This study aims to investigate the bio-refinery process through an enzyme-assisted extraction (EAE) on freeze-dried and fresh macroalgae Gracilaria gracilis for the release of water-soluble components (R-phycoerythrin, proteins, and sugar). Three enzymes, cellulase, protease, and enzyme cocktail (mixture of cellulase and protease), were applied in the study. Results showed that freeze-dried biomass yielded the highest target components in the presence of enzyme cocktail while a single enzyme was better with fresh biomass, either protease for the release of R-PE and protein or cellulase for sugar. The extraction of protein and sugar was improved by 43% and 57%, respectively, from fresh biomass compared to dried biomass. The difference of biomass status was shown to affect the required enzyme and recovery yield during the extraction process. Employing an enzyme cocktail on freeze-dried biomass boosted the extraction yield, which was probably due to the complementary effect between enzymes. On the other hand, single enzyme worked better on fresh biomass, giving economic benefits (enzyme limitation and drying stage) for further implementation of the bio-refinery process. Thus, biomass treatment (fresh or freeze-dried) and enzyme-type determined the efficiency of enzyme-assisted extraction according to the target components.

Due to the urge of human interest in isolating natural products from marine biodiversity for food or health commercial and industrial purposes, marine bioprospecting has increased rapidly in recent years and holds important promise for the future. Indonesian Sargassum species are one of the marine species of brown algae with substantial values. Their wide range of potentiality, especially their bioactive compounds, make them a natural resource for various applications in food, cosmetic and pharmacological sectors. Thus, the bioprospecting of Sargassum species is becoming an interesting topic to discuss, especially for Indonesia where only the red macroalgae Kappaphycus sp and Gracilaria sp are exported. This review illustrates the potential applications of Sargassum species concerning their bioactive chemodiversity. Further, possible challenges for sustainable development of Indonesia are also be described.

Le projet COSELMAR, financé par la Région des Pays de la Loire à hauteur de 2,1 M€ pour une durée de 4 ans, a commencé officiellement le 7 janvier 2013. Cinq unités de recherche de l’Ifremer, onze laboratoires de l’Université de Nantes et un laboratoire de l’Université d’Angers, appuyés par des partenariats académiques et industriels français et internationaux, ont collaboré afin d’apporter une meilleure compréhension des écosystèmes littoraux et marins et des ressources associées, ainsi qu’une réflexion sur la gestion et la prévention des risques engendrés par les facteurs naturels et anthropiques. L’objectif principal de COSELMAR était d’intégrer des travaux scientifiques interdisciplinaires des sciences humaines et sociales, environnementales et de l’ingénieur afin de construire une véritable expertise sur les risques en milieu littoral et marin.Le premier axe du projet était dédié à la compréhension des écosystèmes littoraux et des interactions biotiques dans ces systèmes. Le rôle des huîtres et des oiseaux limicoles dans la structuration des communautés benthiques a été clarifié : il a été démontré que leur présence (ou absence) influence fortement les micro-organismes benthiques (bactéries, micro-algues, foraminifères etc.). La connaissance des modes originaux de nutrition des diatomées et d’un dinoflagellé producteur de toxines diarrhéiques, a permis leur culture en laboratoire, étape préalable indispensable à leur étude approfondie. Enfin, des méthodes de détection des toxines ont été développées et les micro-algues toxiques ont été répertoriées à l’échelle mondiale dans un ouvrage exhaustif bilingue (anglais et français) afin de pouvoir continuer à garantir la production durable de fruits de mer salubres en prévenant d’éventuels risques émergents.Le deuxième axe était focalisé sur l’exploitation durable des ressources marines, depuis le dessalement de l’eau de mer jusqu’à la valorisation de micro- et macro-organismes marins. Des méthodes ont été développées pour détecter les toxines de micro-algues dans l’eau de mer et l’eau potable. Elles permettront de garantir leur absence dans l’eau potable et contribueront ainsi à surmonter la pénurie d’eau potable, un enjeu mondial. Des procédés d’extraction et de transformation douce (procédés enzymatiques) de composés à valeur ajoutée (tels les pigments) ont été mis au point sur deux matrices : les macro-algues et les crabes. Ils permettront de valoriser des matériaux considérés comme nuisibles ou encore des co-produits de la pêche. Enfin, les interactions entre bactéries et la micro-algue responsable du bleuissement de l’huître, Haslea ostrearia, ont été étudiées afin de permettre une meilleure utilisation de cette micro-algue dans les procédés commerciaux.Le troisième axe cherchait à identifier de manière intégrée et interdisciplinaire les éléments de définition des nouveaux risques (induits par les changements globaux) et à modéliser les mécanismes-clés d’émergence et de contrôle des risques, dans la perspective de gestion de l’espace maritime et littoral. Parmi les résultats marquants, une cartographie multi-échelles des enjeux dans les zones submersibles a été produite ainsi qu’une analyse économique coût-efficacité des stratégies pour réduire la vulnérabilité des territoires. L’étude de l’activité de pêche a été très importante dans cet axe : une étude sociologique a permis de mettre en évidence les particularités du travail de pêcheur artisan et les arrangements familiaux spécifiques qu’il induit ; du point de vue économique, la variabilité des prix a été étudiée pour 46 espèces de pêche fraîche vendues dans les 40 halles à marée françaises au cours de la période 2002-2008, ce qui constitue un travail empirique d’une ampleur inégalée internationalement ; un exercice de géoprospective a été réalisé pour susciter du débat autour de devenirs possibles de la gouvernance des pêches du golfe de Gascogne. Enfin, une comparaison France-Canada des processus de gouvernance a été menée à l’échelle de territoires confrontés aux deux principaux risques étudiés : submersion et activité halieutique.L’axe intégrateur a permis d’accroître la structuration du consortium et son rayonnement international, à travers trois actions : une réflexion sur la transdisciplinarité et la création d’une plateforme d’intégration des connaissances sur les risques, la gouvernance des ressources et des espaces marins ; la mise en réseaux des chercheurs de la fédération Institut Universitaire Mer Littoral ; le rayonnement au niveau des formations avec notamment une internationalisation de plusieurs Masters de la fédération IUML.

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Les contributions de ce livrable sont : 1. un état de l'art sur l'utilisation des système multi-agents pour l'évaluation environnementale sur deux critères~ l'exposition au bruit et la pollution de l'air ; 2. la description de la méthodologie du pipeline Eqasim appliquée aux données disponibles pour la métropole de Nantes ; 3. les premiers résultats de simulation obtenu pour l'évaluation environnementale de la ligne Nantes Carquefou sous la forme d'une preuve de concept.

Sustainable development is largely depending on transportation systems ensuring mobility and safety. This mobility requires a sufficient access to the energy, at an affordable price. According to the peak-oil hypothesis, rarefaction and increasing in price of the non-renewable energies are nevertheless expected. Even if transportations systems could partly rely on renewable energies, their optimization should involve energy and mobility criteria. In this study, surrounding wind fields and traveling speed are conjointly investigated in order to evaluate achievable reductions in fuel consumptions for both railways and road transportation systems. In the first part of this work, aerodynamic forces on trains are evaluated with the help of an energy model, validated with experimental tests data. For that, numerical determination of aerodynamic coefficients for several wind and topological situations have been computed with Solid-Works and associated to atmospheric characteristics determined over the considered high-speed line (Rhine-Rhone line; about 140 km) with the AROME model (meteo-france), from measurements at several weather stations. Simulation results show that for a weak wind and a train traveling at high speed, wind influence is about 5% on aerodynamic forces power. Moreover, for a moderate train speed (about 45 m/s), a slightly stronger wind (about 5 m/s) has an influence of about 30%, compared to the without wind case. In a second time, road experiments have been conducted with a passenger car equipped with an air flow meter and an oil flow meter, in addition of dynamical measurements as speed, position, altitude... Similarly to the railway case, wind exposition could be considered as a significant road design parameter. In conclusion, this work based on large sets of full-scale experimental data and the numerical simulation of the aerodynamic forces, points out that wind influence on total aerodynamic power consumption is noticeable, both for railways or road transportation systems.

Efforts to limit climate change should concern the transportation sector which is responsible for roughly a quarter of greenhouse gas emissions. Aside from vehicle’s technical progress and driver eco-driving awareness, road infrastructure has a role to play in this environmental aim. At the project stage, the design of roads can avoid energy losses linked to marked ramps, but afterwards, during the use phase, road management can be a lever too. In this use phase framework, our paper is focused on energy saving that can be achieved by managing speed sectioning. The key point is to ensure consistency between vehicle dynamics, road longitudinal profile and speed policy. Indeed, eco-driving could be impeded if a limiting speed sign is encountered on a steep slope or in a sharp turn. In such a situation the speed sign will be qualified as misplaced. Mechanical braking has then to be used instead of simple natural deceleration. In 2018 the French government lowered authorized speed on secondary roads, from 90 to 80 km/h, with road safety as the primary motivation. In order to assess energy impact of speed-sectioning for these two speed limits, experiments have been carried out in four experimental sites. Furthermore criterion and dissipated energy computation have been developed. The developed energy computation yields to determine the expected fuel economy for the entire traffic over a day on a selected route or network. As a result, over consumption for a misplaced speed sign can reach up to 40 liters of fuel per day with an approaching speed of 80 km/h and 50 liters of fuel per day with an approaching speed of 90 km/h according to traffic data. Significant energy savings could therefore be achieved by sign placement optimization.

Construction site noise significantly contributes to urban noise pollution. To accurately assess the noise exposure from urban construction sites, it is essential to consider the variability and dynamic nature of the urban context. This paper proposes an innovative approach using individual-level activity-based modeling to tackle the given challenge. The methodology introduced encompasses: the modeling of construction site noise emission sources, individual-level activity-based modeling of zone of influences, a modeling chain for assessing individual noise exposure levels, and a novel dedicated exposure indicator. To illustrate the relevance of our methodology, a case study is conducted in Champigny-sur-Marne, France; this study involves the generation of a synthetic population of 1,344,394 individuals distributed among 591,153 households, with 4,854,597 associated activities, using the Eqasim simulation framework. A traffic model is then established using the multi-agent transport simulation software MATSim. NoiseModelling software is employed to compute noise maps both with and without the construction site. Subsequently, the noise exposure for each individual is assessed throughout the day for both configurations. Thanks to this complete modeling chain, the proposed indicator quantifying the cumulative amount of time of exposure above a given threshold can be compute, with the case study result being estimated at more than 3,300 person.quarter-hours above 65 dB(A).

Agent-Based Models (ABMs) are being increasingly used to evaluate urban systems, urban policies and environmental impacts. One prerequisite for using the ABM framework consists of generating a synthetic population representative of the actual population, featuring the appropriate attributes with respect to model objectives. A precise spatial positioning of the synthetic population agents is often key to ensuring ABM modeling quality. This paper considers the problem of allocating synthetic population agents to a finer spatial scale. Such an allocation process is performed from a higher-level statistical area where a synthetic population can be generated, that is, a container statistical area (CSA), to several nested non-overlapping elementary statistical areas (ESAs), where only marginals are available. This allocation step relies not only on common attributes between CSA and ESA, but also on additional discriminatory attributes, that is, attributes of interest, estimated from external data sources. The case study examined herein is based on French census and fiscal data. Common attributes include eight socio-demographic variables, totaling 17 modalities. An additional attribute of interest, that is, income, has also been added. The allocation problem at hand is modeled as an integer quadratic programming problem. An exact algorithm is first applied to solve the problem; the applicability of this algorithm proves to be limited to small-size synthetic populations. A heuristic is proposed to handle the allocation of larger-size synthetic populations. Tests carried out on the case study show that this heuristic yields near-optimal solutions; it is also computationally efficient and may fulfill the needs of a majority of users.

The health and equity issues related to urban pollution, coupled with the need to assess the consequences of implementing transport policies or new mobility solutions, make it imperative to provide decision-makers with a relevant modeling framework. The coupling of activity-based travel models with environmental models presents a promising approach to thoroughly investigate the interconnected issues related to the impact of road traffic on individual exposure to noise and air pollution. Integrated modeling chains provide a robust framework to comprehensively assess traffic scenarios on a city scale and address issues related to environmental inequalities. This article reviews the recent work in this field to identify both the main contributions and key challenges still to be addressed. It illustrates how the use of a dynamic approach, which considers the intraday dynamics of individual mobilities and pollution concentrations, can contribute to more accurate estimates of the exposure of individuals to pollutants. This modeling approach also has the potential to investigate how the level of exposure depends on the characteristics of the individuals (socioeconomic factors, commuting patterns, location of residence, and main activities). Nonetheless, there are still important challenges to overcome to further enhance the accuracy and applicability of these models: taking into account all types of traffic and transport modes; refining the modeling of exposure to pollution during travel; taking better consideration of microenvironments; and so forth.

Ifsttar and RFF have developed an energy model of high-speed trains in order to determine the influence of infrastructure geometry and environment on train power consumptions. Validation of this model is based on full scale tests performed for the Rhine-Rhone high-speed line acceptance of work (in 2011). This paper aims to evaluate if wind influence is a relevant parameter for the model. This involves numerical determination of aerodynamic coefficients for various wind and ground configurations and their use for the calculation of the aerodynamic efforts. Atmospheric characteristics are extrapolated from meteorological measurements thanks to the AROME numerical model, over the whole Rhine-Rhone line. Most remarkable results show for a moderate train speed (about 47 m/s), a rearward oriented wind of only 5,5 m/s lowers by 30% the power due to aerodynamic forces applied on the train, or raises it similarly if it is forwardly oriented. In conclusion, this work points out that wind influence is of first importance for computing energy consumption.

In the context of reducing the global energy consumptions in transport systems, railways offer many advantages. Therefore the construction of new high speed railways has been planned. This article assesses the impact of the gradient of the railway on the train energy efficiency. This key parameter has a direct influence on the construction and operation phases energy amounts, since low gradients could require considerable civil engineering works. The energy model of the railway operation phase presented in this paper takes into account the infrastructure geometry, the train characteristics and the speed profiles. It is applied to different generic cases in order to illustrate the impact of the infrastructure gradients. The corresponding trajectories are compared from an energy efficiency point of view. Results show that the gradient is not the only one element to take into account. The associated length of ramp or slope is crucial too. For example, it is shown that short section with an important gradient can require less operation energy than lower gradient on longer distances. It is therefore possible to determine a couple of slope/ramp length and gradient for a given rolling stock which contributes to the minimization of the global construction cost and of the operating energy consumption.

Macroalgae are a potentially novel source of nutrition and biologically active molecules. Proliferative species such as Eucheuma denticulatum, Solieria chordalis (red algae) and Sargassum muticum (brown alga) constitute a huge biomass that can be exploited. In this study, we focus on the extraction of polysaccharides from these three macroalgae species and the characterization of cell wall polysaccharides such as carrageenans, fucoidans and alginates by Fourier Transform Infrared spectroscopy with Attenuated Reflectance Module (FTIR-ATR). The comparison of purified extracts with commercial solutions of fucoidans, alginates or carrageenans shows a strong similarity between the spectra. It demonstrates that the methods of extraction that have been used are also suitable purifying technics. Moreover, it validates infrared spectroscopy as a quick, simple and non-destructive method for the accurate analysis of polysaccharides. The FTIR technique applied to samples collected at different periods of the year allowed us to highlight differences in the composition of fucoidans, alginates and carrageenans. Different classes corresponding to the season can be distinguished by statistical multidimensionnal analysis (Principal Component Analysis) showing that the structure of algal polysaccharides, related to bioactivity, depends on the period of harvest. FTIR results showed that S. chordalis and E. denticulatum possess a dominant type of carrageenan called iota-carrageenan. This type of carrageenan is in the majority when the alga is at maturity in its development cycle. During its growth phase, iota-carrageenan precursors can be detected by FTIR spectra, enabling a better control of the extraction and an application of these compounds in various economic sectors. When the alga E. denticulatum is in its juvenile stage, we found traces of kappa-carrageenan and nu-carrageenan polysaccharides in some extracts.

The production by enzymatic treatment of fish protein hydrolysates (FPH) is a promising route to add value to fisheries proteinic co-products (fish frames, heads etc.). Indeed, FPH possess good nutritional properties and biological activities for food and feed uses. Pressure-driven membrane separations such as ultrafiltration (UF) and nanofiltration (NF) can be used after the hydrolysis to increase the specific activities of the FPH. This paper discusses the impact of a two-step UF/NF process producing four different fractions on two industrial FPH with different hydrolysis degrees. Fractionation is carried out in “realistic” conditions for an industrial process, on highly concentrated FPH solutions (about 100 g of dry matter/L) at a high volume reduction factor. For each step, UF or NF, the variation of the permeation flux in the course of the fractionation is discussed according to the FPH hydrolysis degree and the membranes cut-offs. The values of performance indicators defined in terms of nitrogen content are also examined, including the concentration factor (CF), the relative recovery in the retentate (ηR) and the mean and final retention factors (RFm and RFf). Computed values of these indicators are validated through the setting of volume and mass balances around each step. The impact of fractionation on the FPH peptidic population is shown. Peptidic populations are described in terms of chromatographic profiles (SEC–FPLC). The UF fractionation produces a permeate enriched with respect to the FPH smaller than a molecular weight of about 600–750 Dalton, and a retentate enriched in large peptides (above the same MW). A similar behaviour is found for the NF fractionation. Comparing the impact of the UF fractionation on the two hydrolysates allows to conclude that the membrane cut-off is well-suited when comprised between the MWs of the biggest and the most abundant peptides in the FPH.

Proliferation of the introduced brown macro alga Sargassum muticum is known as a natural and hard to control phenomenon occurring along the Atlantic coasts. The phenomenon causes serious troubles for local ecosystems including the alteration of ecosystem structure, the reduction in indigenous biodiversity and economic losses (tourism, aquaculture). However, despite the serious troubles caused by S. muticum, this species contains highly remarkable bioactive metabolites. This macro alga is at present under-exploited and the valorization of its metabolites to give a positive value to this seaweed could be a solution of ecosystemic service. Biorefinery process could be one solution to valorize S. muticum. Comprehensive knowledge concerning the biochemical composition of S. muticum and the impact of environmental factors, particularly seasons, on its composition is a prerequisite before its valorization. In this study, the biochemical composition of S. muticum was evaluated by using classic colorimetric methods based on chemical analysis and Fourier Transform Infra-Red spectrometry (FTIR), and was used as a rapid and safe method that could bring advantages in screening studies and a more comprehensive management and use of seaweed products. Our results are globally in accordance, notably for phenolic compounds, showing the relevance of the use of infrared spectrometry. Moreover, based on the absorption bands of some specific and valuable compounds shown by FTIR, there was a seasonal variation in the polysaccharides, i.e., uronic acids and sulphated compounds, together with phenolic contents of S. muticum.

Energy moderation of the road transportation sector is required to limit climate change and to preserve resources. This work is focused on the moderation of vehicle consumption by optimizing the speed policy along an itinerary while taking into account vehicle dynamics, driver visibility and the road's longitudinal profile. First, a criterion is proposed in order to detect speed policies that are impeding drivers' eco-driving ability. Then, an energy evaluation is carried out and an optimization is proposed. A numerical application is performed on a speed limiting point with 20 usage cases and 5 longitudinal slope values. In the hypothesis of a longitudinal slope of zero, energy savings of 27.7 liter per day could be realized by a speed sign displacement of only 153.6 m. Potential energy savings can increase to up to 308.4 L per day for a −4% slope case, or up to 70.5 L per day for an ordinary −2% slope, with a sign displacement of only 391.5 m. This results in a total of 771,975 L of fuel savings over a 30 year infrastructure life cycle period. Therefore a methodology has been developed to help road managers optimize their speed policies with the aim of moderating vehicle consumption.

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The Mobility Allowance Shuttle Transit (MAST) system is a type of public transportation where vehicles may deviate from a fixed route to serve clients who wish to get on or off within a service area. In this area, the system provides an on-demand service. We use Mixed-Integer Linear Programming to tackle the MAST system and consider often neglected realistic constraints, as those linked to vehicle capacity and fleet availability. We perform a sensitivity analysis to understand how the system performance varies as a result of changing client priorities.

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Ce rapport est le deuxième rapport prévu au sein de ce contrat, écrit par l'équipe universitaire à destination de la SNCF dans le cadre de la convention de co-encadrement SNCF/UniversitéGustaveEiffel (anciennement Ifsttar) accompagne cette thèse (No Ifsttar : RP1-E19045) sous la responsabilité scientifique de Jean Calio, expert Data, Mobilité et Territoires et Développement Durable de la SNCF et de Pierre-Olivier Vandanjon.

The current transport system is not sustainable because of its high consumption of finite resources (mainly oil) and its impact on the environment (air quality, global warming, etc.). The design phase of the transport infrastructure has to be reconsidered in order to take into account these environmental burdens. Very recently, a methodology called Sloop (Slope Optimization) has been proposed to optimize the road longitudinal profile according to either an energy consumption or a Global Warming Potential (GWP) criterion calculated for construction and use phases. However, this optimization requires a high computation effort. It is a severe limitation to deploy this application to significant road project . The purpose of this paper is to propose a tailor-made optimization process which improves significantly this methodology. For the construction phase assessment, the methodology is based on a model of earthworks which computes the differences of geometry between the natural terrain and the road longitudinal profile. The use phase is assessed by simulating the traffic. Traffic simulations are based on vehicle dynamic models validated with real experiments. Starting from an initial longitudinal profile proposed by road designers, our algorithm finds a better profile in terms of Energy or GWP. The degrees of freedom of the optimization problem are mainly the road slopes and secondly the radii of the circles which connect them. The main constrain is the connection of the profile with the existing road. The optimization is complex due to the important number of degrees of freedom and the computation cost of the use phase assessment. It is the reason why, we developed a specific algorithm based on the Sequential Quadratic Programming Method (SQP) with suited computations of the linear approximation.

Current transportation systems contributed to one quarter to the Greenhouse Gases (GhG) emissions and the mobility demand increases continuously. The transportation infrastructure design has to be optimized to mitigate these emissions. The methodology ?Sloop? (acronym for Slope Optimization) has recently been set up to optimize the longitudinal road profile with respect to a Global Warming Potential (GWP) criterion calculated for both the construction and operational phases while incorporating accurate vehicle models. This paper proposes a customized optimization strategy that significantly improves the Sloop methodology. From an initial longitudinal profile generated by road designers, our algorithm identifies the optimized profile in terms of GWP. This optimization step is complex due to the large number of degrees of freedom, along with various constraints to obtaining a feasible solution and the computational cost of the operational phase assessment. The most stringent constraint entails connecting the profile with the existing road (i.e., a constraint on the final profile altitude). We have developed a specific algorithm based on the Sequential Quadratic Programming (SQP) method; this algorithm takes into account the quasi-quadratic nature of the constraint on the final altitude. In a real case study, our algorithm outputs a profile whose GWP assessment saves 4% compared to the GWP assessment of the initial profile. The benefit of our specific protocol for treating the final altitude constraint is demonstrated in this example. By means of this new efficient and open-box algorithm, profile evolution at each iteration is analyzed to determine the most sensitive degrees of freedom, and the sensitivity of the optimization with respect to the main construction parameters and traffic assumptions are conducted. The results are consistent and stable.

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Sargassum muticum is an invasive macroalga that uncontrollable widely grows on the European Atlantic coasts. This macroalga is particularly rich in phenolics (phlorotan-nins) presenting biological activities such as antioxidant, radical-scavenging, antibacte-rial, photoprotective properties, etc. Among the diversity of metabolites produced by the species, phenolic compounds could constitute one of the potential ways of valorization of Sargassum muticum.For the needs of the study, algae were harvested along a latitudinal gradient in Norway, Ireland, France, Spain and Portugal.After extraction, the highest phenolic contents and bioactivities were measured for the samples coming from Norway and Portugal. Consequently, both these countries have been retained in the present work.The extracts are composed of a “pool” of phenolic compounds of molecular weight varying, according to the literature, from 126 Da (phloroglucinol) to 650 kDa. But the most common compounds are comprised between 10 and 100 kDa. With the aim of concentrating and eventually fractionating (i.e. isolating the most active fractions) the phenolic extracts of Sargassum muticum, a few membranes of Molecular Weight Cut-Off 10 kDa were chosen. These membranes were tested by using different technologies: ultrafiltration, centrifugal tube and dialysis.Results show that there is no significant activity in the permeates signifying that the retention rate is about 100 % for the active fractions. Concerning the retentates, results show clearly an increase of the total phenolic contents as well as antioxidant and radical scavenging activities. The best performances are obtained with the centrifugal tubes using 10 kDa membranes.

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The objective of this study was to produce, by an enzymatic hydrolysis process at a pilot scale, a saithe (Pollachius virens) hydrolysate with a high antioxidant activity. Design of experiment methodology, based on laboratory-scale experiments, was used to obtain a behavioral reduced model that allows one to determine the optimal operating conditions maximizing the antioxidant activity. Two specifications were studied: the degree of hydrolysis and the antioxidant activity. The effects of the following hydrolysis parameters (temperature, pH, enzyme concentration, and operating time) were studied and presented as response surfaces. From these results, a multifactorial optimization was performed and the Pareto optimal set of efficient solutions was evaluated. The optimal conditions were tested at laboratory scale and then validated by comparison with tests carried out on a pilot plant.

Ultrafiltration membranes can be advantageously used to improve the bioactivity of a saithe protein hydrolysate containing peptides having a size lower than 7 kDa, by fractionating or concentrating some specific molecular weight peptide classes. The aim of this study is to evaluate the behaviour of a 4 kDa membrane in modified polyethersulfone in conditions close to industrial exploitation. This work consists in evaluating the effect of the initial peptide content (30, 90 and 150 mg/mL), the transmembrane pressure (10 and 30 bar), in total recycling modes and the volume reduction factor (until 5) on membrane performances, in terms of permeation flux and selectivity. The peptide content and mass repartition profiles are measured by size exclusion chromatography.It is observed that the pressure and the peptide content, which can act as complementary or antagonist factors, can be adjusted to modify the transmission of peptides and consequently the apparent molecular weight cut-off (MWCO) of the membrane. These results have been directly applied to study the refining of a peptidic fraction lower than 1 kDa having a potential bioactivity (i.e. antioxidant and hypotensive). The change in refining performances with the volume reduction factor (VRF) are discussed, including the recovery yield and the purity of the interesting fraction in permeate and the energy requirements.

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Dans cet article, une présentation des propriétés des polynômes de Tchebychev et une formulation dérivée utilisant ces polynômes sont effectuées. La méthode d'identification présentée consiste à appliquer une transformation linéaire sur le système d'équations qui régit le processus. Pour cela, l'opérateur mathématique utilisé s'appuie sur une décomposition du signal dans une base formée de polynômes orthogonaux. Nous montrons que cette projection se comporte comme un filtre passe-bande, de telle sorte qu'une seule opération est nécessaire pour le pré-traitement des données avant le processus d'identification, à savoir choisir la dimension de la base des polynômes Tchebychev. Une identification expérimentale en boucle fermée d'un robot à 2 axes avec cette méthode est effectuée dans une dernière partie. Les résultats obtenus sont comparés et analysés avec d'autres techniques utilisées en robotique.

Seaweeds are potentially excellent sources of bio-active metabolites that could represent useful leads in the de-velopment of new functional ingredients in pharmaceuticaland cosmetic industries. In the last decade, new marinebioprocess technologies have allowed the isolation of sub-stances with biological properties. The brown algaSargassum muticum (Yendo) Fensholt (Ochrophyta) was en-zymatically hydrolyzed to prepare water-soluble extracts byusing six different commercially available carbohydrate-degrading enzymes and two proteases. Evaluation of 2,2-diphenyl-1-picrylhydrazyl (DPPH) and ferric reducing antiox-idant power (FRAP) antioxidant, tyrosinase, elastase, and bio-film inhibition, antibacterial and antiviral activities as well asevaluation of cytotoxicity were realized for each extract. Totalphenolic content was measured for extract characterization,and solid-phase extraction was useful to purify the enzymaticextract. Soluble total phenolic content of S. muticumViscozyme extract was highest with 6.4% of dry weight.Enzymatic Celluclast and Viscozyme extracts had the lowestvalue of DPPH IC50 indicating a strong antiradical activity,0.6 mg mL−1, in comparison with other enzymes. The ferricreducing antioxidant power ranged between 48.7 μM Fe 2+ Eq,digested with Viscozyme, and 60.8 μM Fe 2+ Eq, digested withAmyloglucosidase. Tyrosinase inhibition activity ofS. muticum Neutrase extract was 41.3% higher compared toother enzymes. Elastase inhibition activity of S. muticumShearzyme extract had highest activity (32.8%). All enzymat-ic extracts showed no cytotoxic effect towards the kidney Verocells. Meanwhile, only S. muticum Neutrase and Alcalase ex-tracts exhibited potential antiviral activity. In addition,S. muticum Viscozyme and Shearzyme extracts showed prom-ising activity in suppressing the biofilm formation againstPseudomonas aeruginosa and Escherichia coli, respectively. Purification of S. muticum Viscozyme extracts by solid-phaseextraction managed to concentrate the phenolic content andimprove the bioactivity. These results indicate the promisingpotential of enzyme-assisted followed by solid-phase extrac-tion in recovering phenolic content and in improving itsbioactivity.

Nova'TP, commission TP de NOVABUILD, a initié un groupe de travail visant à favoriser l'innovation dans les marchés publics de travaux, grâce à l'élaboration de cahier des charges décrivant les performances attendues plutôt que les moyens de les atteindre. Cette démarche s'est appuyée sur l'exemple concret du choix des matériaux des pistes cyclables, qui sont intégrées à la plupart des aménagements réalisés sur l'espace public. La première étape du projet a consisté à définir les critères objectifs de performance de ces matériaux, afin de répondre aux enjeux de confort, de développement durable, de coût... exprimés par les maitres d'ouvrage. Sur certains critères, comme le confort, l'absence de référentiel existant pour les infrastructures cyclables, nous a conduit à développer une démarche innovante, en adaptant l'usage du Vel'Audit (Îlo instrumenté, prévu initialement pour l'inspection à haut rendement d'aménagements cyclables). Les matériaux déjà couramment utilisés pour ce type d'infrastructure ont été caractérisés selon ces critères. Ces données ont ensuite été regroupées dans une application, développée pour faciliter l'analyse et la comparaison des offres par les maîtres d'ouvrage. Cette application permet de comparer des matériaux conventionnels entre eux ou avec des matériaux innovants sous un aspect performentiel. Ce logiciel d'aide à la décision a été testé par Nantes Métropole sur plusieurs opérations d'aménagement, pour Îrifier sa pertinence et proposer certaines évolutions.

Les collectivités françaises développent de plus en plus d'aménagement cyclables, et il apparaît donc plus d'attentes concernant la sécurité de ces aménagements, leur confort, leur durabilité, leur gestion et entretien... Si pour le réseau routier général, les maîtres d'ouvrages disposent d'outils d'aide à la décision, basés sur des critères tels que la sécurité, la mobilité, l'environnement et le coût, les voies cyclables ne sont que partiellement concernées par ces outils. Ce travail vise à répondre aux attentes des gestionnaires de réseaux cyclables et a été réalisé dans le cadre du groupe de travail « Nova-TP » sur l'innovation dans les transports. En première partie de ce travail, L'outil Vel'audit est présenté. DériÎ des outils routiers, Vel'Audit est un Îlo instrumenté qui permet de réaliser des releÎs d'images ensuite exploitables à l'aide du logiciel IREVE. Ce diagnostic permet à la fois de caractériser l'infrastructure et d'identifier des défauts de cyclabilité. Vél'audit dispose aussi d'une centrale inertielle mesurant les vibrations subies par le Îlo et conduisant au calcul d'un indicateur confort indexé à la vitesse d'avancement. Pour l'aspect sécurité, le SRT (Skid Resistance Tester) est ensuite présenté, comme un des moyens de mesure de l'adhérence exploité pour les routes et pouvant être directement transposé aux pistes cyclables. Des essais de validité sur piste cyclable sont ensuite discutés et, en perspectives, le développement d'un matériel utilisable continûment sur itinéraire, inspiré du Grip-Tester, est envisagé en prolongement des mesures ponctuelles du SRT. Ainsi différentes techniques d'auscultations routières éprouÎes peuvent répondre aux besoins émergents des gestionnaires d'infrastructures cyclables, tout en s'adaptant aux caractéristiques de ces déplacements cyclables. Ces transpositions sont encore peu investie alors même que les besoins existent et qu'en finalité cela pourrait aboutir à la construction d'un indice de qualité cyclable (IQCy) à l'image de l'IQRN routier.

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Cooking juices are fish industry effluents with high organic and salt contents, typically 10–20 g COD L−1 and 20–30 g salt L−1. They contain valuable substances (mainly aromas and proteins) that can be recovered, thereby significantly lowering treatment cost and offering a commercial gain for the food industry.The use of membrane processes to concentrate aromas in the cooking juices of fish and shellfish/crustaceans is reviewed, and some issues related to the technical and economic feasibility of industrial processes are highlighted.The issue of process design and economic evaluation is illustrated by a case study of a batch process concentrating shrimp aromas by reverse osmosis, and desalting juices by electrodialysis. The analysis shows that the sizing of this integrated process does not require systematic evaluations of CAPEX and OPEX, but can be done by considering only two simple criteria, namely minimizing energy consumption and the total membrane surface installed. In addition, when the characteristics of the processed juices change in time, the best solution should be determined by considering not only the process costs but also the ability of the process to adequately treat juices with different characteristics.

Two recent techniques based on chemical footprinting analysis, HRMAS NMR and FTIR spectroscopy, were tested on a brown macroalgal model. These powerful and easily-to-use techniques allowed us to discriminate Sargassum muticum specimens collected in five different countries along Atlantic coasts, from Portugal to Norway. HRMAS NMR and FTIR permitted the obtaining of an overview of metabolites produced by the alga. Based on spectra analysis, results allowed us to successfully group the samples according to their geographical origin. HRMAS NMR and FTIR spectroscopy respectively point out the relation between the geographical localization and the chemical composition and demonstrated macromolecules variations regarding to environmental stress. Then, our results are discussed in regard of the powerful of these techniques together with the variability of the main molecules produced by Sargassum muticum along the Atlantic coasts.

The photoadaptive responses of macroalgal communities to abiotic stresses have been studied, and a number of UV-absorbing molecules have been identified. Among these compounds, photoprotective compounds such as mycosporine-like amino acids and carotenoids have been isolated from various red macroalgal species. However, several substances still need to be characterized. We describe the preparation of photoprotective extracts obtained from Solieria chordalis. Two solvents, 2-octyl dodecanol and octyldodecyl ester of L-pyrrolidone carboxylic acid, were selected based on their cosmetic functions for performing an ultrasound-assisted extraction. The efficiency of extraction was monitored by spectrophotometry and in vitro photoprotective activity measurements. 2-Octyl dodecanol and octyldodecyl ester of L-pyrrolidone carboxylic acid extracts showed maximum absorption wavelengths ranging from 280 to 340 nm and 270 to 350 nm, respectively. The anti UV-B capacity for protecting a synthetic chlorophyll solution was assessed by measuring its pseudo first-order degradation kinetics at room temperature. Under irradiation at 312 nm, chlorophyll introduced in the 2-octyl dodecanol S. chordalis extract showed the slowest degradation kinetics with a half-life t(1/2) of 121.0 min. Several compounds were detected in the seaweed

Sargassum muticum, an invasive brown macroalga presently distributed along European Atlantic coasts from southern Portugal to the south coast of Norway, was studied on a large geographical scale for its production of phenolic compounds with potential industrial applications and their chemical and biological activities. S. muticum can produce high biomass in Europe, which could be exploited to supply such compounds. S. muticum was collected in Portugal, Spain, France, Ireland and Norway (three sites/country) to examine the effect of the latitudinal cline and related environmental factors. Assays focused particularly on polyphenols and their activities. Crude acetone-water extracts were purified using solid phase extraction (SPE) and antioxidant and antimicrobial activities of crude extracts and semi-purified fractions measured. Total phenolic content was assessed by colorimetric Folin-Ciocalteu assay and reactive oxygen species activities by 2,2-diphenyl-1-picrylhydrazyl, reducing power, β-carotene bleaching method and xanthine oxidase assay. Antibacterial activities were tested on terrestrial and marine strains to evaluate potential use in biomedical and aquaculture fields. Purified active phlorotannins, isolated by SPE, were identified using NMR. Phenolic contents differ clearly among countries and among sites within countries. Quality did not change between countries, however, although there were some slight differences in phlorethol type. Additionally, some fractions, especially from the extreme north and south, were very active. We discuss this in relation to environmental conditions and the interest of these compounds. S. muticum represents a potential natural source of bioactive compounds and its collection could offer an interesting opportunity for the future management of this species in Europe.

Five organic tubular nanofiltration membranes of various material and molecular weight cut-off (MWCO) were tested for the enrichment of a fish (saithe, Pollachius virens) hydrolysate in low molecular weight antioxidant peptides (< 2 kDa). The effect of membrane material and MWCO on permeation flux, hydrolysate adsorption extent, membrane cleaning and peptides retention was investigated in low fouling conditions in order to limit concentration polarization effects. Water permeation fluxes appeared sensitive both to the MWCO and the membrane material. Membranes in polyethersulfone (PES), a highly hydrophobic material, exhibited lower fluxes than modified polyethersulfone (m-PES) and polysulfone (PS) membranes. The 4 kDa m-PES membrane was found to be the most adapted to the objective in terms of flux and retention factor. The 4 kDa m-PES and 8 kDa PS membranes could also be used in series to recover medium-size peptides. (c) 2008 Elsevier Ltd. All rights reserved.

BACKGROUND: Numerous studies have demonstrated that in vitro controlled enzymatic hydrolysis of fish and shellfish proteins leads to bioactive peptides. Ultrafiltration (UF) and/or nanofiltration (NF) can be used to refine hydrolysates and also to fractionate them in order to obtain a peptide population enriched in selected sizes. This study was designed to highlight the impact of controlled UF and NF on the stability of biological activities of an industrial fish protein hydrolysate (FPH) and to understand whether fractionation could improve its content in bioactive peptides. RESULTS: The starting fish protein hydrolysate exhibited a balanced amino acid composition, a reproducible molecular weight (MW) profile, and a low sodium chloride content, allowing the study of its biological activity. Successive fractionation on UF and NF membranes allowed concentration of peptides of selected sizes, without, however, carrying out sharp separations, some MW classes being found in several fractions. Peptides containing Pro, Hyp, Asp and Glu were concentrated in the UF and NF retentates compared to the unfractionated hydrolysate and UF permeate, respectively. Gastrin/cholecystokinin-like peptides were present in the starting FPH, UF and NF fractions, but fractionation did not increase their concentration. In contrast, quantification of calcitonin gene-related peptide (CGRP)-like peptides demonstrated an increase in CGRP-like activities in the UF permeate, relative to the starting FPH. The starting hydrolysate also showed a potent antioxidant and radical scavenging activity, and a moderate angiotensin-converting enzyme (ACE)-1 inhibitory activity, which were not increased by UF and NF fractionation. CONCLUSION: Fractionation of an FPH using membrane separation, with a molecular weight cut-off adapted to the peptide composition, may provide an effective means to concentrate CGRP-like peptides and peptides enriched in selected amino acids. The peptide size distribution observed after UF and NF fractionation demonstrates that it is misleading to characterize the fractions obtained by membrane filtration according to the MW cut-off of the membrane only, as is currently done in the literature. Copyright © 2010 Society of Chemical Industry

This paper addresses the important topic of joint flexibility identification. Three dynamic models depending on measurements availability are compared. The parameters are estimated by using the ordinary least squares of an over linear system obtained from the sampling of the dynamic model along a closed loop tracking trajectory. An experimental setup exhibits the experimental identification results.

Tyre-road estimation methods have been the objective of many research programmes throughout the world. Most of these methods aim at estimating the friction components such as tyre longitudinal slip rate κ and friction coefficient μ in the contact patch area. In order to estimate the maximum available friction coefficient μmax , these methods generally use a probabilistic relationship between the grip obtained for low tyre excitations (such as constant speed driving) and the grip obtained for high tyre excitations (such as emergency braking manoeuvre). Confirmation or invalidation of this relationship from experimental results is the purpose of this paper. Experiments have been carried out on a reference track including several test boards corresponding to a wide textural spectrum. The main advantage of these experiments lies in the use of a vehicle allowing us to accurately build point-by-point relationship between κ and μ. This relationship has been determined for different tyres and pavement textures. Finally, the curves obtained are analysed to check the validity of the relationship between the current friction coefficient used by the car during normal driving conditions and μmax.

Ce chapitre de livre, accessible par internet, décrit une méthode pour connaître l'influence de l'erreur de mesure sur le résultat final. Elle revient à utiliser une méthode classique utilisée dans l'évaluation de la robustesse des simulations numériques vis à vis de la troncature liée au codage des réels par les ordinateurs. (la méthode CESTAC :Contrôle et Estimation Stochastique des Arrondis de Calculs).

The identification of the dynamic parameters of robot is based on the use of the inverse dynamic model which is linear with respect to the parameters. This model is sampled while the robot is tracking exciting trajectories, in order to get an over determined linear system. The linear least squares solution of this system calculates the estimated parameters. The efficiency of this method has been proved through the experimental identification of a lot of prototypes and industrial robots. However, this method needs joint torque and position measurements and the estimation of the joint velocities and accelerations through the pass band filtering of the joint position at high sample rate. So, the observation matrix is noisy. Moreover identification process takes place when the robot is controlled by feedback. These violations of assumption imply that the LS solution is biased. The Refined Instrumental Variable (SRIV) approach deals with this problem of noisy observation matrix and can be statistically optimal. This paper focuses on this technique which will be applied to a 2 degrees of freedom (DOF) prototype developed by the IRCCyN Robotic team.

The identification of the dynamic parameters of robot is based on the use of the inverse dynamic model which is linear with respect to the parameters. This model is sampled while the robot is tracking exciting trajectories, in order to get an over determined linear system. The linear least squares solution of this system calculates the estimated parameters. The efficiency of this method has been proved through the experimental identification of a lot of prototypes and industrial robots. However, this method needs joint torque and position measurements and the estimation of the joint velocities and accelerations through the pass band filtering of the joint position at high sample rate. So, the observation matrix is noisy. Moreover identification process takes place when the robot is controlled by feedback. These violations of assumption imply that the LS solution is biased. The Simple Refined Instrumental Variable (SRIV) approach deals with this problem of noisy observation matrix and can be statistically optimal. This paper focuses on this technique which will be applied to a 2 degrees of freedom (DOF) prototype developed by the IRCCyN Robotic team.

Usually, the identification of the dynamic parameters of robot makes use of the inverse dynamic model which is linear with respect to the parameters. This model is sampled while the robot is tracking exciting trajectories. This allows using linear least squares (LS) techniques to estimate the parameters. The efficiency of this method has been proved through experimental identifications of a lot of prototypes and industrial robots. However, it is known that LS estimators are sensitive to outliers and leverage points. Thus, it may be helpful to verify their reliability. This is possible by using robust regressions and residual analysis. Then, we compare the results with those obtained with classical LS regression. This paper deals with this issue and introduces the experimental identification and residual analysis of an one degree of freedom (DOF) haptic interface using the Huber's estimator. To verify the pertinence of our analyses, this comparison is also performed on a medical interface consisting of a complex mechanical structure.

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Les contributions de ce livrable sont : 1. un état de l'art sur l'utilisation des système multi-agents pour l'évaluation environnementale sur deux critères~ l'exposition au bruit et la pollution de l'air ; 2. la description de la méthodologie du pipeline Eqasim appliquée aux données disponibles pour la métropole de Nantes ; 3. les premiers résultats de simulation obtenu pour l'évaluation environnementale de la ligne Nantes Carquefou sous la forme d'une preuve de concept.

Sustainable development is largely depending on transportation systems ensuring mobility and safety. This mobility requires a sufficient access to the energy, at an affordable price. According to the peak-oil hypothesis, rarefaction and increasing in price of the non-renewable energies are nevertheless expected. Even if transportations systems could partly rely on renewable energies, their optimization should involve energy and mobility criteria. In this study, surrounding wind fields and traveling speed are conjointly investigated in order to evaluate achievable reductions in fuel consumptions for both railways and road transportation systems. In the first part of this work, aerodynamic forces on trains are evaluated with the help of an energy model, validated with experimental tests data. For that, numerical determination of aerodynamic coefficients for several wind and topological situations have been computed with Solid-Works and associated to atmospheric characteristics determined over the considered high-speed line (Rhine-Rhone line; about 140 km) with the AROME model (meteo-france), from measurements at several weather stations. Simulation results show that for a weak wind and a train traveling at high speed, wind influence is about 5% on aerodynamic forces power. Moreover, for a moderate train speed (about 45 m/s), a slightly stronger wind (about 5 m/s) has an influence of about 30%, compared to the without wind case. In a second time, road experiments have been conducted with a passenger car equipped with an air flow meter and an oil flow meter, in addition of dynamical measurements as speed, position, altitude... Similarly to the railway case, wind exposition could be considered as a significant road design parameter. In conclusion, this work based on large sets of full-scale experimental data and the numerical simulation of the aerodynamic forces, points out that wind influence on total aerodynamic power consumption is noticeable, both for railways or road transportation systems.

The skid resistance analysis is one part of the complex process which is involved in road safety analysis for a road project or for road maintenance. As early as the design phase, the road geometry choice is linked to the skid resistance for a given level of service. In the road maintenance case, different conventional measurements are performed on the road and by analyzing them together, risks can be enlightened. Currently, this analysis is done by comparing these data. This is time consuming and requires dedicated skills. The final objective of our work is to simplify this procedure by developing a software with models of vehicle, digitalized roads, tires, water height and other parameters. With this software, it will be possible to simulate different vehicles on a given road in order to evaluate if the skid resistance supplied by the road meets its demands of the traffic. The key of this development is to assess the prediction capacities of the models. In this paper, a first step in this evaluation process is presented. It consists in a comparison between the skid resistance predicted by simplified vehicle dynamic models, by a reference commercial model and the actual skid resistance measured on a real vehicle taking a curve at high speed. These experiments took place on our test track at Nantes with our fully instrumented car. This study is oriented towards road manager needs which are to know if these dynamic models can improve their analysis. The chosen models have to be versatile enough in order to take into consideration the road characteristics. The objective is not to predict a crash for a given car but to detect a possible source of crash for a class of vehicle due to improper road characteristics. First results show the interest of intermediate models for road manager needs, between the point and commercial model. Their advantages are mainly to be more realistic than the point model and to require less information than commercial software.

The aim of this study is to improve road safety by analyzing road influence on vehicle stability. Nowadays, vehicle stability is enhanced by electronic systems. It can be expected that, in a near future, stability systems will rely on complete vehicle dynamics models. This means measuring, on each axle, the forces and deformations of-especially-tires and suspensions. In that perspective, the contact forces can be obtained by using dynamometrical wheels or load bearing sensors (SKF-TNO), which are likely to become available at a sufficiently low cost. For the deformation concern, we can think of lots of possibilities to gauge the suspension parts, but, at the present time, the tire vertical deformation remains difficult to acquire. Although different techniques exist to determine the tire crushing, the most popular consists in measuring the distance between a laser sensor fitted on a wheel and the corresponding local road surface. Nevertheless, this kind of sensor is expensive, difficult to adapt on standard cars and its measurements are highly influenced by the road texture state (water depth, markings ...). The whole new idea is that, assuming the availability of bearing load sensors on a vehicle for force measurement, tire height could be evaluated without any additional equipment by measuring torques and forces applicated to this wheel. So, we propose to evaluate the effectiveness of equivalence between the tensor components and optical crushing measurements. Experiments have been worked out at various speeds and curves. These experiments tend to show that the longitudinal torque and lateral force at the wheel center can be used to determine the wheel radius in the maximum loaded phases. Dynamometrical measurements present the possibility to be used under extreme weather conditions, for particular road texture contrary to optic techniques and at an expected affordable cost in a near future. More deepened studies will be launched to estimate the influence of tire structure, road curvature, evenness and texture on the proposed methodology.

Efforts to limit climate change should concern the transportation sector which is responsible for roughly a quarter of greenhouse gas emissions. Aside from vehicle’s technical progress and driver eco-driving awareness, road infrastructure has a role to play in this environmental aim. At the project stage, the design of roads can avoid energy losses linked to marked ramps, but afterwards, during the use phase, road management can be a lever too. In this use phase framework, our paper is focused on energy saving that can be achieved by managing speed sectioning. The key point is to ensure consistency between vehicle dynamics, road longitudinal profile and speed policy. Indeed, eco-driving could be impeded if a limiting speed sign is encountered on a steep slope or in a sharp turn. In such a situation the speed sign will be qualified as misplaced. Mechanical braking has then to be used instead of simple natural deceleration. In 2018 the French government lowered authorized speed on secondary roads, from 90 to 80 km/h, with road safety as the primary motivation. In order to assess energy impact of speed-sectioning for these two speed limits, experiments have been carried out in four experimental sites. Furthermore criterion and dissipated energy computation have been developed. The developed energy computation yields to determine the expected fuel economy for the entire traffic over a day on a selected route or network. As a result, over consumption for a misplaced speed sign can reach up to 40 liters of fuel per day with an approaching speed of 80 km/h and 50 liters of fuel per day with an approaching speed of 90 km/h according to traffic data. Significant energy savings could therefore be achieved by sign placement optimization.

Popularity has increased for semi-flexible transit systems because they combine the flexibility of a Demand Responsive Transit with the affordable fare of Conventional Public Transport. In this study, we investigate a Flex-Route Transit: a shuttle performs several trips in a pre-defined service area; it follows a main route between origin and destination terminals and must pass through some mandatory checkpoints along this route. Between any consecutive checkpoints, an available slack time is available for shuttles to deviate from the main route to pick-up and drop-off customers. We implement a Mixed-Integer Linear Programming formulation from the literature with an added shuttle capacity constraint. We use it to perform a sensitivity analysis of the system using a full factorial experimental design over three parameters, namely: shuttle capacity, level of demand and available slack time. The combined influence of the parameters is analyzed using the method called Analysis of Variance.

Construction site noise significantly contributes to urban noise pollution. To accurately assess the noise exposure from urban construction sites, it is essential to consider the variability and dynamic nature of the urban context. This paper proposes an innovative approach using individual-level activity-based modeling to tackle the given challenge. The methodology introduced encompasses: the modeling of construction site noise emission sources, individual-level activity-based modeling of zone of influences, a modeling chain for assessing individual noise exposure levels, and a novel dedicated exposure indicator. To illustrate the relevance of our methodology, a case study is conducted in Champigny-sur-Marne, France; this study involves the generation of a synthetic population of 1,344,394 individuals distributed among 591,153 households, with 4,854,597 associated activities, using the Eqasim simulation framework. A traffic model is then established using the multi-agent transport simulation software MATSim. NoiseModelling software is employed to compute noise maps both with and without the construction site. Subsequently, the noise exposure for each individual is assessed throughout the day for both configurations. Thanks to this complete modeling chain, the proposed indicator quantifying the cumulative amount of time of exposure above a given threshold can be compute, with the case study result being estimated at more than 3,300 person.quarter-hours above 65 dB(A).

Agent-Based Models (ABMs) are being increasingly used to evaluate urban systems, urban policies and environmental impacts. One prerequisite for using the ABM framework consists of generating a synthetic population representative of the actual population, featuring the appropriate attributes with respect to model objectives. A precise spatial positioning of the synthetic population agents is often key to ensuring ABM modeling quality. This paper considers the problem of allocating synthetic population agents to a finer spatial scale. Such an allocation process is performed from a higher-level statistical area where a synthetic population can be generated, that is, a container statistical area (CSA), to several nested non-overlapping elementary statistical areas (ESAs), where only marginals are available. This allocation step relies not only on common attributes between CSA and ESA, but also on additional discriminatory attributes, that is, attributes of interest, estimated from external data sources. The case study examined herein is based on French census and fiscal data. Common attributes include eight socio-demographic variables, totaling 17 modalities. An additional attribute of interest, that is, income, has also been added. The allocation problem at hand is modeled as an integer quadratic programming problem. An exact algorithm is first applied to solve the problem; the applicability of this algorithm proves to be limited to small-size synthetic populations. A heuristic is proposed to handle the allocation of larger-size synthetic populations. Tests carried out on the case study show that this heuristic yields near-optimal solutions; it is also computationally efficient and may fulfill the needs of a majority of users.

Flexible transport systems such as Demand Responsive Transit (DRT) are becoming more and more popular over the last few years due to their convenience for customers. However, this convenience comes at a price. Transport authorities are currently looking for ways to improve service flexibility of Conventional Public Transport (CPT), which is undoubtedly cheaper than DRT. This justifies the need for Flex-Route Transit (FRT), which combines the flexibility of DRT and the low cost of CPT. This paper surveys research developments on FRT, as a promising alternative mode of public transport. Based on this survey, we discuss current research gaps that may be filled to increase FRT applicability. Moreover, we show how literature on classic Operations Research problems is of help to do so. In particular, we study similarities and differences between FRT and Vehicle Routing Problem, and specifically with one of its variant named Dial-a-Ride Problem. The analysis illustrates promising techniques that may be of use for solving FRT.

The health and equity issues related to urban pollution, coupled with the need to assess the consequences of implementing transport policies or new mobility solutions, make it imperative to provide decision-makers with a relevant modeling framework. The coupling of activity-based travel models with environmental models presents a promising approach to thoroughly investigate the interconnected issues related to the impact of road traffic on individual exposure to noise and air pollution. Integrated modeling chains provide a robust framework to comprehensively assess traffic scenarios on a city scale and address issues related to environmental inequalities. This article reviews the recent work in this field to identify both the main contributions and key challenges still to be addressed. It illustrates how the use of a dynamic approach, which considers the intraday dynamics of individual mobilities and pollution concentrations, can contribute to more accurate estimates of the exposure of individuals to pollutants. This modeling approach also has the potential to investigate how the level of exposure depends on the characteristics of the individuals (socioeconomic factors, commuting patterns, location of residence, and main activities). Nonetheless, there are still important challenges to overcome to further enhance the accuracy and applicability of these models: taking into account all types of traffic and transport modes; refining the modeling of exposure to pollution during travel; taking better consideration of microenvironments; and so forth.

Ifsttar and RFF have developed an energy model of high-speed trains in order to determine the influence of infrastructure geometry and environment on train power consumptions. Validation of this model is based on full scale tests performed for the Rhine-Rhone high-speed line acceptance of work (in 2011). This paper aims to evaluate if wind influence is a relevant parameter for the model. This involves numerical determination of aerodynamic coefficients for various wind and ground configurations and their use for the calculation of the aerodynamic efforts. Atmospheric characteristics are extrapolated from meteorological measurements thanks to the AROME numerical model, over the whole Rhine-Rhone line. Most remarkable results show for a moderate train speed (about 47 m/s), a rearward oriented wind of only 5,5 m/s lowers by 30% the power due to aerodynamic forces applied on the train, or raises it similarly if it is forwardly oriented. In conclusion, this work points out that wind influence is of first importance for computing energy consumption.

In the context of reducing the global energy consumptions in transport systems, railways offer many advantages. Therefore the construction of new high speed railways has been planned. This article assesses the impact of the gradient of the railway on the train energy efficiency. This key parameter has a direct influence on the construction and operation phases energy amounts, since low gradients could require considerable civil engineering works. The energy model of the railway operation phase presented in this paper takes into account the infrastructure geometry, the train characteristics and the speed profiles. It is applied to different generic cases in order to illustrate the impact of the infrastructure gradients. The corresponding trajectories are compared from an energy efficiency point of view. Results show that the gradient is not the only one element to take into account. The associated length of ramp or slope is crucial too. For example, it is shown that short section with an important gradient can require less operation energy than lower gradient on longer distances. It is therefore possible to determine a couple of slope/ramp length and gradient for a given rolling stock which contributes to the minimization of the global construction cost and of the operating energy consumption.

Energy moderation of the road transportation sector is required to limit climate change and to preserve resources. This work is focused on the moderation of vehicle consumption by optimizing the speed policy along an itinerary while taking into account vehicle dynamics, driver visibility and the road's longitudinal profile. First, a criterion is proposed in order to detect speed policies that are impeding drivers' eco-driving ability. Then, an energy evaluation is carried out and an optimization is proposed. A numerical application is performed on a speed limiting point with 20 usage cases and 5 longitudinal slope values. In the hypothesis of a longitudinal slope of zero, energy savings of 27.7 liter per day could be realized by a speed sign displacement of only 153.6 m. Potential energy savings can increase to up to 308.4 L per day for a −4% slope case, or up to 70.5 L per day for an ordinary −2% slope, with a sign displacement of only 391.5 m. This results in a total of 771,975 L of fuel savings over a 30 year infrastructure life cycle period. Therefore a methodology has been developed to help road managers optimize their speed policies with the aim of moderating vehicle consumption.

Ce papier présente une nouvelle méthode à erreur de sortie en deux étapes pour l'identification en boucle fermée des paramètres inertiels, de frottements et de raideur d'un axe de robot avec flexibilité articulaire localisée. Cette méthode utilise uniquement l'effort et la position du moteur, sans aucune mesure de flexibilité. La première étape identifie la fréquence du mode rigide asservi et la fréquence du mode flexible à entrée bloquée, en utilisant un modèle haute fréquence double intégrateur du robot. L'identification utilise une méthode à erreur de sortie sur la position du moteur. Ces deux paramètres de fréquence optimaux sont ensuite utilisés dans la deuxième étape pour l'identification des paramètres dynamiques du robot par une méthode à erreur de sortie sur l'effort moteur. Cette méthode récente (DIDIM) qui utilise les modèles d'identification directe et inverse du robot permet d'obtenir une convergence des paramètres très rapide et insensible aux conditions initiales. Une validation expérimentale sur un robot un axe montre l'efficacité de la nouvelle méthode comparée à deux méthodes à erreur de sortie classiques.

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The Mobility Allowance Shuttle Transit (MAST) system is a type of public transportation where vehicles may deviate from a fixed route to serve clients who wish to get on or off within a service area. In this area, the system provides an on-demand service. We use Mixed-Integer Linear Programming to tackle the MAST system and consider often neglected realistic constraints, as those linked to vehicle capacity and fleet availability. We perform a sensitivity analysis to understand how the system performance varies as a result of changing client priorities.

Ce rapport est le deuxième rapport prévu au sein de ce contrat, écrit par l'équipe universitaire à destination de la SNCF dans le cadre de la convention de co-encadrement SNCF/UniversitéGustaveEiffel (anciennement Ifsttar) accompagne cette thèse (No Ifsttar : RP1-E19045) sous la responsabilité scientifique de Jean Calio, expert Data, Mobilité et Territoires et Développement Durable de la SNCF et de Pierre-Olivier Vandanjon.

The current transport system is not sustainable because of its high consumption of finite resources (mainly oil) and its impact on the environment (air quality, global warming, etc.). The design phase of the transport infrastructure has to be reconsidered in order to take into account these environmental burdens. Very recently, a methodology called Sloop (Slope Optimization) has been proposed to optimize the road longitudinal profile according to either an energy consumption or a Global Warming Potential (GWP) criterion calculated for construction and use phases. However, this optimization requires a high computation effort. It is a severe limitation to deploy this application to significant road project . The purpose of this paper is to propose a tailor-made optimization process which improves significantly this methodology. For the construction phase assessment, the methodology is based on a model of earthworks which computes the differences of geometry between the natural terrain and the road longitudinal profile. The use phase is assessed by simulating the traffic. Traffic simulations are based on vehicle dynamic models validated with real experiments. Starting from an initial longitudinal profile proposed by road designers, our algorithm finds a better profile in terms of Energy or GWP. The degrees of freedom of the optimization problem are mainly the road slopes and secondly the radii of the circles which connect them. The main constrain is the connection of the profile with the existing road. The optimization is complex due to the important number of degrees of freedom and the computation cost of the use phase assessment. It is the reason why, we developed a specific algorithm based on the Sequential Quadratic Programming Method (SQP) with suited computations of the linear approximation.

Current transportation systems contributed to one quarter to the Greenhouse Gases (GhG) emissions and the mobility demand increases continuously. The transportation infrastructure design has to be optimized to mitigate these emissions. The methodology ?Sloop? (acronym for Slope Optimization) has recently been set up to optimize the longitudinal road profile with respect to a Global Warming Potential (GWP) criterion calculated for both the construction and operational phases while incorporating accurate vehicle models. This paper proposes a customized optimization strategy that significantly improves the Sloop methodology. From an initial longitudinal profile generated by road designers, our algorithm identifies the optimized profile in terms of GWP. This optimization step is complex due to the large number of degrees of freedom, along with various constraints to obtaining a feasible solution and the computational cost of the operational phase assessment. The most stringent constraint entails connecting the profile with the existing road (i.e., a constraint on the final profile altitude). We have developed a specific algorithm based on the Sequential Quadratic Programming (SQP) method; this algorithm takes into account the quasi-quadratic nature of the constraint on the final altitude. In a real case study, our algorithm outputs a profile whose GWP assessment saves 4% compared to the GWP assessment of the initial profile. The benefit of our specific protocol for treating the final altitude constraint is demonstrated in this example. By means of this new efficient and open-box algorithm, profile evolution at each iteration is analyzed to determine the most sensitive degrees of freedom, and the sensitivity of the optimization with respect to the main construction parameters and traffic assumptions are conducted. The results are consistent and stable.

Dans cet article, une présentation des propriétés des polynômes de Tchebychev et une formulation dérivée utilisant ces polynômes sont effectuées. La méthode d'identification présentée consiste à appliquer une transformation linéaire sur le système d'équations qui régit le processus. Pour cela, l'opérateur mathématique utilisé s'appuie sur une décomposition du signal dans une base formée de polynômes orthogonaux. Nous montrons que cette projection se comporte comme un filtre passe-bande, de telle sorte qu'une seule opération est nécessaire pour le pré-traitement des données avant le processus d'identification, à savoir choisir la dimension de la base des polynômes Tchebychev. Une identification expérimentale en boucle fermée d'un robot à 2 axes avec cette méthode est effectuée dans une dernière partie. Les résultats obtenus sont comparés et analysés avec d'autres techniques utilisées en robotique.

The current transport system is not sustainable because of its high consumption of finite resources (mainly oil) and its impact on the environment (air quality, global warming, etc.). In this context, rail has several advantages: lower consumption and emissions than road and air modes, larger passenger flow, etc. That is why, many rail infrastructures are planned in the world. Today, the impact of energy is not taken into account during the project design. At the project phase of new rail infrastructures, it is nowadays important to characterize accurately the energy that will be induced by its operation phase, in addition to other more classical criteria as construction costs and travel time. Current literature consumption models used to estimate railways operation phase are obsolete or not enough accurate for taking into account the newest train or railways technologies. In this paper, an updated model of consumption for high-speed is proposed, based on experimental data obtained from full-scale tests performed on a new high-speed line. The assessment of the model is achieved by identifying train parameters and measured power consumptions for more than one hundred train routes. Perspectives are then discussed to use this updated model for accurately assess the energy impact of future railway infrastructures.

Nova'TP, commission TP de NOVABUILD, a initié un groupe de travail visant à favoriser l'innovation dans les marchés publics de travaux, grâce à l'élaboration de cahier des charges décrivant les performances attendues plutôt que les moyens de les atteindre. Cette démarche s'est appuyée sur l'exemple concret du choix des matériaux des pistes cyclables, qui sont intégrées à la plupart des aménagements réalisés sur l'espace public. La première étape du projet a consisté à définir les critères objectifs de performance de ces matériaux, afin de répondre aux enjeux de confort, de développement durable, de coût... exprimés par les maitres d'ouvrage. Sur certains critères, comme le confort, l'absence de référentiel existant pour les infrastructures cyclables, nous a conduit à développer une démarche innovante, en adaptant l'usage du Vel'Audit (Îlo instrumenté, prévu initialement pour l'inspection à haut rendement d'aménagements cyclables). Les matériaux déjà couramment utilisés pour ce type d'infrastructure ont été caractérisés selon ces critères. Ces données ont ensuite été regroupées dans une application, développée pour faciliter l'analyse et la comparaison des offres par les maîtres d'ouvrage. Cette application permet de comparer des matériaux conventionnels entre eux ou avec des matériaux innovants sous un aspect performentiel. Ce logiciel d'aide à la décision a été testé par Nantes Métropole sur plusieurs opérations d'aménagement, pour Îrifier sa pertinence et proposer certaines évolutions.

Les collectivités françaises développent de plus en plus d'aménagement cyclables, et il apparaît donc plus d'attentes concernant la sécurité de ces aménagements, leur confort, leur durabilité, leur gestion et entretien... Si pour le réseau routier général, les maîtres d'ouvrages disposent d'outils d'aide à la décision, basés sur des critères tels que la sécurité, la mobilité, l'environnement et le coût, les voies cyclables ne sont que partiellement concernées par ces outils. Ce travail vise à répondre aux attentes des gestionnaires de réseaux cyclables et a été réalisé dans le cadre du groupe de travail « Nova-TP » sur l'innovation dans les transports. En première partie de ce travail, L'outil Vel'audit est présenté. DériÎ des outils routiers, Vel'Audit est un Îlo instrumenté qui permet de réaliser des releÎs d'images ensuite exploitables à l'aide du logiciel IREVE. Ce diagnostic permet à la fois de caractériser l'infrastructure et d'identifier des défauts de cyclabilité. Vél'audit dispose aussi d'une centrale inertielle mesurant les vibrations subies par le Îlo et conduisant au calcul d'un indicateur confort indexé à la vitesse d'avancement. Pour l'aspect sécurité, le SRT (Skid Resistance Tester) est ensuite présenté, comme un des moyens de mesure de l'adhérence exploité pour les routes et pouvant être directement transposé aux pistes cyclables. Des essais de validité sur piste cyclable sont ensuite discutés et, en perspectives, le développement d'un matériel utilisable continûment sur itinéraire, inspiré du Grip-Tester, est envisagé en prolongement des mesures ponctuelles du SRT. Ainsi différentes techniques d'auscultations routières éprouÎes peuvent répondre aux besoins émergents des gestionnaires d'infrastructures cyclables, tout en s'adaptant aux caractéristiques de ces déplacements cyclables. Ces transpositions sont encore peu investie alors même que les besoins existent et qu'en finalité cela pourrait aboutir à la construction d'un indice de qualité cyclable (IQCy) à l'image de l'IQRN routier.

This paper deals with the identification of physical parameters of a one-degree-of-freedom electromechanical system that operates in closed loop. Two models are considered: the inverse dynamic model which is linear in relation to the physical parameters to be identified and the direct dynamic model which is linear in relation to a nonlinear combination of the physical parameters. Three methods are considered and compared: the traditional method which makes use of the inverse dynamic model, tailormade data prefiltering and the least-squares method, an instrumental variable approach which makes use of the direct and inverse dynamic models and another instrumental variable approach which makes use of the direct dynamic model only. The experimental results show that the instrumental variable method based on the use of the inverse and direct dynamic models seems to be more appropriate than the two others because it is robust against noise and the physical parameters are directly identified.

This brief deals with the identification of industrial robots. The usual identification method is based on the use of the inverse dynamic identification model and the least-squares technique. Another approach is the direct and inverse dynamic identification models (DIDIM) method, which is a closed-loop output error (CLOE) method minimizing the quadratic error between the actual and simulated joint torques. In this brief, the DIDIM method is compared with the usual CLOE method, which minimizes the quadratic error between the actual and simulated joint positions. The comparison is carried out on six degrees of freedom industrial robot. The experimental results show that the DIDIM method needs three iterations to identify 60 parameters while the usual CLOE method needs 20 iterations.

This paper deals with the important topic of rigid industrial robots identification. The usual identification method is based on the use of the inverse dynamic model and the least-squares technique. In order to obtain good results, a well-tuned derivative bandpass filtering of joint positions is needed to calculate the joint velocities and accelerations. However, we can doubt whether the bandpass filter is well-tuned or not. Another approach is the instrumental variable (IV) method which is robust to data filtering and which is statistically optimal. In this paper, an IV approach relevant for identification of rigid industrial robots is introduced. The set of instruments is the inverse dynamic model built from simulated data which are calculated from the simulation of the direct dynamic model. The simulation assumes the same reference trajectories and the same control structure for both the actual and the simulated robot and is based on the previous IV estimates. Furthermore, to obtain a rapid convergence, the gains of the simulated controller are updated according to IV estimates. Thus, the proposed approach validates the inverse and direct dynamic models simultaneously and is not sensitive to initial conditions. The experimental results obtained with a 2 degrees of freedom (DOF) planar prototype and with a 6 DOF industrial robot show the effectiveness of our approach: it is possible to identify 60 parameters in 3 iterations and in 11 s.

This paper deals with the important topic of industrial robot identification. The usual identification method is based on the inverse dynamic identification model and the least squares technique. This method has been successfully applied on several industrial robots. Good results can be obtained, provided a well tuned derivative band-pass filtering of joint positions is used to calculate the joint velocities and accelerations. However, one cannot be sure whether or not the band-pass filtering is well tuned. An alternative is the instrumental variable (IV) method, which is robust to data filtering and is statistically optimal. In this paper, a generic IV approach suitable for robot identification is proposed. The instrument set is the inverse dynamic model built from simulated data calculated from simulation of the direct dynamic model. The simulation is based on previous estimates and assumes the same reference trajectories and the same control structure for both actual and simulated robots. Finally, gains of the simulated controller are updated according to IV estimates to obtain a valid instrument set at each step of the algorithm. The proposed approach validates the inverse and direct dynamic models simultaneously, is not sensitive to initial conditions, and converges rapidly. Experimental results obtained on a six-degrees-of-freedom industrial robot show the effectiveness of this approach: 60 dynamic parameters are identified in three itérations.

This paper deals with the topic of industrial robots identification. The usual identification method is based on the use of the inverse dynamic model (IDM) and least squares (LS) technique. Good results can be obtained provided that a well-tuned bandpass filtering is used. However, we are always in doubt if regressors are exogenous i.e. statistically uncorrelated with error terms. Surprisingly, in papers dealing with identification of real-world systems, exogeneity assumption is never verified whereas it is a fundamental condition to obtain unbiased estimates. In Econometrics, the Durbin-Wu-Hausman test (DWH-test) is a theoretical method for investigating whether regressors are exogenous or not. The DWH-test makes of the Two Stage Lesat Squares estimator (2SLS) and an augmented LS regression. However, this test cannot be used as is for robots identification: instruments set is supposed to be valid and restrictive statistical assumptions are made while they are quite implausible in practice. In this paper, we aim at bridging the gap between Econometrics and Control engineering practices by introducing a revisited version relevant for robots identification. An experimental validation performed on a 2 degrees of freedom (DOF) robot shows the effectiveness and the usefulness of this revisited DWH-test.

This paper deals with the topic of robots dynamics identification. In this paper, we focus on the instrumental variable (IV) technique. For robots, the set of instruments is the inverse dynamic model built from simulated data which are calculated from the integration of the direct dynamic model, assuming the same reference trajectories and the same control law structure for both actual and simulated robots. The integration of the direct dynamic model is based on previous IV estimates. This defines an iterative algorithm. Furthermore, gains of the simulated controller are updated to get a fast convergence. Experimental results obtained on a six degrees of freedom robot manufactured by Stäubli show the effectiveness of our approach: 60 dynamic parameters are estimated in only two iterations.

This paper addresses physical parameters identification of mathematical models that are linear in relation to these physical parameters. We can obtain good results with the least squares technique, provided that a well-tuned data filtering is used, and by using instrumental variable (IV) methods, which deal with the problem of noisy observation matrix. However, IV theory is based on instruments validity. In econometrics, statistical tests evaluating instruments quality have been developed. They make use of the two-stage least squares estimator and the concentration parameter introduced by Basmann. In this paper we show how to extend econometric theory to control engineering. An algorithm evaluating instruments quality is presented and experimentally validated on a two-degrees-of-freedom SCARA robot.

Offline robot dynamic identification methods are mostly based on the use of the inverse dynamic model, which is linear with respect to the dynamic parameters. This model is sampled while the robot is tracking reference trajectories that excite the system dynamics. This allows using linear least-squares techniques to estimate the parameters. The efficiency of this method has been proved through the experimental identification of many prototypes and industrial robots. However, this method requires the joint force/torque and position measurements and the estimate of the joint velocity and acceleration, through the bandpass filtering of the joint position at high sampling rates. The proposed new method called DIDIM requires only the joint force/torque measurement, which avoids the calculation of the velocity and acceleration by bandpass filtering of the measured position. It is a closed-loop output error method where the usual joint position output is replaced by the joint force/torque. It is based on a closed-loop simulation of the robot using the direct dynamic model, the same structure of the control law, and the same reference trajectory for both the actual and the simulated robot. The optimal parameters minimize the 2-norm of the error between the actual force/torque and the simulated force/torque. This is a nonlinear least-squares problem which is dramatically simplified using the inverse dynamic model to obtain an analytical expression of the simulated force/torque, linear in the parameters. A validation experiment on a two degree-of-freedom direct drive rigid robot shows that the new method is efficient.

This paper deals with joint stiffness identification with a new Closed-Loop Output Error (CLOE) method which minimizes the quadratic error between the actual motor force/torque and the simulated one. This method is based on the DIDIM (Direct and Inverse Identification Model) procedure which has been validated on rigid robots and which is now applied to a flexible joint robot. DIDIM method requires a gain updating in the simulated robot in order to keep the bandwidth of the rigid controlled degree of freedom (dof ) and to keep the natural frequency of the flexible dof, close to the actual ones, at each step of the recursive Gauss Newton non linear programming algorithm. This gain updating requires a first step of estimating the natural frequency of the flexible dof before applying DIDIM method in a second step. An experimental setup exhibits identification results and shows the effectiveness of our approach.

Ce papier présente une nouvelle méthode à erreur de sortie en deux étapes pour l'identification en boucle fermée des paramètres inertiels, de frottements et de raideur d'un axe de robot avec flexibilité articulaire localisée. Cette méthode utilise uniquement l'effort et la position du moteur, sans aucune mesure de flexibilité. La première étape identifie la fréquence du mode rigide asservi et la fréquence du mode flexible à entrée bloquée, en utilisant un modèle haute fréquence double intégrateur du robot. L'identification utilise une méthode à erreur de sortie sur la position du moteur. Ces deux paramètres de fréquence optimaux sont ensuite utilisés dans la deuxième étape pour l'identification des paramètres dynamiques du robot par une méthode à erreur de sortie sur l'effort moteur. Cette méthode récente (DIDIM) qui utilise les modèles d'identification directe et inverse du robot permet d'obtenir une convergence des paramètres très rapide et insensible aux conditions initiales. Une validation expérimentale sur un robot un axe montre l'efficacité de la nouvelle méthode comparée à deux méthodes à erreur de sortie classiques.

This paper addresses the important topic of joint flexibility identification. Three dynamic models depending on measurements availability are compared. The parameters are estimated by using the ordinary least squares of an over linear system obtained from the sampling of the dynamic model along a closed loop tracking trajectory. An experimental setup exhibits the experimental identification results.

This paper deals with joint stiffness identification with only actual motor force/torque data instead of motor and load positions. The parameters are estimated by using the DIDIM method which needs only input data. This method was previously validated on a 6 DOF rigid robot and is now extended to flexible systems. The criterion to be minimized is the quadratic error between the measured actual motor force/torque and the simulated one. The optimal parameters are calculated with the Nelder - Mead simplex algorithm. An experimental setup exhibits the experimental identification results and shows the effectiveness of our approach.

L'identification des paramètres dynamiques des robots est généralement basée sur l'utilisation du modèle dynamique inverse (MDI) qui est linéaire par rapport aux paramètres dynamiques à identifier et les moindres carrés linéaires (MCL). Cette méthode nécessite une mesure précise d'une image du couple et de la position à une fréquence assez élevée d'une part, et de l'estimation des vitesses et accélérations par un filtrage passe bande de la position d'autre part. La matrice d'observation étant bruitée et l'identification étant effectuée en boucle fermée, nous avons adapté la méthode de la variable instrumentale (IV) pour remédier au problème de la matrice d'observation bruitée. Récemment, une méthode d'identification originale ne nécessitant que la mesure des couples (grandeurs d'entrée) a été appliquée avec succès sur un robot industriel à 6 degrés de liberté (DDL). Cette méthode appelée DIDIM est une méthode erreur de sortie en boucle fermée (CLOE) basée sur une simulation en boucle fermée du robot. Les résultats expérimentaux ont montré que les résultats IV et DIDIM sont numériquement égaux. Dans ce papier, nous comparons ces deux méthodes grâce à une analyse basée sur les doubles moindres carrés (2SLS). Cette étude est expérimentalement validée sur un robot 2 DDL plan.

Off-line robot dynamic identification methods are mostly based on the use of the inverse dynamic model, which is linear with respect to the dynamic parameters. This model is sampled while the robot is tracking reference trajectories that excite the system dynamics. This allows using linear least-squares techniques to estimate the parameters. This method requires the joint force/torque and position measurements and the estimate of the joint velocity and acceleration, through the bandpass filtering of the joint position at high sampling rates. A new method called DIDIM has been proposed and validated on a 2 degree-of-freedom robot. DIDIM method requires only the joint force/torque measurement. It is a closed-loop output error (CLOE) method based on a closed-loop simulation of the robot using the direct dynamic model, the same structure of the control law, and the same reference trajectory for both the actual and the simulated robot. The optimal parameters minimize the 2-norm of the error between the actual force/torque and the simulated force/torque. A validation experiment on a 6 dof Staubli TX40 robot shows that DIDIM method is very efficient on industrial robots.

Off-line robot dynamic identification methods are mostly based on the use of the inverse dynamic model, which is linear with respect to the dynamic parameters. This model is calculated with torque and position sampled data while the robot is tracking reference trajectories that excite the system dynamics. This allows using linear least-squares techniques to estimate the parameters. This method requires the joint force/torque and position measurements and the estimate of the joint velocity and acceleration, through the bandpass filtering of the joint position at high sampling rates. A new method called DIDIM (Direct and Inverse Dynamic Identification Models) has been proposed and validated on a 2 degree-of- freedom robot [1]. DIDIM method requires only the joint force/torque measurement. It is based on a closed-loop simulation of the robot using the direct dynamic model, the same structure of the control law, and the same reference trajectory for both the actual and the simulated robot. The optimal parameters minimize the 2-norm of the error between the actual force/torque and the simulated force/torque. A validation experiment on a 6 dof Staubli TX40 robot shows that DIDIM method is very efficient on industrial robots.

Tyre-road estimation methods have been the objective of many research programmes throughout the world. Most of these methods aim at estimating the friction components such as tyre longitudinal slip rate κ and friction coefficient μ in the contact patch area. In order to estimate the maximum available friction coefficient μmax , these methods generally use a probabilistic relationship between the grip obtained for low tyre excitations (such as constant speed driving) and the grip obtained for high tyre excitations (such as emergency braking manoeuvre). Confirmation or invalidation of this relationship from experimental results is the purpose of this paper. Experiments have been carried out on a reference track including several test boards corresponding to a wide textural spectrum. The main advantage of these experiments lies in the use of a vehicle allowing us to accurately build point-by-point relationship between κ and μ. This relationship has been determined for different tyres and pavement textures. Finally, the curves obtained are analysed to check the validity of the relationship between the current friction coefficient used by the car during normal driving conditions and μmax.

Off-line robot dynamic identification methods are mostly based on the use of the inverse dynamic model, which is linear with respect to the dynamic parameters. This model is sampled while the robot is tracking reference trajectories that excite the system dynamics. This allows using linear least-squares techniques to estimate the parameters. This method requires the joint force/torque and position measurements and the estimate of the joint velocity and acceleration, through the bandpass filtering of the joint position at high sampling rates. Recently, the DIDIM method has been proposed and validated on a 2 degree-of-freedom robot. DIDIM method requires only the joint force/torque measurement. It is a closed-loop output error (CLOE) method based on a closed-loop simulation of the robot using the direct dynamic model, the same structure of the control law, and the same reference trajectory for both the actual and the simulated robot. The optimal parameters minimize the 2-norm of the error between the actual force/torque and the simulated force/torque. A validation experiment on a 6 dof Staubli TX40 robot shows that DIDIM method is very efficient on industrial robots.

Off-line robot dynamic identification methods are mostly based on the use of the inverse dynamic model, which is linear with respect to the dynamic parameters. This model is sampled while the robot is tracking reference trajectories that excite the system dynamics. This allows using linear least-squares techniques to estimate the parameters. This method requires the joint force/torque and position measurements and the estimate of the joint velocity and acceleration, through the bandpass filtering of the joint position at high sampling rates. A new method called DIDIM has been proposed and validated on a 2 degree-of-freedom robot. DIDIM method requires only the joint force/torque measurement. It is based on a closed-loop simulation of the robot using the direct dynamic model, the same structure of the control law, and the same reference trajectory for both the actual and the simulated robot. The optimal parameters minimize the 2-norm of the error between the actual force/torque and the simulated force/torque. A validation experiment on a 6 dof Staubli TX40 robot shows that DIDIM method is very efficient on industrial robots

The identification of the dynamic parameters of robot is based on the use of the inverse dynamic identification model which is linear with respect to the parameters. This model is sampled while the robot is tracking “exciting” trajectories, in order to get an over determined linear system. The linear least squares solution of this system calculates the estimated parameters. The efficiency of this method has been proved through the experimental identification of a lot of prototypes and industrial robots. However, this method needs joint torque and position measurements and the estimation of the joint velocities and accelerations through the bandpass filtering of the joint position at high sample rate. So, the observation matrix is noisy. Moreover identification process takes place when the robot is controlled by feedback. These violations of assumption imply that the LS estimator is not consistent. This paper focuses on the Refined Instrumental Variable (RIV) approach to over-come this problem of noisy observation matrix. This technique is applied to a 2 degrees of freedom (DOF) prototype devel-oped by the IRCCyN Robotic team.

Off-line robot dynamic identification methods are mostly based on the use of the inverse dynamic model, which is linear with respect to the dynamic parameters. This model is sampled while the robot is tracking reference trajectories that excite the system dynamics. This allows using linear least-squares techniques to estimate the parameters. The efficiency of this method has been proved through the experimental identification of many prototypes and industrial robots. However, this method requires the joint force/torque and position measurements and the estimate of the joint velocity and acceleration, through the bandpass filtering of the joint position at high sampling rates. The proposed new method requires only the joint force/torque measurement. It is a closed-loop output error method where the usual joint position output is replaced by the joint force/torque. It is based on a closed-loop simulation of the robot using the direct dynamic model, the same structure of the control law, and the same reference trajectory for both the actual and the simulated robot. The optimal parameters minimize the 2-norm of the error between the actual force/torque and the simulated force/torque. This is a non-linear least-squares problem which is dramatically simplified using the inverse dynamic model to obtain an analytical expression of the simulated force/torque, linear in the parameters. A validation experiment on a 2 degree-of-freedom direct drive robot shows that the new method is efficient.

L'identification des paramètres dynamiques des robots est généralement basée sur l'utilisation du modèle inverse qui est linéaire par rapport aux paramètres dynamiques à identifier. Afin d'obtenir un système surdéterminé, ce modèle est échantillonné pendant que le robot est piloté avec des mouvements excitants. La solution optimale est obtenue à l'aide des moindres carrés linéaires (MCL). L'efficacité de cette approche a été prouvée à travers de l'identification expérimentale de nombreux prototypes et robots industriels. Elle a été étendue à d'autres systèmes tels que les compacteurs, les voitures, les moteurs et les interfaces haptiques. Cependant, cette méthode nécessite une mesure précise d'une image du couple et de la position à une fréquence assez élevée. Elle nécessite également l'estimation des vitesses et accélérations par un filtrage passe bande de la position. La matrice d'observation est donc bruitée. De plus, l'identification est effectuée en boucle fermée du fait du caractère double intégrateur instable des robots. La méthode de la variable instrumentale (IV) traite du problème de la matrice d'observation bruitée et peut être statistiquement optimale. Ce papier se focalise sur cette technique et propose une extension de cette méthode. Elle est appliquée sur un robot 2 degrés de liberté (DDL) plan développé par l'équipe robotique de l'IRCCyN.

Usually, the identification of the dynamic parameters of robot is based on the use of the inverse dynamic model which is linear with respect to the parameters. This model is sampled while the robot is tracking exciting trajectories. This allows using linear least squares techniques to estimate the parameters. The efficiency of this method has been proved through the experimental identification of a lot of prototypes and industrial robots. However, this method needs joint torque and position measurements and the estimation of the joint velocities and accelerations through the pass band filtering of the joint position at high sample rate. The new method overcomes these drawbacks. It is based on a closed loop simulation using the direct dynamic model. The optimal parameters minimize the 2 norms of the error between the actual torque and the simulated torque assuming the same control law and the same tracking trajectory. This non linear least squares problem dramatically is simplified using the inverse model to calculate the simulated torque.

Ce chapitre de livre, accessible par internet, décrit une méthode pour connaître l'influence de l'erreur de mesure sur le résultat final. Elle revient à utiliser une méthode classique utilisée dans l'évaluation de la robustesse des simulations numériques vis à vis de la troncature liée au codage des réels par les ordinateurs. (la méthode CESTAC :Contrôle et Estimation Stochastique des Arrondis de Calculs).

The identification of the dynamic parameters of robot is based on the use of the inverse dynamic model which is linear with respect to the parameters. This model is sampled while the robot is tracking exciting trajectories, in order to get an over determined linear system. The linear least squares solution of this system calculates the estimated parameters. The efficiency of this method has been proved through the experimental identification of a lot of prototypes and industrial robots. However, this method needs joint torque and position measurements and the estimation of the joint velocities and accelerations through the pass band filtering of the joint position at high sample rate. So, the observation matrix is noisy. Moreover identification process takes place when the robot is controlled by feedback. These violations of assumption imply that the LS solution is biased. The Refined Instrumental Variable (SRIV) approach deals with this problem of noisy observation matrix and can be statistically optimal. This paper focuses on this technique which will be applied to a 2 degrees of freedom (DOF) prototype developed by the IRCCyN Robotic team.

The identification of the dynamic parameters of robot is based on the use of the inverse dynamic model which is linear with respect to the parameters. This model is sampled while the robot is tracking exciting trajectories, in order to get an over determined linear system. The linear least squares solution of this system calculates the estimated parameters. The efficiency of this method has been proved through the experimental identification of a lot of prototypes and industrial robots. However, this method needs joint torque and position measurements and the estimation of the joint velocities and accelerations through the pass band filtering of the joint position at high sample rate. So, the observation matrix is noisy. Moreover identification process takes place when the robot is controlled by feedback. These violations of assumption imply that the LS solution is biased. The Simple Refined Instrumental Variable (SRIV) approach deals with this problem of noisy observation matrix and can be statistically optimal. This paper focuses on this technique which will be applied to a 2 degrees of freedom (DOF) prototype developed by the IRCCyN Robotic team.

Usually, the identification of the dynamic parameters of robot makes use of the inverse dynamic model which is linear with respect to the parameters. This model is sampled while the robot is tracking exciting trajectories. This allows using linear least squares (LS) techniques to estimate the parameters. The efficiency of this method has been proved through experimental identifications of a lot of prototypes and industrial robots. However, it is known that LS estimators are sensitive to outliers and leverage points. Thus, it may be helpful to verify their reliability. This is possible by using robust regressions and residual analysis. Then, we compare the results with those obtained with classical LS regression. This paper deals with this issue and introduces the experimental identification and residual analysis of an one degree of freedom (DOF) haptic interface using the Huber's estimator. To verify the pertinence of our analyses, this comparison is also performed on a medical interface consisting of a complex mechanical structure.

Motivation: Metagenomics is a powerful approach to study genetic content of environmental samples, which has been strongly promoted by next-generation sequencing technologies. To cope with massive data involved in modern metagenomic projects, recent tools rely on the analysis of k-mers shared between the read to be classified and sampled reference genomes.Results: Within this general framework, we show that spaced seeds provide a significant improvement of classification accuracy, as opposed to traditional contiguous k-mers. We support this thesis through a series of different computational experiments, including simulations of large-scale metagenomic projects.Availability and implementation, Supplementary information: Scripts and programs used in this study, as well as supplementary material, are available from http://github.com/gregorykucherov/spaced-seeds-for-metagenomics.

We consider the Low Rank Approximation problem, where the input consists of a matrix A and an integer k, and the goal is to find a matrix B of rank at most k that minimizes $‖A−B‖_0$, which is the number of entries where A and B differ. For any constant k and ε>0, we present a polynomial time (1+ε)-approximation time for this problem, which significantly improves the previous best poly(k)-approximation. Our algorithm is obtained by viewing the problem as a Constraint Satisfaction Problem (CSP) where each row and column becomes a variable that can have a value from $\mathbb{R}^k$. In this view, we have a constraint between each row and column, which results in a dense CSP, a well-studied topic in approximation algorithms. While most of previous algorithms focus on finite-size (or constant-size) domains and involve an exhaustive enumeration over the entire domain, we present a new framework that bypasses such an enumeration in $\mathbb{R}^k$. We also use tools from the rich literature of Low Rank Approximation in different objectives (e.g., $\ell_p$ with p∈(0,∞)) or domains (e.g., finite fields/generalized Boolean). We believe that our techniques might be useful to study other real-valued CSPs and matrix optimization problems. On the hardness side, when k is part of the input, we prove that Low Rank Approximation is NP-hard to approximate within a factor of Ω(logn). This is the first superconstant NP-hardness of approximation for any p∈[0,∞] that does not rely on stronger conjectures (e.g., the Small Set Expansion Hypothesis).

A matroid $M$ is an ordered pair $(E,I)$, where $E$ is a finite set called the ground set and a collection $I\subset 2^{E}$ called the independent sets which satisfy the conditions: (i) $\emptyset \in I$, (ii) $I'\subset I \in I$ implies $I'\in I$, and (iii) $I_1,I_2 \in I$ and $|I_1| < |I_2|$ implies that there is an $e\in I_2$ such that $I_1\cup \{e\} \in I$. The rank $rank(M)$ of a matroid $M$ is the maximum size of an independent set. We say that a matroid $M=(E,I)$ is representable over the reals if there is a map $\varphi \colon E \rightarrow \mathbb{R}^{rank(M)}$ such that $I\in I$ if and only if $\varphi(I)$ forms a linearly independent set. We study the problem of matroid realizability over the reals. Given a matroid $M$, we ask whether there is a set of points in the Euclidean space representing $M$. We show that matroid realizability is $\exists \mathbb R$-complete, already for matroids of rank 3. The complexity class $\exists \mathbb R$ can be defined as the family of algorithmic problems that is polynomial-time is equivalent to determining if a multivariate polynomial with integers coefficients has a real root. Our methods are similar to previous methods from the literature. Yet, the result itself was never pointed out and there is no proof readily available in the language of computer science.

<div><p>In this research, we extend the traditional model of recurrent real-time tasks to incorporate the cost of context switches; we introduce the concept of Non-Resumable delays (i.e., loading phases are preemptive, but the processing time already expended in attempting to load a task is forfeited). We propose a model that addresses previous methodology flaws regarding the pessimism and scheduling anomalies, which could be implementable on certain Real-Time Operating System (RTOS) and models high-level abstraction of what is a context switch activity. We provide two exact schedulability tests for two popular scheduling families (Fixed Task Priority as Rate Monotonic and Fixed Job Priority as Earliest Deadline First). These tests are sustainable regarding the execution times and the preemption costs. They are based on the notion of simulation interval and properties of the schedule repetition. We conclude with a discussion of the advantages and drawbacks of our model and the future perspectives it opens up.</p></div>

DAGs are commonly used for modelling successive versions of a project in systems such as Git or Mercurial, where each version (node) is based on one or two former versions (with arcs from each node to its former versions). Some specificities make these graphs unique: they grow in "append-only" mode, i.e. nodes are created with in-degree 0, so the out-degree of existing nodes remains constant. Also, several copies of the graph grow in parallel, with synchronisation steps when an agent sends their new nodes to other agents.<p>For the largest graphs, which can reach up to several million revisions, sub-linear algorithms become necessary for recurring tasks, creating a need for a pre-computed index that can grow and be shared along with the graph nodes. In this paper we focus on the reachability problem (deciding if a path exists between two nodes), as well as label discovery: if each agent has a label attached to each node, determine the set of nodes with distinct labels between two agents. Algorithms are to be designed not only in a dynamic setting, i.e. allowing for node insertions, but also with the multi-agent constraint that different agents, having received different nodes and common nodes in different orders, may need to share large sets of nodes among themselves. Very efficient reachability indices exist in static DAGs, but they often do not apply well to rapidly-growing graphs in a multi-agent model. We present a framework allowing for fast algorithms for both tasks, using a compact index (with a few bytes per node in practice) that can easily be updated when the graph grows and nodes are shared. At the core of our approach lies the notion of ranges, i.e. specific sets of nodes that admit a concise representation and can be partitioned (split) into smaller ranges.</p>

<div><p>Defining script types and establishing classification criteria for medieval handwriting is a central aspect of palaeographical analysis. However, existing typologies often encounter methodological challenges, such as descriptive limitations and subjective criteria. We propose an interpretable deep learning-based approach to morphological script type analysis, which enables systematic and objective analysis and contributes to bridging the gap between qualitative observations and quantitative measurements. More precisely, we adapt a deep instance segmentation method to learn comparable character prototypes, representative of letter morphology, and provide qualitative and quantitative tools for their comparison and analysis. We demonstrate our approach by applying it to the Textualis Formata script type and its two subtypes formalized by A. Derolez: Northern and Southern Textualis.</p></div>

<div><p>Inverse folding is a classic instance of negative RNA design which consists in finding a sequence that uniquely folds into a target secondary structure with respect to energy minimization. A breakthrough result of Bonnet et al. shows that, even in simple base pairs-based (BP) models, the decision version of a mildly constrained version of inverse folding is NP-hard. In this work, we show that inverse folding can be solved in linear time for a large collection of targets, including every structure that contains no isolated BP and no isolated stack (or, equivalently, when all helices consist of 3 + base pairs). For structures featuring shorter helices, our linear algorithm is no longer guaranteed to produce a solution, but still does so for a large proportion of instances. Our approach introduces a notion of modulo m-separability, generalizing a property pioneered by Hales et al. Separability is a sufficient condition for the existence of a solution to the inverse folding problem. We show that, for any input secondary structure of length n, a modulo m-separated sequence can be produced in time O(n m 2 m ) anytime such a sequence exists. Meanwhile, we show that any structure consisting of 3 + base pairs is either trivially non-designable, or always admits a modulo-2 separated solution. Solution sequences can thus be produced in linear time, and even be uniformly generated within the set of modulo-2 separable sequences.</p></div>

Les objectifs de la thèse sont de développer des méthodes et des cadres d'analyse de la posture humaine 3D en environnement de travail pour l'ergonomie. L’ergonomie est une discipline qui consiste à comprendre le fonctionnement du corps lorsqu'il travaille pour l’objectif de préserver la santé des opérateurs tout en permettant l’atteinte de la qualité attendue. Les postures des opérateurs au poste de travail sont un des facteurs d'apparition des maladies professionnelles, et la caractérisation d’une posture est une étape du pré-diagnostic d’une situation de travail. Les méthodes d’intelligence artificielle autour de l’estimation 3D de la pose humaine pour détecter les postures inadaptées au travail peuvent ainsi aider l’ergonome à établir son diagnostic sur un grand nombre de données. Cette thèse propose trois travaux autour de l'estimation de pose humaine 3D pour s'attaquer aux difficultés de mise en œuvre en environnement non contraint tels que les postes de travail.La première contribution propose un algorithme synthétique de génération de poses humaines en 3D. Nous abordons le problème de l'écart de domaine selon lequel les scénarios de travail a plus de variété d'actions et d'environnement que les données de recherche publique. Ce travail présente un algorithme qui permet de générer des squelettes humains 3D synthétiques pendant l'entraînement de réseau des neurons, suivant une distribution de type arbre de Markov qui évolue au fil du temps pour créer des nouvelles postures. Ce travail propose également un processus d'entraînement multi-vues sans échelle basé sur des données purement synthétiques générées à partir de quelques postures initiales. Nous évaluons notre approche sur les deux ensembles de données de référence et obtenons des résultats prometteurs dans une configuration sans aucune donnée réelle. Le deuxième travail propose un cadre de création d'annotations 3D du corps entier à partir d'images multi-vues ainsi qu'un benchmark construit sur la base de ce cadre. Les données couramment utilisées ne comportent normalement qu'une vingtaine d'articulations, ce qui n'est pas suffisant pour qu'un ergonome puisse mesurer certains aspects comme les angles de supination-pronation, c'est pourquoi nous proposons un squelette du corps entier compte 133 articulations, capables de contenir les informations nécessaires. Le cadre de création d'annotations contient 3 étapes allant de la reconstruction géométrique 3D multi-vues à la complétion des squelettes incomplets, et enfin au raffinement main/visage par diffusion. Avec ce cadre, nous introduisons 3 ensembles de données en tant qu'extensions des ensembles de données Human3.6M, CMU-Panoptic et MPI-INF-3DHP existants avec des annotations de points clés 2D et 3D du corps entier pour le corps, le visage et les mains. Un benchmark de trois tâches est proposé sur la base de l'extension du corps entier de Human3.6M.Le troisième travail propose un algorithme qui permet une prédiction continue des poses humaines à travers le temps avec des images d'entrée très limitées pour aborder des séquences vidéo potentiellement corrompues dans un environnement sans contrainte où les travailleurs ne sont pas toujours observés ou même à l'écran en raison de leurs mouvements. Ce travail propose une nouvelle approche qui modélise le mouvement humain comme une fonction continue mise en œuvre par un réseau neuronal, semblable à des représentations neuronales implicites. Nous effectuons une comparaison complète de cette approche avec des méthodes de prédiction de mouvement de pointe sur trois ensembles de données populaires, démontrant des améliorations significatives par rapport aux lignes de base dans la plupart des cas. Enfin, nous avons réalisé un démonstrateur qui effectue une estimation de la pose humaine en 2D et 3D, ainsi qu'une détection des poses critiques pour une analyse ergonomique, capable d'analyse rapide même sur un ordinateur équipé uniquement d'un CPU.

Generative modeling methods can generate images from textual or visual inputs. However, diversity in the generated images persists as a major challenge of the existing approaches. In this work, we address this issue head-on by demonstrating that: (a) the diversity of a generated batch of images is intrinsically linked to the diversity within the latent variables; (b) leveraging the geometry of the latent space, we can establish an effective metric for quantifying diversity; and (c) employing this insight allows one to achieve a significantly enhanced diversity in image generation beyond the capabilities of traditional random independent sampling. This advancement is consistent across a variety of generative models, including Generative Adversarial Networks (GANs) and latent diffusion models. To facilitate further research and application in this field, we are also releasing a comprehensive package that enables easy reproduction of our experiments. We integrate our contributions into a widely recognized tool for generative image modeling, ensuring that our improvements are accessible to the broader community.

In this paper, we develop the notion of a Morse sequence, which provides an alternative approach to discrete Morse theory, and which is both simple and effective. A Morse sequence on a finite simplicial complex is a sequence composed solely of two elementary operations, that is, expansions (the inverse of a collapse), and fillings (the inverse of a perforation). In a dual manner, a Morse sequence may be obtained by considering only collapses and perforations. Such a sequence is another way to represent the gradient vector field of an arbitrary discrete Morse function. To each Morse sequence, we assign a reference map and an extension map. A reference map associates a set of critical simplexes to each simplex of a given complex, and an extension map associates a set of simplexes to each critical simplex. By considering the boundary of each critical simplex, we obtain a chain complex from these maps, which corresponds precisely to the Morse complex. We show that, when restricted to homology, an extension map is the inverse of a reference map. Also we show that these two maps allow us to recover directly the isomorphism theorem between the homology of an object and the homology of its Morse complex. At last, we introduce the notion of a flow complex, which is based solely on extension maps. We prove that this notion is equivalent to the classical one based on gradient flows.

Annotating 3D medical images demands expert knowledge and is time-consuming. As a result, semi-supervised learning (SSL) approaches have gained significant interest in 3D medical image segmentation. The significant size differences among various organs in the human body lead to imbalanced class distribution, which is a major challenge in the real-world application of these SSL approaches. To address this issue, we develop a novel Shape Transformation driven by Active Contour (STAC), that enlarges smaller organs to alleviate imbalanced class distribution across different organs. Inspired by curve evolution theory in active contour methods, STAC employs a signed distance function (SDF) as the level set function, to implicitly represent the shape of organs, and deforms voxels in the direction of the steepest descent of SDF (i.e., the normal vector). To ensure that the voxels far from expansion organs remain unchanged, we design an SDF-based weight function to control the degree of deformation for each voxel. We then use STAC as a data-augmentation process during the training stage. Experimental results on two benchmark datasets demonstrate that the proposed method significantly outperforms some state-of-the-art methods. Source code is publicly available at https://github.com/GuGuLL123/STAC.

Communication needs in avionics and transportation have radically changed over the recent years. Traditionally, the underlying hard real-time networks were designed in a centralized way, focusing on redundancy and isolation. Today, real-time communication is ubiquitous, from large airplanes to small vehicles. The associated networks must support a wide range of applications, and large amounts of data. Centralized approaches from the avionics domain, e.g., AFDX, are too costly, too heavyweight, and not flexible enough for these applications.In this paper we explore a new distributed network architecture designed to support jumbo airliners, but also small aircraft and drones. Communication redundancy is achieved using redundant paths, which have to be adapted and optimized to the application. The main challenge then is to build an optimized network configuration ensuring safety, fault tolerance, timing, and performance of both critical, and non-critical communication. Minimizing volume and weight of the equipment is also mandatory. Since the solution space is too large to be explored in reasonable time, we propose a genetic algorithm. Our experiments show that our algorithm converges quickly and offers solutions of excellent quality. The computed solutions are in the top 2% among the best solutions obtained using an exhaustive exploration. Our approach thus enables system engineers to quickly explore and choose very good solution for their systems.

AfterLearnER (After Learning Evolutionary Retrofitting) consists in applying non-differentiable optimization, including evolutionary methods, to refine fully-trained machine learning models by optimizing a set of carefully chosen parameters or hyperparameters of the model, with respect to some actual, exact, and hence possibly non-differentiable error signal, performed on a subset of the standard validation set. The efficiency of AfterLearnER is demonstrated by tackling non-differentiable signals such as threshold-based criteria in depth sensing, the word error rate in speech re-synthesis, image quality in 3D generative adversarial networks (GANs), image generation via Latent Diffusion Models (LDM), the number of kills per life at Doom, computational accuracy or BLEU in code translation, and human appreciations in image synthesis. In some cases, this retrofitting is performed dynamically at inference time by taking into account user inputs. The advantages of AfterLearnER are its versatility (no gradient is needed), the possibility to use non-differentiable feedback including human evaluations, the limited overfitting, supported by a theoretical study and its anytime behavior. Last but not least, AfterLearnER requires only a minimal amount of feedback, i.e., a few dozens to a few hundreds of scalars, rather than the tens of thousands needed in most related published works. Compared to fine-tuning (typically using the same loss, and gradient-based optimization on a smaller but still big dataset at a fine grain), AfterLearnER uses a minimum amount of data on the real objective function without requiring differentiability.

Composed Image Retrieval (CoIR) has recently gained popularity as a task that considers both text and image queries together, to search for relevant images in a database. Most CoIR approaches require manually annotated datasets, comprising image-text-image triplets, where the text describes a modification from the query image to the target image. However, manual curation of CoIR triplets is expensive and prevents scalability. In this work, we instead propose a scalable automatic dataset creation methodology that generates triplets given video-caption pairs, while also expanding the scope of the task to include composed video retrieval (CoVR). To this end, we mine paired videos with a similar caption from a large database, and leverage a large language model to generate the corresponding modification text. Applying this methodology to the extensive WebVid2M collection, we automatically construct our WebVid-CoVR dataset, resulting in 1.6 million triplets. Moreover, we introduce a new benchmark for CoVR with a manually annotated evaluation set, along with baseline results. We further validate that our methodology is equally applicable to image-caption pairs, by generating 3.3 million CoIR training triplets using the Conceptual Captions dataset. Our model builds on BLIP-2 pretraining, adapting it to composed video (or image) retrieval, and incorporates an additional caption retrieval loss to exploit extra supervision beyond the triplet, which is possible since captions are readily available for our training data by design. We provide extensive ablations to analyze the design choices on our new CoVR benchmark. Our experiments also demonstrate that training a CoVR model on our datasets effectively transfers to CoIR, leading to improved state-of-the-art performance in the zero-shot setup on the CIRR, FashionIQ, and CIRCO benchmarks. Our code, datasets, and models are publicly available at https://imagine.enpc.fr/ ventural/covr.

We initiate the study of computing shortest non-separating simple closed curves with some given topological properties on non-orientable surfaces. While, for orientable surfaces, any two non-separating simple closed curves are related by a self-homeomorphism of the surface, and computing shortest such curves has been vastly studied, for non-orientable ones the classification of non-separating simple closed curves up to ambient homeomorphism is subtler, depending on whether the curve is one-sided or two-sided, and whether it is orienting or not (whether it cuts the surface into an orientable one). We prove that computing a shortest orienting (weakly) simple closed curve on a non-orientable combinatorial surface is NP-hard but fixed-parameter tractable in the genus of the surface. In contrast, we can compute a shortest non-separating non-orienting (weakly) simple closed curve with given sidedness in g^{O(1)} ⋅ n log n time, where g is the genus and n the size of the surface. For these algorithms, we develop tools that can be of independent interest, to compute a variation on canonical systems of loops for non-orientable surfaces based on the computation of an orienting curve, and some covering spaces that are essentially quotients of homology covers.

We present a new description of the known large deviation function of the classical symmetric simple exclusion process by exploiting its connection with the quantum symmetric simple exclusion processes and using tools from free probability. This may seem paradoxal as free probability usually deals with non commutative probability while the simple exclusion process belongs to the realm of classical probability. On the way, we give a new formula for the free energy -- alias the logarithm of the Laplace transform of the probability distribution -- of correlated Bernoulli variables in terms of the set of their cumulants with non-coinciding indices. This latter result is obtained either by developing a combinatorial approach for cumulants of products of random variables or by borrowing techniques from Feynman graphs.

L’adverbe de phrase "évidemment" ("Évidemment, c’est moi / C’est évidemment moi") porte sur la proposition qui le suit ou dans laquelle il apparaît. Le même adverbe employé en tête de phrase, en réponse à la question ("— C’est toi ? — Évidemment (oui)"), est généralement considéré comme appartenant au même emploi. Or, cet adverbe peut être employé « anaphoriquement », ayant sous sa portée non pas la phrase qui le suit, mais une proposition récupérée dans le contexte précédent ("— C’est toi ? — Évidemment, Paul n’est pas là — Ah, je ne le savais pas"). Nous montrons que l’emploi « anaphorique » est à distinguer de l’adverbe de phrase « canonique ». La capacité qu’a cet emploi d’entrer en relation avec une proposition « en suspens » permet deux emplois supplémentaires : l’un situationnel et l’autre ironique. Dans ce dernier emploi, notamment, l’adverbe peut être suivi d’une proposition mais elle n’est en fait qu’une « mention » de la proposition que le locuteur ne prend pas en charge ("— C’est toi encore ? — Évidemment, c’est moi ! Selon toi, c’est toujours moi le coupable"), et la prise en charge opère ici sur l’acte de production de cette proposition ("— Évidemment, que tu dis que c’est moi !").

The Tree Containment problem consists in deciding whether a phylogenetic tree is displayed by a phylogenetic network. It is NP-hard in general, although polynomial-time algorithms were found for restricted cases when constraints are given on thestructure of the phylogenetic network. Considering this problem as a special case of directed subgraph homeomorphism allows us to design several new algorithmic approaches to address it efficiently in practice, using the pebble game algorithm introduced to solve this problem. After proposing a SAT formulation of the problem, we turn to database theory, by first showing how to design a Datalog program to solve the problem. We then show how to optimize this approach to solve the problem faster in practice.

Après un échange épistolaire avec Hippolyte-Romain Duthillœul, alors bibliothécaire de la ville de Douai, à propos de la publication d’une notice biographique qu’elle a rédigée sur Constant Desbordes, Marceline Desbordes-Valmore donne à sa ville natale, début 1844, un portrait de son père réalisé par cet oncle peintre, mort en 1828. Ce don sera suivi d’autres, par Hippolyte et Prosper Valmore, de portraits par Constant Desbordes de la poète ou de son père, au musée de Douai ainsi qu’à la Bibliothèque nationale de France.

Voies de circulation, établissements scolaires, bibliothèques, salles de spectacle, une trentaine de lieux en France sont nommés d’après Marceline Desbordes-Valmore. Ces choix de dénomination correspondent à des logiques variées : hommages à l’enfant du pays, liens avec l’itinéraire de la poète et avec son œuvre, dénominations en série dans des quartiers aux noms de poètes. Une comparaison avec les autres poètes françaises montre une évolution du nombre d’odonymes relatifs à Marceline Desbordes-Valmore similaire à celle de Lucie Delarue-Mardrus. Avec une quinzaine d’odonymes chacune, elles sont devancées par Louise Labé et plusieurs poètes du XXe siècle, ainsi que par d’autres femmes connues pour d’autres activités que la poésie, la reine Marguerite de Valois et l’écrivaine et militante Louise Michel.

From the very beginnings of modern theater in France, dramatic creation has been using several practices of imitation and adaptation of earlier texts, from biblical, antique or historical sources. Here, we focus on plays inspired by other plays, and on ways of automatically detecting similarities between the texts of such plays. We present approaches based on structural similarities and others based on semantic similarities between play texts, using word embedding methods. Finally, we look at the advantages of developing hybrid approaches that combine these two criteria, using on the one hand the corpus of sources on dramatic creation provided on the <em>Hyperpièces</em> website for comedies, tragi-comedies and tragedies published in France from 1550 to 1650, and on the other hand the <em>Dracor</em> corpus.

<div><p>This paper aims to develop the study of historical printed ornaments with modern unsupervised computer vision. We highlight three complex tasks that are of critical interest to book historians: clustering, element discovery, and unsupervised change localization. For each of these tasks, we introduce an evaluation benchmark, and we adapt and evaluate state-of-the-art models. Our Rey's Ornaments dataset is designed to be a representative example of a set of ornaments historians would be interested in. It focuses on an XVIIIth century bookseller, Marc-Michel Rey, providing a consistent set of ornaments with a wide diversity and representative challenges. Our results highlight the limitations of state-of-the-art models when faced with real data and show simple baselines such as k-means or congealing can outperform more sophisticated approaches on such data. Our dataset and code can be found at https://printed-ornaments.github.io/.</p></div>

We extend classical methods of computational complexity to the setting of distributed computing, where they are sometimes more effective than in their original context. Our focus is on distributed decision in the local model, where multiple networked computers communicate via synchronous message-passing to collectively answer a question about their network topology. Rather unusually, we impose two orthogonal constraints on the running time of this model: the number of communication rounds is bounded by a constant, and the number of computation steps of each computer is polynomially bounded by the size of its local input and the messages it receives. By letting two players take turns assigning certificates to all computers in the network, we obtain a generalization of the polynomial hierarchy (and hence of the complexity classes P and NP). We then extend some key results of complexity theory to this setting, in particular the Cook-Levin theorem (which identifies Boolean satisfiability as a complete problem for NP), and Fagin's theorem (which characterizes NP as the problems expressible in existential second-order logic). The original results can be recovered as the special case where the network consists of a single computer. But perhaps more surprisingly, the task of separating complexity classes becomes easier in the general case: we can show that our hierarchy is infinite, while it remains notoriously open whether the same is true in the case of a single computer. (By contrast, a collapse of our hierarchy would have implied a collapse of the polynomial hierarchy.) As an application, we propose quantifier alternation as a new approach to measuring the locality of problems in distributed computing.

Digital Twin technology has revolutionised overseeing newly built structures. This study suggests employing digital twin-based automatic progress monitoring on construction sites, comparing 3D point clouds with their Building Information Modelling to track progress and predict completion. It highlights integrating semi-continuous monitoring with a building’s digital twin for efficient construction management. Leveraging precise data enhances understanding and identifies schedule deviations, enabling timely actions. Demonstrated through real-world construction data, visualised Gantt charts showcase its efficacy, offering insights into task status, schedule deviations, and projected completion dates. This underscores digital twin technology’s potential to transform construction oversight.

We consider a discrete-time system of n coupled random vectors, a.k.a. interacting particles. The dynamics involve a vanishing step size, some random centered perturbations, and a mean vector field which induces the coupling between the particles. We study the doubly asymptotic regime where both the number of iterations and the number n of particles tend to infinity, without any constraint on the relative rates of convergence of these two parameters. We establish that the empirical measure of the interpolated trajectories of the particles converges in probability, in an ergodic sense, to the set of recurrent Mc-Kean-Vlasov distributions. A first application example is the granular media equation, where the particles are shown to converge to a critical point of the Helmholtz energy. A second example is the convergence of stochastic gradient descent to the global minimizer of the risk, in a wide two-layer neural networks using random features.

In many cases, adversarial attacks are based on specialized algorithms specifically dedicated to attacking automatic image classifiers. These algorithms perform well, thanks to an excellent ad hoc distribution of initial attacks. However, these attacks are easily detected due to their specific initial distribution. We therefore consider other black-box attacks, inspired from generic black-box optimization tools, and in particular the log-normal algorithm. We apply the log-normal method to the attack of fake detectors, and get successful attacks: importantly, these attacks are not detected by detectors specialized on classical adversarial attacks. Then, combining these attacks and deep detection, we create improved fake detectors.

In this paper we show that enumerating the set MM(G,R), defined below, cannot be done with polynomial-delay in its input G and R, unless P=NP. R is a regular expression over an alphabet $\Sigma$, G is directed graph labeled over $\Sigma$, and MM(G,R) contains walks of G. First, consider the set Match(G,R) containing all walks G labeled by a word (over $\Sigma$) that conforms to $R$. In general, M(G,R) is infinite, and MM(G,R) is the finite subset of Match(G,R) of the walks that are minimal according to a well-quasi-order <. It holds w

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For any natural number d, a graph G is a (disjoint) d-interval graph if it is the intersection graph of (disjoint) d-intervals, the union of d (disjoint) intervals on the real line. Two important subclasses of d-interval graphs are unit and balanced d-interval graphs (where every interval has unit length or all the intervals associated to a same vertex have the same length, respectively). A celebrated result by Roberts gives a simple characterization of unit interval graphs being exactly claw-free interval graphs. Here, we study the generalization of this characterization for d-interval graphs. In particular, we prove that for any d ⩾ 2, if G is a K_{1,2d+1}-free interval graph, then G is a unit d-interval graph. However, somehow surprisingly, under the same assumptions, G is not always a disjoint unit d-interval graph. This implies that the class of disjoint unit d-interval graphs is strictly included in the class of unit d-interval graphs. Finally, we study the relationships between the classes obtained under disjoint and non-disjoint d-intervals in the balanced case and show that the classes of disjoint balanced 2-intervals and balanced 2-intervals coincide, but this is no longer true for d > 2.

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<div><p>In this paper, we investigate deep neural network (DNN)-based power allocation policies maximizing the opportunistic rate of a relay-aided cognitive radio network under a quality of service (QoS) constraint protecting the primary transmission. The full-duplex relay performs either Decode-and-Forward (DF) or Compress-and-Forward (CF) and assists the opportunistic communication. The considered primary QoS constraint is expressed in terms of the tolerated primary rate degradation compared to the case of no opportunistic interference. In order to cope with imperfect channel state information (CSI) especially regarding the links to/from the primary network, we propose a self-supervised learning approach that skillfully exploits both perfect and imperfect CSI knowledge within the training phase. Since none of the two relaying schemes is optimal in all system setups (e.g., relative position of the different transmitters, receiver and of the relay), we then propose a novel supervised DNN-based relaying scheme selection. Finally, we extend all these results by proposing a self-supervised DNN-based power allocation policy that is able to generalize over system parameters such as the individual power budget, and the allowed level of primary degradation. Our extensive numerical results on synthetic data demonstrate the effectiveness of our proposed deep learning approaches.</p></div>

Satellite imagery plays a crucial role in monitoring changes happening on Earth's surface and aiding in climate analysis, ecosystem assessment, and disaster response. In this paper, we tackle semantic change detection with satellite image time series (SITS-SCD) which encompasses both change detection and semantic segmentation tasks. We propose a new architecture that improves over the state of the art, scales better with the number of parameters, and leverages long-term temporal information. However, for practical use cases, models need to adapt to spatial and temporal shifts, which remains a challenge. We investigate the impact of temporal and spatial shifts separately on global, multi-year SITS datasets using DynamicEarthNet and MUDS. We show that the spatial domain shift represents the most complex setting and that the impact of temporal shift on performance is more pronounced on change detection than on semantic segmentation, highlighting that it is a specific issue deserving further attention.

We show how to efficiently count and generate uniformly at random finitely generated subgroups of the modular group PSL 2 (Z) of a given isomorphism type. The method to achieve these results relies on a natural map of independent interest, which associates with any finitely generated subgroup of PSL 2 (Z) a graph which we call its silhouette, and which can be interpreted as a conjugacy class of free finite index subgroups of PSL 2 (Z).

Accurate skeleton segmentation of the entire anteroposterior bone scintigrams of the human body is essential for diagnosing bone metastases. However, conventional methods lack a loss design incorporating prior anatomical information, leading to segmentation failures, particularly when dealing with the irregular shapes of organs or high concentrations of positive accumulation. Cases where diagnostic support systems present anatomically abnormal findings may shatter the confidence of doctors and their reliability in these systems. In this paper, we propose a novel multi-factor component tree loss function to resolve the topological issues in segmentation failures. The proposed loss function, computed based on the component trees, comprises two factors: image maxima vanishment and reconnection. We aim to discard the false positive connected components (FPCCs) and reconnect the disconnected true positive connected components (TPCCs) for each bone. Experiments conducted on a private bone scintigrams dataset show that our proposed method outperforms state-of-the-art approaches in dice similarity coefficient (DSC) while efficiently addressing topological issues at a low computational cost. Code is available at https://github.com/MultiCTree/MultiCTree.

Initiated by Davis, Nelson, Petersen and Tenner (2018), the enumerative study of pinnacle sets of permutations has attracted a fair amount of attention recently. In this article, we provide a recurrence that can be used to compute efficiently the number $|\mathfrak{S}_n(P)|$ of permutations of size $n$ with a given pinnacle set $P$, with arithmetic complexity $O(k^4 + k\log n)$ for $P$ of size $k$. A symbolic expression can also be computed in this way for pinnacle sets of fixed size. A weighted sum $q_n(P)$ of $|\mathfrak{S}_n(P)|$ proposed in Davis, Nelson, Petersen and Tenner (2018) seems to have a simple form, and a conjectural form is given recently by Flaque, Novelli and Thibon (2021+). We settle the problem by providing and proving an alternative form of $q_n(P)$, which has a strong combinatorial flavor. We also study admissible orderings of a given pinnacle set, first considered by Rusu (2020) and characterized by Rusu and Tenner (2021), and we give an efficient algorithm for their counting.

L’énoncé on dit ça (ODC) peut être considéré comme une unité formellement et sémantico-pragmatiquement figée. Il permet à son locuteur d’exprimer la contestation implicite d’une énonciation préalable d’un autre locuteur, qui annonce ou sous-entend un acte futur. Son caractère générique et son occurrence dans une structure oppositive p et q latente ou manifeste différencie ODC de ses homonymes compositionnels.

Blockchains are used to store and transmit digital and secure information. This is made possible by creating a chain of chronologically and cryptographically linked data blocks. An oracle is often used to feed the blockchain to ensure the data inserted are reliable. The nature of data sent to the oracle can vary a lot from one application to another. In the scope of the Internet of Things, data provided by sensors may be corrupted when the sensor is damaged or has been corrupted. In this case, it is important for the oracle to include an efficient tool able to detect these outliers. The Grubbs test is such an efficient tool. This article aims at presenting the impact of the use of the Grubbs test on the performance of the oracle. It shows that not only it allows to significantly increase the quality of the data inserted in the blockchain by identifying outliers, it also keeps the algorithmic complexity of the oracle as low as possible.

Compiler optimizations play a crucial role in enhancing software performance by improving execution speed and reducing resource consumption. However, these optimizations can also introduce variability in execution times, a significant concern for real-time systems where predictability is paramount. This paper investigates the relationship between GCC compiler optimizations and the execution time variability. By compiling a set of benchmark programs under different optimization levels (O0, O1, O2, O3), we analyze the impact on execution time variability using the WCET/BCET ratio, and the dispersion of the execution times around the WCET.

In mixed-criticality systems, classical models assume an estimated execution time budget for tasks in each possible criticality, and when a job presents a budget overrun, they tend to drop the lower criticality tasks to preserve the schedulability of the higher criticality tasks. However, few studies focus on how the different execution time budgets are calculated, and it has been emphasized that tasks of lower criticality must provide a minimum service for the system to function properly. In this paper, we present a greedy heuristic for calculating the execution time budget to be allocated to real-time tasks according to their criticality and the shape of their execution time distribution. This budget is calculated with the aim of minimizing potential budget overruns. We first introduce a new statistical dispersion parameter called the maximum coefficient of variation (VWCET). This parameter describes the tendency of execution times towards the worst-case execution time and can be adjusted according to the criticality of the task. We show through experiments that the proposed heuristic reduces the probability of exceeding the time budget allocated to the task, and improves the performance of low-criticality tasks.

A method to improve dynamic service provision is presented, catering to variable future demands while minimizing energy consumption and waiting times, maximizing customer satisfaction. The approach involves dynamic service dispatch reconfiguration at predefined intervals, addressing optimal solutions through iterative single-variable resolution. We propose a pseudo-optimal minimization problem approximating the optimal solution without requiring demand statistics, offering a simple expression. The research establishes effectiveness via a mathematical model and practical implementation using a Google-provided dataset, illustrating real-world applicability.

We introduce a model of tree-rooted planar maps weighted by their number of 2-connected blocks. We study its enumerative properties and prove that it undergoes a phase transition. We give the distribution of the size of the largest 2-connected blocks in the three regimes (subcritical, critical and supercritical) and further establish that the scaling limit is the Brownian Continuum Random Tree in the critical and supercritical regimes, with respective rescalings √{n/log(n)} and √n.

Software Defined Networking (SDN) is gaining momentum not only in research but also in IT industry representing the drivers of 5G networks, due to its capabilities of increasing the flexibility of a network and address a variety of network challenges, by logically centralizing the intelligence in software-based controllers. Thanks to Machine Learning (ML) techniques, the network performances and utilization can be optimized and enhanced. Neural Networks (NN) and Reinforcement Learning (RL), in particular, have demonstrated great success in cooperating with complex problems arising in network operation and management. To this end, we exploit in this paper, an SDN-based rules placement approach that aims to dynamically predict the traffic congestion by using mainly NN and learn optimal paths and reroute traffic to improve network utilization by deploying a Deep Q-Network (DQN) agent. To this end, we first formulate the Quality-of-Service (QoS)-aware routing problem as a Linear Program (LP), whose objective is to minimize the end-to-end (E2E) delay and link utilization. Then, we propose a simple yet efficient heuristic algorithm to solve it. Numerical results through emulation using ONOS controller and Mininet demonstrate that the proposed approach can significantly improve network performances in terms of decreasing the link utilization, the packet loss and the E2E delay.

To efficiently serve heterogeneous demands in terms of data rate, reliability, latency and mobility, network operators must optimize the utilization of their infrastructure resources. In this context, we propose a framework to orchestrate resources for 5G networks by leveraging Machine Learning (ML) techniques. We start by classifying the demands for resources into groups in order to adequately serve them by dedicated logical virtual networks or Network Slices (NSs). To optimally implement these heterogeneous NSs that share the same infrastructure, we develop a new dynamic slicing approach of Physical Resource Blocks (PRBs). On first hand, we propose a predictive approach to achieve optimal slicing decisions of the PRBs from a limited resource pool. On second hand, we design an admission controller and a slice scheduler and formalize them as Knapsack problems. Finally, we design an adaptive resource manager by leveraging Deep Reinforcement Learning (DRL). Using our 5G experimental prototype based on OpenAirInterface (OAI), we generate a realistic dataset for evaluating ML based approaches as well as two baselines solutions (i.e. static slicing and uninformed random slicing-decisions). Simulation results show that using regression trees for both classification and prediction, coupled with the DRL-based adaptive resource manager, outperform alternative approaches in terms of prediction accuracy, resource smoothing, system utilization and network throughput.

We introduce a new pair of mutually dual bases of noncommutative symmetric functions and quasi-symmetric functions, and use it to derive generalizations of several results on the reduced incidence algebra of the lattice of noncrossing partitions. As a consequence, we obtain a quasi-symmetric version of the Farahat-Higman algebra.

Phasor Measurement Units (PMUs) are used in the measurement, control and protection of power grids. However, deploying PMUs at every bus in a power system is prohibitively expensive, necessitating their optimal placement to ensure the system observability with minimum cost. This partial PMU placement renders the system vulnerable to False Data Injection Attacks (FDIAs). This paper proposes a zero-sum game-based approach to strategically place an additional PMU (after the initial optimal PMU deployment for full observability) to bolster robustness against FDIAs by introducing redundancy in attack-susceptible areas. To compute the Nash equilibrium (NE) solution, we leverage a reinforcement learning algorithm that mitigates the need for complete knowledge of the opponent's actions. The proposed PMU deployment algorithm increases the detection rate of FDIA by 36% compared to benchmark approaches.

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Recent road crack detection methods obtain appealing scores but typically allow a few pixel tolerance margin. This is acceptable for locating cracks, but not for measuring their width (indicator of the cracks’ severity). Our baseline model, U-VGG19, obtains an F-score of 71.77% on CrackForest, which is superior to other approaches when no tolerance is admitted. However, increasing the scores without tolerance is difficult due to inaccurate annotations. We propose a novel synthetic dataset, Syncrack, as a benchmark for the evaluation of training with inaccurate annotations. Our results show that inaccurate annotations have a detrimental impact on the F-measure, decreasing it by up to 20%. To overcome this, we study label noise correction techniques using weakly supervised learning. Training U-VGG19 with these corrected labels improves the results on Syncrack by up to 12%. Obtained results on the CrackForest and Aigle-RN datasets support that these approaches are useful for real-life data too.

Recent advances in generative modeling have led to promising progress on synthesizing 3D human motion from text, with methods that can generate character animations from short prompts and specified durations. However, using a single text prompt as input lacks the fine-grained control needed by animators, such as composing multiple actions and defining precise durations for parts of the motion. To address this, we introduce the new problem of timeline control for text-driven motion synthesis, which provides an intuitive, yet fine-grained, input interface for users. Instead of a single prompt, users can specify a multi-track timeline of multiple prompts organized in temporal intervals that may overlap. This enables specifying the exact timings of each action and composing multiple actions in sequence or at overlapping intervals. To generate composite animations from a multi-track timeline, we propose a new test-time denoising method. This method can be integrated with any pre-trained motion diffusion model to synthesize realistic motions that accurately reflect the timeline. At every step of denoising, our method processes each timeline interval (text prompt) individually, subsequently aggregating the predictions with consideration for the specific body parts engaged in each action. Experimental comparisons and ablations validate that our method produces realistic motions that respect the semantics and timing of given text prompts. Our code and models are publicly available at https://mathis.petrovich.fr/stmc.

The diversity and complementarity of sensors available for Earth Observations (EO) calls for developing bespoke self-supervised multimodal learning approaches. However, current multimodal EO datasets and models typically focus on a single data type, either mono-date images or time series, which limits their impact. To address this issue, we introduce OmniSat, a novel architecture able to merge diverse EO modalities into expressive features without labels by exploiting their alignment. To demonstrate the advantages of our approach, we create two new multimodal datasets by augmenting {existing ones} with new modalities. As demonstrated for three downstream tasks---forestry, land cover classification, and crop mapping---OmniSat can learn rich representations without supervision, leading to state-of-the-art performances in semi- and fully supervised settings. Furthermore, our multimodal pretraining scheme improves performance even when only one modality is available for inference. The code and dataset are available at The diversity and complementarity of sensors available for Earth Observations (EO) calls for developing bespoke self-supervised multimodal learning approaches. However, current multimodal EO datasets and models typically focus on a single data type, either mono-date images or time series, which limits their impact. To address this issue, we introduce OmniSat, a novel architecture able to merge diverse EO modalities into expressive features without labels by exploiting their alignment. To demonstrate the advantages of our approach, we create two new multimodal datasets by augmenting {existing ones} with new modalities. As demonstrated for three downstream tasks—forestry, land cover classification, and crop mapping—OmniSat can learn rich representations without supervision, leading to state-of-the-art performances in semi- and fully supervised settings. Furthermore, our multimodal pretraining scheme improves performance even when only one modality is available for inference. The code and dataset are available at https://github.com/gastruc/OmniSat.

Recently, we have witnessed an operational deployment of 5G in big cities to increase the communication capacity of users. However, with the ever-increasing of devices in dense networks, the current 5G could fail due to its unscalable issues. Scientists in academia and industry are actively analyzing this open issue within the context of Beyond 5G (B5G) and 6G before their realistic deployment. Moreover, the high densities of devices lead to the challenges of over-energy consumption and unfair Quality of Service (QoS) caused by the distances from the base station and existing obstacles separating the communicating devices. The network relay election method can be seen as an appropriate option to overcome these challenges, but this solution should be optimized according to the environment's dynamics. Therefore, in this paper, we propose an energy-efficient election-based method that selects relays so that it maximizes the overall network lifetime while maintaining a certain level of QoS. Considering that our system is deployed in an unknown dynamic environment, we employ a deep reinforcement learning technique that aims to optimize the network relay selection, maximize the residual energy levels of devices, and ensure fair and high data rates. Our system has been tested through a discrete-event simulator, and its performances have been evaluated and compared to a set of baseline and benchmark methods. It has been shown that our proposed system outperforms existing relevant solutions.