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.

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.

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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Seaweeds are potentially excellent sources of bio- active metabolites that could represent useful leads in the de- velopment of new functional ingredients in pharmaceutical and cosmetic industries. In the last decade, new marine bioprocess technologies have allowed the isolation of sub- stances with biological properties. The brown alga Sargassum muticum (Yendo) Fensholt (Ochrophyta) was en- zymatically hydrolyzed to prepare water-soluble extracts by using 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 as evaluation of cytotoxicity were realized for each extract. Total phenolic content was measured for extract characterization, and solid-phase extraction was useful to purify the enzymatic extract. Soluble total phenolic content of S. muticum Viscozyme extract was highest with 6.4% of dry weight. Enzymatic Celluclast and Viscozyme extracts had the lowest value of DPPH IC50 indicating a strong antiradical activity, 0.6 mg mL−1, in comparison with other enzymes. The ferric reducing antioxidant power ranged between 48.7 μM Fe 2+ Eq, digested with Viscozyme, and 60.8 μM Fe 2+ Eq, digested with Amyloglucosidase. Tyrosinase inhibition activity of S. muticum Neutrase extract was 41.3% higher compared to other enzymes. Elastase inhibition activity of S. muticum Shearzyme extract had highest activity (32.8%). All enzymat- ic extracts showed no cytotoxic effect towards the kidney Vero cells. 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 against Pseudomonas aeruginosa and Escherichia coli, respectively. Purification of S. muticum Viscozyme extracts by solid-phase extraction managed to concentrate the phenolic content and improve the bioactivity. These results indicate the promising potential of enzyme-assisted followed by solid-phase extrac- tion in recovering phenolic content and in improving its bioactivity.

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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.

A comparative study between "alternative" extraction processes such as centrifugal partition extraction (CPE), supercritical fluid extraction (SFE) and pressurized liquid extraction (PLE) and classical solid/liquid used in the laboratory are currently focusing on the efficiency (selectivity and productivity) to obtain bioactive phenolic compounds from the phaeophyte Sargassum muticum model. The choice of the best process was based on several measurements: (i) the total phenolic content measured by the colorimetric Folin-Ciocalteu assay, (ii) radical scavenger and antioxidant activities assessed by the DPPH (2,2-diphenyl-1-picrylhydrazyl) radical scavenging assay, and the β-carotene bleaching method and finally (iii) the method productivity. Irrespective of the solvent used in the processes, alternative methods are always sharply more effective than classical ones. With the exception of SFE which does not allow extracting the totality of the active phenolic compounds, two of the other extraction methods were particularly promising. First, CPE afforded the most important yields in concentrated phenolic compounds (PC) (22.90±0.65% DW) also displaying the best activities (0.52±0.02 and 0.58±0.19 mg/mL for IC50 and AAC700, respectively). Secondly, PLE using an EtOH:water mixture 75:25 (v/v) allowed a good PC extraction (10.18±0.25% DW) with huge efficiency. Despite a lesser activity of the extracts (0.77±0.01 and 1.59±0.15 mg/mL for IC50 and AAC700, respectively) PLE is a green process and potentially complies European norms requirements for the prospective valorization of phenolic compounds from S. muticum in Brittany.

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

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.

This paper addresses the topic of robot identification. The usual identification method makes use of the inverse dynamic model (IDM) and the least squares (LS) technique while robot is tracking exciting tra- jectories. Assuming an appropriate bandpass filtering, good results can be obtained. However, the users are in doubt whether the columns of the observation matrix (the regressors) are uncorrelated (exo- genous) or correlated (endogenous) with the error terms. The exogeneity condition is rarely verified in a formal way whereas it is a fundamental condition to obtain unbiased LS estimates. In Econometrics, the Durbin-Wu-Hausman test (DWH-test) is a formal statistic for investigating whether the regressors are exogenous or endogenous. However, the DWH-test cannot be straightforwardly used for robot identifi- cation because it is assumed that the set of instruments is valid. In this paper, a Revised DWH-test suitable for robot identification is proposed. The revised DWH-test validates/invalidates the instruments chosen by the user and validates the exogeneity assumption through the calculation of the QR factor- ization of the augmented observation matrix combined with a F-test if required. The experimental results obtained with a 6 degrees-of-freedom (DOF) industrial robot validate the proposed statistic.

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.

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 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 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.

The goal of this document is to openly contribute with our comments to the Open Letter "Establishing a national research software award" published in the Open Research Europe(ORE), see https://open-research-europe.ec.europa.eu/articles/3-185/v1. We hope that they can contribute to enrich the discussion on the involved subjects. Moreover, we think that these suggestions could be also useful for the authors to react to the Referees’ comments when they discuss and ask for more precision regarding both the concept of research software presented in the Open Letter, and the proposed evaluation criteria and steps. These comments are now available at the ORE platform, see https://open-research-europe.ec.europa.eu/articles/3-185/v1?commentId=3859

Description détaillée de l’intervention de T. Gomez-Diaz le 9 avril lors de l’atelier BlueHats. Nous présenterons l’expérience de productions des logiciels de la recherche au Laboratoire d’informatique Gaspard-Monge, dont plus de 80% sont diffusés sous licence libre [1]. Depuis 2006 le laboratoire mène une politique qui favorise la visibilité de cette production, avec l’amélioration des pratiques de diffusion et la réalisation et maintenance d’un catalogue. Le concept de “logiciel d’un laboratoire” [1,2,5], que nous avons proposé initialement en 2006 a évolué vers le concept de logiciel de la recherche, comme la traduction du terme anglais research software, mais aussi pour mieux faire le parallèle avec les évolutions récentes du paysage scientifique liées aux données de la recherche. Il s’agît d’un concept central pour les infrastructures et les services [3,4] qui sont actuellement en construction dans le cadre des évolutions de la Science Ouverte [3], dont l’objectif est de rendre les productions scientifiques visibles, accessibles et réutilisables. Il est également un concept central dans la proposition des protocoles d’évaluation de cette production scientifique [5]. [1] T. Gomez-Diaz (2013) Patrimoine logiciel des laboratoires https://hal.archives-ouvertes.fr/cel-01804283 [2] T. Gomez-Diaz (2015) Article vs. Logiciel : questions juridiques et de politique scientifique dans la production de logiciels http://www.societe-informatique-de-france.fr/wp-content/uploads/2015/04/1024-5-gomez-diaz.pdf [3] T. Gomez-Diaz, T. Recio (2020) Towards an Open Science definition as a political and legal framework: on the sharing and dissemination of research outputs POLIS N. 19, 2020, http://uet.edu.al/polis/images/1.pdf V3 du 28/02/2021 : https://zenodo.org/record/4577066 [4] T. Gomez-Diaz, T. Recio (2020) Open comments on the Task Force SIRS report: Scholarly Infrastructures for Research Software, Research Ideas and Outcomes (RIO) 7: e63872, https://doi.org/10.3897/rio.7.e63872 [5] T. Gomez-Diaz, T. Recio (2019) On the evaluation of research software: the CDUR procedure https://doi.org/10.12688/f1000research.19994.2

Nous cherchons à caractériser des événements liés à la pollution industrielle, extraits de textes administratifs. Chaque événement peut être caractérisé par un déclencheur qui rend compte de sa nature (administrative, de dépollution, etc.). Nous proposons une typologie pour catégoriser ces déclencheurs et une méthode hybride pour réaliser automatiquement cette catégorisation. Cette méthode combine une approche d'apprentissage automatique (fondée sur des plongements de mots et un apprentissage supervisé) avec une approche textométrique (fondée sur la visualisation de la cooccurrence des mots fréquents). Elle permet de caractériser les déclencheurs d’événements automatiquement avec une f-mesure de 80%.

Optimal transport has recently started to be successfully employed to define misfit or loss functions in inverse problems. However, it is a problem intrinsically defined for positive (probability) measures and therefore strategies are needed for its applications in more general settings of interest. In this paper we introduce an unbalanced optimal transport problem for vector valued measures starting from the $L^1$ optimal transport. By lifting data in a self-dual cone of a higher dimensional vector space, we show that one can recover a meaningful transport problem. We show that the favorable computational complexity of the $L^1$ problem, an advantage compared to other formulations of optimal transport, is inherited by our vector extension. We consider both a one-homogeneous and a two-homogeneous penalization for the imbalance of mass, the latter being potentially relevant for applications to physics based problems. In particular, we demonstrate the potential of our strategy for full waveform inversion, an inverse problem for high resolution seismic imaging.

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.

The article describes the structural and algorithmic relations between Cartesian trees and Lyndon Trees. This leads to a uniform presentation of the Lyndon table of a word corresponding to the Next Nearest Smaller table of a sequence of numbers. It shows how to efficiently compute runs, that is, maximal periodicities occurring in a word.

Using the dual formulation only, we show that regularity of unbalanced optimal transport also called entropy-transport inherits from regularity of standard optimal transport. We then provide detailed examples of Riemannian manifolds and costs for which unbalanced optimal transport is regular. Among all entropy-transport formulations, Wasserstein-Fisher-Rao metric, also called Hellinger-Kantorovich, stands out since it admits a dynamic formulation, which extends the Benamou-Brenier formulation of optimal transport. After demonstrating the equivalence between dynamic and static formulations on a closed Riemannian manifold, we prove a polar factorization theorem, similar to the one due to Brenier and Mc-Cann. As a byproduct, we formulate the Monge-Ampère equation associated with Wasserstein-Fisher-Rao metric, which also holds for more general costs.

Research around Robust Estimator in the context of 3D Stereo Reconstruction is complicated by the difficulty to obtain reliable Ground Truth data. The literature around this subject proposes a number of RanSaC like algorithms that are compared to each other using either fully artificial data or estimated real data which leads to unreliable conclusions. Recent work focuses on methods that do not use user-defined thresholds which require strong hypothesis on the data distribution. To efficiently compare this new methods we propose a new benchmarking solution that will help leveraging recent work to improve the State of the Art. Our first contribution is a novel data generation method that relies on real data to obtain a realistic model and distribution of data points to create reliable benchmarks with precise metrics. We then used this method to compare most recent adaptative methods on a variety of Multi-View Stereo (MVS) and Structure-from-Motion(SfM) problems and obtain insight in the capabilities of each algorithm. Using these conclusions, we tried to improve ColMap using user-set-threshold-free methods. Finally, we provide the complete code base used to generate the benchmarks, with all tested methodsin a unified framework. This includes an implementation of an algorithm making use of Likelihood Ratio Tests which had not been made available by the original authors

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We study parabolic aligned elements associated with the type-$B$ Coxeter group and the so-called linear Coxeter element. These elements were introduced algebraically in (Mühle and Williams, 2019) for parabolic quotients of finite Coxeter groups and were characterized by a certain forcing condition on inversions. We focus on the type-$B$ case and give a combinatorial model for these elements in terms of pattern avoidance. Moreover, we describe an equivalence relation on parabolic quotients of the type-$B$ Coxeter group whose equivalence classes are indexed by the aligned elements. We prove that this equivalence relation extends to a congruence relation for the weak order. The resulting quotient lattice is the type-$B$ analogue of the parabolic Tamari lattice introduced for type $A$ in (Mühle and Williams, 2019).

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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.

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.

Nowadays, most applications hosted on public cloud data centers (DCs) disseminate data from a single source to a group of receivers for service deployment, data replication, software upgrade, etc. For such one-to-many data communication paradigm, multicast routing is the natural choice as it reduces network traffic and improves application throughput. Unfortunately, recent approaches adopting IP multicast routing suffer from scalability and load balancing issues, and do not scale well with the number of supported multicast groups when used for cloud DC networks. Furthermore, IP multicast does not exploit the topological properties of DCs, such as the presence of multiple parallel paths between end hosts. Despite the recent efforts aimed at addressing these challenges, there is still a need for multicast routing protocol designs that are both scalable and load-balancing aware. This paper proposes $\mathsf {Ernie}$ , a scalable load-balanced multicast source routing for large-scale DCs. At its heart, $\mathsf {Ernie}$ further exploits DC network structural properties and switch programmability capabilities to encode and organize multicast group information inside packets in a way that minimizes downstream header sizes significantly, thereby reducing overall network traffic. Additionally, $\mathsf {Ernie}$ introduces an efficient load balancing strategy, where multicast traffic is adequately distributed at downstream layers. To study the effectiveness of $\mathsf {Ernie}$ , we extensively evaluate $\mathsf {Ernie}$ ’s scalability behavior (i.e., switch memory, packet size overheads, and CPU overheads), and load balancing ability through a mix of simulation and analysis of its performances. For example, experiments of large-scale DCs with 27k+ servers show that $\mathsf {Ernie}$ requires a downstream header sizes that are $10\times $ smaller than those needed under state-of-the-art schemes while keeping end-host overheads at low levels. Our simulation results also indicate that at highly congested links, $\mathsf {Ernie}$ can achieve a better multicast load balancing than other existing schemes.

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Network slicing is a key enabler in the Next Generation 5G Radio Access Network (RAN) to build the RAN-as-a-Service concept. Cloud-RAN, Network Function Virtualization, Software Defined Network and RAN functional splits are the main pillars expected to be integrated to provide the required flexibility. One of the major concerns is to efficiently allocate RAN resources for slices, while supporting multiple use-cases with heterogeneous Quality-of-Service (QoS) requirements. Current related work is adopting radio resource allocation scheme by considering a cell-centric deployment approach for slice embedding. However, to achieve greater flexibility and fine-grained tunable resource utilization, we believe that the deployment scheme should be integrated in the slice design. In this paper, we go a step further and propose a RAN slicing approach with customized deployment scheme on user basis. As the corresponding optimization problem is NP-Hard, we propose a low-cost and efficient heuristic algorithm for RAN Slice allocation based on the Particle Swarm Optimization approach. Our proposal jointly harnesses radio, processing and link resources at user level tailored to the QoS requirements, while customizing efficiently the underlying physical RAN resource usage.

This thesis explores the challenges and strategies associated with Class-Incremental Learning (CIL), a branch of machine learning that focuses on the efficient integration of new classes into existing models. The work addresses key difficulties, including catastrophic forgetting and the trade-off between stability and plasticity, especially in scenarios where past class data is not storable. A number of innovative methods are proposed, ranging from strategic management of the model parameter distribution to the creation of a pseudo-feature generator for improved stability-plasticity balance. The study also provides a thorough experimental analysis of various factors influencing CIL performance, such as initial training strategies, neural architecture selection, and CIL algorithm choice. The research culminates in the provision of practical guidelines for effective incremental learning to facilitate the real-world application of CIL. In summary, the thesis contributes significant insights and methods to the field of machine learning, fostering a better understanding and use of Class-Incremental Learning.

We prove essentially tight lower bounds, conditionally to the Exponential Time Hypothesis, for two fundamental but seemingly very different cutting problems on surface-embedded graphs: the Shortest Cut Graph problem and the Multiway Cut problem. A cut graph of a graph G embedded on a surface S is a subgraph of G whose removal from S leaves a disk. We consider the problem of deciding whether an unweighted graph embedded on a surface of genus G has a cut graph of length at most a given value. We prove a time lower bound for this problem of nΩ(g log g) conditionally to the ETH. In other words, the first nO(g)-time algorithm by Erickson and Har-Peled [SoCG 2002, Discr. Comput. Geom. 2004] is essentially optimal. We also prove that the problem is W[1]-hard when parameterized by the genus, answering a 17-year-old question of these authors. A multiway cut of an undirected graph G with t distinguished vertices, called terminals, is a set of edges whose removal disconnects all pairs of terminals. We consider the problem of deciding whether an unweighted graph G has a multiway cut of weight at most a given value. We prove a time lower bound for this problem of nΩ(gt+ g2+tlog (g+t)), conditionally to the ETH, for any choice of the genus g ≥ 0 of the graph and the number of terminals t≥ 4. In other words, the algorithm by the second author [Algorithmica 2017] (for the more general multicut problem) is essentially optimal; this extends the lower bound by the third author [ICALP 2012] (for the planar case). Reductions to planar problems usually involve a gridlike structure. The main novel idea for our results is to understand what structures instead of grids are needed if we want to exploit optimally a certain value G of the genus.

Online Continual Learning (CL) solves the problem of learning the ever-emerging new classification tasks from a continuous data stream. Unlike its offline counterpart, in online CL, the training data can only be seen once. Most existing online CL research regards catastrophic forgetting (i.e., model stability) as almost the only challenge. In this paper, we argue that the model's capability to acquire new knowledge (i.e., model plasticity) is another challenge in online CL. While replay-based strategies have been shown to be effective in alleviating catastrophic forgetting, there is a notable gap in research attention toward improving model plasticity. To this end, we propose Collaborative Continual Learning (CCL), a collaborative learning based strategy to improve the model's capability in acquiring new concepts. Additionally, we introduce Distillation Chain (DC), a novel collaborative learning scheme to boost the training of the models. We adapted CCL-DC to existing representative online CL works. Extensive experiments demonstrate that even if the learners are well-trained with state-of-the-art online CL methods, our strategy can still improve model plasticity dramatically, and thereby improve the overall performance by a large margin.

Les communications 5G véhiculaires (5G-V2X) continueront à jouer un rôle essentiel dans le développement de l’industrie automobile. En effet, grâce entre autres au concept de découpage du réseau 5G en plusieurs sous-réseaux, de nouveaux cas d’usage futuristes pourront être pris en charge sur un même réseau physique. Cependant, l’utilisation du découpage du réseau 5G-V2X entraîne de nouveaux défis en matière de cybersécurité que les standards actuels, que ce soit ceux du domaine automobile que ceux de la 5G, n’ont pas encore traités. Un élément de vulnérabilité particulier est celui des environnements transfrontaliers où il est nécessaire d’assurer une transition optimale pour les véhicules d’un opérateur à un autre. Cet article vise donc à examiner les problèmes de sécurité et de protection de la vie privée du réseau 5G-V2X tout en recensant les questions ouvertes et les opportunités en zones transfrontalières, en mettant l’accent sur la détection des attaques, leur atténuation et les solutions potentielles pour la protection de la vie privée.

The method concerns a method of communication between end-user terminals and user terminals that are static or have low relative mobility with respect to each other, wherein a control server: - selects the terminals that are static or have low relative mobility from among the assembly of terminals, the other terminals being referred to as end terminals; - determines a graph, the nodes of which are static or low relative mobility terminals, - determines, on this graph of nodes, a sub-graph of dominant nodes; - selects, in the sub-graph of dominant nodes, at least one gateway node capable of establishing a communication with the Cloud Computing type network over their second cellular communication link; - selects the nodes of the graph which do not belong to the nodes of the sub-graph, as nodes having only the access/relay function to the dynamic multi-hop proximity network for the end-user terminals; - transmits its hierarchy status over the second cellular communication link, only individually to each of the various static or low relative mobility terminals.

Although automatic sequences are very simple algorithmically, some of them have pseudo-random properties. In particular, some automatic sequences such as the Golay–Shapiro sequence are known to be 2-uncorrelated, meaning that they have the same correlations of order 2 as a uniform random sequence. However, the existence of ℓ -uncorrelated automatic sequences (for ℓ ⩾ 3) was left as an open question in a recent paper of Marcovici, Stoll and Tahay. We exhibit binary block-additive sequences that are 3-uncorrelated and, with the help of analytical results supplemented by an exhaustive search, we present a complete picture of the correlation properties of binary block-additive sequences of rank r ⩽ 5, and ternary sequences of rank r ⩽ 3.

The dataset contains images of concrete printing process monitoring camera. The dataset images are completed by the annotations dedicated to the detection of defects and anomalies that can be used in the close-loop control as feedback to an automated quality assessment system. It could stem from a rule-based expert system, vector-to-action dictionaries, supervised machine learning models, etc.

Sickle cell disease is a rare, chronic and potentially fatal inherited disorder affecting red blood cells. In particular, acute chest syndrome (ACS) is a feared complication due to its association with increased mortality in hospitalized patients. To reduce this mortality, biomarker variables are examined on a patient's arrival in the emergency department, followed by a statistical prediction if the syndrome will occur. When making these statistical predictions, the use of conventional statistical methods or machine learning algorithms often leads to problems such as over-fitting and high variability. This is due to the detection of acute chest syndrome (ACS) being a typical example of an imbalanced classification problem. Our main objective is to find syndrome cases which represent a subset within the set of patients unaffected by ACS.The concept of imbalanced datasets is key in this context. A binary dataset is considered imbalanced when there is a significant disparity between the occurrences of the minority class (in our case, the occurrence of ACS) and the majority class (patients unaffected by ACS). Traditional machine learning techniques are generally designed under the assumption of balanced datasets. Consequently, their application to imbalanced datasets often produces non-optimal performance and introduces biases. Due to these challenges, a high number of methods, called imbalanced methods, have been designed and implemented.Nonetheless, our investigation unveiled the absence of an established framework to systematically compare distinct imbalanced methods. Furthermore, we will demonstrate that despite the profusion of methodologies, none of them yields an enhancement in the acute chest syndrome (ACS) detection. In response, a structured pipeline has been formulated to systematically assess and compare diverse imbalanced methods. Simultaneously, recognizing the inherent limitations of existing methodologies, an innovative approach using mathematical morphology with watershed approaches was proposed. The idea of combining mathematical morphology and watersheds is based on the fact that the detection of small objects in an image has been explored more algorithmically than the detection of small classes in a digital dataset. This concept is also based on the progressive correlation established between watersheds applied to images and their application in classification. Consequently, we designed three distinct algorithms based on watershed graph theory and hierarchical principles. The first algorithm is rooted in the traditional oversampling approach, while the second algorithm adopts a method centered on learning based metric optimization. Finally, the third algorithm introduces an in-place algorithm built on watershed hierarchies, specifically designed to reduce dependence on the dataset's structure.The findings highlight the efficacy of watershed-based imbalanced classification algorithms in improving the detection of acute chest syndrome (ACS). These algorithms effectively address concerns related to overfitting and variability, translating to better reproducibility. For instance, the watershed-based oversampling technique, known as WSSMOTE, reduced overfitting from 13.3% to 1.2%. Moreover, the applicability of these algorithms can be extended to real-world classification problems. In certain cases, these algorithms led to improvements in performance metrics. For example, the accuracy of type of glasses predictions was notably enhanced through the application of the watershed optimization metric learning method. This underlines the practical benefits and adaptability of the proposed watershed-based algorithms

We propose a new grammar-based language for defining information extractors from documents (text) that is built upon the well-studied framework of document spanners for extracting structured data from text. While previously studied formalisms for document spanners are mainly based on regular expressions, we use an extension of context-free grammars, called extraction grammars, to define the new class of context-free spanners. Extraction grammars are simply context-free grammars extended with variables that capture interval positions of the document, namely spans. While regular expressions are efficient for tokenizing and tagging, context-free grammars are also efficient for capturing structural properties. Indeed, we show that context-free spanners are strictly more expressive than their regular counterparts. We reason about the expressive power of our new class and present a pushdown-automata model that captures it. We show that extraction grammars can be evaluated with polynomial data complexity. Nevertheless, as the degree of the polynomial depends on the query, we present an enumeration algorithm for unambiguous extraction grammars that, after quintic preprocessing, outputs the results sequentially, without repetitions, with a constant delay between every two consecutive ones.

We study the problems of finding a shortest synchronizing word and its length for a given prefix code. This is done in two different settings: when the code is defined by an arbitrary decoder recognizing its star and when the code is defined by its literal decoder (whose size is polynomially equivalent to the total length of all words in the code). For the first case for every epsilon > 0 we prove n^(1 - epsilon)-inapproximability for recognizable binary maximal prefix codes, Theta(log n)-inapproximability for finite binary maximal prefix codes and n^(1/2 - epsilon)-inapproximability for finite binary prefix codes. By c-inapproximability here we mean the non-existence of a c-approximation polynomial time algorithm under the assumption P != NP, and by n the number of states of the decoder in the input. For the second case, we propose approximation and exact algorithms and conjecture that for finite maximal prefix codes the problem can be solved in polynomial time. We also study the related problems of finding a shortest mortal and a shortest avoiding word.

We study a problem of scheduling n jobs on machines of two types: in‐house machines and third‐party machines. Scheduling on in‐house machines incurs no additional costs, while using third‐party machines implies costs depending on their number and the time of usage. Each job has a fixed time interval for being processed which can be divided and allocated among several machines, as long as there is only one machine processing the job at any time. No machine can process more than one job at a time. Jobs can be rejected, and they are of different importance that is reflected in the weight of each job. The objective is to find a subset of the jobs and the number of third‐party machines for any period of time so that the accepted jobs can be feasibly scheduled, the total weight of the accepted jobs is maximized, and the total machine usage costs does not exceed a given upper bound. We also study a similar problem in which the objective is to maximize the total time at which at least one job is processed. Both problems are encountered in situations in which certain activities with given start and completion times have to be serviced by human operators. Examples are air traffic control and the monitoring safe vehicle unloading. Other examples are the employment of subcontractors in agriculture, construction or transportation. We will present NP‐hardness proofs, polynomial and pseudo‐polynomial optimal algorithms and an approximation algorithm for these problems and their special cases. These problems admit graph‐theoretical interpretations associated with finding independent sets and a proper vertex coloring in interval graphs.

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We say that a pure simplicial complex K of dimension d satisfies the removal-collapsibility condition if K is either empty or K becomes collapsible after removing β~d(K;Z2) facets, where β~d(K;Z2) denotes the dth reduced Betti number. In this paper, we show that if the link of each face of a pure simplicial complex K (including the link of the empty face which is the whole K) satisfy the removal-collapsibility condition, then the second barycentric subdivision of K is vertex decomposable and in particular shellable. This is a higher dimensional generalization of a result of Hachimori, who proved that that if the link of each vertex of a pure 2-dimensional simplicial complex K is connected, and K becomes simplicially collapsible after removing χ~(K) facets, where χ~(K) denotes the reduced Euler characteristic, then the second barycentric subdivision of K is shellable. For the proof, we introduce a new variant of decomposability of a simplicial complex, stronger than vertex decomposability, which we call star decomposability. This notion may be of independent interest.

Inspired by the classical Riemannian systolic inequality of Gromov, we present a combinatorial analogue providing a lower bound on the number of vertices of a simplicial complex in terms of its edge-path systole. Similarly to the Riemannian case, where the inequality holds under a topological assumption of “essentiality”, our proofs rely on a combinatorial analogue of that assumption. Under a stronger assumption, expressed in terms of cohomology cup-length, we improve our results quantitatively. We also illustrate our methods in the continuous setting, generalizing and improving quantitatively the Minkowski principle of Balacheff and Karam; a corollary of this result is the extension of the Guth–Nakamura cup-length systolic bound from manifolds to complexes.

The quality and generality of deep image features is crucially determined by the data they have been trained on, but little is known about this often overlooked effect. In this paper, we systematically study the effect of variations in the training data by evaluating deep features trained on different image sets in a few-shot classification setting. The experimental protocol we define allows to explore key practical questions. What is the influence of the similarity between base and test classes? Given a fixed annotation budget, what is the optimal trade-off between the number of images per class and the number of classes? Given a fixed dataset, can features be improved by splitting or combining different classes? Should simple or diverse classes be annotated? In a wide range of experiments, we provide clear answers to these questions on the miniImageNet, ImageNet and CUB-200 benchmarks. We also show how the base dataset design can improve performance in few-shot classification more drastically than replacing a simple baseline by an advanced state of the art algorithm.

Deep image generation is becoming a tool to enhance artists and designers creativity potential. In this paper, we aim at making the generation process more structured and easier to interact with. Inspired by vector graphics systems, we propose a new deep image reconstruction paradigm where the outputs are composed from simple layers, defined by their color and a vector transparency mask. This presents a number of advantages compared to the commonly used convolutional network architectures. In particular, our layered decomposition allows simple user interaction, for example to update a given mask, or change the color of a selected layer. From a compact code, our architecture also generates vector images with a virtually infinite resolution, the color at each point in an image being a parametric function of its coordinates. We validate the efficiency of our approach by comparing reconstructions with state-of-the-art baselines given similar memory resources on CelebA and ImageNet datasets. Most importantly, we demonstrate several applications of our new image representation obtained in an unsupervised manner, including editing, vectorization and image search.

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The goal of this work is to efficiently identify visually similar patterns in images, e.g. identifying an artwork detail copied between an engraving and an oil painting, or recognizing parts of a night-time photograph visible in its daytime counterpart. Lack of training data is a key challenge for this co-segmentation task. We present a simple yet surprisingly effective approach to overcome this difficulty: we generate synthetic training pairs by selecting segments in an image and copy-pasting them into another image. We then learn to predict the repeated region masks. We find that it is crucial to predict the correspondences as an auxiliary task and to use Poisson blending and style transfer on the training pairs to generalize on real data. We analyse results with two deep architectures relevant to our joint image analysis task: a transformer-based architecture and Sparse Nc-Net, a recent network designed to predict coarse correspondences using 4D convolutions. We show our approach provides clear improvements for artwork details retrieval on the Brueghel dataset and achieves competitive performance on two place recognition benchmarks, Tokyo247 and Pitts30K. We also demonstrate the potential of our approach for unsupervised image collection analysis by introducing a spectral graph clustering approach to object discovery and demonstrating it on the object discovery dataset of \cite{rubinstein2013unsupervised} and the Brueghel dataset. Our code and data are available at http://imagine.enpc.fr/~shenx/SegSwap/.

A word u is a shuffle of words v and w, which we denote by u ∈ v⧢w, if u can be obtained by mixing the letters from v and w in a way that preserves the left-to-right ordering of the letters from v and w. In case u∈v⧢v for some word v, the word u is called a shuffled-square. A word u is shuffled-square-free if it does not have a non-empty factor (i.e., non-empty sequence of adjacent letters) that is a shuffled-square. Our contribution in this context is two-fold. First, we prove that there exist arbitrarily long shuffled-square-free words in any alphabet with six letters or more, thereby improving on a previous result of Guégan and Ochem. Furthermore, we show that recognizing shuffled-square-free words on arbitrary alphabets is NP-complete.

We study the <span class="u-small-caps">Maximum Zero-Sum Partition</span> problem (or <span class="u-small-caps">MZSP</span>), defined as follows: given a multiset S={a<small><sub>1</sub></small>, a<small><sub>2</sub></small>, ..., a<small><sub>n</sub></small>} of integers a<small><sub>i</sub></small>∈Z* such that Σ<sub>i=1..n</sub> a<small><sub>i</sub></small>=0, find a maximum cardinality partition {S<small><sub>1</sub></small>, S<small><sub>2</sub></small>, ... , S<small><sub>k</sub></small>} of S such that, for every 1≤ i ≤ k, Σ<sub>a<small><sub>j</sub></small> ∈ S<small><sub>i</sub></small></sub> a<small><sub>j</sub></small>=0. Solving <span class="u-small-caps">MZSP</span> is useful in genomics for computing evolutionary distances between pairs of species. Our contributions are a series of algorithmic results concerning <span class="u-small-caps">MZSP</span>, in terms of complexity, (in)approximability, with a particular focus on the fixed-parameter tractability of <span class="u-small-caps">MZSP</span> with respect to either (i) the size <i>k</i> of the solution, (ii) the number of negative (resp. positive) values in S and (iii) the largest integer in S.

The shuffle of two words <em>u</em> and <em>v</em> of <span class="math"><math><msup is="true"><mrow is="true"><mi is="true">A</mi></mrow><mrow is="true"><mo is="true">⁎</mo></mrow></msup></math></span> is the language <span class="math"><math><mi is="true">u</mi><mo is="true">⧢</mo><mi is="true">v</mi></math></span> consisting of all words <span class="math"><math><msub is="true"><mrow is="true"><mi is="true">u</mi></mrow><mrow is="true"><mn is="true">1</mn></mrow></msub><msub is="true"><mrow is="true"><mi is="true">v</mi></mrow><mrow is="true"><mn is="true">1</mn></mrow></msub><msub is="true"><mrow is="true"><mi is="true">u</mi></mrow><mrow is="true"><mn is="true">2</mn></mrow></msub><msub is="true"><mrow is="true"><mi is="true">v</mi></mrow><mrow is="true"><mn is="true">2</mn></mrow></msub><mo is="true">…</mo><msub is="true"><mrow is="true"><mi is="true">u</mi></mrow><mrow is="true"><mi is="true">k</mi></mrow></msub><msub is="true"><mrow is="true"><mi is="true">v</mi></mrow><mrow is="true"><mi is="true">k</mi></mrow></msub></math></span>, where <span class="math"><math><mi is="true">k</mi><mo is="true">≥</mo><mn is="true">0</mn></math></span> and the <span class="math"><math><msub is="true"><mrow is="true"><mi is="true">u</mi></mrow><mrow is="true"><mi is="true">i</mi></mrow></msub></math></span> and <span class="math"><math><msub is="true"><mrow is="true"><mi is="true">v</mi></mrow><mrow is="true"><mi is="true">i</mi></mrow></msub></math></span> are words of <span class="math"><math><msup is="true"><mrow is="true"><mi is="true">A</mi></mrow><mrow is="true"><mo is="true">⁎</mo></mrow></msup></math></span> such that <span class="math"><math><mi is="true">u</mi><mo linebreak="goodbreak" linebreakstyle="after" is="true">=</mo><msub is="true"><mrow is="true"><mi is="true">u</mi></mrow><mrow is="true"><mn is="true">1</mn></mrow></msub><msub is="true"><mrow is="true"><mi is="true">u</mi></mrow><mrow is="true"><mn is="true">2</mn></mrow></msub><mo is="true">…</mo><msub is="true"><mrow is="true"><mi is="true">u</mi></mrow><mrow is="true"><mi is="true">k</mi></mrow></msub></math></span> and <span class="math"><math><mi is="true">v</mi><mo linebreak="goodbreak" linebreakstyle="after" is="true">=</mo><msub is="true"><mrow is="true"><mi is="true">v</mi></mrow><mrow is="true"><mn is="true">1</mn></mrow></msub><msub is="true"><mrow is="true"><mi is="true">v</mi></mrow><mrow is="true"><mn is="true">2</mn></mrow></msub><mo is="true">…</mo><msub is="true"><mrow is="true"><mi is="true">v</mi></mrow><mrow is="true"><mi is="true">k</mi></mrow></msub></math></span>. In other words, <span class="math"><math><mi is="true">u</mi><mo is="true">⧢</mo><mi is="true">v</mi></math></span> is the finite set of all words obtainable from merging the words <em>u</em> and <em>v</em> from left to right, but choosing the next symbol arbitrarily from <em>u</em> or <em>v</em>. A word <span class="math"><math><mi is="true">u</mi><mo is="true">∈</mo><msup is="true"><mrow is="true"><mi is="true">A</mi></mrow><mrow is="true"><mo is="true">⁎</mo></mrow></msup></math></span> is a <em>square for the shuffle product</em> if it is the shuffle of two identical words (<em>i.e.</em>, <span class="math"><math><mi is="true">u</mi><mo is="true">∈</mo><mi is="true">v</mi><mo is="true">⧢</mo><mi is="true">v</mi></math></span> for some <span class="math"><math><mi is="true">v</mi><mo is="true">∈</mo><msup is="true"><mrow is="true"><mi is="true">A</mi></mrow><mrow is="true"><mo is="true">⁎</mo></mrow></msup></math></span>).</p><p id="sp0160">Whereas it can be decided in polynomial-time whether or not <span class="math"><math><mi is="true">u</mi><mo is="true">∈</mo><msub is="true"><mrow is="true"><mi is="true">v</mi></mrow><mrow is="true"><mn is="true">1</mn></mrow></msub><mo is="true">⧢</mo><msub is="true"><mrow is="true"><mi is="true">v</mi></mrow><mrow is="true"><mn is="true">2</mn></mrow></msub></math></span> for given words <em>u</em>, <span class="math"><math><msub is="true"><mrow is="true"><mi is="true">v</mi></mrow><mrow is="true"><mn is="true">1</mn></mrow></msub></math></span> and <span class="math"><math><msub is="true"><mrow is="true"><mi is="true">v</mi></mrow><mrow is="true"><mn is="true">2</mn></mrow></msub></math></span> (J.-C. Spehner, 1986), we show in this paper that it is <strong>NP</strong>-complete to determine whether or not a word <em>u</em> is a square for the shuffle product. The novelty in our approach lies in representing words as <em>linear graphs</em>, in which deciding whether or not a given word is a square for the shuffle product reduces to computing some <em>inclusion-free perfect matching</em>. Finally, we prove that it is <strong>NP</strong>-complete to determine whether or not an input word is in the shuffle of a word with its reverse.

We propose a self-supervised approach to deep surface deformation. Given a pair of shapes, our algorithm directly predicts a parametric transformation from one shape to the other respecting correspondences. Our insight is to use cycle-consistency to define a notion of good correspondences in groups of objects and use it as a supervisory signal to train our network. Our method does not rely on a template, assume near isometric deformations or rely on point-correspondence supervision. We demonstrate the efficacy of our approach by using it to transfer segmentation across shapes. We show, on Shapenet, that our approach is competitive with comparable state-of-the-art methods when annotated training data is readily available, but outperforms them by a large margin in the few-shot segmentation scenario.

We address the problem of visually guided rearrangement planning with many movable objects, i.e., finding a sequence of actions to move a set of objects from an initial arrangement to a desired one, while relying on visual inputs coming from an RGB camera. To do so, we introduce a complete pipeline relying on two key contributions. First, we introduce an efficient and scalable rearrangement planning method, based on a Monte-Carlo Tree Search exploration strategy. We demonstrate that because of its good trade-off between exploration and exploitation our method (i) scales well with the number of objects while (ii) finding solutions which require a smaller number of moves compared to the other state-of-the-art approaches. Note that on the contrary to many approaches, we do not require any buffer space to be available. Second, to precisely localize movable objects in the scene, we develop an integrated approach for robust multi-object workspace state estimation from a single uncalibrated RGB camera using a deep neural network trained only with synthetic data. We validate our multi-object visually guided manipulation pipeline with several experiments on a real UR-5 robotic arm by solving various rearrangement planning instances, requiring only 60 ms to compute the plan to rearrange 25 objects. In addition, we show that our system is insensitive to camera movements and can successfully recover from external perturbations. Supplementary video, source code and pre-trained models are available at https://ylabbe.github.io/rearrangement-planning/.

We study a second-order variational problem on the group of diffeomorphisms of the interval [0, 1] endowed with a right-invariant Sobolev metric of order 2, which consists in the minimization of the acceleration. We compute the relaxation of the problem which involves the so-called Fisher–Rao functional, a convex functional on the space of measures. This relaxation enables the derivation of several optimality conditions and, in particular, a sufficient condition which guarantees that a given path of the initial problem is also a minimizer of the relaxed one. Based on these sufficient conditions, the main result is that, when the value of the (minimized) functional is small enough, the minimizers are classical, that is the defect measure vanishes.

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Continuous-depth neural networks can be viewed as deep limits of discrete neural networks whose dynamics resemble a discretization of an ordinary differential equation (ODE). Although important steps have been taken to realize the advantages of such continuous formulations, most current techniques are not truly continuous-depth as they assume \textit{identical} layers. Indeed, existing works throw into relief the myriad difficulties presented by an infinite-dimensional parameter space in learning a continuous-depth neural ODE. To this end, we introduce a shooting formulation which shifts the perspective from parameterizing a network layer-by-layer to parameterizing over optimal networks described only by a set of initial conditions. For scalability, we propose a novel particle-ensemble parametrization which fully specifies the optimal weight trajectory of the continuous-depth neural network. Our experiments show that our particle-ensemble shooting formulation can achieve competitive performance, especially on long-range forecasting tasks. Finally, though the current work is inspired by continuous-depth neural networks, the particle-ensemble shooting formulation also applies to discrete-time networks and may lead to a new fertile area of research in deep learning parametrization.

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