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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.

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

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

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.

Road transport largely dominates the transportation demand and energy consumption. Passenger light-duty vehicles (passenger cars and light trucks) are responsible for almost half of the energy consumption of road transport. This energy is mainly supplied by fuels produced using oil resources, increasingly difficult and expensive to exploit. The use of these fuels also generates greenhouse gases, mainly carbon dioxide. This paper analyzes and compares strategies from different domains – the vehicle, the driver and the environment – for reducing carbon dioxide emissions from the use phase of passenger cars. This analysis is based on a general litterature review primarily focused on strategies applicable to OECD countries. A framework for classifying these strategies and the interactions between measures coming from different domains is proposed. The results highlight the possibilities of integration between domains that remain unexplored and that could bring significant gains in terms of emissions reductions.

Identifying the drag force coefficient and the rolling resistance coefficient of a vehicle is not an easy task, especially when not using a wind tunnel. This paper proposes an easy method using light experimental means to assess these coefficients in the case of a cargo-bike. Free deceleration measurement is performed. An identification method based on the least squares method is then proposed and discussed.

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.

Analyzing spatial structures of transportation data at various scales can be of prime interest to transportation planning and governance. In recent years, multi-scale spatial analysis methods have been developed and used in fields like ecology and geography, but only a few studies have applied these methods to transportation data. However, such methods can provide an efficient exploratory tool for: identifying those scales at which transportation data vary spatially; modeling the spatial structures at each scale; and determining the processes at work that explain these spatial structures. This paper describes and demonstrates how a multi-scale spatial analysis method, namely distance-based Moran's eigenvector maps (dbMEM), can be applied to study the spatial layout of car ownership. For this analysis, we rely on aggregated census data for small statistical areas within France's Loire-Atlantique administrative region. At first, 176 spatial vectors representing spatial patterns with a positive autocorrelation are constructed. Among the 176 vectors, only 23 significant ones are retained after performing a regression with car ownership as the dependent variable. Next, we divide these spatial vectors into three sub-models representing three spatial scales: broad scale, medium scale, and fine scale. Lastly, we identify a set of sociodemographic factors capable of explaining the spatial variation at each scale, i.e.: the broad-scale variation is mainly explained by population density, couples with children and income variables; the medium scale by couples with children, share of individuals in the 25-54 year age range and income; and the fine scale by couples with children and income variables.

In a context of climate change, lowering road vehicles consumption is a key point to meet CO2 reduction requirements. In addition to car technological advances, eco-driving is part of the solution but the road infrastructure should ensure its development. In a previous study, a gain of 5% in the spent energy was estimated on specific route by slightly moving some speed signs, but under the assumptions that drivers practice eco-driving and the traffic is free-flow. This paper deepens and widens these first results. The base of this research is to provide a simulation model to study the impact of traffic and speed-sectioning on the environment. Inside this model, the impact of different approach speeds to a speed-sectioning is assessed. The simulation is conducted within the Trafficware Synchro environment where parameters according to road infrastructure, vehicle and driver are based on real traffic data. Moving a speed limitation sign can contribute to a reduction of fuel consumption up to 8% depending on driver structure. This new methodology improves the accuracy of our first results and detects adverse effects as the possible emergence of congestion due to the modification of speed sectioning. In perspective this methodology represents a significant argument in road managers strategy. In addition it also represents an orienting point to investigate different action scenarios and a first step to a global optimization policy in managing road infrastructure.

Synthetic population is used in many transport models ranging from trip-based, hybrid trip, tour-based, and activity-based models. As mobility decisions depend on both individuals' characteristics and family situation, generating a two-layered population that takes into account not only the individual level but also household level is essential. In the literature, three main categories of methods for two-layered population generation have been proposed. These categories are synthetic reconstruction (SR), combinatorial optimization (CO), and statistical learning (SL). SR and CO methods produce synthetic populations by means of replicating individuals, whereas SL methods generate a population following a joint probability estimation. However, selecting a generation process is not straightforward as it depends on input data and synthetic population characteristics. To the best of our knowledge, no clear methodology for selecting between these methods exists. The main objectives of this paper are to provide (1) a detailed description of the available methods, (2) a comparison between these methods, and (3) a decision-making procedure for selecting between these methods. The description and comparison of the methods relies on different criteria: marginals availability, sample size, number of potential attributes that can be handled, population size to generate, possibility of zero-cell problem, and so forth. The advantages and shortcomings of each method are illustrated, and method performance is assessed. The decision-making procedure is carried out through the proposal of a decision tree. Researchers and practitioners have now access to a comprehensive and unified framework to select the appropriate method depending on available data and features of their modeling purposes.

La Métropole Rouen Normandie souhaitait concevoir une nouvelle passerelle nécessitant des appuis provisoires en Seine. Aussi a-t-elle sollicité l’Ifsttar pour conduire une étude statistique des données de trajectoires, afin de définir les fuseaux de mobilité des navires en fonction notamment de leur vitesse et de leur gabarit, deux facteurs susceptibles d’avoir un impact sur la manoeuvrabilité des navires. Le traitement des données au format SIA a nécessité de recourir à des outils dédiés aux gros fichiers de données. Puis un modèle de régression linéaire a été retenu pour l’analyse des trajectoires rectilignes hors manoeuvres d’accostage. La détermination d’intervalles de prédiction a permis de délimiter les zones de navigation, et donc de mettre en évidence les surfaces du fleuve aptes à accueillir les appuis provisoires.

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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.

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

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

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.

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.

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.

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.

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.

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.

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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.

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.

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.

Road transport largely dominates the transportation demand and energy consumption. Passenger light-duty vehicles (passenger cars and light trucks) are responsible for almost half of the energy consumption of road transport. This energy is mainly supplied by fuels produced using oil resources, increasingly difficult and expensive to exploit. The use of these fuels also generates greenhouse gases, mainly carbon dioxide. This paper analyzes and compares strategies from different domains – the vehicle, the driver and the environment – for reducing carbon dioxide emissions from the use phase of passenger cars. This analysis is based on a general litterature review primarily focused on strategies applicable to OECD countries. A framework for classifying these strategies and the interactions between measures coming from different domains is proposed. The results highlight the possibilities of integration between domains that remain unexplored and that could bring significant gains in terms of emissions reductions.

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.

Identifying the drag force coefficient and the rolling resistance coefficient of a vehicle is not an easy task, especially when not using a wind tunnel. This paper proposes an easy method using light experimental means to assess these coefficients in the case of a cargo-bike. Free deceleration measurement is performed. An identification method based on the least squares method is then proposed and discussed.

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.

The French metropolis, Rouen Normandie, has a project of a new bridge requiring temporary pier in the river Seine during the building phase. The location of this pier in the river is constrained by mechanical reasons related to the construction of the bridge. The purpose of this study is to find a position of this pier in the mechanically constrained area which minimizes vessel traffic obstruction and therefore collision risk. This type of study is classically carried out by using complex and multiple vessel dynamics simulation software in order to assess the risk for a given vessel to crash into the pier. The novelty of this study is to propose a statistical study based on the database of Automatic Identification System (AIS) of the Vessel Traffic Service (VTS) that is an automatic tracking system relying on ships transceivers. The technical objective is to identify low risk areas according to vessel speeds and sizes. Both factors should have an impact on the maneuverability of ships to avoid pier collision risk. Among all trajectories, straight line trajectories are selected based on statistical methods. The computation of prediction intervals of these trajectories delimits navigation zone. If all the straight trajectories are taken into account, the navigation zone extends to the whole river surface, which is not an helping result. However, by focusing on large and high speed vessels trajectories the navigation zone of these weak maneuverable ships is more centered in the middle of the river and represents only 25% of the river area. The complement of this area delineates possible locations of the temporary pier of the bridge in the river that do not disrupt vessel traffic.

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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.

Analyzing spatial structures of transportation data at various scales can be of prime interest to transportation planning and governance. In recent years, multi-scale spatial analysis methods have been developed and used in fields like ecology and geography, but only a few studies have applied these methods to transportation data. However, such methods can provide an efficient exploratory tool for: identifying those scales at which transportation data vary spatially; modeling the spatial structures at each scale; and determining the processes at work that explain these spatial structures. This paper describes and demonstrates how a multi-scale spatial analysis method, namely distance-based Moran's eigenvector maps (dbMEM), can be applied to study the spatial layout of car ownership. For this analysis, we rely on aggregated census data for small statistical areas within France's Loire-Atlantique administrative region. At first, 176 spatial vectors representing spatial patterns with a positive autocorrelation are constructed. Among the 176 vectors, only 23 significant ones are retained after performing a regression with car ownership as the dependent variable. Next, we divide these spatial vectors into three sub-models representing three spatial scales: broad scale, medium scale, and fine scale. Lastly, we identify a set of sociodemographic factors capable of explaining the spatial variation at each scale, i.e.: the broad-scale variation is mainly explained by population density, couples with children and income variables; the medium scale by couples with children, share of individuals in the 25-54 year age range and income; and the fine scale by couples with children and income variables.

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.

In a context of climate change, lowering road vehicles consumption is a key point to meet CO2 reduction requirements. In addition to car technological advances, eco-driving is part of the solution but the road infrastructure should ensure its development. In a previous study, a gain of 5% in the spent energy was estimated on specific route by slightly moving some speed signs, but under the assumptions that drivers practice eco-driving and the traffic is free-flow. This paper deepens and widens these first results. The base of this research is to provide a simulation model to study the impact of traffic and speed-sectioning on the environment. Inside this model, the impact of different approach speeds to a speed-sectioning is assessed. The simulation is conducted within the Trafficware Synchro environment where parameters according to road infrastructure, vehicle and driver are based on real traffic data. Moving a speed limitation sign can contribute to a reduction of fuel consumption up to 8% depending on driver structure. This new methodology improves the accuracy of our first results and detects adverse effects as the possible emergence of congestion due to the modification of speed sectioning. In perspective this methodology represents a significant argument in road managers strategy. In addition it also represents an orienting point to investigate different action scenarios and a first step to a global optimization policy in managing road infrastructure.

Synthetic population is used in many transport models ranging from trip-based, hybrid trip, tour-based, and activity-based models. As mobility decisions depend on both individuals' characteristics and family situation, generating a two-layered population that takes into account not only the individual level but also household level is essential. In the literature, three main categories of methods for two-layered population generation have been proposed. These categories are synthetic reconstruction (SR), combinatorial optimization (CO), and statistical learning (SL). SR and CO methods produce synthetic populations by means of replicating individuals, whereas SL methods generate a population following a joint probability estimation. However, selecting a generation process is not straightforward as it depends on input data and synthetic population characteristics. To the best of our knowledge, no clear methodology for selecting between these methods exists. The main objectives of this paper are to provide (1) a detailed description of the available methods, (2) a comparison between these methods, and (3) a decision-making procedure for selecting between these methods. The description and comparison of the methods relies on different criteria: marginals availability, sample size, number of potential attributes that can be handled, population size to generate, possibility of zero-cell problem, and so forth. The advantages and shortcomings of each method are illustrated, and method performance is assessed. The decision-making procedure is carried out through the proposal of a decision tree. Researchers and practitioners have now access to a comprehensive and unified framework to select the appropriate method depending on available data and features of their modeling purposes.

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

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In a context of decreasing resources and of climate change, lowering road vehicles consumption is a key point to meet CO2 reduction requirements. In addition to car technological advances, eco-driving is part of the solution but the road infrastructure should ensure its development. This work aims to demonstrate that road energy demand and associated pollutant emissions can be reduced by working out minor optimization of road infrastructure itself. For this to happen, a simple eco-driving potential criterion is built upon infrastructure parameters such as slopes and sight distances. This criterion aims to detect Misplaced Speed-sectioning Positions (MSP) with regard to the Starting Point of Deceleration (SPD); with speed-sectioning being the succession of speed changes along a given route. An enhanced energy waste formulation is then developed to quantify the vehicles energy waste due to misplaced road-signs. Thirdly, a traffic simulation constitutes a framework for energy evaluation; considering a full flow of vehicles, based on real traffic data, and by modeling several driver behaviors. Simulation results show that a significant fuel reduction of up to 5.5% can be achieved locally, simply by moving a road sign, for rural areas and without degrading road safety.

La Métropole Rouen Normandie souhaitait concevoir une nouvelle passerelle nécessitant des appuis provisoires en Seine. Aussi a-t-elle sollicité l’Ifsttar pour conduire une étude statistique des données de trajectoires, afin de définir les fuseaux de mobilité des navires en fonction notamment de leur vitesse et de leur gabarit, deux facteurs susceptibles d’avoir un impact sur la manoeuvrabilité des navires. Le traitement des données au format SIA a nécessité de recourir à des outils dédiés aux gros fichiers de données. Puis un modèle de régression linéaire a été retenu pour l’analyse des trajectoires rectilignes hors manoeuvres d’accostage. La détermination d’intervalles de prédiction a permis de délimiter les zones de navigation, et donc de mettre en évidence les surfaces du fleuve aptes à accueillir les appuis provisoires.

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.

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.

Rouen métropole a sollicité les capacités de l'Ifsttar dans le domaine du traitement du signal appliqué à l'usage des infrastructures à travers un courriel envoyé le 1er mars 2017 par M Toynan, responsable du service Ouvrage d'art de la métropole de Rouen, à MM Coiret et Vandanjon, chercheurs à l'Ifsttar. Cette sollicitation concerne un ouvrage en projet pour le franchissement de la Seine. L'ouvrage sera de type léger, sous la forme d'une passerelle dédiée aux modes doux (cycles, piétons). Il complètera l'offre de franchissement existante et reliera efficacement des pôles générateurs de déplacements dans l'agglomération, et notamment l'Ecoquartier Luciline en cours de réalisation en rive droite et l'Ecoquartier Flaubert en projet en rive gauche dans le cadre du projet global Seine Cité. L'ouvrage est donc envisagé dans le secteur Ouest de l'agglomération rouennaise, entre les ponts Guillaume Le Conquérant et Gustave Flaubert, soit en traverse de la partie de Seine accueillant régulièrement la grande Armada. Les appuis principaux de la structure seraient situés dans l'alignement des hangars existants. La structure provisoire dite > sera déconstruite préalablement aux travaux de passerelle. A ce jour, il n'est pas envisagé d'implanter d'appui définitif en Seine. Néanmoins, le recours à des appuis intermédiaires en phase réalisation semble incontournable. De même, la définition d'un gabarit de passe navigable influe sur la définition de la limite basse d'intrados d'ouvrage et donc sur la définition des accès. Seule une connaissance plus précise des trajectoires fluviales entre le Pont Guillaume le Conquérant en amont et le Pont Flaubert en aval permettra donc de gagner en précision sur la définition des implantations possibles d'appuis provisoires puis sur la définition du gabarit fluvial définitif. À cet effet, l'Ifsttar a été sollicité pour une étude statistique de trajectoire de navires, selon un cahier des charges qui est donné en annexe au présent document. L'Ifsttar a répondu à ce cahier des charges en rédigeant une proposition technique, également en annexe à ce document, dont le produit est un rapport d'étude sur les trajectoires fluviales sur la section considérée. Une réunion de lancement a eu lieu le 11 mai 2017 dans les locaux de Rouen métropole. Ce document est la troisième version de ce rapport d'étude. Une première version a été envoyée le 17 juin afin d'échanger avec la Métropole Rouen Normandie. Ce document a été le support d'une réunion téléphonique avec la métropole de Rouen le 27 juin afin, notamment, de préparer une réunion entre la métropole et le grand port maritime de Rouen. Nous avons abouti à une deuxième version envoyée le 3 juillet 2017 afin de fournir des données opérationnelles pour la métropole. Suite à des relectures internes, nous avons modifié cette deuxième version en améliorant sa forme afin d'aboutir à cette version qui est remise lors de la réunion de présentation des résultats du 11 juillet 2017. Ce rapport est découpé en 10 chapitres. Tout d'abord, la structure des données, que nous avons obtenues par le port maritime, est expliquée. Les grandes étapes du traitement de ces données sont présentées dans le chapitre suivant. Puis, les résultats de ce traitement sont affichées sous forme de graphiques pour chaque trimestre de l'année 2016 dans les chapitres 4 à 8. Enfin, les deux dernier chapitres discutent les résultats et conclut le rapport.

In railway planning, the timetabling step needs, as input, the train running times, which are calculated from a train dynamic model. Usually, this model determines the most energy-efficient train trajectory for a predefined time. However, this time may not correspond to the timetable-makers' needs. They should have the choice among a set of solutions, more or less energy-consuming. This study proposes a method capable of producing a set of alternative running times with the associated mechanical energy required. To this end, the authors' contribution is to set up an efficient evolutionary multi-objective algorithm builds a set of well-spread and diversified solutions which approximate a Pareto front. The solutions are all compromises between running time and energy-consumption, the two minimisation objectives concurrently optimised. Given that an evolutionary algorithm is strongly dependent on the initialisation phase, the efficiency of the algorithm is improved through a specific and original mechanism connecting multiple initialisations in cascade in order to accelerate the convergence towards the best solutions. A set of results obtained on randomly-generated instances is analysed and discussed.

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

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

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

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

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

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

In the hardening energy context, the transport sector which constitutes a large worldwide energy demand has to be improving for decrease energy demand and global warming impacts. In a controversial situation where subsists an increasing demand for long-distance and high-speed travels, high-speed trains offer many advantages, as consuming significantly less energy than road or air transports. 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 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 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.

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.

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.

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.

France Grilles - Software On Line (FG-SOL) est proposé par l'infrastructure de recherche France Grilles (http://www.france-grilles.fr/accueil/) . Les codes développés dans nos laboratoires, appelés ici logiciels de la recherche, jouent un rôle fondamental dans le fonctionnement de la recherche puisque la plupart des résultats obtenus dans l'ensemble des domaines scientifiques s’appuient sur des analyses de données, simulations, ou calculs obtenus grâce à ces logiciels. Mais les algorithmes implémentés et les savoirs sous-jacents sont souvent peu décrits dans les publications associées. Il est alors difficile voire impossible de reproduire les résultats ou de faire évoluer cette recherche sans l'accès à ces logiciels. FG-SOL a pour premier objectif de permettre aux scientifiques qui produisent des logiciels et souhaitent les faire connaître, de pouvoir les mettre à disposition facilement, accompagnés si besoin d'un jeu de données et des liens vers les articles scientifiques associés. FG-SOL fournit donc à la communauté scientifique un environnement qui permet d'une part la reproduction des résultats, et d'autre part l'utilisation de ces codes avec de nouveaux jeux de données, l'accès au code source et son étude. Le second objectif de FG-SOL est de permettre aux scientifiques à la recherche d'un logiciel pour leurs propres travaux de pouvoir découvrir, tester, ré-utiliser les logiciels mis ainsi à disposition sur une infrastructure de production sans avoir à installer eux-mêmes ces logiciels. Après une introduction consacrée au contexte et aux besoins, nous présenterons le service du point de vue des utilisateurs puis l'architecture technique basée sur VIP (https://www.creatis.insa-lyon.fr/vip/) et nous conclurons sur les retours des premiers utilisateurs et les perspectives d'évolution.

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

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

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

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

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

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

The objectives of the thesis is to develop the methods and frameworks to analysis 3D human postures in the working environment for ergonomic propose.Ergonomics is a discipline which consists of understanding body work with the objective of preserving the health of operators while allowing achievement of the expected quality. The postures of operators at work stations are one of the factors in the appearance of occupational diseases, and the characterization of a posture is a step in the pre-diagnosis of a work situation. Artificial intelligence methods using 3D human pose estimation to detect unsuitable postures at work could help ergonomist to establish their diagnosis on a large quantity of data.This thesis proposes three works on 3D human pose estimation to attack the difficulties of unconstrained environment such as work stations.The first work proposes a synthetic 3D human pose generation algorithm for training 2D to 3D human pose lifting. We tackle with the domain gap problem between public research data which have constrained environment plus limited number of action and unconstrained working environment data with much more variety of actions. This work presents an algorithm which allows to generate synthetic 3D human skeletons on the fly during the training, following a Markov-tree type distribution which evolve through out time to create unseen poses. This work also proposes a scaleless multi-view training process based on purely synthetic data generated from a few initial poses. We evaluate our approach on two benchmark datasets and achieve promising results in a zero shot setup.The second work proposes a framework of making 3D wholebody annotations from multi-view image data as well as a few datasets and a benchmark built based on this framework to tackle with the skeleton model capability problem. Public research data normally only has around 20 keypoints which is not enough for ergonomist to measure certain aspects like supination-pronation angles, while the wholebody skeleton has 133 keypoints, capable for obtaining the necessary information. The annotation-making framework contains 3 steps from multi-view 3D geometry reconstruction, to incomplete skeleton completion, and finally hand/face refinement through diffusion. With this framework, we introduce 3 datasets as extensions of existing Human3.6M, CMU-Panoptic and MPI-INF-3DHP datasets with wholebody 2D and 3D keypoint annotations for body, face, and hands. A benchmark of three tasks is proposed based on Human3.6M wholebody extension: 3D whole-body lifting from complete 2D keypoints, from incomplete 2D keypoints, and from monocular images.The third work proposes an algorithm which allows a continuous estimation of human poses through time with very limited input frames to tackle potential corrupted video sequences in unconstrained environment where the workers are not always observed or even in the screen due to their movements. This work proposes a novel approach that models human motion as a continuous function implemented by a neural network, akin to neural implicit representations. We conduct a comprehensive comparison of this approach with state-of-the-art motion prediction methods on three popular datasets, demonstrating significant improvements over the baselines in most cases.Finally, we made a demonstration that perform 2D and 3D human pose estimation, as well as critical pose detection for ergonomic analysis, capable of fast processing even on a CPU-only computer.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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