Multi-criteria Performance Assessment of Adaptive Radar Resources Management: Application to Naval Scenario

International audienceMultifunction radars (MFR) must achieve their capability requirements in an increasingly complex environment, populated with diverse and hostile targets (e.g. low Radar Cross-Section, low speed targets in clutter or high speed, ballistic targets) in saturating scenarios (due to e.g. RF interference or threats). These radar systems are increasingly exploiting active electronically scanned array (AESA) technology to dynamically schedule the use of multiple functions in a short duration. However, the increasing complexity and adaptive nature of MFR radar makes it very difficult to specify their performance in a way which can both deliver the required capability and which can be verified in a cost-effective manner. Due to their multi-function feature, the operational scenarios have a strong impact on MFR radars; their performances should be specified accordingly. Addressing the challenges in this area will benefit via better understood requirements which can be more easily interpreted. After definition of FoM (Figures of Merit) for phased array radar operation and performance, we have computed them on benchmark test scenarios of varying complexity. We describe a new methodology to aggregate these metrics to provide a global notation of MFR radar performances

The relationship between traits optimism and anxiety and health-related quality of life in patients hospitalized for chronic diseases: data from the SATISQOL study.

International audienceBACKGROUND: The impact of psychological factors is often taken into account in the evaluation of quality of life. However, the effect of optimism and trait anxiety remains controversial and they are rarely studied simultaneously. We aimed to study the effect of this factor on health-related quality of life (HRQOL) of patients after a hospitalization in relation with their chronic disease. METHODS: Using cross-sectional data from the SATISQOL cohort, we conducted a multicentric study, including patients hospitalized for an intervention in connection with their chronic disease. Six months after hospitalization, patients completed a generic HRQOL questionnaire (SF-36), and the STAI and LOT-R questionnaires to evaluate optimism and trait anxiety. We studied the effect of each trait on HRQOL separately, and simultaneously, taking account of their interaction in 3 models, using an ANOVA. RESULTS: In this study, 1529 patients were included in three participating hospitals and there existed wide diversity in the chronic diseases in our population. The HRQOL score increased for all dimensions of SF36 between 15,8 and 44,5 when the level of anxiety decreased (p < 0.0001) for the model 1, assessing the effect of anxiety on HRQOL and increased for all dimensions of SF36 between 3.1 and 12.7 with increasing level of optimism (< 0.0001) in the model 2 assessing the effect of optimism on HRQOL. In the model 3, assessing the effect of both anxiety and optimism on HRQOL, and their interaction, the HRQOL score for all dimensions of the SF36 increased when the level of anxiety decreased (p < 0.0001). It increased with increasing level of optimism (p < 0.006) in the model for all dimensions of SF36 except the Role Physical dimension. In this model, interaction between anxiety and optimism was significant for the Social Functioning dimension (p = 0.0021). CONCLUSIONS: Optimism and trait anxiety appeared to be significantly correlated with HRQOL. Furthermore, an interaction existed between the trait anxiety and optimism for some dimensions of SF36. Contrary to optimism, it seems essential to evaluate trait anxiety in future studies about HRQOL, since it could represent a confounding factor

Un système multi-agents pour une place de marché de facture

For most companies, the time gap between the moment when they begin to spend money and the moment when they receive payment from their customer generates a working capital requirement. The latter may create issues such as contract lost or bankruptcy. In the current context, getting credits from banks may be either costly or even impossible for Small and Medium Enterprises. Indeed, many banks refuse to lend money for small companies or when they estimate that there is a risk. It is then possible to rely on other solutions as factoring and get immediate credit from the invoices. In this thesis, we propose a factoring marketplace based on related to curious agents that try to infer private information from their partners. This kind of behavior is harmful both for the agents and for the platform more generally. We thus propose to design a negotiation protocol resistant to such behavior by endowing the agents with an incentive to negotiate only when they really try to get a good. We then propose an automated negotiation agent which relies on Monte Carlo Tree Search techniques. Those techniques have proved to be quite efficient in AI for games. Our agent is able to learn information from its partner, though it is not its main objective in order to get to a beneficial agreement. For this purpose, it relies on opponent modeling techniques and machine learning such as Bayesian Learning and Gaussian Process Regression.Dans les entreprises, l’écart entre la date où les entreprises engagent leurs dépenses et la date où elles sont payées entraîne un besoin en fonds de roulement à l’origine de problèmes dont la sévérité va de la perte de contrat au dépôt de bilan. Dans le contexte actuel, le recours à l’emprunt pour éviter ces situations est hélas complexe pour les PME, les banques refusant de prêter lorsqu’elles estiment que le risque est trop important. Une possibilité est alors de recourir au factoring, et de faire financer ses créances. Dans cette thèse, nous proposons une place de marché de factoring intelligente, basée sur des travaux de négociation automatique et de mechanism design: nous proposons d’analyser théoriquement et expérimentalement les problématiques liées aux agents curieux cherchant à inférer les informations privées des autres agents, entraînant des surcoûts de leur point vue comme de celui de la plate-forme. Nous proposons également de concevoir un protocole de négociation qui s’oppose à de tels comportements, donnant aux agents une incitation à ne négocier que lorsqu’ils souhaitent vraiment obtenir un bien. Nous proposons ensuite un agent de négociation automatique. Pour la conception de la stratégie de notre agent, nous proposons d’exploiter les méthodes de Monte Carlo Tree Search, qui ont récemment fait leur preuve dans l’IA pour les jeux. Notre agent est capable d’apprendre de son partenaire bien que ce ne soit pas son objectif premier, afin de trouver un accord qui lui soit favorable. Il s’appuie pour cela sur des technique de modélisation d’adversaire impliquant des méthodes d’apprentissage tels que l’apprentissage bayésien et la régression de processus gaussiens

A multiagent system for an invoice marketplace

Dans les entreprises, l’écart entre la date où les entreprises engagent leurs dépenses et la date où elles sont payées entraîne un besoin en fonds de roulement à l’origine de problèmes dont la sévérité va de la perte de contrat au dépôt de bilan. Dans le contexte actuel, le recours à l’emprunt pour éviter ces situations est hélas complexe pour les PME, les banques refusant de prêter lorsqu’elles estiment que le risque est trop important. Une possibilité est alors de recourir au factoring, et de faire financer ses créances. Dans cette thèse, nous proposons une place de marché de factoring intelligente, basée sur des travaux de négociation automatique et de mechanism design: nous proposons d’analyser théoriquement et expérimentalement les problématiques liées aux agents curieux cherchant à inférer les informations privées des autres agents, entraînant des surcoûts de leur point vue comme de celui de la plate-forme. Nous proposons également de concevoir un protocole de négociation qui s’oppose à de tels comportements, donnant aux agents une incitation à ne négocier que lorsqu’ils souhaitent vraiment obtenir un bien. Nous proposons ensuite un agent de négociation automatique. Pour la conception de la stratégie de notre agent, nous proposons d’exploiter les méthodes de Monte Carlo Tree Search, qui ont récemment fait leur preuve dans l’IA pour les jeux. Notre agent est capable d’apprendre de son partenaire bien que ce ne soit pas son objectif premier, afin de trouver un accord qui lui soit favorable. Il s’appuie pour cela sur des technique de modélisation d’adversaire impliquant des méthodes d’apprentissage tels que l’apprentissage bayésien et la régression de processus gaussiens.For most companies, the time gap between the moment when they begin to spend money and the moment when they receive payment from their customer generates a working capital requirement. The latter may create issues such as contract lost or bankruptcy. In the current context, getting credits from banks may be either costly or even impossible for Small and Medium Enterprises. Indeed, many banks refuse to lend money for small companies or when they estimate that there is a risk. It is then possible to rely on other solutions as factoring and get immediate credit from the invoices. In this thesis, we propose a factoring marketplace based on related to curious agents that try to infer private information from their partners. This kind of behavior is harmful both for the agents and for the platform more generally. We thus propose to design a negotiation protocol resistant to such behavior by endowing the agents with an incentive to negotiate only when they really try to get a good. We then propose an automated negotiation agent which relies on Monte Carlo Tree Search techniques. Those techniques have proved to be quite efficient in AI for games. Our agent is able to learn information from its partner, though it is not its main objective in order to get to a beneficial agreement. For this purpose, it relies on opponent modeling techniques and machine learning such as Bayesian Learning and Gaussian Process Regression

Collaborative Multi-Radars Tracking by Distributed Auctions

International audienceIn this paper, we present an algorithm which lies in the domain of task allocation for a set of static autonomous radars with rotating antennas. It allows a set of radars to allocate in a fully decentralized way a set of active tracking tasks according to their location, considering that a target can be tracked by several radars, in order to improve accuracy with which the target is tracked. The allocation algorithm proceeds through a collaborative and fully decentralized auction protocol, using a collaborative auction protocol (Consensus Based Bundle Auction algorithm). Our algorithm is based on a double use of our allocation protocol among the radars. The latter begin by allocating targets, then launch a second round of allocation if theyhave resources left, in order to improve accuracy on targets already tracked. Our algorithm is also able to adapt to dynamism, i.e. to take into account the fact that the targets are moving and that the radar(s) most suitable for Tracking them changes as the mission progresses. To do this, the algorithm is restarted on a regular basis, to ensure that a bid made by a radar can decrease when the target moves away from it. Since our algorithm is based on collaborative auctions, it does not plan the following rounds, assuming that the targets are not predictable enough for this. Our algorithm is however based on radars capable of anticipating the positions of short-term targets, thanks to a Kalman filter. The algorithm will be illustrated based on a multi-radar tracking scenario where the radars, autonomous, must follow a set of targets in order to reduce the position uncertainty of the targets. Standby aspects will not be considered in this scenario. It is assumed that the radars can pick up targets in active pursuit, with an area ofuncertainty corresponding to their distance

Thickness-controlled porous NiO films by Ni(OH)2/alginate layer-by-layer assembly

International audienceNi(OH)2/alginate multilayer thin films were prepared directly by dip-coating using Layer-by-Layer assembly method. β-Ni(OH)2 nanoplatelets were first synthetized by chemical precipitation from nickel nitrate in ammonia under ultrasound. These particles, stabilized at pH ≈ 8, present an equivalent diameter of ≈ 250 nm and a zeta potential of ≈ + 35 mV. Sodium alginate was used as a negatively charged polyelectrolyte. In this work, the multilayer growth was first investigated in situ using optical fixed-angle reflectometry showing that particles/polyelectrolyte multilayer film can be successfully carried out. Ni(OH)2/alginate multilayer thin films were elaborated using several parameters: number of adsorbed bilayers, alginate and Ni(OH)2 particles concentrations, showing that multilayer films thickness can be mainly controlled by the number of adsorbed bilayers. The elaborated films were then heated at 325 °C in air for 1 h to calcinate the incorporated polyelectrolyte and to form NiO films. Thermal treatment experiments show an alginate content-dependent film adhesion. Indeed, multilayer films are more adherent and homogeneous with low alginate concentration (< 0.2 g.L−1). Finally, the specific surface area (SSA) of the NiO films was determined by adsorption-desorption of nitrogen gas using BET method