The first part of this thesis relies on stated and revealed preference survey results across a sample of U.S. households to first ascertain vehicle acquisition, disposal, and use patterns, and then simulate these for a synthetic population over time. Results include predictions of future U.S. household-fleet composition, use, and greenhouse gas (GHG) emissions under nine different scenarios, including variations in fuel and plug-in-electric-vehicle (PHEV) prices, new-vehicle feebate policies, and land-use-density settings. The adoption and widespread use of plug-in vehicles will depend on thoughtful marketing, competitive pricing, government incentives, reliable driving-range reports, and adequate charging infrastructure. This work highlights the impacts of various directions consumers may head with such vehicles. For example, twenty-five-year simulations at gas prices at $7 per gallon resulted in the highest market share predictions (16.30%) for PHEVs, HEVs, and Smart Cars (combined) — and the greatest GHG-emissions reductions. Predictions under the two feebate policy scenarios suggest shifts toward fuel-efficient vehicles, but with vehicle miles traveled (VMT) rising slightly (by 0.96% and 1.42%), thanks to lower driving costs. The stricter of the two feebate policies – coupled with gasoline at $5 per gallon – resulted in the highest market share (16.37%) for PHEVs, HEVs, and Smart Cars, but not as much GHG emissions reduction as the $7 gas price scenario. Total VMT values under the two feebate scenarios and low-PHEV-pricing scenarios were higher than those under the trend scenario (by 0.56%, 0.96%, and 1.42%, respectively), but only the low-PHEV-pricing scenario delivered higher overall GHG emission estimates (just 0.23% more than trend) in year 2035. The high-density scenario (where job and household densities were quadrupled) resulted in the lowest total vehicle ownership levels, along with below-trend VMT and emissions rates. Finally, the scenario involving a $7,500 rebate on all PHEVs still predicted lower PHEV market share than the $7 gas price scenario (i.e., 2.85% rather than 3.78%). The second part of this thesis relies on data from the U.S. Consumer Expenditure Survey (CEX) to estimate the welfare impacts of carbon taxes and household-level capping of emissions (with carbon-credit trading allowed). A translog utility framework was calibrated and then used to anticipate household expenditures across nine consumer goods categories, including vehicle usage and vehicle expenses. An input-output model was used to estimate the impact of carbon pricing on goods prices, and a vehicle choice model determined vehicle type preferences, along with each household’s effective travel costs. Behaviors were predicted under two carbon tax scenarios ($50 per ton and $100 per ton of CO2-equivalents) and four cap-and-trade scenarios (10-ton and 15-ton cap per person per year with trading allowed at $50 per ton and $100 per ton carbon price). Results suggest that low-income households respond the most under a $100-per-ton tax but increase GHG emissions under cap-and-trade scenarios, thanks to increased income via sale of their carbon credits. High-income households respond the most across all the scenarios under a 10-ton cap (per household member, per year) and trading at $100 per ton scenario. Highest overall emission reduction (47.2%) was estimated to be under $100 per ton carbon tax. High welfare loss was predicted for all households (to the order of 20% of household income) under both the policies. Results suggest that a carbon tax will be regressive (in terms of taxes paid per dollar of expenditure), but a tax-revenue redistribution can be used to offset this regressivity. In the absence of substitution opportunities (within each of the nine expenditure categories), these results represent highly conservative (worst-case) results, but they illuminate the behavioral response trends while providing a rigorous framework for future work. / text

The aim of this work is to analytically treat the dynamic ion behavior during the evolution of the ion matrix sheath, considering the industrial application plasma source ion implantation for both planar and cylindrical targets, and then to de-velop a code that simulates this dynamic ion behavior numerically. If the sepa-ration between the electrodes in a discharge tube is small, upon the application of a large potential between the electrodes, an ion matrix sheath is formed, which fills the whole inter-electrode space. After a short time, the ion matrix sheath starts moving towards the cathode and disappears there. Two regions are formed as the matrix sheath evolves. The potential profiles of these two regions are derived and the ion flux on the cathode is estimated. Then, by us-ing the finite-differences method, the problem is simulated numerically. It has been seen that the results of both analytical calculations and numerical simula-tions are in a good agreement.

Les systèmes de recherche ont facilité l’accès à l’information disponible sur le web à l’aide de mécanismes de collecte, d’indexation et de stockage de contenus hétérogènes.Ils génèrent des traces résultant de l’activité des internautes. Il s’agit ensuite d’analyser ces données à l’aide d’outils de data mining afin d’améliorer la qualité de réponse de ces systèmes ou de la personnaliser en fonction des profils des utilisateurs. Certains acteurs, comme la société Marketshot, se positionnent comme intermédiaires entre les consommateurs et les professionnels. Ils mettent en relation les acheteurs potentiels avec les grandes marques et leurs réseaux de distribution à travers leurs sites Internet d’aide à l’achat. Pour cela, ces intermédiaires ont développé des portails efficaces et stockent de gros volumes de données liées à l’activité des internautes sur leurs sites. Ces gisements de données sont exploités pour répondre favorablement aux besoins des internautes, ainsi qu’à ceux des professionnels qui cherchent à comprendre le comportement de leurs clients et anticiper leurs actes d’achats. C’est dans ce contexte, où on cherche à fouiller les données collectées du web, que se placent mes travaux de recherche. L’idée est de construire des modèles qui permettent d’expliciter une corrélation entre les activités des internautes sur les sites d’aide à l’achat et les tendances de ventes de produits dans la « vraie vie ». En effet, ma thèse se place dans le cadre de l’apprentissage probabiliste et plus particulièrement des modèles graphiques « Topic Models ». Elle consiste à modéliser les comportements des internautes à partir des données d’usages de sites web. / Research systems have facilitated access to information available on the web using mechanisms for collecting, indexing and storage of heterogeneous content. They generate data resulting from the activity of users on Internet (queries, logfile). The next step is to analyze the data using data mining tools in order to improve the response’s quality of these systems, or to customize the response based on users’ profiles. Some actors, such as the company Marketshot, are positioned as intermediaries between consumers and professionals. Indeed, they link potential buyers with the leading brands and distribution networks through their websites. For such purposes, these intermediaries have developed effective portals, and have stored large volumes of data related to the activity of users on their websites. These data repositories are exploited to respond positively to the needs of users as well as those of professionals who seek to understand the behavior of their customers and anticipate their purchasing actions. My thesis comes within the framework of searching through the data collected from the web. The idea is to build models that explain the correlation between the activities of users on websites of aid for the purchase, and sales trends of products in « real life ». In fact, my research concerns probabilistic learning, in particular Topic Models. It involves modeling the users’ behavior from uses of trader websites.

With the advancement of CMOS process and fabrication, it has been a trend to maximize digital design while minimize analog correspondents in mixed-signal system designs. So is the case for PLL. PLL has always been a traditional mixed-signal system limited by analog part performance. Around 2000, there emerged ADPLL of which all the blocks besides oscillator are implemented in digital circuits. There have been successful examples in application of Bluetooth, and it is moving to improve results for application of WiMax and ad-hoc frequency hopping communication link. Based on the theoretic and measurement results of existing materials, ADPLL has shown advantages such as fast time-to-market, low area, low cost and better system integration; but it also showed disadvantages in frequency resolution and phase noise, etc. Also this new topic still opens questions in many researching points important to PLL such as tracking behavior and quantization effect. In this thesis, a non-linear phase domain model for all digital phase-locked loop (ADPLL) was established and validated. Based on that, we analyzed that ADPLL phase noise prediction derived from traditional linear quantization model became inaccurate in non-linear cases because its probability density of quantization error did not meet the premise assumption of linear model. The phenomena of bandwidth expansion and in-band phase noise decreasing peculiar to integer-N ADPLL were demonstrated and explained by matlab and verilog behavior level simulation test bench. The expression of threshold quantization step was defined and derived as the method to distinguish whether an integer-N ADPLL was in non-linear cases or not, and the results conformed to those of matlab simulation. A simplified approximation model for non-linear integer-N ADPLL with noise sources was established to predict in-band phase noise, and the trends of the results conformed to those of matlab simulation. Other basic analysis serving for the conclusions above covered: ADPLL loop dynamics, traditional linear theory and its quantitative limitations and numerical analysis of random number. Finally, a present measurement setup was demonstrated and the results were analyzed for future work.

<p dir="ltr">In the context of a Human-in-the-Loop (HITL) system, the accuracy of reachability analysis plays a significant role in ensuring the safety and reliability of HITL systems. In addition, one can avoid unnecessary conservativeness by explicitly considering human control behavior compared to those methods that rely on the system dynamics alone. One possible approach is to use a Gaussian Mixture Model (GMM) to encode human control behavior using the Expectation-Maximization (EM) algorithm. However, relatively few works consider the admissible control input ranges due to physical limitations when modeling human control behavior. This could make the following reachability analysis overestimate the system's capability, thereby affecting the performance of the HITL system. To address this issue, this work presents a constrained stochastic reachability analysis algorithm that can explicitly account for the admissible control input ranges. By confining the ellipsoidal confidence region of each Gaussian component using Sequential Quadratic Programming (SQP), we probabilistically constrain the GMM as well as the corresponding stochastic reachable sets. A comprehensive mathematical analysis of how the constrained GMM can affect the stochastic reachable sets is provided in this work. Finally, the proposed stochastic reachability analysis algorithm is validated via an illustrative numerical example.</p>

In Multi-Agenten-Systemen (MAS) kooperieren und konkurrieren Agenten um ihre jeweiligen Ziele zu erreichen. Für optimierte Agenten-Interaktionen sind Kenntnisse über die aktuellen und zukünftigen Handlungen anderer Agenten (Interaktionsparter, IP) hilfreich. Bei der Ermittlung und Nutzung solcher Kenntnisse kommt dem automatischen Erkennen und Verstehen sowie der Vorhersage von Verhalten der IP auf Basis von Beobachtungen besondere Bedeutung zu. Die Dissertation beschäftigt sich mit der automatischen Bestimmung und Vorhersage von Verhalten der IP durch einen Modellierenden Agenten (MA). Der MA generiert fallbasierte, adaptive Verhaltens-Modelle seiner IP und verwendet diese zur Vorhersage ihrer Verhalten. Als Anwendungsszenario wird mit dem virtuellen Fußballspiel des RoboCup ein komplexes und populäres MAS betrachtet. Der Hauptbeitrag dieser Arbeit besteht in der Ausarbeitung, Realisierung und Evaluierung eines Ansatzes zur automatischen Verhaltens-Modellierung für ein komplexes Multi-Agenten-System. / In multi-agent-systems agents cooperate and compete to reach their personal goals. For optimized agent interactions it is helpful for an agent to have knowledge about the current and future behavior of other agents. Ideally the recognition and prediction of behavior should be done automatically. This work addresses a way of automatically classifying and an attempt at predicting the behavior of a team of agents, based on external observation only. A set of conditions is used to distinguish behaviors and to partition the resulting behavior space. From observed behavior, team specific behavior models are then generated using Case Based Reasoning. These models, which are derived from a number of virtual soccer games (RoboCup), are used to predict the behavior of a team during a new game. The main contribution of this work is the design, realization and evaluation of an automatic behavior modeling approach for complex multi-agent systems.

Managed lanes are a form of congestion pricing that use occupancy and toll payment requirements to utilize capacity more efficiently. How socio-spatial characteristics impact users’ travel behavior toward managed lanes is the main research question of this study. This research is a case study of the conversion of a High Occupancy Vehicle (HOV) lane to a High Occupancy Toll (HOT) lane, implemented in Atlanta I-85 on 2011. To minimize the cost and maximize the size of the collected data, an innovative and cost-effective modeling framework for socioeconomic analysis of managed lanes has been developed. Instead of surveys, this research is based on the observation of one and a half million license plates, matched to household locations, collected over a two-year study period. Purchased marketing data, which include detailed household socioeconomic characteristics, supplemented the household corridor usage information derived from license plate observations. Generalized linear models have been used to link users’ travel behavior to socioeconomic attributes. Furthermore, GIS raster analysis methods have been utilized to visualize and quantify the impact of the HOV-to-HOT conversion on the corridor commutershed. At the local level, this study conducted a comprehensive socio-spatial analysis of the Atlanta I-85 HOV to HOT conversion. At the general scale, this study enhances managed lanes’ travel demand models with respect to users’ characteristics and introduces a comprehensive modeling framework for the socioeconomic analysis of managed lanes. The methods developed through this research will inform future Traffic and Revenue Studies and help to better predict the socio-spatial characteristics of the target market.

The mission of the research presented in this thesis is to give computers the power to sense and react to human activities. Without the ability to sense the surroundings and understand what humans are doing, computers will not be able to provide active, timely, appropriate, and considerate services to the humans. To accomplish this mission, the work stands on the shoulders of two giants: Machine learning and ubiquitous computing. Because of the ubiquity of sensor-enabled mobile and wearable devices, there has been an emerging opportunity to sense, learn, and infer human activities from the sensor data by leveraging state-of-the-art machine learning algorithms. While having shown promising results in human activity recognition, most existing approaches using supervised or semi-supervised learning have two fundamental problems. Firstly, most existing approaches require a large set of labeled sensor data for every target class, which requires a costly effort from human annotators. Secondly, an unseen new activity cannot be recognized if no training samples of that activity are available in the dataset. In light of these problems, a new approach in this area is proposed in our research. This thesis presents our novel approach to address the problem of human activity recognition when few or no training samples of the target activities are available. The main hypothesis is that the problem can be solved by the proposed NuActiv activity recognition framework, which consists of modeling the hierarchical and sequential structure of human activities, as well as bringing humans in the loop of model training. By injecting human knowledge about the hierarchical nature of human activities, a semantic attribute representation and a two-layer attribute-based learning approach are designed. To model the sequential structure, a probabilistic graphical model is further proposed to take into account the temporal dependency of activities and attributes. Finally, an active learning algorithm is developed to reinforce the recognition accuracy using minimal user feedback. The hypothesis and approaches presented in this thesis are validated by two case studies and real-world experiments on exercise activities and daily life activities. Experimental results show that the NuActiv framework can effectively recognize unseen new activities even without any training data, with up to 70-80% precision and recall rate. It also outperforms supervised learning with limited labeled data for the new classes. The results significantly advance the state of the art in human activity recognition, and represent a promising step towards bridging the gap between computers and humans.

In order to make computers proactive and assistive, we must enable them to perceive, learn, and predict what is happening in their surroundings. This presents us with the challenge of formalizing computational models of everyday human activities. For a majority of environments, the structure of the in situ activities is generally not known a priori. This thesis therefore investigates knowledge representations and manipulation techniques that can facilitate learning of such everyday human activities in a minimally supervised manner. A key step towards this end is finding appropriate representations for human activities. We posit that if we chose to describe activities as finite sequences of an appropriate set of events, then the global structure of these activities can be uniquely encoded using their local event sub-sequences. With this perspective at hand, we particularly investigate representations that characterize activities in terms of their fixed and variable length event subsequences. We comparatively analyze these representations in terms of their representational scope, feature cardinality and noise sensitivity. Exploiting such representations, we propose a computational framework to discover the various activity-classes taking place in an environment. We model these activity-classes as maximally similar activity-cliques in a completely connected graph of activities, and describe how to discover them efficiently. Moreover, we propose methods for finding concise characterizations of these discovered activity-classes, both from a holistic as well as a by-parts perspective. Using such characterizations, we present an incremental method to classify a new activity instance to one of the discovered activity-classes, and to automatically detect if it is anomalous with respect to the general characteristics of its membership class. Our results show the efficacy of our framework in a variety of everyday environments.

La réalité augmentée (Augmented Reality ou AR) peut potentiellement changer significativement l’expérience utilisateur. Au contraire les applications sur Smartphone ou tablette, les technologies d’affichage tête haute (Head Up Display ouHUD) aujourd’hui sont capables de projeter localement sur une zone du pare-brise ou globalement sur tout le pare-brise. Le conducteur peut alors percevoir l’information directement dans son champ de vision. Ce ne sont pas que les informations basiques comme vitesse ou navigation, le système peut aussi afficher des aides, des indicateurs qui guident l’attention du conducteur vers les dangers possibles. Il existe alors un chalenge scientifique qui est de concevoir des visualisations d’interactions qui s’adaptent en fonction de l’observation de la scène mais aussi en fonction de l’observation du conducteur. Dans le contexte des systèmes d’alerte de collision avec les piétons (Pedestrian Collision Warning System ou PCWS), l’efficacité de la détection du piéton a atteint un niveau élevé grâce à la technologie de vision. Pourtant, les systèmes d’alerte ne s’adaptent pas au conducteur et à la situation, ils deviennent alors une source de distraction et sont souvent négligés par le conducteur. Pour ces raisons, ce travail de thèse consiste à proposer un nouveau concept de PCWS avec l’AR (nommé the AR-PCW system). Premièrement, nous nous concentrons sur l’étude de la conscience de la situation (Situation Awareness ou SA) du conducteur lorsqu’il y a un piéton présent devant le véhicule. Nous proposons une approche expérimentale pour collecter les données qui représentent l’attention du conducteur vis-à-vis du piéton (Driver Awareness of Pedestrian ou DAP) et l’inattention du conducteur vis-à-vis de celui-ci (Driver Unawareness of Pedestrian ou DUP). Ensuite, les algorithmes basées sur les charactéristiques, les modèles d’apprentissage basés sur les modèles discriminants (ex, Support Vector Machine ou SVM) ou génératifs (Hidden Markov Model ou HMM) sont proposés pour estimer le DUP et le DAP. La décision de notre AR-PCW system est effectivement basée sur ce modèle. Deuxièmement, nous proposons les aides ARs pour améliorer le DAP après une étude de l’état de l’art sur les ARs dans le contexte de la conduite automobile. La boite englobante autour du piéton et le panneau d’alerte de danger sont utilisés. Finalement, nous étudions expérimentalement notre système AR-PCW en analysant les effets des aides AR sur le conducteur. Un simulateur de conduite est utilisé et la simulation d’une zone HUD dans la scène virtuelle sont proposés. Vingt-cinq conducteurs de 2 ans de permis de conduite ont participé à l’expérimentation. Les situations ambigües sont créées dans le scénario de conduite afin d’analyser le DAP. Le conducteur doit suivre un véhicule et les piétons apparaissent à différents moments. L’effet des aides AR sur le conducteur est analysé à travers ses performances à réaliser la tâche de poursuite et ses réactions qui engendrent le DAP. Les résultats objectifs et subjectifs montrent que les aides AR sont capables d’améliorer le DAP défini en trois niveaux : perception, vigilance et anticipation. Ce travail de thèse a été financé sur une bourse ministère et a été réalisé dans le cadre des projets FUI18 SERA et Labex MS2T qui sont financé par le Gouvernement Français, à travers le programme « Investissement pour l’avenir » géré par le ANR (Référence ANR-11-IDEX-0004-02). / Augmented reality (AR) can potentially change the driver’s user experience in significant ways. In contrast of the AR applications on smart phones or tablets, the Head-Up-Displays (HUD) technology based on a part or all wind-shield project information directly into the field of vision, so the driver does not have to look down at the instrument which maybe causes to the time-critical event misses. Until now, the HUD designers try to show not only basic information such as speed and navigation commands but also the aids and the annotations that help the driver to see potential dangers. However, what should be displayed and when it has to be displayed are still always the questions in critical driving context. In another context, the pedestrian safety becomes a serious society problem when half of traffic accidents around the world are among pedestrians and cyclists. Several advanced Pedestrian Collision Warning Systems (PCWS) have been proposed to detect pedestrians using the on-board sensors and to inform the driver of their presences. However, most of these systems do not adapt to the driver’s state and can become extremely distracting and annoying when they detect pedestrian. For those reasons, this thesis focuses on proposing a new concept for the PCWS using AR (so called the AR-PCW system). Firstly, for the «When» question, the display decision has to take into account the driver’s states and the critical situations. Therefore, we investigate the modelisation of the driver’s awareness of a pedestrian (DAP) and the driver’s unawareness of a pedestrian (DUP). In order to do that, an experimental approach is proposed to observe and to collect the driving data that present the DAP and the DUP. Then, the feature-based algorithms, the data-driven models based on the discriminative models (e.g. Support Vector Machine) or the generative models (e.g. Hidden Markov Model) are proposed to recognize the DAP and the DUP. Secondly, for the «What» question, our proposition is inspired by the state-of-the-art on the AR in the driving context. The dynamic bounding-box surrounding the pedestrian and the static danger panel are used as the visual aids. Finally, in this thesis, we study experimentally the benefits and the costs of the proposed AR-PCW system and the effects of the aids on the driver. A fixed-based driving simulator is used. A limited display zone on screen is proposed to simulate the HUD. Twenty five healthy middle-aged licensed drivers in ambiguous driving scenarios are explored. Indeed, the heading-car following is used as the main driving task whereas twenty three pedestrians appear in the circuit at different moment and with different behaviors. The car-follow task performance and the awareness of pedestrian are then accessed through the driver actions. The objective results as well as the subjective results show that the visual aids can enhance the driver’s awareness of a pedestrian which is defined with three levels: perception, vigilance and anticipation. This work has been funded by a Ministry scholarship and was carried out in the framework of the FUI18 SERA project, and the Labex MS2T which is funded by the French Government, through the program ”Investments for the future” managed by the National Agency for Research (Reference ANR-11-IDEX-0004-02).