Computing driver tiredness and fatigue in automobile via eye tracking and body movements

The aim of this paper is to classify the driver tiredness and fatigue in automobile via eye tracking and body movements using deep learning based Convolutional Neural Network (CNN) algorithm. Vehicle driver face localization serves as one of the most widely used real-world applications in fields like toll control, traffic accident scene analysis, and suspected vehicle tracking. The research proposed a CNN classifier for simultaneously localizing the region of human face and eye positioning. The classifier, rather than bounding rectangles, gives bounding quadrilaterals, which gives a more precise indication for vehicle driver face localization. The adjusted regions are preprocessed to remove noise and passed to the CNN classifier for real time processing. The preprocessing of the face features extracts connected components, filters them by size, and groups them into face expressions. The employed CNN is the well-known technology for human face recognition. One we aim to extract the facial landmarks from the frames, we will then leverage classification models and deep learning based convolutional neural networks that predict the state of the driver as 'Alert' or 'Drowsy' for each of the frames extracted. The CNN model could predict the output state labels (Alert/Drowsy) for each frame, but we wanted to take care of sequential image frames as that is extremely important while predicting the state of an individual. The process completes, if all regions have a sufficiently high score or a fixed number of retries are exhausted. The output consists of the detected human face type, the list of regions including the extracted mouth and eyes with recognition reliability through CNN with an accuracy of 98.57% with 100 epochs of training and testing

Actes de JFSMA 2010 - 18e Journées francophones sur les systèmes multi-agents

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Simulation multi-agents des systèmes économiques (Vers des systèmes multi-agents adaptatifs)

Deux principales approches ont été identifiées parmi les théories économiques issues des travaux de Simon : l'écologie organisationnelle et le management stratégique. Elles étudient séparément les deux principaux problèmes de sélection (matérialisée par l'intégration des formes organisationnelles) et d'adaptation. De récentes réflexions s'orientent vers l'unification des deux approches. Cependant, aucun modèle, à notre connaissance, n'a encore été proposé pour étudier simultanément les deux problèmes jusque-là abordés indépendamment. Ceci est notamment dû à la complexité des systèmes économiques. Le but de la thèse est de palier cette lacune en suggérant un modèle qui intègre les deux niveaux firmes et formes organisationnelles. Notre solution est basée sur l'utilisation des systèmes multi-agents adaptatifs. Nous montrons donc que les systèmes multi-agents adaptatifs permettent d'étudier les deux problèmes (adaptation et sélection) simultanément et que l'adaptation ne se limite pas aux agents mais est aussi présente au niveau des formes organisationnelles. La première partie de la thèse a permis de montrer l'intérêt des agents adaptatifs pour modéliser les firmes. Elle nous a permis aussi de mettre en évidence l'intérêt et les problèmes engendrés par l'utilisation des techniques d'apprentissage dans un contexte multi-agents. La deuxième partie correspond à la modélisation des formes organisationnelles et leur relation avec les firmes afin de vérifier qu'il existe une boucle où toute variation des formes organisationnelles est interprétée par les firmes pour leur adaptation et où l'adaptation des firmes engendre des variations dans les formes organisationnelles.Two main economic theories were identified among the economic theories which appear after Simon work: organizational ecology and strategic management. They investigate independently the problems of selection (embodying the integration of organizational forms) and adaptation. Recent research orientations focused on unifying the two approaches. However, no research studied merged these two approaches considered so far as independent. This is most likely due to the complexity of economic systems. This thesis defines a complete economic model integrating firms and organisational forms using an approach based on adaptive multi-agent systems. We show that adaptive multi-agent systems are well suited for the adaptation and selection problem and that the adaptation is not simply a feature of agents but also present at the organizational forms level. The first part of the thesis shows the advantages of using adaptive agents in modelling firms while highlighting the problems caused by the use of learning techniques in a multi-agent context. The second part models organizational forms and their interaction with firms. It proves the existence of a loop where each variation in the organizational forms is interpreted by firms which accordingly adapt, and where the adaptation of firms generates variations at the form level.REIMS-BU Sciences (514542101) / SudocSudocFranceF

Firms Adaptation in Dynamic Economic Systems

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Detecting Physiological Needs Using Deep Inverse Reinforcement Learning

Smart health-care assistants are designed to improve the comfort of the patient where smart refers to the ability to imitate the human intelligence to facilitate his life without, or with limited, human intervention. As a part of this, we are proposing a new Intelligent Communication Assistant capable of detecting physiological needs by following a new efficient Inverse Reinforcement learning algorithm designed to be able to deal with new time-recorded states. The latter processes the patient’s environment data, learns from the patient previous choices and becomes capable of suggesting the right action at the right time. In this paper, we took the case study of Locked-in Syndrome patients, studied their actual communication methods and tried to enhance the existing solutions by adding an intelligent layer. We showed that by using Deep Inverse Reinforcement Learning using Maximum Entropy, we can learn how to regress the reward amount of new states from the ambient environment recorded states. After that, we can suggest the highly rewarded need to the target patient. Also, we proposed a full architecture of the system by describing the pipeline of the information from the ambient environment to the different actors

Using XCS to build adaptive agents

International audienceTo deal with dynamic changes of their environment, agents need an adaptive mechanism. This paper proposes an integration of classifier-based framework (named XCS) and an agent-based framework (named DIMA). The result of this integration is an adaptive- agent framework. It has been applied to simulate economic models

Comparative study of the effect of vanadium and sulfate on the performance of molybdenum-titanium co-pillared clay for selective catalytic reduction of NO by ammonia.

International audienceA series of vanadium supported on molybdenum and titanium co-pillared montmorillonite, for use in the selective catalytic reduction (SCR) of NO by ammonia, were prepared and characterized with different technique. The surface properties of the samples were compared to the support prepared in absence and in presence of sulphate groups. The catalysts are porous solids and the support (MoTi-PILC) prepared in absence of sulfate shows highest surface area and number of acid sites. The experimental results showed that the addition of different amount of vanadium to the support affects the textural, acid and redox properties as well as the NO removal efficiency for the selective catalytic reduction of NO by ammonia. However, the best catalytic activity is obtained for MoTi-PILC which is correlated to the highest acid sites of this catalyst. The excellent performance of MoTi-PILC is attributed to the combination of the better porous structure, the good dispersion of the actives phases mainly arising by molybdenum species well dispersed on the high surface area and to the appropriate blend of redox with acid sites

The Use of DCNN for Road Path Detection and Segmentation

In this study, various organizations that have participated in several road path-detecting experiments are analyzed. However, the majority of techniques rely on attributes or form models built by humans to identify sections of the path. In this paper, a suggestion was made regarding a road path recognition structure that is dependent on a deep convolutional neural network. A tiny neural network has been developed to perform feature extraction to a massive collection of photographs to extract the suitable path feature. The parameters obtained from the model of the route classification network are utilized in the process of establishing the parameters of the layers that constitute the path detection network. The deep convolutional path discovery network’s production is pixel-based and focuses on the identification of path types and positions. To train it, a detection failure job is provided. Failure in path classification and regression are the two components that make up a planned detection failure function. Instead of laborious postprocessing, a straightforward solution to the problem of route marking can be found using observed path pixels in conjunction with a consensus of random examples. According to the findings of the experiments, the classification precision of the network for classifying every kind is higher than 98.3%. The simulation that was trained using the suggested detection failure function is capable of achieving an accuracy of detection that is 85.5% over a total of 30 distinct scenarios on the road

The Exploration-Exploitation Dilemma for Adaptive Agents

Learning agents have to deal with the exploration-exploitation dilemma