La thèse défendue dans ce manuscrit s'intéresse à la simulation multi-agents appliquée à la simulation d'individus dans des bâtiments 3D virtuels.Pour ce faire, nos travaux proposent de capitaliser l'expérience acquise dans le domaine du web sémantique sur les ontologies et les moteurs d'inférence associés pour faciliter la conception et le développement de comportements intelligents pour des agents évoluant dans des univers virtuels.L'objectif est de fournir aux agents une approche générique pour gérer leur représentation du monde et raisonner sur cette représentation.Pour cela, la problématique centrale repose sur la définition d'une ontologie décidable modélisant l'ensemble des connaissances contenues dans l'environnement virtuel 3D pour enrichir sémantiquement l'environnement d'une simulation multi-agents.Cette ontologie décidable a pour but d'offrir la possibilité d'intégrer les moteurs d'inférence sémantique au c\oe{}ur de la modélisation de comportements d'agents mobiles dans un environnement virtuel. / This thesis focuses on multi-agent simulation applied to the simulation of individuals in virtual 3D buildings. To do this, our work suggests to capitalize on the experience gained in the field of semantic web ontologies and inference engines to facilitate the design and development of intelligent behavior for agents operating in virtual worlds. The goal is to provide to agents a generic approach to managing their representation of the world and reason about this representation. For this, the central problem is based on the definition of a decidable ontology modeling all of the knowledge contained in the virtual 3D environment to enrich semantically the environment of a multi-agent simulation. This decidable ontology aims to provide an opportunity to integrate semantic inference engine at the heart of modeling behavior of mobile agents in a virtual environment.

Submitted by Haroudo Xavier Filho (haroudo.xavierfo@ufpe.br) on 2016-04-06T18:16:38Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) SISTEMA DE PREDIÇÃO DE ESTADOS DE TRANSDUTORES PARA AMBIEN.pdf: 2277201 bytes, checksum: 566bf1bfadd889426473a7471d98ef39 (MD5) / Made available in DSpace on 2016-04-06T18:16:38Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) SISTEMA DE PREDIÇÃO DE ESTADOS DE TRANSDUTORES PARA AMBIEN.pdf: 2277201 bytes, checksum: 566bf1bfadd889426473a7471d98ef39 (MD5) Previous issue date: 2015-08-26 / CNPq / Nos Ambientes Inteligentes, os dispositivos colaboram entre si para auxiliar o usuário de forma não intrusiva. Uma forma de auxílio é antecipar as ações do usuário e realizá-las por ele ou facilitar a sua realização. Esse trabalho propõe um framework para a predição das ações do usuário pelo aprendizado do seu comportamento e hábitos enquanto ele interage com o Ambiente Inteligente. As ações do usuário é considerada como sendo a troca do valor de um transdutor (sensor ou atuador). A interação do usuário com o Ambiente Inteligente produz o contexto que é utilizado para a predição das ações. O preditor é um algoritmo de classificação supervisionada que aprende os padrões de comportamento do habitante do Ambiente Inteligente. Portanto, a solução proposta pode prover um serviço personalizado e adaptativo ao invés de um conjunto de regras predefinido por humanos. O preditor trabalha apenas com um transdutor alvo e para prever valores de mais transdutores, mais preditores devem ser treinados. A solução proposta é projetada para funcionar automaticamente sem a necessidade de interferência humana. Isso faz com que o habitante do Ambiente Inteligente sinta-se mais confortável já que sua privacidade estará protegida. Todas as informações para treinar o preditor podem ser obtidas diretamente dos transdutores do Ambiente Inteligente. Não existe a necessidade de anotação manual dos dados e nem dados extras como tipo do transdutor, localização do transdutor ou objeto ao qual o transdutor está acoplado. Isso aumenta a facilidade de instalação dos transdutores no Ambiente Inteligente. A saída do preditor pode tanto controlar diretamente um atuador ou ser enviada a um agente de software. Esse agente pode verificar condições de segurança ou requisitos de gerenciamento de energia antes de tomar a decisão. O foco desse trabalho é a geração de uma base de dados com os dados do contexto para o treinamento do preditor responsável por decidir se o transdutor alvo deverá ou não mudar seu valor. Vários parâmetros são considerados como o tamanho do período de treinamento, quantidade de ativações passadas que serão consideradas e quais são os transdutores mais relevantes para a predição. A solução proposta atinge uma melhora significativa para todos os transdutores estudados e a maioria das combinações de parâmetros da geração da base de dados possuem resultados melhores que o caso base. Além disso, os nossos resultados são superiores às outras soluções da literatura. / Smart environments possess devices that collaborate to help the user non-intrusively. One possible aid smart environment offer is to anticipate user’s tasks and perform them on his/her behalf or facilitate the action completion. In this work, we propose a framework that predicts user’s actions by learning his/her behavior when interacting with the smart environment. The user actions are considered as being the value change of a transducer (sensor or actuator). The user interaction with the smart environment produces the context used to predict the actions. The predictor is a supervised classification algorithm that learns the smart environment inhabitant behavior patterns. Therefore, the proposed solution can provide a personalized and adaptive service instead of a human predefined set of rules. The predictor works with only one transducer and to predict the values of several transducers, more predictors must be trained. The proposed solution is designed to work automatically without the need of human interference. That makes the smart environment inhabitant more comfortable since his/her privacy is protected. All the information needed to train the predictor can be obtained directly from the smart environment transducers. There is no need for manual data annotation or extra data such as transducer type, transducer location or which object the transducer is attached to. This facilitates the transducer installation in the smart environment. The predictor output can either control directly an actuator or be sent to an software agent. This software agent can check for security or energy constraints before making the decision. This work focus on prepare datasets and train a predictor that is responsible to decide whether a target transducer value should be changed or not. Several parameters are considered such as the training period size, amount of previous transducer activations considered and which are the most relevant transducers for the prediction. Our solution achieves a significant improvement for all target transducers studied and most combinations of parameters yields better results than the base case. Our results are superior to other solutions in the literature.

The aim of the thesis Development of Smart Cities in the region of Latin America applied on the case of Mexico City and Rio de Janeiro stands on their comparison provided according to application of six axes smart city concept. Both cities provide their individual approach in their formulation and implementation of smart city initiative. According to this comparison this paper will prove that even though that there is not yet a uniform smart city definition there are indicators according to which it is possible to form a general a framework to identify smart cities. This framework will be important to prove several things about smart cities. They will be necessary for the future growth of humanity as cities become more and more important. This will happen by allowing for better functioning of cities and better use of existing resources. These cities will start to operate for their citizens in ways that lessen the impact of the environment while allowing cities to grow across multiple sectors while improving quality of life among a city s residents. This implementation of ITC technologies will prove a rising tide that will lift the city s poor by empowering their economic lives by improving quality of life and giving better access to resources. The comparison of the two cities will also prove that Rio de Janeiro through its many smart initiatives is further along in its path to becoming a smart city than Mexico City. The difference between the two will also prove just how important smart cities are to the region s future. Mexico City s projects have not been as holistic as those taken in Brazil s largest city. Rio de Janeiro s implementation of projects like COR have transformed the city allowing it to become one of the smartest cities in the region and the world. The COR has implemented ITC technologies and initiatives that have transformed every sector of the six-axes approach model.

De nos jours, l'informatique omniprésente fait face à un progrès croissant. Ce paradigme est caractérisé par de multiples capteurs intégrés dans des objets du monde physique. Le développement d'applications personnelles utilisant les données fournies par ces capteurs a conduit à la création d'environnements intelligents, conçus comme un framework de superposition avancé qui aide de manière proactive les individus dans leur vie quotidienne. Une application d’environnement intelligent collecte les données de capteurs deployés d'une façon en continu , traite ces données et les analyse avant de prendre des décisions pour exécuter des actions sur l’environnement physique. Le traitement de données en ligne consiste principalement en une segmentation des données pour les diviser en fragments. Généralement, dans la littérature, la taille des fragments est fixe. Cependant, une telle vision statique entraîne généralement des problèmes de résultats imprécis. Par conséquent, la segmentation dynamique utilisant des tailles variables de fenêtres d’observation est une question ouverte. La phase d'analyse prend en entrée un segment de données de capteurs et extrait des connaissances au moyen de processus de raisonnement ou d'extraction. La compréhension des activités quotidiennes des utilisateurs et la prévention des situations anormales sont une préoccupation croissante dans la littérature, mais la résolution de ces problèmes à l'aide de données de petite taille et imparfaites reste un problème clé. En effet, les données fournies par les capteurs sont souvent imprécises, inexactes, obsolètes, contradictoires ou tout simplement manquantes. Par conséquent, l'incertitude liée à la gestion est devenue un aspect important. De plus, il n'est pas toujours possible et trop intrusif de surveiller l'utilisateur pour obtenir une grande quantité de données sur sa routine de vie. Les gens ne sont pas souvent ouverts pour être surveillés pendant une longue période. Évidemment, lorsque les données acquises sur l'utilisateur sont suffisantes, la plupart des méthodes existantes peuvent fournir une reconnaissance précise, mais les performances baissent fortement avec de petits ensembles de données. Dans cette thèse, nous avons principalement exploré la fertilisation croisée d'approches d'apprentissage statistique et symbolique et les contributions sont triples: (i) DataSeg, un algorithme qui tire parti à la fois de l'apprentissage non supervisé et de la représentation ontologique pour la segmentation des données. Cette combinaison choisit de manière dynamique la taille de segment pour plusieurs applications, contrairement à la plupart des méthodes existantes. De plus, contrairement aux approches de la littérature, Dataseg peut être adapté à toutes les fonctionnalités de l’application; (ii) AGACY Monitoring, un modèle hybride de reconnaissance d'activité et de gestion des incertitudes qui utilise un apprentissage supervisé, une inférence de logique possibiliste et une ontologie permettant d'extraire des connaissances utiles de petits ensembles de données; (iii) CARMA, une méthode basée sur les réseaux de Markov et les règles d'association causale pour détecter les causes d'anomalie dans un environnement intelligent afin d'éviter leur apparition. En extrayant automatiquement les règles logiques concernant les causes d'anomalies et en les intégrant dans les règles MLN, nous parvenons à une identification plus précise de la situation, même avec des observations partielles. Chacune de nos contributions a été prototypée, testée et validée à l'aide de données obtenues à partir de scénarios réels réalisés. / Nowadays, pervasive computing is facing an increasing advancement. This paradigm is characterized by multiple sensors highly integrated in objects of the physical world.The development of personal applications using data provided by these sensors has prompted the creation of smart environments, which are designed as an overlay advanced framework that proactively, but sensibly, assist individuals in their every day lives. A smart environment application gathers streaming data from the deployed sensors, processes and analyzes the collected data before making decisions and executing actions on the physical environment. Online data processing consists mainly in data segmentation to divide data into fragments. Generally, in the literature, the fragment size is fixed. However, such static vision usually brings issues of imprecise outputs. Hence, dynamic segmentation using variable sizes of observation windows is an open issue. The analysis phase takes as input a segment of sensor data and extract knowledge by means of reasoning or mining processes. In particular, understanding user daily activities and preventing anomalous situations are a growing concern in the literature but addressing these problems with small and imperfect data is still a key issue. Indeed, data provided by sensors is often imprecise, inaccurate, outdated, in contradiction, or simply missing. Hence, handling uncertainty became an important aspect. Moreover, monitoring the user to obtain a large amount of data about his/her life routine is not always possible and too intrusive. People are not often open to be monitored for a long period of time. Obviously, when the acquired data about the user are sufficient, most existing methods can provide precise recognition but the performances decline sharply with small datasets.In this thesis, we mainly explored cross-fertilization of statistic and symbolic learning approaches and the contributions are threefold: (i) DataSeg, an algorithm that takes advantage of both unsupervised learning and ontology representation for data segmentation. This combination chooses dynamically the segment size for several applications unlike most of existing methods. Moreover, unlike the literature approaches, Dataseg is able to be adapted to any application features; (ii) AGACY Monitoring, a hybrid model for activity recognition and uncertainty handling which uses supervised learning, possibilistic logic inference, and an ontology to extract meaningful knowledge from small datasets; (iii) CARMA, a method based on Markov Logic Networks (MLN) and causal association rules to detect anomaly causes in a smart environment so as to prevent their occurrence. By automatically extracting logic rules about anomalies causes and integrating them in the MLN rules, we reach a more accurate situation identification even with partial observations. Each of our contributions was prototyped, tested and validated through data obtained from real scenarios that are realized.

貼心的智慧型生活環境，必須能在不同的情緒狀態提供適當服務，因此我們希望能開發出一個情緒辨識系統，透過對於形於外的生理感測資料的變化來觀察形於內的情緒狀態。 首先我們採用國際情緒圖庫系統(IAPS: International Affective Picture System) 及維度式分析方法，透過心理實驗的操弄，收集了20位的受測者生理數值與主觀評定情緒的強度與正負向。我們提出了一個情緒辨識學習演算法，經由交叉驗證訓練出每個情緒的特徵，並藉由即時測試資料來修正情緒特徵的個人化，經由學習趨勢的評估，準確率有明顯提升。其次，我們更進一步引用了維度式與類別式情緒的轉換概念來驗證受測者主觀評定的結果。相較於相關研究實驗結果，我們在維度式上的強度與正負向辨識率有較高的表現，在類別式上的驗證我們也達到明顯區分效果。 更重要的是，我們所實作出的系統，是搭載了無線生理感測器，使用時更具行動性，而且可即時反映情緒，提供線上智慧型服務。 / A living smart environment should be able to provide thoughtful services by considering different states of emotions. The goal of our research is to develop an emotion recognition system which can detect the internal emotion states from external varieties of physiological data. First we applied the dimensional analysis approach and adopted IAPS (International Affective Picture System) to manipulate psychological experiments. We collected physiological data and subjective ratings for arousal and valence from 20 subjects. We proposed an emotion recognition learning algorithm. It would extract each pattern of emotions from cross validation training and can further learn adaptively by feeding personalized testing data. We measured the learning trend of each subject. The recognition rate reveals incremental enhancement. Furthermore, we adopted a dimensional to discrete emotion transforming concept for validating the subjective rating. Compared to the experiment results of related works, our system outperforms both in dimensional and discrete analyses. Most importantly, the system is implemented based on wireless physiological sensors for mobile usage. This system can reflect the image of emotion states in order to provide on-line smart services.

Les entités physiques prises en compte par les applications dites M2M dans les télécoms sont aujourd’hui de plus en plus hétérogènes. Le défi adressé par ce travail est donc l’intégration, et la configuration automatiques de toutes ces différentes variétés d’entités physiques d’une façon homogène dans les systèmes M2M, en généralisant les approches de configuration automatique déjà connues et utilisées pour les objets communicants numériques. Cette thèse présente un cadre théorique général et des mécanismes de base pour l’identification de modèles de telles entités physiques dans les systèmes d’information embarqués répartis, en englobant dans une même approche les équipements et les sous-ensembles de l’espace, faisant se rejoindre les points de vue ”internet des objets” et ”environnement interactif” dans une nouvelle vision unifiée de l’intelligence ambiante. Ce travail, motivé initialement par les applications à la gestion d’énergie domestique, cherche à intégrer au réseau local de la maison des entités physiques qui ont un impact énergétique mais ne sont dotés d’aucune connexion réseau, ce qui correspond à une extension qualitative du périmètre de l’Internet des Objets. Cette intégration se fait de manière tout à fait similaire à ce qui est fait classiquement pour des équipements numériques état de l’art, c’est-à-dire par des mécanismes de découverte et configuration spontanés. Ces mécanismes comportent les étapes suivantes : détection de la présence d’une entité physique par analyse de la coïncidence d’évènements significatifs reçus de capteurs ; sélection d’un premier modèle générique représentatif de l’entité physique détectée depuis une ontologie de référence en analysant des données reçues les capteurs ; création d’un composant logiciel représentant l’entité physique détectée, à partir du modèle sélectionné, et associant les capteurs et actionneurs utiles ; supervision et contrôle de l’entité cible par l’intermédiaire de ce composant logiciel ; mise à jour incrémentale du modèle de l’entité identifiée par analyse des données issues des capteurs associés. Ce travail est parti d’applications dans l’environnement de la maison, pour lesquelles il a été validé et mis en œuvre. Mais notre approche a vocation à être généralisée et étendue à des environnements comme les bâtiments ou la ville, en offrant suivant le même principe une infrastructure partagée pour toutes les applications M2M dans ces environnements / The physical entities which are taken into account by Machine to Machine (M2M) telecom applications are more and more heterogeneous. The challenge addressed by our research is the automatic integration and configuration of all these types of physical entities in M2M systems, with a homogeneous solution that generalizes self-configuration approaches used for networked digital devices. This thesis presents a general theoretical framework and basic mechanisms for the identification and configuration of such physical entity models in distributed embedded information systems. Our approach deals jointly with equipment and space entities encompassing the ”Internet of Things” (IoT) and ”interactive environment” viewpoints in a renewed interpretation of ambient intelligence. This work has been motivated initially by home energy management applications, trying to integrate into the Home Area Network all home entities that play a role in energy management, but do not have a networked interface of their own. This corresponds to a qualitative extension of the perimeter of the Home Area Network. This integration is achieved in a way similar to what is done for state of the art digital devices, through a spontaneous discovery and configuration mechanism, with the following stages: detection of the presence of a physical entity by analyzing the coincidence of significant events detected by sensors; selection of the first generic model corresponding to the detected physical entity from a reference ontology, on the basis of received sensors data; creation of a software component representing the detected physical entity, based on the selected model, associated with relevant sensors and actuators; provision of application interface for monitoring and control of the target entity through this intermediate software component; iterative update of the identified entity model on the basis of data from associated sensors. The proposed approach has been validated and implemented in home environments, but it is intended to be generalized and expanded to environments such as buildings or cities, offering a similarly shared infrastructure for all M2M applications in these environments