O reconhecimento de atividaes é vital no contexto dos ambientes inteligentes. Mesmo com a facilidade de acesso a sensores móveis baratos, reconhecer atividades continua sendo um problema difícil devido à incerteza nas leituras dos sensores e à complexidade das atividades. A teoria da evidência provê um modelo de reconhecimento de atividades que detecta atividades mesmo na presença de incerteza nas leituras dos sensores, mas ainda não é capaz de modelar atividades complexas ou mudanças na configuração dos sensores ou do ambiente. Este trabalho propõe combinar abordagens baseadas em modelagem de conhecimento com a teoria da evidência, melhorando assim a construção dos modelos da última trazendo a reusabilidade, flexibilidade e semântica rica da primeira. / Activity recognition is a vital need in the field of ambient intelligence. It is essential for many internet of things applications including energy management, healthcare systems and home automation. But, even with the many cheap mobile sensors envisioned by the internet of things, activity recognition remains a hard problem. This is due to uncertainty in sensor readings and the complexity of activities themselves. Evidence theory models provide activity recognition even in the presence of uncertain sensor readings, but cannot yet model complex activities or dynamic changes in sensor and environment configurations. This work proposes combining knowledge-based approaches with evidence theory, improving the construction of evidence theory models for activity recognition by bringing reusability, flexibility and rich semantics.

O reconhecimento de atividaes é vital no contexto dos ambientes inteligentes. Mesmo com a facilidade de acesso a sensores móveis baratos, reconhecer atividades continua sendo um problema difícil devido à incerteza nas leituras dos sensores e à complexidade das atividades. A teoria da evidência provê um modelo de reconhecimento de atividades que detecta atividades mesmo na presença de incerteza nas leituras dos sensores, mas ainda não é capaz de modelar atividades complexas ou mudanças na configuração dos sensores ou do ambiente. Este trabalho propõe combinar abordagens baseadas em modelagem de conhecimento com a teoria da evidência, melhorando assim a construção dos modelos da última trazendo a reusabilidade, flexibilidade e semântica rica da primeira. / Activity recognition is a vital need in the field of ambient intelligence. It is essential for many internet of things applications including energy management, healthcare systems and home automation. But, even with the many cheap mobile sensors envisioned by the internet of things, activity recognition remains a hard problem. This is due to uncertainty in sensor readings and the complexity of activities themselves. Evidence theory models provide activity recognition even in the presence of uncertain sensor readings, but cannot yet model complex activities or dynamic changes in sensor and environment configurations. This work proposes combining knowledge-based approaches with evidence theory, improving the construction of evidence theory models for activity recognition by bringing reusability, flexibility and rich semantics.

O reconhecimento de atividaes é vital no contexto dos ambientes inteligentes. Mesmo com a facilidade de acesso a sensores móveis baratos, reconhecer atividades continua sendo um problema difícil devido à incerteza nas leituras dos sensores e à complexidade das atividades. A teoria da evidência provê um modelo de reconhecimento de atividades que detecta atividades mesmo na presença de incerteza nas leituras dos sensores, mas ainda não é capaz de modelar atividades complexas ou mudanças na configuração dos sensores ou do ambiente. Este trabalho propõe combinar abordagens baseadas em modelagem de conhecimento com a teoria da evidência, melhorando assim a construção dos modelos da última trazendo a reusabilidade, flexibilidade e semântica rica da primeira. / Activity recognition is a vital need in the field of ambient intelligence. It is essential for many internet of things applications including energy management, healthcare systems and home automation. But, even with the many cheap mobile sensors envisioned by the internet of things, activity recognition remains a hard problem. This is due to uncertainty in sensor readings and the complexity of activities themselves. Evidence theory models provide activity recognition even in the presence of uncertain sensor readings, but cannot yet model complex activities or dynamic changes in sensor and environment configurations. This work proposes combining knowledge-based approaches with evidence theory, improving the construction of evidence theory models for activity recognition by bringing reusability, flexibility and rich semantics.

Deoiling of petroleum wastewater is a major concern in petroleum process engineering. The conventional design procedure for deoiling systems consists of nine consecutive stages involving different people or groups. This thesis considers reengineering the conventional procedure, with the aim of increasing the efficiency of the process and productivity of people involved. The thesis then investigates the development of the appropriate computer support tools for the new procedure. The reengineering of the procedure is based on concepts proposed by Hammer (1990). The resulting procedure consists of four stages, where parallelism of the different tasks in the conventional procedure is explicitly considered. A model of the new procedure is described in UML (Unified Modelling Language). The advantage of using UML instead of using just natural language description is that UML uses graphical representation that is easy to use and less prone to ambiguity. Based on the new procedure, a decision support system called CODES (Conceptual DEsign of deoiling Systems) was designed and implemented. CODES supports the stages of wastewater assessment, equipment options at different locations, treatment disposal options and recommendations for final process configuration. Two Case Studies using operating field data are reported. The Case Studies using CODES proved to be straightforward to use and produced appropriate comments on the performance of the existing designs and proposed alternatives to satisfy the design requirements. CODES results agreed with knowledge from previous plant trials. The contribution of this thesis to the deoiling design process is twofold. Firstly, a critical review of the conventional procedure has resulted in a new one, which has potential benefits of improving a company's efficiency by reducing the number of people and activities involved, and shortening the design time. Secondly, the feasibility of applying the new procedure and CODES is successfully demonstrated through the case studies. Limitations and areas of extensions are also identified.

Comprendre la genèse d’une situation de conduite requiert d’analyser les choixfaits par le conducteur au volant de son véhicule pendant l’activité de conduite, dans sacomplexité naturelle et dans sa dynamique située. Le LESCOT a développé le modèleCOSMODRIVE, fournissant un cadre conceptuel pour la simulation cognitive de l’activitéde conduite automobile. Pour exploiter ce modèle en simulation, il est nécessairede produire les connaissances liées à la situation de conduite sous forme d’un automatepar exemple. La conception d’un tel automate nécessite d’une part de disposer de donnéesissues de la conduite réelle, enregistrées sur un véhicule instrumenté et d’autrepart d’une expertise humaine pour les interpréter.Pour accompagner ce processus d’ingénierie des connaissances issues de l’analysed’activité, ce travail de thèse propose une méthode de découverte interactive deconnaissances à partir de traces d’activité. Les données de conduite automobile sontconsidérées comme des M-Traces, associant une sémantique explicite aux données,exploitées en tant que connaissances dans un Système à Base de Traces (SBT). Le SBTpermet de filtrer, transformer, reformuler et abstraire les séquences qui serviront à alimenterla synthèse de modèles automates de l’activité de conduite. Nous reprenons destechniques de fouille de workflow permettant de construire des automates (réseaux dePetri) à partir de logs. Ces techniques nécessitent des données complètes ou statistiquementreprésentatives. Or les données collectées à bord d’un véhicule en situationde conduite sont par nature des cas uniques, puisqu’aucune situation ne sera jamaisreproductible à l’identique, certaines situations particulièrement intéressantes pouvanten outre être très rarement observées. La gageure est alors de procéder à une forme degénéralisation sous la forme de modèle, à partir d’un nombre de cas limités, mais jugéspertinents, représentatifs, ou particulièrement révélateurs par des experts du domaine.Pour compléter la modélisation de telles situations, nous proposons donc de rendreinteractifs les algorithmes de synthèse de réseau de Petri à partir de traces, afin depermettre à des experts-analystes de guider ces algorithmes et de favoriser ainsi la découvertede connaissances pertinentes pour leur domaine d’expertise. Nous montreronscomment rendre interactifs l’algorithme α et l’algorithme α+ et comment généralisercette approche à d’autres algorithmes.Nous montrons comment l’utilisation d’un SBT et de la découverte interactived’automates impacte le cycle général de découverte de connaissances. Une méthodologieest proposée pour construire des modèles automates de l’activité de conduiteautomobile.Une étude de cas illustre la méthodologie en partant de données réelles de conduiteet en allant jusqu’à la construction de modèles avec un prototype logiciel développédans le cadre de cette thèse / Driving is a dynamic and complex activity. Understanding the origin of a driving situationrequires the analysis of the driver’s choices made while he/she drives. In addition,a driving situation has to be studied in its natural complexity and evolution. LESCOThas developed a model called COSMODRIVE, which provides a conceptual frameworkfor the cognitive simulation of the activity of car driving. In order to run themodel for a simulation, it is necessary to gather knowledge related to the driving situation,for example in the form of an automaton. The conception of such an automatonrequires : 1) the use of real data recorded in an instrumented car, and, 2) the use of humanexpertise to interpret these data. These data are considered in this thesis as activitytraces.The purpose of this thesis is to assist the Knowledge Engineering process of activityanalysis. The present thesis proposes a method to interactively discover knowledgefrom activity traces. For this purpose, data from car driving are considered as M-traces– which associate an explicit semantic to these data. This semantic is then used asknowledge in a Trace Based System. In a Trace Based System, M-traces can be filtered,transformed, reformulated, and abstracted. The resulting traces are then used as inputsin the production of an automaton model of the activity of driving. In this thesis,Workflow Mining techniques have been used to build automata (Petri nets) from logs.These techniques require complete or statistically representative data sets. However,data collected from instrumented vehicles are intrinsically unique, as no two drivingsituations will ever be identical. In addition, situations of particular interest, such ascritical situations, are rarely observed in instrumented vehicle studies. The challenge isthen to produce a model which is a form of generalisation from a limited set of cases,which have been judged by domain experts as being relevant and representative of whatactually happens.In the current thesis, algorithms synthesising Petri nets from traces have been madeinteractive, in order to achieve the modelling of such driving situations. This thenmakes it possible for experts to guide the algorithms and therefore to support the discoveryof knowledge relevant to the experts. The process involved in making the α-algorithm and the α+-algorithm interactive is discussed in the thesis in a way that canbe generalised to other algorithms.In addition, the current thesis illustrates how the use of a Trace Based System andthe interactive discovery of automata impacts the global cycle of Knowledge Discovery.A methodology is also proposed to build automaton models of the activity of cardriving. Finally, a case study is presented to illustrate how the proposed methodologycan be applied to real driving data in order to construct models with the softwaredeveloped in this thesis