Esta tese propõe a utilização de sistemas inteligentes para, automaticamente, realizar diagnósticos e localizar falhas na instalação e na operação de redes de comunicação industrial que utilizam o protocolo Profibus DP. Para tais tarefas, uma série de análises é realizada a partir dos sinais transmitidos pela camada física, de telegramas transmitidos pela camada de enlace e de funções da camada de usuário do protocolo Profibus DP. Para a análise da camada física, amostras dos sinais elétricos transmitidos são processadas e apresentadas a algumas Redes Neurais Artificiais para que sejam classificadas de acordo com a sua forma de onda. Caso estes sinais apresentem alguma deformação, o sistema indica uma provável causa para o problema, afinal, os problemas das redes Profibus originam padrões específicos e característicos impressos nas formas de onda do sinal digital. Ainda através da análise das amostras dos sinais oriundos da camada física, algumas fontes de problemas são detectadas a partir da análise do nível médio de tensão do sinal que um determinado dispositivo está transmitindo. Tal análise é realizada a partir de um Sistema Especialista. Também utilizando Sistemas Especialistas, os telegramas transmitidos pela camada de enlace deste protocolo são analisados e a partir destes, falhas de configuração são detectadas. Por fim, é proposto um sistema nebuloso responsável por indicar ao usuário um valor próximo ao ideal para a variável de tempo denominada target rotation time. A proposta foi testada e validada a partir de dados obtidos de redes Profibus estabelecidas em laboratório e de alguns dados sintéticos originados por software. Os resultados obtidos foram suficientes para a comprovação da tese de que sistemas computacionais inteligentes podem contribuir de maneira efetiva no diagnóstico de problemas em redes Profibus DP e até mesmo em outros tipos de rede. / This thesis proposes the use of intelligent systems to automatically perform diagnostics and locate faults during the installation and operation of industrial communication networks that use the Profibus DP protocol. For such tasks, some analyzes are performed from the signals transmitted by the physical layer, from telegrams transmitted by the data link layer and from some user layer functions of the Profibus DP protocol. For physical layer analysis, the transmitted electrical signals samples are processed and submitted for some artificial neural networks that classifies each signal according to its waveshape. If these signals have some deformation, the system indicates a probable cause for the problem, after all, the Profibus problems originate specific and characteristic patterns printed on the digital signal waveform. Still analyzing the physical layer signal samples, some problems sources are detected from the signal voltage analysis. Such analysis is performed from an Expert System. Also using expert systems, the data link layer telegrams are analyzed and configuration faults are detected. Finally, it is proposed a fuzzy system responsible for specify a value close to ideal for the target rotation time variable. The proposal has been tested and validated with data from Profibus networks established in laboratory. Besides, some synthetic data were generated by software. The results were sufficient to prove the thesis that intelligent computational systems can contribute effectively to diagnose problems in Profibus DP networks and even in other types of networks.

Intresset för agenter som forskningsområde är någonting relativt nytt. Agenter i sig är en naturlig del av Artificiell intelligens men det är först på senare tid som de hamnat i fokus. Begreppet agent har spridit sig som en löpeld utanför AI-forskningen och kan hittas i många andra områden. Vad detta intresse för agenter kommer ifrån kan debatteras men det är troligtvis dels en följd av att internet visat hur kraftfullt ett distribuerat system kan vara, och att agenter och botar bokar våra biljetter samt söker igenom webbsidor åt oss.Arbetets huvudfråga är att undersöka hur ett intelligent system (för värmedistributionen i ett hus) kan uppnås genom att koppla ihop ett antal agenter med begränsade funktionalitet och distinkta uppgifter. Intelligensen i systemet ska uppnås genom samverkan mellan de olika agenterna. Denna studies förslag för att lösa problemet är en teoristudie av existerande litteratur för designprinciper samt design och implementering av ett exempelsystem, som sedan kommer att simuleras i för att samla in data gällande validitet.Experimentet visade att ett agentsystem kan ge en klar ekonomisk besparing men kommer också med ett relativt högt pris för att implementeras. Om agenterna kan dela infrastruktur genom att installeras samtidigt som ett nytt hus byggs och kan använda tänkt infrastruktur. Dessutom kan besparingen utökas genom att agentsystemet appliceras på en större nybyggnation.

O objetivo deste trabalho é estudar a identificação, classificação, localização e setorização de faltas em redes de distribuição radiais, verificar a maneira de aplicar e integrar diversas ferramentas numéricas convencionais, assim como ferramentas de sistemas inteligentes, visando identificar a ocorrência de uma falta, classificar as fases envolvidas com a mesma, e aplicar as diversas ferramentas existentes a fim de localizar em tempo real o eventual local onde houve a ocorrência da falta, permitindo que a mesma possa ser setorizada dentro do sistema da concessionária e informada ao centro de operações, usando uma nova abordagem inteligente baseada em análise espectral de sinais. / The aim of this work is study the identification, classification, location and sectorization of a fault in distribution radial networks, check how to implement and integrate various conventional numerical tools, as well as intelligent systems based tools, to identify the occurrence of a fault, classify the phases involved with it, and apply the various tools available to locate the place where a fault was occurred in real time, enabling it to be sectorized into the utility system and informed to operational center using a new intelligent approach based on spectral signals analysis.

É cada vez mais notável o desenvolvimento da indústria micro-eletrônica. A criação de dispositivos eletrônicos menores, que apresentam maior autonomia de energia, aliados ao aumento do poder de processamento, armazenamento e comunicação sem fio de alta velocidade favoreceram o surgimento e disseminação de novas tecnologias e paradigmas, dentre elas a Internet das Coisas (IoT). Do ponto de vista tecnológico, IoT é uma rede de objetos físicos que possuem tecnologia embarcada de sensoriamento e atuação. Agências de consultoria empresarial, tais como a McKinsey & Company, afirmam que IoT apresenta valor de mercado bilionário e poderá ultrapassar a casa dos trilhões antes de 2020. Dessa forma, o mercado de IoT vem se apresentando como um dos mercados mais promissores para os próximos anos. Alguns dos problemas que podem postergar este crescimento são os problemas decorrentes da dificuldade de integração e escalabilidade das aplicações de IoT. Em IoT, problemas de interoperabilidade são corriqueiros, seja pela alta diversidade de dispositivos empregados, seja pela incompatibilidade entre fabricantes. Em relação a escalabilidade, sistemas de IoT possuem uma demanda natural por alta escala, visto que buscam atender demandas comuns a vários setores, seja na indústria, transporte, domótica, segurança pública, comércio, entre outros. Este trabalho apresenta uma solução para esses problemas através do SWoTPAD, um framework formal que auxilia o projetista no desenvolvimento de soluções para IoT. SWoTPAD oferece uma linguagem para especificar dispositivos e serviços, descrever o ambiente e realizar requisições. Adicionalmente, ele gera o módulo de descoberta, composição automática de serviços e execução. Aplicações SWoTPAD são facilmente integráveis, pois usam e estendem um mesmo conjunto de ontologias, o que garante a compatibilidade nos dados gerados e consumidos por essas aplicações. A escalabilidade advém da associação de anotações semânticas a cada um dos elementos que compõem a aplicação de IoT. Essas anotações permitem ao SWoTPAD descobrir, classificar, selecionar e compor automaticamente serviços do ambiente. Dessa forma, SWoTPAD pode procurar por soluções alternativas, quando o serviço original apto a atender uma determinada demanda se encontra sobrecarregado ou indisponível. Para validação do framework, foram adotados dois estudos de caso. O primeiro deles, o problema de implantação de serviços em um ambiente de nuvem, e o segundo, uma aplicação de segurança residencial. O estudo de caso demonstrou que é possível desenvolver aplicações completas de IoT no framework proposto. Adicionalmente, o mecanismo de composição automática gerado pelo framework para essas aplicações apresenta uma piora média de 45% de desempenho quando comparado à composição manual. / The development of the micro-electronics industry is becoming more and more remarkable. The creation of smaller electronic devices, with higher degree of autonomy, processing, storage, and wireless communication favor the emergence and dissemination of new technologies and paradigms, such as the Internet of Things (IoT ). From the technological point of view, IoT is a network of physical objects that have embedded technology of sensing and actuation. McKinsey & Company says the IoT market is already reaching billionaire numbers and may exceed the trillions by 2020. Thus, the IoT market is proving to be one of the most promising markets in the next years. Problems that can delay this growth come from the difficulty of integration and scalability of IoT applications. In IoT, interoperability problems are common, either because of the high diversity of devices used, or because of the incompatibility between manufacturers. Regarding scalability, IoT systems have a natural demand for high scale, since they seek to meet common demands in various sectors, be it in industry, transportation, home automation, public safety, commerce, among others. This work solves these problems through SWoTPAD, a formal framework that assists the designer in developing solutions for IoT. SWoTPAD provides a language for specifying devices and services, describing the environment, and performing requests. Additionally, it generates the discovery, automatic service composition, and execution module. SWoTPAD applications are easily integrable, since they use and extend the same set of ontologies, which guarantees compatibility in the data generated and consumed by these applications. Scalability comes from the association of semantic annotations to each of the elements that compose the IoT application. These annotations allow SWoTPAD to discover, rank, select, and automatically compose services. In this way, SWoTPAD can search for alternative solutions, when the original service able to meet a particular demand is overloaded or unavailable. Two case studies were developed for validation of the framework. The first one, the problem of deploying services in a cloud environment, and the second, a home security system. The case study demonstrated that it is possible to develop complete IoT applications in the proposed framework. Also, the automatic service composition module generated by SWoTPAD for these applications has a mean worsening of 45 % of performance when compared to the manual composition.

No contexto metodológico desse trabalho, foram desenvolvidas as tecnologias lógicas, baseadas em arquiteturas de sistemas inteligentes, que auxiliem nos diagnósticos de avarias em transformadores de transmissão. O sistema desenvolvido tem como dados de entrada os resultados de ensaios de isolação, de relação de transformação, do fator de potência da isolação, de ensaios de tensão induzida, além de outros e, por meio de técnicas de inferência, indicarão a existência de possíveis avarias que devem ser averiguadas (de forma mais detalhada) antes de se retornar o transformador ao serviço. A particularidade que caracteriza a inovação sobre este assunto está justamente na elaboração de um novo ferramental, a ser disponibilizado por meio de um software inteligente de suporte à decisão, o qual produzirá respostas que permitirão o completo diagnóstico de falhas e avarias em transformadores de transmissão submetidos a esforços eletromagnéticos severos. As respostas serão produzidas a partir da análise integrada de resultados advindos de módulos inteligentes incumbidos de apoiar os diversos ensaios a serem realizados no equipamento. Assim, será possível estimar, inclusive, qual o momento de se proceder com a manutenção preventiva no equipamento. / In methodological context of this work, the logical technologies were developed based on smart systems architectures, which help in diagnosis of faults in transmission transformers. The developed system has as data input the results of insulation tests, the transformation ratio of the insulation power factor of induced voltage test, and others, and by inference techniques, indicate the existence of possible faults that must be ascertained (in more detail) before the processor returns to service. The special featuring innovation on this subject is exactly in the drafting of a new tooling, to be made available through intelligent decision support software, which will produce answers that will allow the full fault diagnosis and failure in transmission submitted transformers the severe electromagnetic efforts. The answers will be produced from the integrated analysis of results arising from intelligent modules responsible for assisting the various tests to be performed on the equipment. Thus, you can estimate, even when it\'s time to proceed with preventive maintenance on the equipment.

O objetivo deste trabalho é estudar a identificação, classificação, localização e setorização de faltas em redes de distribuição radiais, verificar a maneira de aplicar e integrar diversas ferramentas numéricas convencionais, assim como ferramentas de sistemas inteligentes, visando identificar a ocorrência de uma falta, classificar as fases envolvidas com a mesma, e aplicar as diversas ferramentas existentes a fim de localizar em tempo real o eventual local onde houve a ocorrência da falta, permitindo que a mesma possa ser setorizada dentro do sistema da concessionária e informada ao centro de operações, usando uma nova abordagem inteligente baseada em análise espectral de sinais. / The aim of this work is study the identification, classification, location and sectorization of a fault in distribution radial networks, check how to implement and integrate various conventional numerical tools, as well as intelligent systems based tools, to identify the occurrence of a fault, classify the phases involved with it, and apply the various tools available to locate the place where a fault was occurred in real time, enabling it to be sectorized into the utility system and informed to operational center using a new intelligent approach based on spectral signals analysis.

Governmental and private institutions rely heavily on reliable computer networks for their everyday business transactions. The downtime of their infrastructure networks may result in millions of dollars in cost. Fault management systems are used to keep today’s complex networks running without significant downtime cost, either by using active techniques or passive techniques. Active techniques impose excessive management traffic, whereas passive techniques often ignore uncertainty inherent in network alarms,leading to unreliable fault identification performance. In this research work, new algorithms are proposed for both types of techniques so as address these handicaps. Active techniques use probing technology so that the managed network can be tested periodically and suspected malfunctioning nodes can be effectively identified and isolated. However, the diagnosing probes introduce extra management traffic and storage space. To address this issue, two new CSP (Constraint Satisfaction Problem)-based algorithms are proposed to minimize management traffic, while effectively maintain the same diagnostic power of the available probes. The first algorithm is based on the standard CSP formulation which aims at reducing the available dependency matrix significantly as means to reducing the number of probes. The obtained probe set is used for fault detection and fault identification. The second algorithm is a fuzzy CSP-based algorithm. This proposed algorithm is adaptive algorithm in the sense that an initial reduced fault detection probe set is utilized to determine the minimum set of probes used for fault identification. Based on the extensive experiments conducted in this research both algorithms have demonstrated advantages over existing methods in terms of the overall management traffic needed to successfully monitor the targeted network system. Passive techniques employ alarms emitted by network entities. However, the fault evidence provided by these alarms can be ambiguous, inconsistent, incomplete, and random. To address these limitations, alarms are correlated using a distributed Dempster-Shafer Evidence Theory (DSET) framework, in which the managed network is divided into a cluster of disjoint management domains. Each domain is assigned an Intelligent Agent for collecting and analyzing the alarms generated within that domain. These agents are coordinated by a single higher level entity, i.e., an agent manager that combines the partial views of these agents into a global one. Each agent employs DSET-based algorithm that utilizes the probabilistic knowledge encoded in the available fault propagation model to construct a local composite alarm. The Dempster‘s rule of combination is then used by the agent manager to correlate these local composite alarms. Furthermore, an adaptive fuzzy DSET-based algorithm is proposed to utilize the fuzzy information provided by the observed cluster of alarms so as to accurately identify the malfunctioning network entities. In this way, inconsistency among the alarms is removed by weighing each received alarm against the others, while randomness and ambiguity of the fault evidence are addressed within soft computing framework. The effectiveness of this framework has been investigated based on extensive experiments. The proposed fault management system is able to detect malfunctioning behavior in the managed network with considerably less management traffic. Moreover, it effectively manages the uncertainty property intrinsically contained in network alarms,thereby reducing its negative impact and significantly improving the overall performance of the fault management system.

Governmental and private institutions rely heavily on reliable computer networks for their everyday business transactions. The downtime of their infrastructure networks may result in millions of dollars in cost. Fault management systems are used to keep today’s complex networks running without significant downtime cost, either by using active techniques or passive techniques. Active techniques impose excessive management traffic, whereas passive techniques often ignore uncertainty inherent in network alarms,leading to unreliable fault identification performance. In this research work, new algorithms are proposed for both types of techniques so as address these handicaps. Active techniques use probing technology so that the managed network can be tested periodically and suspected malfunctioning nodes can be effectively identified and isolated. However, the diagnosing probes introduce extra management traffic and storage space. To address this issue, two new CSP (Constraint Satisfaction Problem)-based algorithms are proposed to minimize management traffic, while effectively maintain the same diagnostic power of the available probes. The first algorithm is based on the standard CSP formulation which aims at reducing the available dependency matrix significantly as means to reducing the number of probes. The obtained probe set is used for fault detection and fault identification. The second algorithm is a fuzzy CSP-based algorithm. This proposed algorithm is adaptive algorithm in the sense that an initial reduced fault detection probe set is utilized to determine the minimum set of probes used for fault identification. Based on the extensive experiments conducted in this research both algorithms have demonstrated advantages over existing methods in terms of the overall management traffic needed to successfully monitor the targeted network system. Passive techniques employ alarms emitted by network entities. However, the fault evidence provided by these alarms can be ambiguous, inconsistent, incomplete, and random. To address these limitations, alarms are correlated using a distributed Dempster-Shafer Evidence Theory (DSET) framework, in which the managed network is divided into a cluster of disjoint management domains. Each domain is assigned an Intelligent Agent for collecting and analyzing the alarms generated within that domain. These agents are coordinated by a single higher level entity, i.e., an agent manager that combines the partial views of these agents into a global one. Each agent employs DSET-based algorithm that utilizes the probabilistic knowledge encoded in the available fault propagation model to construct a local composite alarm. The Dempster‘s rule of combination is then used by the agent manager to correlate these local composite alarms. Furthermore, an adaptive fuzzy DSET-based algorithm is proposed to utilize the fuzzy information provided by the observed cluster of alarms so as to accurately identify the malfunctioning network entities. In this way, inconsistency among the alarms is removed by weighing each received alarm against the others, while randomness and ambiguity of the fault evidence are addressed within soft computing framework. The effectiveness of this framework has been investigated based on extensive experiments. The proposed fault management system is able to detect malfunctioning behavior in the managed network with considerably less management traffic. Moreover, it effectively manages the uncertainty property intrinsically contained in network alarms,thereby reducing its negative impact and significantly improving the overall performance of the fault management system.

Changes in work, along with improvements in techniques to statistically model uncertainty, have resulted in a class of groupware tools able to forecast the activities and/or attentional state of their users. This thesis represents an exploration into the design, development, and use of one such system. I describe the design and development of a groupware calendar system called Augur that is augmented with the ability to predict the attendance of its users. Using Bayesian networks, Augur models the uncertain problem of event attendance, drawing inferences based on the attributes of calendar events as well as a history of attendance provided by each user. This system was deployed to an academic workgroup and studied over the course of a semester. To more deeply explore the social implications of Augur and systems like it, I conducted a structured privacy analysis of Augur to examine the vulnerabilities inherent in this type of forecasting groupware system. I present an architecture, user interface, and probabilistic model for Augur. This work also addresses the feasibility of such a system and the challenges faced when deploying it to an academic workgroup. I also report on an exploration of the systems use by individuals, its effects on communication within working relationships, and its effectiveness with respect to the presence of domestic calendars. Finally, I present a set of implications for the workplace social environment with the introduction of Augur. Specifically, I show how the integrity of predictions generated by Augur can have consequences for the privacy of users and their representations through the shared calendar. Overall, this thesis is presented as an early exploration into the potential for a new class of forecasting groupware applications. It offers guidance and lessons learned for both designers and researchers seeking to work in this area. It also presents a complete calendar application as an example for building and studying such systems.

Tecnologia de montagem de superfície (SMT) é um método para construção de circuitos eletrônicos, nos quais os componentes são montados diretamente sobre a superfície da placa de circuito impresso. Tais dispositivos eletrônicos são chamados de dispositivos de montagens de superfície ou SMDs. Paralelamente, as vantagens oferecidas pelo componente eletrônico LED SMD têm causado uma grande aplicação desse dispositivo em substituição ao LED convencional. O presente trabalho apresenta um sistema inteligente baseado em sistemas de inferência fuzzy para estimar valores de intensidade luminosa de equipamentos automotivos a partir de dados de projeto. Embora o trabalho esteja direcionado para a aplicação de LEDs SMD em lanternas traseiras, o trabalho aqui desenvolvido pode ser generalizado e usado em outras aplicações industriais, tais como semáforos de trânsito, painéis eletrônicos de mensagens ou qualquer outra aplicação onde use LEDs SMD em conjunto. Resultados de protótipos são apresentados para validar a técnica proposta. Por meio desses resultados, pode-se observar que a aplicação de sistemas inteligentes é uma abordagem atrativa para este tipo de problema. / Surface mount technology (SMT) is a method for making electronic circuits in which the components are mounted directly onto the surface of printed circuit boards. Such electronic devices are called surface-mount devices or SMDs. The advantages offered by the electronic component SMD LED (Light Emitting Diode) have caused a wide application of this device in replacement of conventional LEDs. This work shows an intelligent system using fuzzy interference systems to estimate values of luminous intensity in automotive equipments from design data. Although this work is aimed to the application of SMD LEDs in rear lights, methods hereby developed and described can also be used in other applications, such as traffic lights, electronic panels of messages or any other application where SMD LEDs are used in groups. Results of prototypes are presented to validate the proposed technique. From these results, it can be observed that the application of intelligent systems is an attractive approach to this type of problem.