O presente trabalho apresenta um estudo feito para a elaboração de um modelo de análise de crédito para micro, pequenas e médias empresas (MPME) utilizando Inteligência Artificial. Apresenta, também, uma contribuição de um novo método de raciocínio baseado em casos, denominado FISKNN, que utiliza medida de similaridade presente nos métodos KNN e KNN-Fuzzy, e um sistema de inferência Fuzzy para decidir se a classe de um determinado caso é a classe do elemento mais próximo ou a classe da maioria dos K elementos selecionados para análise. Compara-se o método FISKNN com os métodos tradicionais KNN e KNN-Fuzzy utilizando os dados do Machine Learning Repository da Universidade da Califórnia, e apresentam-se três estudos de casos com bases de dados selecionadas das informações provenientes de solicitações de financiamento através do Cartão BNDES. / This work presents an investigation of a model of credit analysis for micro, small and medium size enterprises based on artificial intelligence techniques. The novelty is a cases-based reasoning, denoted by FISKNN, which uses a measure of similarity present in the KNN and KNN-Fuzzy methods, and a Fuzzy Inference System to decide between the class of the nearest case and the class of the majority of K elements selected for the analysis. One compares the FISKNN methods with the more traditional ones, KNN and KNNFuzzy, using data from the Machine Learning Repository of the University of California, and one presents three study cases with data bases selected from the set of financing applications to the BNDES Card.

This thesis aims to solve the mathematical inverse problem of characterizing sea waves based on the responses obtained from a marine vessel sailing under certain sea conditions. By researching this problem the thesis contributes to the marine industry by improving products that are using ocean behavior for controlling ship's dynamics. Knowledge about the current state of the sea, such as the wave frequency and height, is important for navigation, control, and for the safety of a vessel. This information can be retrieved from specialized weather reports. However, such information is not at all time possible to obtain during a voyage, and if so usually comes with a certain delay. Therefore this thesis seeks solutions that can estimate on-line the waves' state using methods in the field of Artificial Intelligence. The specific investigation methods are Transfer Functions augmented with Genetic Algorithm, Artificial Neural Networks and Case-Based Reasoning. These methods have been configured and validated using the n-fold cross validation method. All the methods have been tested with an actual implementation. The algorithms have been trained with data acquired from a marine simulation program developed in Simulink. The methods have also been trained and tested using monitored data acquired from an actual ship sailing on the Baltic Sea as well as wave data obtained from a buoy located nearby the vessel's route. The proposed methods have been compared with state-of-the art reports in order evaluate the novelty of the research and its potential applications in industry. The results in this thesis show that the proposed methods can in fact be used for solving the inverse problem. It was also found that among the investigated methods it is the Transfer Function augmented with Genetic Algorithm which yields best results. This Master Thesis is conducted under the Master of Engineering Program in Robotics at Mälardalens högskola in Västerås, Sweden. The thesis was proposed by Q-TAGG R&D AB in Västerås, Sweden, a company which specializes in marine vessel dynamics research.

Nos travaux de thèses s’intéressent au pronostic de défaillance de composant critique et à l’estimation de la durée de vie résiduelle avant défaillance (RUL). Nous avons développé des méthodes basées sur l’expérience. Cette orientation nous permet de nous affranchir de la définition d’un seuil de défaillance, point problématique lors de l’estimation du RUL. Nous avons pris appui sur le paradigme de Raisonnement à Partir de Cas (R à PC) pour assurer le suivi d’un nouveau composant critique et prédire son RUL. Une approche basée sur les instances (IBL) a été développée en proposant plusieurs formalisations de l’expérience : une supervisée tenant compte de l’ état du composant sous forme d’indicateur de santé et une non-supervisée agrégeant les données capteurs en une série temporelle mono-dimensionnelle formant une trajectoire de dégradation. Nous avons ensuite fait évoluer cette approche en intégrant de la connaissance à ces instances. La connaissance est extraite à partir de données capteurs et est de deux types : temporelle qui complète la modélisation des instances et fréquentielle qui, associée à la mesure de similarité permet d’affiner la phase de remémoration. Cette dernière prend appui sur deux types de mesures : une pondérée entre fenêtres parallèles et fixes et une pondérée avec projection temporelle. Les fenêtres sont glissantes ce qui permet d’identifier et de localiser l’état actuel de la dégradation de nouveaux composants. Une autre approche orientée donnée a été test ée. Celle-ci est se base sur des caractéristiques extraites des expériences, qui sont mono-dimensionnelles dans le premier cas et multi-dimensionnelles autrement. Ces caractéristiques seront modélisées par un algorithme de régression à vecteurs de support (SVR). Ces approches ont été évaluées sur deux types de composants : les turboréacteurs et les batteries «Li-ion». Les résultats obtenus sont intéressants mais dépendent du type de données traitées. / Our thesis work is concerned with the development of experience based approachesfor criticalcomponent prognostics and Remaining Useful Life (RUL) estimation. This choice allows us to avoidthe problematic issue of setting a failure threshold.Our work was based on Case Based Reasoning (CBR) to track the health status of a new componentand predict its RUL. An Instance Based Learning (IBL) approach was first developed offering twoexperience formalizations. The first is a supervised method that takes into account the status of thecomponent and produces health indicators. The second is an unsupervised method that fuses thesensory data into degradation trajectories.The approach was then evolved by integrating knowledge. Knowledge is extracted from the sensorydata and is of two types: temporal that completes the modeling of instances and frequential that,along with the similarity measure refine the retrieval phase. The latter is based on two similaritymeasures: a weighted one between fixed parallel windows and a weighted similarity with temporalprojection through sliding windows which allow actual health status identification.Another data-driven technique was tested. This one is developed from features extracted from theexperiences that can be either mono or multi-dimensional. These features are modeled by a SupportVector Regression (SVR) algorithm. The developed approaches were assessed on two types ofcritical components: turbofans and ”Li-ion” batteries. The obtained results are interesting but theydepend on the type of the treated data.

Die demografische Entwicklung und die fachliche Spezialisierung medizinischer Organisationen führen zu immer komplexeren Behandlungswegen. Eine grundsätzliche Herausforderung komplexer Behandlungswege besteht darin, die Versorgungskontinuität zu wahren, obschon viele verschiedene Einrichtungen am Behandlungsweg agieren. Die Versorgungskontinuität hängt dabei maßgeblich von einer effektiven Kooperation und effizienten Kommunikation der beteiligten Akteure ab. Die folgende Arbeit ist der Frage gewidmet, welches Potenzial die elektronische Gesundheitskarte (eGK) in diesem transinstitutionellen Szenario für die Patienten birgt. Eine entscheidende Empfehlung im Rahmen des Requirements Engineering (RE) besteht darin, als Erstes die Anforderungen der Anwender zu analysieren und erst daran anschließend die Funktionalität des technischen Systems hinsichtlich der Anforderungskonformität zu evaluieren. Dieser Empfehlung folgend führte der Verfasser dieser Abhandlung dreizehn teilstrukturierte Interviews, fünf Projektsteuerungsmeetings und einen Workshop mit Patienten und medizinischen Experten durch, um fünf komplexe Behandlungswege aus der Perspektive der Patienten zu rekonstruieren. Die Auswertung, Strukturierung und Validierung der dem Behandlungskontext inhärenten Probleme mündeten in fünfundvierzig Einzelproblemen, die zu fünf finalen Patientenanforderungen verdichtet werden konnten. Im zweiten Schritt analysierte der Verfasser dieser Arbeit die Funktionalität der eGK hinsichtlich der Fähigkeit, diesen fünf Anforderungen gerecht zu werden. Die vorliegende Potenzialanalyse hebt sich von den bisherigen Forschungsarbeiten zur eGK insbesondere durch zwei Merkmale ab: Erstens erfolgt die Analyse konsequent aus der Perspektive der Patienten und zweitens sind dabei die organisatorischen Rahmenbedingungen intensiv berücksichtigt worden. In dieser Abhandlung werden die Behandlungswege von fünf Patienten fallstudienbasiert aus einer ganzheitlichen Perspektive beleuchtet. Es wird gezeigt, dass fünf grundsätzliche Patientenanforderungen zu erfüllen sind. Diese fünf Anforderungen basieren auf Problemen, die erstens mit einer effizienten Informationslogistik, zweitens mit dem Recht auf informationelle Selbstbestimmung, drittens mit der Versorgungskontinuität, viertens mit der Gesamtsteuerung der Behandlung und fünftens mit administrativen Aufgaben zusammenhängen. Auf dieser Basis konnten insgesamt zehn Fachanwendungen identifiziert und im Rahmen eines visionären Anwendungsfalles exemplarisch veranschaulicht werden, von denen angenommen wird, dass sie das Potenzial der geplanten Telematik-Infrastruktur (TI) hinsichtlich der Anforderungen der Patienten besonders gut ausschöpfen.

Dem Web 2.0 werden - nicht selten mit euphorischem Unterton - hinsichtlich Interaktion, Selbstorganisation und Nutzbarmachung kollektiver Intelligenz enorme Nutzenpotentiale nachgesagt. Ansätze mit Bezug zum Unternehmenskontext werden unter dem Stichwort Enterprise 2.0 behandelt und beschäftigen sich vorrangig mit der Unterstützung des betrieblichen Wissensmanagements. Speziell für die zunehmend durch Komplexität sowie intensive Interaktionsprozesse geprägte Fabrikplanung lassen sich durch einen zielgerichteten Einsatz von Web 2.0-Werkzeugen positive Effekte erwarten. Zielstellung dieser Arbeit ist die Entwicklung einer Methode zum Einsatz von Web 2.0-Werkzeugen in der Fabrikplanung. Hierfür erfolgt zunächst eine Bestandsaufnahme relevanter Ansätze und Begriffe in diesen Bereichen. Anschließend wird auf Grundlage system-, handlungs- und tätigkeitstheoretischer Überlegungen ein situativer Forschungsansatz begründet. Die Methodenentwicklung erfolgt als problemspezifische Ausgestaltung des Fall-basierten Schließens. Sie ist in ein entsprechend angepasstes Vorgehen der morphologisch-typologischen Theorieentwicklung eingebettet und basiert auf einer umfassenden Analyse hierfür relevanter Theorien, Modelle und Ansätze. Die Methode beruht auf einer kontinuierlichen Erfassung und Wiederverwendung von Erfahrungswissen. Sie wird abschließend evaluiert, wobei u.a. ein Prototyp entwickelt wird, der den praktischen Einsatz der entwickelten Methode unterstützt.:Abbildungsverzeichnis Tabellenverzeichnis Anlagenverzeichnis Symbol- und Abkürzungsverzeichnis 1 Einleitung 1.1 Motivation 1.2 Problemstellung 1.3 Zielstellung 1.4 Aufbau der Arbeit 2 Grundlagen 2.1 Phänomen Web 2.0 2.2 Fabrikplanung 3 Forschungsansatz 3.1 Betrachtungsrahmen 3.2 Bearbeitungsmethodik 4 Methodenentwicklung 4.1 Systemmodell 4.2 Morphologische Fallbasis 4.3 Bewertungssystematik 4.4 Gesamtmethode 5 Evaluation 5.1 Ansätze und Methoden 5.2 Methodenauswahl 5.3 Durchführung 5.4 Gesamtbewertung 6 Schlussbetrachtung 6.1 Zusammenfassung 6.2 Ausblick Literaturverzeichnis Anlagen / The Web 2.0 is supposed to have huge potential for the support of interaction, selforganization and the utilization of collective intelligence. Approaches related to an enterprise context are discussed with the keyword Enterprise 2.0 and mainly deal with potentials to support the operational knowledge management. A systematic approach for the use of web-based collaborative tools is expected to generate positive effects on modern factory planning, which faces increasing complexity and dynamic interactions. The objective of this work is to develop a methodical approach for the use of web-based collaborative tools in factory planning. Therefore, in the first part of this thesis an overview of relevant approaches and terms in the areas of Web 2.0 and factory planning is being worked out. In a second step, a situational approach is identified as an appropriate view after due consideration and contextual discussion of system, action and activity theory. The development of the methodical approach is based on a problem-specific adaptation of case-based reasoning. It is embedded into an elaborated procedure of morphologic-typological theory building and bases on a comprehensive analysis of relevant theories, models and approaches. The evolved method relies on continuous collection and reutilisation of experiential knowledge. It is evaluated through different methods, inter alia by the construction of a prototype that supports its practical use.:Abbildungsverzeichnis Tabellenverzeichnis Anlagenverzeichnis Symbol- und Abkürzungsverzeichnis 1 Einleitung 1.1 Motivation 1.2 Problemstellung 1.3 Zielstellung 1.4 Aufbau der Arbeit 2 Grundlagen 2.1 Phänomen Web 2.0 2.2 Fabrikplanung 3 Forschungsansatz 3.1 Betrachtungsrahmen 3.2 Bearbeitungsmethodik 4 Methodenentwicklung 4.1 Systemmodell 4.2 Morphologische Fallbasis 4.3 Bewertungssystematik 4.4 Gesamtmethode 5 Evaluation 5.1 Ansätze und Methoden 5.2 Methodenauswahl 5.3 Durchführung 5.4 Gesamtbewertung 6 Schlussbetrachtung 6.1 Zusammenfassung 6.2 Ausblick Literaturverzeichnis Anlagen

Fundação de Amparo a Pesquisa do Estado de Minas Gerais / Agentes que operam em ambientes onde as tomadas de decisão precisam levar em conta, além do ambiente, a atuação minimizadora de um oponente (tal como nos jogos), é fundamental que o agente seja dotado da habilidade de, progressivamente, traçar um perĄl de seu adversário que o auxilie em seu processo de seleção de ações apropriadas. Entretanto, seria improdutivo construir um agente com um sistema de tomada de decisão baseado apenas na elaboração desse perĄl, pois isso impediria o agente de ter uma Şidentidade própriaŤ, o que o deixaria a mercê de seu adversário. Nesta direção, este trabalho propõe um sistema automático jogador de Damas híbrido, chamado ACE-RL-Checkers, dotado de um mecanismo dinâmico de tomada de decisões que se adapta ao perĄl de seu oponente no decorrer de um jogo. Em tal sistema, o processo de seleção de ações (movimentos) é conduzido por uma composição de Rede Neural de Perceptron Multicamadas e biblioteca de casos. No caso, a Rede Neural representa a ŞidentidadeŤ do agente, ou seja, é um módulo tomador de decisões estático já treinado e que faz uso da técnica de Aprendizagem por Reforço TD( ). Por outro lado, a biblioteca de casos representa o módulo tomador de decisões dinâmico do agente que é gerada pela técnica de Elicitação Automática de Casos (um tipo particular de Raciocínio Baseado em Casos). Essa técnica possui um comportamento exploratório pseudo-aleatório que faz com que a tomada de decisão dinâmica do agente seja guiada, ora pelo perĄl de jogo do adversário, ora aleatoriamente. Contudo, ao conceber tal arquitetura, é necessário evitar o seguinte problema: devido às características inerentes à técnica de Elicitação Automática de Casos, nas fases iniciais do jogo Ű em que a quantidade de casos disponíveis na biblioteca é extremamente baixa em função do exíguo conhecimento do perĄl do adversário Ű a frequência de tomadas de decisão aleatórias seria muito elevada, o que comprometeria o desempenho do agente. Para atacar tal problema, este trabalho também propõe incorporar à arquitetura do ACE-RLCheckers um terceiro módulo, composto por uma base de regras de experiência extraída a partir de jogos de especialistas humanos, utilizando uma técnica de Mineração de Padrões Sequenciais. O objetivo de utilizar tal base é reĄnar e acelerar a adaptação do agente ao perĄl de seu adversário nas fases iniciais dos confrontos entre eles. Resultados experimentais conduzidos em torneio envolvendo ACE-RL-Checkers e outros agentes correlacionados com este trabalho, conĄrmam a superioridade da arquitetura dinâmica aqui proposta. / ake into account, in addition to the environment, the minimizing action of an opponent (such as in games), it is fundamental that the agent has the ability to progressively trace a proĄle of its adversary that aids it in the process of selecting appropriate actions. However, it would be unsuitable to construct an agent with a decision-making system based on only the elaboration of this proĄle, as this would prevent the agent from having its Şown identityŤ, which would leave it at the mercy of its opponent. Following this direction, this work proposes an automatic hybrid Checkers player, called ACE-RL-Checkers, equipped with a dynamic decision-making mechanism, which adapts to the proĄle of its opponent over the course of the game. In such a system, the action selection process (moves) is conducted through a composition of Multi-Layer Perceptron Neural Network and case library. In the case, Neural Network represents the ŞidentityŤ of the agent, i.e., it is an already trained static decision-making module and makes use of the Reinforcement Learning TD( ) techniques. On the other hand, the case library represents the dynamic decision-making module of the agent, which is generated by the Automatic Case Elicitation technique (a particular type of Case-Based Reasoning). This technique has a pseudo-random exploratory behavior, which makes the dynamic decision-making on the part of the agent to be directed, either by the game proĄle of the opponent or randomly. However, when devising such an architecture, it is necessary to avoid the following problem: due to the inherent characteristics of the Automatic Case Elicitation technique, in the game initial phases, in which the quantity of available cases in the library is extremely low due to low knowledge content concerning the proĄle of the adversary, the decisionmaking frequency for random decisions is extremely high, which would be detrimental to the performance of the agent. In order to attack this problem, this work also proposes to incorporate onto the ACE-RL-Checkers architecture a third module composed of a base of experience rules, extracted from games played by human experts, using a Sequential Pattern Mining technique. The objective behind using such a base is to reĄne and accelerate the adaptation of the agent to the proĄle of its opponent in the initial phases of their confrontations. Experimental results conducted in tournaments involving ACE-RL-Checkers and other agents correlated with this work, conĄrm the superiority of the dynamic architecture proposed herein. / Tese (Doutorado)