Dissertations / Theses on the topic 'Fuzzy Pattern Classification'

COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
Esta dissertação investiga a utilização de sistemas Neuro- Fuzzy Hierárquicos BSP (Binary Space Partitioning) para classificação de padrões e para extração de regras fuzzy em bases de dados. O objetivo do trabalho foi criar modelos específicos para classificação de registros a partir do modelo Neuro-Fuzzy Hierárquico BSP que é capaz de gerar sua própria estrutura automaticamente e extrair regras fuzzy, lingüisticamente interpretáveis, que explicam a estrutura dos dados. O princípio da tarefa de classificação de padrões é descobrir relacionamentos entre os dados com a intenção de prever a classe de um padrão desconhecido. O trabalho consistiu fundamentalmente de quatro partes: um estudo sobre os principais métodos de classificação de padrões; análise do sistema Neuro-Fuzzy Hierárquico BSP (NFHB) original na tarefa de classificação; definição e implementação de dois sistemas NFHB específicos para classificação de padrões; e o estudo de casos. No estudo sobre os métodos de classificação foi feito um levantamento bibliográfico da área, resultando em um "survey" onde foram apresentadas as principais técnicas utilizadas para esta tarefa. Entre as principais técnicas destacaram-se: os métodos estatísticos, algoritmos genéticos, árvores de decisão fuzzy, redes neurais, e os sistemas neuro-fuzzy. Na análise do sistema NFHB na classificação de dados levou- se em consideração as peculiaridades do modelo, que possui: aprendizado da estrutura, particionamento recursivo do espaço de entrada, aceita maior número de entradas que os outros sistemas neuro-fuzzy, além de regras fuzzy recursivas. O sistema NFHB, entretanto, não é um modelo exatamente desenvolvido para classificação de padrões. O modelo NFHB original possui apenas uma saída e para utilizá- lo como um classificador é necessário criar um critério de faixa de valores (janelas) para representar as classes. Assim sendo, decidiu-se criar novos modelos que suprissem essa deficiência. Foram definidos dois novos sistemas NFHB para classificação de padrões: NFHB-Invertido e NFHB-Class. O primeiro utiliza a arquitetura do modelo NFHB original no aprendizado e em seguida a inversão da mesma para a validação dos resultados. A inversão do sistema consistiu de um meio de adaptar o novo sistema à tarefa específica de classificação, pois passou-se a ter o número de saídas do sistema igual ao número de classes ao invés do critério de faixa de valores utilizado no modelo NFHB original. Já o sistema NFHB-Class utilizou, tanto para a fase de aprendizado, quanto para a fase de validação, o modelo NFHB original invertido. Ambos os sistemas criados possuem o número de saídas igual ao número de classes dos padrões, o que representou um grande diferencial em relação ao modelo NFHB original. Além do objetivo de classificação de padrões, o sistema NFHB-Class foi capaz de extrair conhecimento em forma de regras fuzzy interpretáveis. Essas regras são expressas da seguinte maneira: SE x é A e y é B então padrão pertence à classe Z. Realizou-se um amplo estudo de casos, abrangendo diversas bases de dados Benchmark para a tarefa de classificação, tais como: Iris Dataset, Wine Data, Pima Indians Diabetes Database, Bupa Liver Disorders e Heart Disease, e foram feitas comparações com diversos modelos e algoritmos de classificação de padrões. Os resultados encontrados com os modelos NFHB-Invertido e NFHB-Class mostraram-se, na maioria dos casos, superiores ou iguais aos melhores resultados encontrados pelos outros modelos e algoritmos aos quais foram comparados.O desempenho dos modelos NFHB-Invertido e NFHB-Class em relação ao tempo de processamento também se mostrou muito bom. Para todas as bases de dados descritas no estudo de casos (capítulo 8), os modelos convergiram para uma ótima solução de classificação, além da extração das regras fuzzy, em
This dissertation investigates the use of Neuro-Fuzzy Hierarchical BSP (Binary Space Partitioning) systems for pattern classification and extraction of fuzzy rules in databases. The objective of this work was to create specific models for the classification of registers based on the Neuro-Fuzzy BSP model that is able to create its structure automatically and to extract linguistic rules that explain the data structure. The task of pattern classification is to find relationships between data with the intention of forecasting the class of an unknown pattern. The work consisted of four parts: study about the main methods of the pattern classification; evaluation of the original Neuro-Fuzzy Hierarchical BSP system (NFHB) in pattern classification; definition and implementation of two NFHB systems dedicated to pattern classification; and case studies. The study about classification methods resulted in a survey on the area, where the main techniques used for pattern classification are described. The main techniques are: statistic methods, genetic algorithms, decision trees, neural networks, and neuro-fuzzy systems. The evaluation of the NFHB system in pattern classification took in to consideration the particularities of the model which has: ability to create its own structure; recursive space partitioning; ability to deal with more inputs than other neuro-fuzzy system; and recursive fuzzy rules. The original NFHB system, however, is unsuited for pattern classification. The original NFHB model has only one output and its use in classification problems makes it necessary to create a criterion of band value (windows) in order to represent the classes. Therefore, it was decided to create new models that could overcome this deficiency. Two new NFHB systems were developed for pattern classification: NFHB-Invertido and NFHB-Class. The first one creates its structure using the same learning algorithm of the original NFHB system. After the structure has been created, it is inverted (see chapter 5) for the generalization process. The inversion of the structure provides the system with the number of outputs equal to the number of classes in the database. The second system, the NFHB-Class uses an inverted version of the original basic NFHB cell in both phases, learning and validation. Both systems proposed have the number of outputs equal to the number of the pattern classes, what means a great differential in relation to the original NFHB model. Besides the pattern classification objective, the NFHB- Class system was able to extract knowledge in form of interpretable fuzzy rules. These rules are expressed by this way: If x is A and y is B then the pattern belongs to Z class. The two models developed have been tested in many case studies, including Benchmark databases for classification task, such as: Iris Dataset, Wine Data, Pima Indians Diabetes Database, Bupa Liver Disorders and Heart Disease, where comparison has been made with several traditional models and algorithms of pattern classification. The results found with NFHB-Invertido and NFHB-Class models, in all cases, showed to be superior or equal to the best results found by the others models and algorithms for pattern classification. The performance of the NFHB- Invertido and NFHB-Class models in terms of time-processing were also very good. For all databases described in the case studies (chapter 8), the models converged to an optimal classification solution, besides the fuzzy rules extraction, in a time-processing inferior to a minute.
Esta disertación investiga el uso de sistemas Neuro- Fuzzy Herárquicos BSP (Binary Space Partitioning) en problemas de clasificación de padrones y de extracción de reglas fuzzy en bases de datos. El objetivo de este trabajo fue crear modelos específicos para clasificación de registros a partir del modelo Neuro-Fuzzy Jerárquico BSP que es capaz de generar automáticamente su propia extructura y extraer reglas fuzzy, lingüisticamente interpretables, que explican la extructura de los datos. El principio de la clasificación de padrones es descubrir relaciones entre los datos con la intención de prever la clase de un padrón desconocido. El trabajo está constituido por cuatro partes: un estudio sobre los principales métodos de clasificación de padrones; análisis del sistema Neuro-Fuzzy Jerárquico BSP (NFHB) original en la clasificación; definición e implementación de dos sistemas NFHB específicos para clasificación de padrones; y el estudio de casos. En el estudio de los métodos de clasificación se realizó un levatamiento bibliográfico, creando un "survey" donde se presentan las principales técnicas utilizadas. Entre las principales técnicas se destacan: los métodos estadísticos, algoritmos genéticos, árboles de decisión fuzzy, redes neurales, y los sistemas neuro-fuzzy. En el análisis del sistema NFHB para clasificación de datos se tuvieron en cuenta las peculiaridades del modelo, que posee : aprendizaje de la extructura, particionamiento recursivo del espacio de entrada, acepta mayor número de entradas que los otros sistemas neuro-fuzzy, además de reglas fuzzy recursivas. El sistema NFHB, sin embargo, no es un modelo exactamente desarrollado para clasificación de padrones. El modelo NFHB original posee apenas una salida y para utilizarlo conmo un clasificador fue necesario crear un criterio de intervalos de valores (ventanas) para representar las clases. Así, se decidió crear nuevos modelos que supriman esta deficiencia. Se definieron dos nuevos sistemas NFHB para clasificación de padrones: NFHB- Invertido y NFHB-Clas. El primero utiliza la arquitectura del modelo NFHB original en el aprendizaje y en seguida la inversión de la arquitectura para la validación de los resultados. La inversión del sistema es un medio para adaptar el nuevo sistema, específicamente a la clasificación, ya que el sistema pasó a tener número de salidas igual al número de clases, al contrario del criterio de intervalo de valores utilizado en el modelo NFHB original. En el sistema NFHB-Clas se utilizó, tanto para la fase de aprendizajeo, cuanto para la fase de validación, el modelo NFHB original invertido. Ambos sistemas poseen el número de salidas igual al número de clases de los padrones, lo que representa una gran diferencia en relación al modelo NFHB original. Además del objetivo de clasificación de padrones, el sistema NFHB-Clas fue capaz de extraer conocimento en forma de reglas fuzzy interpretables. Esas reglas se expresan de la siguiente manera: Si x es A e y es B entonces el padrón pertenece a la clase Z. Se realizó un amplio estudio de casos, utilizando diversas bases de datos Benchmark para la clasificación, tales como: Iris Dataset, Wine Data, Pima Indians Diabetes Database, Bupa Liver Disorders y Heart Disease. Los resultados se compararon con diversos modelos y algoritmos de clasificación de padrones. Los resultados encontrados con los modelos NFHB-Invertido y NFHB-Clas se mostraron, en la mayoría de los casos, superiores o iguales a los mejores resultados encontrados por los otros modelos y algoritmos con los cuales fueron comparados. El desempeño de los modelos NFHB-Invertido y NFHB-Clas en relación al tiempo de procesamiento tambiém se mostró muy bien. Para todas las bases de datos descritas en el estudio de casos (capítulo 8), los modelos convergieron para una solución óptima, además de la extracción de las reglas fuzzy, con tiemp

Die Arbeit ordnet sich in das Gebiet der unscharfen Klassifikation ein und stellt im Detail eine Weiterführung der Forschung zur Fuzzy-Pattern-Klassifikation dar. Es handelt sich dabei um eine leistungsfähige systemtheoretische Methodik zur klassifikatorischen Modellierung komplexer, hochdimensionaler, technischer oder nichttechnischer Systeme auf der Basis von metrischen Messgrößen und/oder nichtmetrischen Experten-Bewertungen. Die Beschreibung der Unschärfe von Daten, Zuständen und Strukturen wird hierbei durch einen einheitlichen Typ einer Zugehörigkeitsfunktion des Potentialtyps realisiert. Ziel der Betrachtungen ist die weiterführende Nutzung des bestehenden Klassenmodells zur unscharfen Beschreibung nichtkonvexer Objektmengenmorphologien. Ausgehend vom automatischen datengetriebenen Aufbau der konvexen Klassenbeschreibung, deren vorteilhaften Eigenschaften sowie Defiziten wird im Rahmen der Arbeit eine Methodik vorgestellt, die eine Modellierung beliebiger Objektmengenmorphologien erlaubt, ohne das bestehende Klassifikationskonzept zu verlassen. Kerngedanken des Vorgehens sind: 1.) Die Aggregation von Fuzzy-Pattern-Klassen auf der Basis so genannter komplementärer Objekte. 2.) Die sequentielle Verknüpfung von Fuzzy-Pattern-Klassen und komplementären Klassen im Sinne einer unscharfen Mengendifferenz. 3.) Die Strukturierung des Verknüpfungsprozesses durch die Clusteranalyse von Komplementärobjektmengen und damit der Verwendung von Konfigurationen aus komplementären Fuzzy-Pattern-Klassen. Das dabei gewonnene nichtkonvexe Fuzzy-Klassifikationsmodell impliziert eine Vernetzung von Fuzzy-Klassifikatoren in Form von Klassifikatorbäumen. Im Ergebnis entstehen Klassifikatorstrukturen mit hoher Transparenz, die - neben der üblichen zustandsorientierten klassifikatorischen Beschreibung in den Einzelklassifikatoren - zusätzliche Informationen über den Ablauf der Klassifikationsentscheidungen erfassen. Der rechnergestützte Entwurf und die Eigenschaften der entstehenden Klassifikatorstruktur werden an akademischen Teststrukturen und realen Daten demonstriert. Die im Rahmen der Arbeit dargestellte Methodik wird in Zusammenhang mit dem Fuzzy-Pattern-Klassifikationskonzept realisiert, ist jedoch aufgrund ihrer Allgemeingültigkeit auf eine beliebige datenbasierte konvexe Klassenbeschreibung übertragbar
This work contributes to the field of fuzzy classification. It dedicates itself to the subject of "Fuzzy-Pattern-Classification", a versatile method applied for classificatory modeling of complex, high dimensional systems based on metric and nonmetric data, i.e. sensor readings or expert statements. Uncertainties of data, their associated morphology and therewith classificatory states are incorporated in terms of fuzziness using a uniform and convex type of membership function. Based on the properties of the already existing convex Fuzzy-Pattern-Class models and their automatic, data-driven setup a method for modeling nonconvex relations without leaving the present classification concept is introduced. Key points of the elaborated approach are: 1.) The aggregation of Fuzzy-Pattern-Classes with the help of so called complementary objects. 2.) The sequential combination of Fuzzy-Pattern-Classes and complementary Fuzzy-Pattern-Classes in terms of a fuzzy set difference. 3.) A clustering based structuring of complementary Fuzzy-Pattern-Classes and therewith a structuring of the combination process. A result of this structuring process is the representation of the resulting nonconvex fuzzy classification model in terms of a classifier tree. Such a nonconvex Fuzzy-Classifier features high transparency, which allows a structured understanding of the classificatory decision in working mode. Both the automatic data-based design as well as properties of such tree-like fuzzy classifiers will be illustrated with the help of academic and real word data. Even though the proposed method is introduced for a specific type of membership function, the underlying idea may be applied to any convex membership function

Der Gesetzgeber fordert von den Energieversorgungsunternehmen, ¨die Versorgung so sicher und billig wie moeglich zu gestalten¨. Um jederzeit ein stoerungsfreies und kostenguenstiges Angebot von elektrischer Energie zu gewaehrleisten, bedarf es einer moeglichst genauen Prognose der zu erwartenden Belastung im Versorgungsgebiet. Aufbauend auf den Erfahrungen bei der Prognose von Zeitreihen aus den Bereichen Umwelt und Verkehr wird in dieser Arbeit eine kurzfristige Vorhersage der elektrischen Belastung mit Hilfe der ¨Fuzzy-Pattern- Klassifikation¨ dargestellt. Dabei erfolgt die Modellbildung nicht allein auf der Basis der elektrischen Leistung, sondern wird durch zusaetzliche energiewirtschaftlich relevante Informationen, wie z.B. Klimadaten unterstuetzt. Zentraler Gegenstand der Untersuchungen ist die Frage, ob durch den Einsatz ergaenzender Informationen die Genauigkeit der Prognose bei kurzfristigen Vorhersagehorizonten (15 bis 120 Minuten) verbessert werden kann. Die mannigfaltigen Abhaengigkeiten zwischen elektrischer Belastung und ursaechlich wirkenden Einflussgroessen fuehren auf differenzierte Strategien zur Analyse und Prognose des Datenmaterials. Ausfuehrlich werden die Vorstrukturierung der Datenbasis, eine Prototypenmaskierung sowie die dynamische Parametrierung der Prognose erlaeutert und deren Wirksamkeit an realen Daten ueberprueft. Die Einschaetzungen zur Brauchbarkeit der Zusatzinformationen beruhen auf einem Vergleich von Prognoseresultaten der unterschiedlichen Modelle.

Classification of terrain cover from satellite radar imagery represents an area of considerable current interest and research. Most satellite sensors used for land applications are of the imaging type. They record data in a variety of spectral channels and at a variety of ground resolutions. Spectral pattern recognition refers to classification procedures utilizing pixel-by-pixel spectral information as the basis for automated land cover classification. A number of methods have been developed in the past to classify pixels [resolution cells] from multispectral imagery to a priori given land cover categories. Their ability to provide land cover information with high classification accuracies is significant for work where accurate and reliable thematic information is needed. The current trend towards the use of more spectral bands on satellite instruments, such as visible and infrared imaging spectrometers, and finer pixel and grey level resolutions will offer more precise possibilities for accurate identification. But as the complexity of the data grows, so too does the need for more powerful tools to analyse them. It is the major objective of this study to analyse the capabilities and applicability of the neural pattern recognition system, called fuzzy ARTMAP, to generate high quality classifications of urban land cover using remotely sensed images. Fuzzy ARTMAP synthesizes fuzzy logic and Adaptive Resonance Theory (ART) by exploiting the formal similarity between the computations of fuzzy subsethood and the dynamics of category choice, search and learning. The paper describes design features, system dynamics and simulation algorithms of this learning system, which is trained and tested for classification (8 a priori given classes) of a multispectral image of a Landsat-5 Thematic Mapper scene (270 x 360 pixels) from the City of Vienna on a pixel-by-pixel basis. Fuzzy ARTMAP performance is compared with that of an error-based learning system based upon the multi-layer perceptron, and the Gaussian maximum likelihood classifier as conventional statistical benchmark on the same database. Both neural classifiers outperform the conventional classifier in terms of classification accuracy. Fuzzy ARTMAP leads to out-of-sample classification accuracies, very close to maximum performance, while the multi-layer perceptron - like the conventional classifier - shows difficulties to distinguish between some land use categories. (authors' abstract)
Series: Discussion Papers of the Institute for Economic Geography and GIScience

This dissertation introduces advanced artificial intelligence based algorithm for detecting and classifying faults on the power system transmission line. The proposed algorithm is aimed at substituting classical relays susceptible to possible performance deterioration during variable power system operating and fault conditions. The new concept relies on a principle of pattern recognition and detects the existence of the fault, identifies fault type, and estimates the transmission line faulted section. The approach utilizes self-organized, Adaptive Resonance Theory (ART) neural network, combined with fuzzy decision rule for interpretation of neural network outputs. Neural network learns the mapping between inputs and desired outputs through processing a set of example cases. Training of the neural network is based on the combined use of unsupervised and supervised learning methods. During training, a set of input events is transformed into a set of prototypes of typical input events. During application, real events are classified based on the interpretation of their matching to the prototypes through fuzzy decision rule. This study introduces several enhancements to the original version of the ART algorithm: suitable preprocessing of neural network inputs, improvement in the concept of supervised learning, fuzzyfication of neural network outputs, and utilization of on-line learning. A selected model of an actual power network is used to simulate extensive sets of scenarios covering a variety of power system operating conditions as well as fault and disturbance events. Simulation results show improved recognition capabilities compared to a previous version of ART neural network algorithm, Multilayer Perceptron (MLP) neural network algorithm, and impedance based distance relay. Simulation results also show exceptional robustness of the novel ART algorithm for all operating conditions and events studied, as well as superior classification capabilities compared to the other solutions. Consequently, it is demonstrated that the proposed ART solution may be used for accurate, high-speed distinction among faulted and unfaulted events, and estimation of fault type and fault section.

Este trabalho propõe uma alternativa para o problema de classificação de espécies de abelhas a partir da implementação de um algoritmo com base na Morfométria Geométrica e estudo das Formas dos marcos anatômicos das imagens obtidas pelas asas das abelhas. O algoritmo implementado para este propósito se baseia no algoritmo dos k-Vizinho mais Próximos (do inglês, kNN) e na Lógica Fuzzy kNN (Fuzzy k-Nearest Neighbor) aplicados a dados analisados e selecionados de pontos bidimensionais referentes as características geradas por marcos anatômicos. O estudo apresentado envolve métodos de seleção e ordenação de marcos anatômicos para a utilização no algoritmo por meio da implementação de um método matemático que utiliza o calculo dos marcos anatômicos mais significativos (que são representados por marcos matemáticos) e a formulação da Ordem de Significância onde cada elemento representa variáveis de entrada para a Fuzzy kNN. O conhecimento envolvido neste trabalho inclui uma perspectiva sobre a seleção de características não supervisionada como agrupamentos e mineração de dados, analise de pré-processamento dos dados, abordagens estatísticas para estimação e predição, estudo da Forma, Analise de Procrustes e Morfométria Geométrica sobre os dados e o tópico principal que envolve uma modificação do algoritmo dos k- Vizinhos mais Próximos e a aplicação da Fuzzy kNN para o problema. Os resultados mostram que a classificação entre amostras de abelhas no seu próprio grupo apresentam acuracia de 90%, dependendo da espécie. As classificações realizadas entre as espécies de abelhas alcançaram acuracia de 97%.
This work presents a proposal to solve the bees classification problem by implementing an algorithm based on Geometrics Morphometrics and the Shape analysis of landmarks generated from bees wings images. The algorithm is based on the K-Nearest Neighbor (K-Nearest Neighbor) algorithm and Fuzzy Logic KNN applied to the analysis and selection of two-dimensional data points relating to landmarks. This work is part of the Architecture Reference Model for Automatic identification and Taxonomic Classification System of Stingless Bee using the Wing Morphometry. The study includes selection and ordering methods for landmarks used in the algorithm by developing a mathematical model to represent the significance order, generating the most significant mathematical landmarks as input variables for Fuzzy Logic kNN. The main objective of this work is to develop a classification system for bee species. The knowledge involved in the development of this work include an overview of feature selection, unsupervised clustering and data mining, analysis of data pre-processing, statistical approaches for estimation and prediction, study of Shape, Procrustes Analysis on data that comes from Geometric Morphometry and the modification of the k-Nearest Neighbors algorithm and the Fuzzy Logic kNN. The results show that the classification in bee samples of the same species presents a accuracy above 90%, depending on the specie in analysis. The classification done between the bees species reach accuracies of 97%.

Die Berechnung der Netzverluste in Niederspannungsnetzen kann nur über Umwege erfolgen, da viele Netzbetreiber keine digitalisierten Daten ihrer Netze besitzen. Hier wird ein Ansatz zur Ermittlung der technischen Verluste anhand vorhandener Netzdaten beschrieben
The major part of network operator of low voltage nets do not have digitised data of their nets. That’s why net losses must be calculated indirectly. This article describes an approach for getting results in a direct way

Orientador: Jurandir Zullo Junior
Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Agricola
Made available in DSpace on 2018-08-05T08:07:53Z (GMT). No. of bitstreams: 1 Antunes_JoaoFranciscoGoncalves_M.pdf: 7524504 bytes, checksum: e36a3c933615dc4ef031bf119f6c09ff (MD5) Previous issue date: 2005
Resumo: A estimativa precisa com antecedência à época da colheita de áreas plantadas com culturas agrícolas, como a soja, é de fundamental importância para a economia brasileira. A previsão do escoamento e comercialização da produção agrícola é estratégica para o Brasil, pois estão diretamente relacionados com o planejamento, custos e preço. Com o recente avanço tecnológico na obtenção de dados por sensoriamento remoto orbital é possível melhorar a previsão de safras, diminuindo cada vez mais o nível de subjetividade. Embora designadas para fins meteorológicos, as imagens AVHRR-NOAA de elevada repetitividade temporal, têm sido utilizadas para o monitoramento agrícola. Porém, a sua baixa resolução espacial faz com que possa ocorrer a mistura espectral das classes de cobertura do solo dentro de um mesmo pixel e isso pode acarretar problemas de imprecisão na estimativa de área plantada de uma cultura agrícola. O objetivo principal do trabalho foi desenvolver uma metodologia de classificação automática com a aplicação de lógica fuzzy para o reconhecimento de padrões em imagens AVHRR-NOAA, utilizando índices de vegetação para estimar a área plantada de soja no nível sub-pixel. Para oito municípios produtores de soja da região oeste do Estado do Paraná, foi possível obter a estimativa de área no final de janeiro de 2004, com antecedência em relação à época da colheita, ao contrário dos levantamentos oficiais que se estendem até o final da safra, além de utilizarem dados subjetivos vindos do campo. As estimativas de área de soja baseadas em classificação fuzzy mostraram-se altamente correlacionadas com as estimativas de área de referência obtidas a partir da máscara de soja e por expansão direta, sendo um indicativo de boa precisão. E também apresentaram alta correlação, balizadas com as estimativas oficiais da SEAB/DERAL e do IBGE. Em ambas comparações, o nível de erro relativo geral foi aceitável. O sistema desenvolvido para processamento e geração de produtos das imagens AVHRR-NOAA mostrou-se uma ferramenta fundamental de infra-estrutura, por aliar automação e precisão a metodologia do trabalho
Abstract: An early accurate estimation of agricultural crop areas, such as soybean, is fundamental for the Brazilian economy. The draining forecast and the estimation of agricultural production commercialization are strategic to Brazil, since they are directly related to planning, costs and price. Recent technological progress of data acquisition from orbital remote sensing makes possible to improve harvest forecast, reducing more and more the level of subjectivity. Although designed for meteorological aims, the AVHRR-NOAA images of high temporal resolution, have been used for the crop monitoring. However, its low spatial resolution might cause the spectral mixture of the different land cover classes within the same pixel and it can lead to accuracy problems on crop area estimation. The main objective of the work was to develop an automatic classification methodology with the application of fuzzy logic for pattern recognition in AVHRR-NOAA images, using vegetation indices to estimate the soybean crop areas at sub-pixel level. For eight soybean producer counties in the West region of the Paraná State, it was possible to obtain the crop area estimation at the end of january 2004, prior to the harvest period, on the contrary of the official surveys that extend until the end of the harvest, besides using subjective data collected on the field. The soybean crop area estimation based on fuzzy classification showed to be highly correlated with the reference area estimation obtained from the soybean mask and by direct expansion, being an indicative of good accuracy. And also presented high correlation, marked out with the official estimations from SEAB/DERAL and IBGE. In both comparisons, the level of general relative error was acceptable. The system developed for processing and products generation of AVHRR-NOAA images had proved to be a fundamental infrastructure tool, due to its capacity to combine automation and accuracy to the work methodology
Mestrado
Planejamento e Desenvolvimento Rural Sustentável
Mestre em Engenharia Agrícola

Esta dissertação apresenta um sistema de indução de classificadores fuzzy. Ao invés de utilizar a abordagem tradicional de sistemas fuzzy baseados em regras, foi utilizado o modelo de Árvore de Padrões Fuzzy(APF), que é um modelo hierárquico, com uma estrutura baseada em árvores que possuem como nós internos operadores lógicos fuzzy e as folhas são compostas pela associação de termos fuzzy com os atributos de entrada. O classificador foi obtido sintetizando uma árvore para cada classe, esta árvore será uma descrição lógica da classe o que permite analisar e interpretar como é feita a classificação. O método de aprendizado originalmente concebido para a APF foi substituído pela Programação Genética Cartesiana com o intuito de explorar melhor o espaço de busca. O classificador APF foi comparado com as Máquinas de Vetores de Suporte, K-Vizinhos mais próximos, florestas aleatórias e outros métodos Fuzzy-Genéticos em diversas bases de dados do UCI Machine Learning Repository e observou-se que o classificador APF apresenta resultados competitivos. Ele também foi comparado com o método de aprendizado original e obteve resultados comparáveis com árvores mais compactas e com um menor número de avaliações.
This work presents a system for induction of fuzzy classifiers. Instead of the traditional fuzzy based rules, it was used a model called Fuzzy Pattern Trees (FPT), which is a hierarchical tree-based model, having as internal nodes, fuzzy logical operators and the leaves are composed of a combination of fuzzy terms with the input attributes. The classifier was obtained by creating a tree for each class, this tree will be a logic class description which allows the interpretation of the results. The learning method originally designed for FPT was replaced by Cartesian Genetic Programming in order to provide a better exploration of the search space. The FPT classifier was compared against Support Vector Machines, K Nearest Neighbour, Random Forests and others Fuzzy-Genetics methods on several datasets from the UCI Machine Learning Repository and it presented competitive results. It was also compared with Fuzzy Pattern trees generated by the former learning method and presented comparable results with smaller trees and a lower number of functions evaluations.

Die vorliegende Arbeit befaßt sich mit instationären, uni- oder multivariaten Zeitreihen, die bei der Beobachtung komplexer nichtlinearer dynamischer Systeme entstehen und sich der Modellierung durch ein globales Modell entziehen. In vielen natürlichen oder gesellschaftlichen Prozessen kann man jedoch wiederkehrende Phänomene beobachten, die von deren Rhythmen beeinflußt sind; ebenso lassen sich in technischen Prozessen beispielsweise aufgrund einer bedarfsorientierten Steuerung wiederholte, aber nicht periodische Verhaltensweisen ausmachen. Für solche Systeme und Zeitreihen wird deshalb vorgeschlagen, eine partielle Modellierung durch mehrere lokale Modelle vorzunehmen, die wiederkehrende Phänomene in Form zeitlich begrenzter Muster beschreiben. Um den Unwägbarkeiten dieser und sich anschließender Aufgabenstellungen Rechnung zu tragen, werden in dieser Arbeit durchgehend unscharfe Ansätze zur Modellierung von Mustern und ihrer Weiterverarbeitung gewählt und ausgearbeitet. Die Aufgabenstellung der Erkennung von Mustern in fortlaufenden Zeitreihen wird dahingehend verallgemeinert, daß unvollständige, sich noch in Entwicklung befindliche Musterinstanzen erkannt werden können. Basierend auf ebendieser frühzeitigen Erkennung kann der Verlauf der Zeitreihe -- und damit das weitere Systemverhalten -- lokal prognostiziert werden. Auf Besonderheiten und Schwierigkeiten, die sich aus der neuartigen Aufgabe der Online-Erkennung von Mustern ergeben, wird jeweils vermittels geeigneter Beispiele eingegangen, ebenso die praktische Verwendbarkeit des musterbasierten Vorhersageprinzips anhand realer Daten dokumentiert.
This dissertation focuses on non-stationary multivariate time series stemming from the observation of complex nonlinear dynamical systems. While one global model for such systems and time series may not always be feasible, we may observe recurring phenomena (patterns) in some of these time series. These phenomena might, for example, be caused by the rhythms of natural or societal processes, or a demand-oriented control of technical processes. For such systems and time series a partial modelling by means of multiple local models is being proposed. To cope with the intrinsic uncertainties of this task, fuzzy methods and models are being used throughout this work. Means are introduced for modelling and recognition of patterns in multivariate time series. Based on a novel method for the early recognition of incomplete patterns in streaming time series, a short-time prediction becomes feasible. Peculiarities and intrinsic difficulties of an online recognition of incomplete patterns are being discussed with the help of suitable examples. The usability of the pattern-based prediction approach is being demonstrated by means of real-world data.

Die vorliegende Arbeit befaßt sich mit instationären, uni- oder multivariaten Zeitreihen, die bei der Beobachtung komplexer nichtlinearer dynamischer Systeme entstehen und sich der Modellierung durch ein globales Modell entziehen. In vielen natürlichen oder gesellschaftlichen Prozessen kann man jedoch wiederkehrende Phänomene beobachten, die von deren Rhythmen beeinflußt sind; ebenso lassen sich in technischen Prozessen beispielsweise aufgrund einer bedarfsorientierten Steuerung wiederholte, aber nicht periodische Verhaltensweisen ausmachen. Für solche Systeme und Zeitreihen wird deshalb vorgeschlagen, eine partielle Modellierung durch mehrere lokale Modelle vorzunehmen, die wiederkehrende Phänomene in Form zeitlich begrenzter Muster beschreiben. Um den Unwägbarkeiten dieser und sich anschließender Aufgabenstellungen Rechnung zu tragen, werden in dieser Arbeit durchgehend unscharfe Ansätze zur Modellierung von Mustern und ihrer Weiterverarbeitung gewählt und ausgearbeitet. Die Aufgabenstellung der Erkennung von Mustern in fortlaufenden Zeitreihen wird dahingehend verallgemeinert, daß unvollständige, sich noch in Entwicklung befindliche Musterinstanzen erkannt werden können. Basierend auf ebendieser frühzeitigen Erkennung kann der Verlauf der Zeitreihe -- und damit das weitere Systemverhalten -- lokal prognostiziert werden. Auf Besonderheiten und Schwierigkeiten, die sich aus der neuartigen Aufgabe der Online-Erkennung von Mustern ergeben, wird jeweils vermittels geeigneter Beispiele eingegangen, ebenso die praktische Verwendbarkeit des musterbasierten Vorhersageprinzips anhand realer Daten dokumentiert
This dissertation focuses on non-stationary multivariate time series stemming from the observation of complex nonlinear dynamical systems. While one global model for such systems and time series may not always be feasible, we may observe recurring phenomena (patterns) in some of these time series. These phenomena might, for example, be caused by the rhythms of natural or societal processes, or a demand-oriented control of technical processes. For such systems and time series a partial modelling by means of multiple local models is being proposed. To cope with the intrinsic uncertainties of this task, fuzzy methods and models are being used throughout this work. Means are introduced for modelling and recognition of patterns in multivariate time series. Based on a novel method for the early recognition of incomplete patterns in streaming time series, a short-time prediction becomes feasible. Peculiarities and intrinsic difficulties of an online recognition of incomplete patterns are being discussed with the help of suitable examples. The usability of the pattern-based prediction approach is being demonstrated by means of real-world data

Este trabalho apresenta os resultados obtidos com o desenvolvimento de um sistema robusto como uma alternativa de reconhecimento da qualidade de vapor de álcool combustível e do poder calorífico do gás combustível GLP em um nariz eletrônico. Foram implementadas duas metodologias experimentais para a extração de atributos dos padrões de vapor de álcool combustível e de gás GLP. Na primeira abordagem de tratamento dos dados, foram usados um Sistema de Inferência Fuzzy (FIS), e dois algoritmos de treinamento de Redes Neurais Artificiais (RNA) para reconhecer padrões de vapor de álcool combustível: a Backpropagation e Learning Vector Quantization. A segunda abordagem para o tratamento dos dados foi desenvolver um sistema reconhecedor do poder calorífico do gás GLP robusto à perda aleatória de um dos sensores. Foram usados três sistemas. No primeiro foi implementada uma RNA para reconhecer todos os dados que simulavam a falha de um sensor aleatório. O resultado desse sistema foi de 97% de acertos. O segundo implementou sete RNA’s treinadas com subconjuntos dos dados de entrada, tais que seis RNA’s foram treinadas com um sensor diferente com falha; e a sétima RNA foi treinada com dados dos sensores sem falhas. O resultado desse sistema foi de 99% de acertos. O terceiro implementou uma Máquina de Comitê Estática Ensemble constituída de dez RNA’s em paralelo para resolver o problema. O resultado foi de 97% de acertos. As RNA’s tiveram melhores respostas que os FIS. Foram sugeridas algumas formas de implementação em hardware do sistema reconhecedor em sistemas pré-fabricados com DSP’s e micro-controladores.
This work shows the results of a robust system development as an alternative to recognize the quality of an alcohol fuel vapor sample and Liquid Petrol Gas (LPG) heat power in an electric nose. Two experimental methodologies were implemented to extract the features of alcohol fuel vapor and LPG gas patterns. The first approach to process the data used an Fuzzy Inference System (FIS) and two training algorithms of Artificial Neural Networks (ANN) to recognize alcohol fuel vapor patterns: Backpropagation and Learning Vector Quantization. The second approach consists of process data to develop an LPG heat power recognizing system robust to one-random-sensor-loss. Three systems were used. The first implemented an ANN to recognize all data that simulated the failure of a random sensor. This system had 97% of right responses. The second implemented seven ANN’s trained with input data subsets, such that six ANN’s were trained with a different failure sensor, and the seventh ANN was trained with data of all sensors without failure. This system had 99% of right responses. The third implemented an Ensemble Static Learning Machine containing ten parallel RNA’s to solve the problem. The result were 97% of right responses. RNA’s had better results than FIS. Some ways of hardware implementation of the recognizing system were suggested in DSP and micro-controllers pre-built systems.

Cette thèse aborde le problème du manque de performance des outils exploitant des représentationsà base de graphes en reconnaissance des formes. Nous proposons de contribuer aux nouvellesméthodes proposant de tirer partie, à la fois, de la richesse des méthodes structurelles et de la rapidité des méthodes de reconnaissance de formes statistiques. Deux principales contributions sontprésentées dans ce manuscrit. La première correspond à la proposition d'une nouvelle méthode deprojection explicite de graphes procédant par analyse multi-facettes des graphes. Cette méthodeeffectue une caractérisation des graphes suivant différents niveaux qui correspondent, selon nous,aux point-clés des représentations à base de graphes. Il s'agit de capturer l'information portéepar un graphe au niveau global, au niveau structure et au niveau local ou élémentaire. Ces informationscapturées sont encapsulés dans un vecteur de caractéristiques numériques employantdes histogrammes flous. La méthode proposée utilise, de plus, un mécanisme d'apprentissage nonsupervisée pour adapter automatiquement ses paramètres en fonction de la base de graphes àtraiter sans nécessité de phase d'apprentissage préalable. La deuxième contribution correspondà la mise en place d'une architecture pour l'indexation de masses de graphes afin de permettre,par la suite, la recherche de sous-graphes présents dans cette base. Cette architecture utilise laméthode précédente de projection explicite de graphes appliquée sur toutes les cliques d'ordre 2pouvant être extraites des graphes présents dans la base à indexer afin de pouvoir les classifier.Cette classification permet de constituer l'index qui sert de base à la description des graphes etdonc à leur indexation en ne nécessitant aucune base d'apprentissage pré-étiquetées. La méthodeproposée est applicable à de nombreux domaines, apportant la souplesse d'un système de requêtepar l'exemple et la granularité des techniques d'extraction ciblée (focused retrieval)
This thesis addresses the problem of lack of efficient computational tools for graph based structural pattern recognition approaches and proposes to exploit computational strength of statistical pattern recognition. It has two fold contributions. The first contribution is a new method of explicit graph embedding. The proposed graph embedding method exploits multilevel analysis of graph for extracting graph level information, structural level information and elementary level information from graphs. It embeds this information into a numeric feature vector. The method employs fuzzy overlapping trapezoidal intervals for addressing the noise sensitivity of graph representations and for minimizing the information loss while mapping from continuous graph space to discrete vector space. The method has unsupervised learning abilities and is capable of automatically adapting its parameters to underlying graph dataset. The second contribution is a framework for automatic indexing of graph repositories for graph retrieval and subgraph spotting. This framework exploits explicit graph embedding for representing the cliques of order 2 by numeric feature vectors, together with classification and clustering tools for automatically indexing a graph repository. It does not require a labeled learning set and can be easily deployed to a range of application domains, offering ease of query by example (QBE) and granularity of focused retrieval

A inspeção visual automática é uma tarefa importante para a produtividade industrial. Ela pode ser aplicada em controle de qualidade para substituir operadores humanos em trabalhos perigosos ou repetitivos. O estágio de classificação em controle de qualidade da produção industrial é freqüentemente baseado no conhecimento humano. Portanto, torna-se importante alimentar um sistema visual automático com dados nebulosos ou ambíguos. Um sistema \"neuro-fuzy\" é uma forma adequada de implementar isto. O trabalho contribui na área tecnológica de inspeção visual com o desenvolvimento de uma nova abordagem para avaliação da qualidade de placas de madeira utilizadas na fabricação de lápis. Outra contribuição foi a divisão do vetor de características, fazendo com que cada característica específica seja tratada em uma rede neural própria. O método é baseado em duas redes neurais, cada uma tratando com apenas uma característica de entrada. Os resultados das redes neurais são combinados através de lógica nebulosa (\"fuzzy) fornecendo um sistema com maior poder discriminante do que aqueles que utilizam métodos tradicionais. O sistema se caracteriza por ser ágil, repetitivo, com um padrão de classificação definido e por possuir baixo custo.
Automatic visual inspection is an important task for industrial productivity. It could be applied for quality control or for replacing manual work under dangerous or repetitive activity. The classification stage in quality control of the industrial production is often based on the human knowledge. It seems, therefore, to be a great concern to supply an automated visual inspection system with fuzzy or ambiguous data. The Neuro-Fuzzy system is a good way to do this. The objective of this work is to develop a new approach for the classification of wooden plates used in the pencil production. This new method is based on two neural networks, each one working with just an input feature. The results of neural networks are combined through fuzzy logic giving the system a greater discriminating power than those that use traditional methods. The proposed method is characterized by being agile, repetitive, with a defined classification pattern and having low cost.

Les méthodes de Reconnaissance est l’ensemble des méthodes permettant de classifier des formes dans des classes. Nous avons choisi, parmi les méthodes de classifications existantes, la méthode possibiliste Fuzzy Pattern Matching (FPM) pour réaliser le diagnostic des systèmes dynamiques. FPM est simple et son temps de classification est constant et faible. De plus, elle est capable de sélectionner les sources d’informations les plus pertinentes et de traiter des données qui sont à la fois incertaines et imprécises. Cependant, FPM est une méthode de classification naïve, c'est-à-dire qu’elle classifie un nouveau point par la sélection d'une des décisions partielles. Chaque décision partielle est calculée pour chaque classe et par rapport à chaque attribut. FPM ne tient donc pas compte de la corrélation entre les attributs et considère la forme des classes comme convexe. Ces inconvénients rendent FPM inutilisable pour les applications réelles qui souvent nécessitent une discrimination non linéaire entre les classes. De plus, FPM n’est pas une méthode de classification adaptative ou prédictive. Elle ne peut pas extraire l’information manquante des points rejetés en quantifiant leur représentativité vis a vis des classes connues. Ces points, portent l’information sur l’apparition d’une nouvelle classe ou l’évolution entre deux classes. Les travaux de ce mémoire de thèse portent donc sur l’amélioration de la méthode FPM afin de remédier à ses limites. Les performances des solutions proposées sont illustrées à travers plusieurs exemples académiques et réels
The problem of diagnosis by Pattern Recognition can be posed as a problem of classification, i. E. , the actual functioning mode can be determined by knowing the class of the actual pattern. We use the method Fuzzy Pattern Matching (FPM) to realize the diagnosis because it is a simple method based on a feature selection. In addition it has a small and constant classification time, and it takes into account both the imprecision and uncertainty. However FPM is marginal, i. E. , its global decision is based on the selection of one of the intermediate decisions. Each intermediate decision is based on one attribute. Thus, FPM does not take into account the correlation between attributes. Additionally, FPM considers the shape of classes as convex one. Also, FPM cannot realize the adaptive and predictive diagnosis because it rejects all the points which carry the information about the class evolution or the creation of a new class. These drawbacks make FPM unusable for many real world applications. In this thesis, we propose to improve FPM to solve these drawbacks. Several synthetic and real data sets are used to show the performances of the improved FPM with respect to classical one

Die vorliegende Diplomarbeit befasst sich mit dem diagnostischen Schließen bei Widersprüchen. Diagnostisches Schließen wird in Anlehnung an Johnson und Krems (2001) als sequentieller Verstehensprozess aufgefasst, bei dem ein Situationsmodell aufgebaut wird, dass die Problemlösung enthält. Es wurde eine Experimentalaufgabe aus dem Bereich der medizinischen Diagnostik konstruiert, bei der sequentiell Krankheitssymptome präsentiert wurden. Die studentischen Versuchspersonen sollten auf mögliche Ursachen schließen und eine Diagnose stellen. Erfasst wurden die Häufigkeit richtiger Lösungen sowie Ratingwerte einer Plausibilitätseinschätzung über den gesamten Schlussfolgerungsprozess hinweg. Neben widerspruchsfreien Versuchsdurchgängen wurden zwei Arten von Widersprüchen erzeugt. Zum einen Widersprüche, in denen ein Wechsel zwischen verschiedenen Klassen von Hypothesen notwendig ist, und zum anderen Widersprüche, bei denen zwischen Einzelhypothesen der gleichen Hypothesenklasse gewechselt werden soll. Erwartet wurde, dass Widersprüche schwerer zu lösen sein sollten als widerspruchsfreie Durchgänge und dass sich der Prozess des Schließens je nach Widerspruchsart unterschiedlich gestaltet. Im Vergleich der Durchgänge ließen sich die Hypothesen bestätigen. Bezüglich der beiden Widerspruchsarten wurde davon ausgegangen, dass ein Wechsel zwischen Hypothesenklassen schwieriger sein sollte, als wenn Einzelhypothesen innerhalb der gleichen Klasse geändert werden müssen. Dies konnte in der experimentellen Untersuchung nicht bestätigt werden, sondern hat sich eher gegenteilig gezeigt. Mit Hilfe des Verfahrens der Fuzzy Pattern Klassifikation, dass insbesondere die Unsicherheit und Unschärfe von Daten berücksichtigt, wurden die Ratings ausgewertet. Die Modellierung des Schlussfolgerungsprozesses speziell für die Fälle von Widersprüchen verdeutlicht die Ergebnisse.

This article deals with the recognition of recurring multivariate time series patterns modelled sample-point-wise by parametric fuzzy sets. An efficient classification-based approach for the online recognition of incompleted developing patterns in streaming time series is being presented. Furthermore, means are introduced to enable users of the recognition system to restrict results to certain stages of a pattern’s development, e. g. for forecasting purposes, all in a consistently fuzzy manner.

This article proposes knowledge-based short-time prediction methods for multivariate streaming time series, relying on the early recognition of local patterns. A parametric, well-interpretable model for such patterns is presented, along with an online, classification-based recognition procedure. Subsequently, two options are discussed to predict time series employing the fuzzified pattern knowledge, accompanied by an example. Special emphasis is placed on comprehensible models and methods, as well as an easy interface to data mining algorithms.

Reducing unscheduled maintenance is important for aircraft operators. There are significant costs if flights must be delayed or cancelled, for example, if spares are not available and have to be shipped across the world. This thesis describes three methods of aircraft health condition monitoring and prediction; one for system monitoring, one for forecasting and one combining the two other methods for a complete monitoring and prediction process. Together, the three methods allow organizations to forecast possible failures. The first two use decision trees for decision-making and genetic optimization to improve the performance of the decision trees and to reduce the need for human interaction. Decision trees have several advantages: the generated code is quickly and easily processed, it can be altered by human experts without much work, it is readable by humans, and it requires few resources for learning and evaluation. The readability and the ability to modify the results are especially important; special knowledge can be gained and errors produced by the automated code generation can be removed. A large number of data sets is needed for meaningful predictions. This thesis uses two data sources: first, data from existing aircraft sensors, and second, sound and vibration data from additionally installed sensors. It draws on methods from the field of big data and machine learning to analyse and prepare the data sets for the prediction process.

Il est appelé que le sylogisme indirect n'est pas parfait quelque soit l'opérateur de composition floue. Un opérateur de maximalisation (ou de minimalisation) est déterminé pour la composition sup-T norme (ou INF-T conorme). Après la reprise des résultats des mesures d'incertain il est donné une application numérique au problème de classification des phonèmes

Problemi klasifikacije i segmentacije digitalnih slika su veomaaktuelni i zastupljeni u praksi. Potreba za modelima koji razmatrajuovu problematiku u poslednjih nekoliko decenija ubrzanim tempompoprima sve veći značaj i obim u svakodnevnom životu. Koriste se uračunarskoj grafici, prepoznavanju oblika, medicinskoj analizi slika,saobraćaju, analizi dokumenata, pokreta i izraza lica i sl.U okviru ove disertacije, predstavljeno istraživanje motivisano jeprimenama razvijenih modela u klasifikaciji i segmentacijidigitalnih slika. Istraživanje obuhvata dva segmenta. Ovi segmentipovezani su terminom neodređenosti, koji je uz upotrebu adekvatnogmatematičkog aparata (teorije fazi skupova), ugrađen u modele razvijeza primenu u obradi slike.Jedan pravac istraživanja baziran je na teoriji fazi skupova, t-normama, t-konormama, operatorima agregacije i agregiranimfunkcijama rastojanja. U okviru toga, istraživanje je sprovedeno sastruktuiranom matematičkom podlogom, izložene su osnovnedefinicije, teoreme, kao i osobine korištenih operatora, proširenisu teorijski koncepti t-normi i t-konormi. Definisani su novi tipovioperatora agregacije i njihovom primenom konstruisane su novefunkcije rastojanja, čija je upotreba diskutovana kroz uspešnost uprocesu segmentacije digitalnih slika.Drugi pravac istraživanja, izložen u ovoj disertaciji, obuhvata višeinženjerski pristup rešavanju problema klasifikacije teksturadigitalnih slika. U skladu sa tim, detaljno je analizirana idiskutovana klasa lokalnih binarnih deskriptora teksture.Inspirisana uspešnošću pomenute LBP klase deskriptora, uvedena jejedna nova podfamilija α-deskriptora teksture. Uvedeni modeldeskriptora formiran je na temeljima idejnih principa lokalnihbinarnih kodova i bazičnih pojmova iz teorije fazi skupova. Praktičnaupotreba i značaj predstavljenog modela demonstrirani su kroz veomauspešne procese klasifikacije na nekoliko javno dostupnih baza slika.
Classification and segmentation problems of digital images is a very attractivetopic and has been making impact in many different applied disciplines. In thepast few decades, the demand for models that address these issues has beengaining momentum and applications in everyday life. These models are used incomputer graphics, shape recognition, medical image analysis, traffic, documentanalysis, facial movements and expressions, etc.The research within this doctoral dissertation was motivated by the application ofdeveloped methods in classification and segmentation tasks. The conductedresearch covered two segments, which were linked by the term of indeterminacy,with the usage of the theory of fuzzy sets, which is incorporated into methodsdeveloped for application in image processing.One direction of the research was founded on the theory of fuzzy sets, t-norms,t-conorms, aggregation operators, and aggregated distance functions. Within thisframework, the research was conducted with a structured mathematicalbackground. Firstly, basic definitions, theorems and characteristics of the usedoperators were presented, followed by the theoretical concepts of t-norms and tconormsthat were extended. New types of aggregation operators and distancefunctions were defined, and finally, their contribution in the digital imagesegmentation process was explored and discussed.The second direction of the research presented in this dissertation involved moreof an engineering-type of approach to solving the problem of the classification ofdigital image textures. To that end, a class of local binary texture descriptors(LBPs) was analyzed and discussed in detail. Inspired by the results of theabove-mentioned LBP descriptors, one new sub-family of the $\alpha$-descriptors was introduced by the author. The introduced descriptor model wasbased on the conceptual principles of LBPs and basic definitions from the fuzzyset theory. Its practical usage and importance were established and reflected invery successful classification results, achieved in the application on severalpublicly available image datasets.

L'avènement des réseaux sociaux a suscité un intérêt considérable pour la société au cours de notre décennie. Ces plateformes permettent aux utilisateurs de produire, partager et échanger des contenus divers. Twitter est l'un des réseaux sociaux les plus populaires permettant à ses utilisateurs de publier des messages, appelés tweets. Ces derniers peuvent contenir des textes offensifs, tels que les messages de harcèlement, ou encore des informations liées à des sujets controversés. De nombreux travaux de recherche ont montré comment ces contenus sociaux peuvent avoir une influence sur les utilisateurs. Un système de détection de ce type de messages est nécessaire afin de protéger l'utilisateur et prédire l'apparition des évènements. Dans ce travail de thèse, nous proposons un système de détection de tweets suspects basé sur les modèles thématiques probabilistes et la logique floue. Afin d'identifier les tweets de harcèlement, nous introduisons un modèle de classification exploitant un ensemble de caractéristiques et utilisant des algorithmes d'apprentissage supervisé. Les utilisateurs effectuent également des recherches sur ces plateformes pour trouver des informations qui répondent à un besoin exprimé par une requête. Cependant, les tweets sont courts et l'accès à l'information est parfois difficile. Une partie de nos travaux se situe plus particulièrement dans le contexte de la recherche d'information sociale et vise à améliorer la recherche de tweets. Nous proposons une méthode d'expansion de requêtes, afin de pallier le problème de concision des messages ainsi que des requêtes, basée sur l’extraction des motifs fermés fréquents et utilisant des plongements lexicaux
Social networks have gained a significant interest for society during our decade. These platforms allow users to produce, share and exchange various content. Twitter is one of the most popular social networks that allow users to publish messages, called tweets. These tweets may contain offensive texts, such as harassment or bullying messages, or information related to abnormal topics. Many research studies have shown how such social content can have an impact on users and cause psychological harm. Developing a system for detecting such type of messages is necessary to protect the user and predict tragic events. The work presented in this thesis is brought into the context of data mining from Twitter to identify and detect such messages. We propose a suspicious tweets detection system based on probabilistic topic models and fuzzy logic. In order to identify harassment tweets, we introduce a classification model that exploits a set of features and uses supervised learning algorithms. People also use social networks to search for relevant posts that satisfy their information need where this need is usually formulated using a textual query. Twitter’s messages are short and access to information is sometimes difficult because of the variety of published content and huge amount of data generated. The second part of this work deals with the context of social information retrieval and aims to improve tweets retrieval quality. We propose a query expansion approach to overcome the shortness of user queries and tweets by extracting frequent closed patterns and using word embeddings

碩士
國立臺灣科技大學
電機工程研究所
82
This paper proposes a new approach for object recognition systems(ORS) which have the capability of knowing the candidates by themselves. The idea of this paper is to implement a low-cost ORS that can distinguish objects among a finite number of candidates. Usually, in order to achieve better performance, an ORS must be equipped with a high resolution image processor and use complicate feature extraction techniques to obtain more information from the images. As a result, the cost of the system is very expensive. Therefore, in our research, we intend to use low-cost image processing system to process image and use learning techniques to build up the characteristics of candidates and the rules of recognition so that the system only needs to distinguish the images among those candidates. We implement the concept of "different objects will have different characteristics" as the main theme in classifying unknown objects. Our system consists of two subsystems: a classifier and a recogniter. In the classifier, an adaptive fuzzy classifier is proposed. Such a classifier adopts the fuzzy concept in the clustering of unknown objects. Our approach not only can learn fast, but also has stable adaptive classification. In the recogniter, fuzzy reasoning rules are built from the results of the adaptive fuzzy classifier to recognize objects from different views. Finally, several candidates and objects are tested in our system. The result has shown the effectness of our approach.

Thesis (Ph. D.)--Florida State University, 2006.
Advisor: Ladislav J. Kohout, Florida State University, College of Arts and Sciences, Dept. of Computer Science. Title and description from dissertation home page (viewed Sept. 20, 2006). Document formatted into pages; contains xiii, 103 pages. Includes bibliographical references.

碩士
國立臺灣科技大學
電機工程系
100
ABSTRACT Partial discharge (PD) measurement is among the most important diagnostics methods of insulation systems in high voltage equipment, which makes it convenient to assess the insulation status. Partial discharge activities may stem from various defects, and correspondingly behave differently. Here, the PD patterns produced by 3 different laboratory models representing defects in GIS are recorded and analyzed. The research aimed at conducting PD tests with three GIS apparatus including prefabricated defects. From the PD pattern data, statistical features were extracted and these features were reduced by linear discriminant Analysis (LDA). Adaptive neuro-fuzzy inference system (ANFIS) was used to train the fuzzy inference system (FIS). The trained FIS was then used to recognize the source of the PDs. Results show that ANFIS classification has a high success rate and highest average success rate at 38kV reaches 95.83%.

博士
國立臺灣科技大學
電機工程系
92
To construct an unsupervised fuzzy pattern classification system, generating standard patterns from a given set of sample data for classification is an important first step. Then, an accurate system model and discrimination function for efficient pattern classification can therefore be defined based on the generated standard patterns. Recent researches have proposed methods of automatic construction of such systems, drawbacks, however, have been encountered through conducting these researches such as not considering the characteristic of data distribution, the constructed model is too complex to understand and its associated computation cost is too high to become practical. This research intends to improve the drawbacks and propose a fuzzy pattern classification approach based on fuzzy clustering, the structure of data distribution and the features of data to improve the performance of fuzzy pattern classification. To extract standard patterns from a set of sample data, this research proposes an approach that can appropriately cluster a given data set automatically based on both the data distribution and the distance gaps existing among the data points of a given data set without the need of specifying the number of resultant clusters and setting up subjective parameters. On the other hand, statistical concept is also applied to define weights of pattern features (dimensions) in the training data set so that the weight of a pattern feature is proportional to the contribution the feature can provide to the task of pattern classification. The proposed weight definition not only reduces the dimensionality of feature space so as to speed up the classification process but also increases the accuracy rate of classification result.

碩士
義守大學
資訊工程學系
104
In this study, we propose an image texture extraction and classification approach based on local fuzzy pattern (LFP). LFP is originally proposed by Ouyang et al. [26] and is a kind of texture descriptor for moving object detection. It is mainly extended from the well-known local binary pattern (LBP) by replacing the binary logic-based coding with fuzzy logic-based coding, and therefore, possesses the more robustness. In this study, we employ the LFP texture descriptor to extract texture features from texture images and apply five classifiers to obtain the classification results. Besides, a comparison between our approach with other texture descriptors, including local binary pattern, local ternary pattern, and local N-ary pattern, is also made by performing experiments on four datasets of texture images. Experimental results have shown that our approach can produce the higher classification accuracy than other approaches.