88 research outputs found
Aide à la décision pour l'expertise des barrages
National audienceUn modèle d'évaluation des performances des barrages vis-à-vis de leurs principaux modes de rupture et de dégradation a été développé (Curt, 2008). Les données d'entrée (indicateurs) et de sortie (performance du barrage) de ce modèle sont des distributions de possibilité. Cette communication est axée sur la problématique de la prise de décision associée à ce résultat possibiliste : comment prioriser les actions de maintenance à entreprendre sur le barrage et comment transmettre l'information aux gestionnaires ? Nous proposons une analyse comparative des méthodes de défuzzification afin de sélectionner les méthodes répondant le mieux à cette problématique d'aide à l'expertise des barrages. / An assessment model of dam performances as regards their main failure modes and degradation modes was developed (Curt, 2008). The input data (indicators) and the ouput data (dam performance) of this model are possibility distributions. This paper focuses on the problematic of decision making associated to this possibility result: how to classify maintenance actions that have to be made on that dam and how to convey this result to dam managers? A comparative analysis of defuzzyfication methods is provided; those methods allow to best answer to this problematic of dam expertise
Defuzzification of groups of fuzzy numbers using data envelopment analysis
Defuzzification is a critical process in the implementation of fuzzy systems that converts fuzzy numbers to crisp representations. Few researchers have focused on cases where the crisp outputs must satisfy a set of relationships dictated in the
original crisp data. This phenomenon indicates that these crisp outputs are mathematically dependent on one another. Furthermore, these fuzzy numbers may
exist as a group of fuzzy numbers. Therefore, the primary aim of this thesis is to develop a method to defuzzify groups of fuzzy numbers based on Charnes, Cooper, and Rhodes (CCR)-Data Envelopment Analysis (DEA) model by modifying the Center of Gravity (COG) method as the objective function. The constraints represent the relationships and some additional restrictions on the allowable crisp outputs with their dependency property. This leads to the creation of crisp values with preserved
relationships and/or properties as in the original crisp data. Comparing with Linear Programming (LP) based model, the proposed CCR-DEA model is more efficient, and also able to defuzzify non-linear fuzzy numbers with accurate solutions. Moreover, the crisp outputs obtained by the proposed method are the nearest points to the fuzzy numbers in case of crisp independent outputs, and best nearest points to the fuzzy numbers in case of dependent crisp outputs. As a conclusion, the proposed
CCR-DEA defuzzification method can create either dependent crisp outputs with preserved relationship or independent crisp outputs without any relationship. Besides, the proposed method is a general method to defuzzify groups or individuals
fuzzy numbers under the assumption of convexity with linear and non-linear membership functions or relationships
Fuzzy Controllers
Trying to meet the requirements in the field, present book treats different fuzzy control architectures both in terms of the theoretical design and in terms of comparative validation studies in various applications, numerically simulated or experimentally developed. Through the subject matter and through the inter and multidisciplinary content, this book is addressed mainly to the researchers, doctoral students and students interested in developing new applications of intelligent control, but also to the people who want to become familiar with the control concepts based on fuzzy techniques. Bibliographic resources used to perform the work includes books and articles of present interest in the field, published in prestigious journals and publishing houses, and websites dedicated to various applications of fuzzy control. Its structure and the presented studies include the book in the category of those who make a direct connection between theoretical developments and practical applications, thereby constituting a real support for the specialists in artificial intelligence, modelling and control fields
Joint inversion of magnetotelluric and seismic data for crustal characterization
Tese de doutoramento, Ciências Geofísicas e da Geoinformação (Geofisíca), Universidade de Lisboa, Faculdade de Ciências, 2014The research that has been performed in this thesis contributes to integration two different geophysical methods (magnetotellurics and seismic refraction). Pattern recognition has not been used for the cooperative inversion of these two methods before. This study demonstrates potential of soft clustering methods for integration of two data sets. Gathering information from both inversions, a multiparameter model was made. This multi-parameter model contains regions, each of which is characterized by a consistent relationship between the model parameters. Both synthetic and field data results were used to discuss an assumption that mutual exchange of information can lead to better results of inversions in both cases. The method developed in this thesis was successfully applied at experimental data collected in the Iberian Pyrite Belt.A investigação desenvolvida nesta tese é uma contribuição para a integração de dois métodos geofísicos (magneto-telúrico e sísmica de refracção). A técnica de reconhecimento de padrões não tinha sido ainda utilizada na inversão cooperativa de dados destes dois métodos. Este estudo mostra as potencialidades dos métodos de análise de “clusters” na integração dos dois conjuntos de dados.Um modelo “multi-parâmetros” foi desenvolvido para extrair informação de ambos os métodos geofísicos.Este modelo contém regiões caracterizadas por relações consistentes entre os parâmetros. Resultados obtidos com dados sintéticos e de campo foram usados para mostrar que o uso da informação contida nos dois conjuntos de dados conduz a melhores resultados. O método desenvolvido nesta tese foi aplicado, com resultados satisfatórios, a um conjunto de dados reais obtidos na Faixa Piritosa.Fundação para a Ciência e Tecnologia (FCT, SFRH/BD/66455/2009
Study and Development of Some Novel Image Segmentation Techniques
Some fuzzy technique based segmentation methods are studied and implemented and some fuzzy c means clustering based segmentation algorithms are developed in this thesis to suppress high and low uniform random noise. The reason for not developing fuzzy rule based segmentation method is that they are application dependent
In many occasions, the images in real life are affected with noise. Fuzzy c means clustering based segmentation does not give good segmentation result under such condition. Various extension of the FCM method for segmentation are present in the literature. But most of them modify the objective function hence changing the basic FCM algorithm present in MATLAB toolboxes. Hence efforts have been made to develop FCM algorithm without modifying their objective function for better segmentation .
The fuzzy technique based segmentation methods that are studied and developed are summarized here.
(A) Fuzzy edge detection based segmentation: Two fuzzy edge detection methods are studied and implemented for segmentation: (i) FIS based edge detection and (ii) Fast multilevel fuzzy edge detector (FMFED).
(i): The Fuzzy Inference system (FIS) based edge detector consists of some fuzzy inference rules which are defined in such a way that the FIS system output (“edges”) is high only for those pixels belonging to edges in the input image. A robustness to contrast and lightining variations were also taken into consideration while developing these rules.The output of the FIS based edge detector is then compared with the existing Sobel, LoG and Canny edge detector results. The algorithm is seen to be application dependent and time consuming.
(ii) Fast Multilevel Fuzzy Edge Detector: To realise the fast and accurate detection of edges, the FMFED algorithm is proposed. It first enhances the image contrast by means of a fast multilevel fuzzy enhancement algorithm using simple transformation function based on two image thresholds. Second, the edges are extracted from the enhanced image by using a two stage edge detector operator that identifies the edge candidates based on local characteristics of the image and then determines the true edge pixels using edge detector operator based on extremum of the gradient values.
Finally the segmentation of the edge image is done by morphological operator by edge linking.
(B) FCM based segmentation: Two fuzzy clustering based segmentation methods are developed: (i) Modified Spatial Fuzzy c-Means (MSFCM) (ii) Neighbourhood Attraction Fuzzy c-Means (NAFCM). .
(i) Contrast-Limited Adaptive Histogram Equalization Fuzzy c-Means (CLAHEFCM): This proposed algorithm presents a color segmentation process for low contrast images or unevenly illuminated images. The algorithm presented in this paper first enhances the contrast of the image by using contrast limited adaptive histogram equalization. After the enhancement of the image this method divides the color space into a given number of clusters, the number of cluster are fixed initially. The image is converted from RGB color space to LAB color space before the clustering process. Clustering is done here by using Fuzzy c means algorithm. The image is segmented based on color of a region, that is, areas having same color are grouped together. The image segmentation is done by taking into consideration, to which cluster a given pixel belongs the most. The method has been applied on a number of color test images and it is observed to give good segmentation results
(ii) Modified Spatial Fuzzy c-means (MSFCM): The proposed algorithm divides the color space into a given number of clusters, the number of cluster are fixed initially. The image is converted from RGB color space to LAB color space before the clustering process. A robust segmentation technique based on extension to the traditional fuzzy c-means (FCM) clustering algorithm is proposed. The spatial information of each pixel in an image has been taken into consideration to get a noise free segmentation result. The image is segmented based on color of a region, that is, areas having same color are grouped together. The image segmentation is done by taking into consideration, to which cluster a given pixel belongs the most. The method has been applied to some color test images and its performance has been compared to FCM and FCM based methods to show its superiority over them. The proposed technique is observed to be an efficient and easy method for segmentation of noisy images.
(iv) Neighbourhood Attraction Fuzzy c Means Algorithm: A new algorithm based on the IFCM neighbourhood attraction is used without changing the distance function of the FCM and hence avoiding an extra neural network optimization step for the adjusting parameters of the distance function, it is called Neighborhood Atrraction FCM (NAFCM). During clustering, each pixel attempts to attract its neighbouring pixels towards its own cluster. This neighbourhood attraction depends on two factors: the pixel intensities or feature attraction, and the spatial position of the neighbours or distance attraction, which also depends on neighbourhood structure. The NAFCM algorithm is tested on a synthetic image (chapter 6, figure 6.3-6.6) and a number of skin tumor images. It is observed to produce excellent clustering result under high noise condition when compared with the other FCM based clustering methods
Fuzzy Logic
The capability of Fuzzy Logic in the development of emerging technologies is introduced in this book. The book consists of sixteen chapters showing various applications in the field of Bioinformatics, Health, Security, Communications, Transportations, Financial Management, Energy and Environment Systems. This book is a major reference source for all those concerned with applied intelligent systems. The intended readers are researchers, engineers, medical practitioners, and graduate students interested in fuzzy logic systems
Object Tracking
Object tracking consists in estimation of trajectory of moving objects in the sequence of images. Automation of the computer object tracking is a difficult task. Dynamics of multiple parameters changes representing features and motion of the objects, and temporary partial or full occlusion of the tracked objects have to be considered. This monograph presents the development of object tracking algorithms, methods and systems. Both, state of the art of object tracking methods and also the new trends in research are described in this book. Fourteen chapters are split into two sections. Section 1 presents new theoretical ideas whereas Section 2 presents real-life applications. Despite the variety of topics contained in this monograph it constitutes a consisted knowledge in the field of computer object tracking. The intention of editor was to follow up the very quick progress in the developing of methods as well as extension of the application
Evaluation of optimal solutions in multicriteria models for intelligent decision support
La memoria se enmarca dentro de la optimización y su uso para la toma de decisiones. La secuencia lógica ha sido la modelación, implementación, resolución y validación que conducen a una decisión. Para esto, hemos utilizado herramientas del análisis multicrerio, optimización multiobjetivo y técnicas de inteligencia artificial.
El trabajo se ha estructurado en dos partes (divididas en tres capítulos cada una) que se corresponden con la parte teórica y con la parte experimental. En la primera parte se analiza el contexto del campo de estudio con un análisis del marco histórico y posteriormente se dedica un capítulo a la optimización multicriterio en el se recogen modelos conocidos, junto con aportaciones originales de este trabajo. En el tercer capítulo, dedicado a la inteligencia artificial, se presentan los fundamentos del aprendizaje estadístico , las técnicas de aprendizaje automático y de aprendizaje profundo necesarias para las aportaciones en la segunda parte.
La segunda parte contiene siete casos reales a los que se han aplicado las técnicas descritas. En el primer capítulo se estudian dos casos: el rendimiento académico de los estudiantes de la Universidad Industrial de Santander (Colombia) y un sistema objetivo para la asignación del premio MVP en la NBA. En el siguiente capítulo se utilizan técnicas de inteligencia artificial a la similitud musical (detección de plagios en Youtube), la predicción del precio de cierre de una empresa en el mercado bursátil de Nueva York y la clasificación automática de señales espaciales acústicas en entornos envolventes. En el último capítulo a la potencia de la inteligencia artificial se le incorporan técnicas de análisis multicriterio para detectar el fracaso escolar universitario de manera precoz (en la Universidad Industrial de Santander) y, para establecer un ranking de modelos de inteligencia artificial de se recurre a métodos multicriterio.
Para acabar la memoria, a pesar de que cada capítulo contiene una conclusión parcial, en el capítulo 8 se recogen las principales conclusiones de toda la memoria y una bibliografía bastante exhaustiva de los temas tratados. Además, el trabajo concluye con tres apéndices que contienen los programas y herramientas, que a pesar de ser útiles para la comprensión de la memoria, se ha preferido poner por separado para que los capítulos resulten más fluidos
Transformation of graphical models to support knowledge transfer
Menschliche Experten verfügen über die Fähigkeit, ihr Entscheidungsverhalten flexibel auf die jeweilige Situation abzustimmen. Diese Fähigkeit zahlt sich insbesondere dann aus, wenn Entscheidungen unter beschränkten Ressourcen wie Zeitrestriktionen getroffen werden müssen. In solchen Situationen ist es besonders vorteilhaft, die Repräsentation des zugrunde liegenden Wissens anpassen und Entscheidungsmodelle auf unterschiedlichen Abstraktionsebenen verwenden zu können. Weiterhin zeichnen sich menschliche Experten durch die Fähigkeit aus, neben unsicheren Informationen auch unscharfe Wahrnehmungen in die Entscheidungsfindung einzubeziehen.
Klassische entscheidungstheoretische Modelle basieren auf dem Konzept der Rationalität, wobei in jeder Situation die nutzenmaximale Entscheidung einer Entscheidungsfunktion zugeordnet wird. Neuere graphbasierte Modelle wie Bayes\u27sche Netze oder Entscheidungsnetze machen entscheidungstheoretische Methoden unter dem Aspekt der Modellbildung interessant. Als Hauptnachteil lässt sich die Komplexität nennen, wobei Inferenz in Entscheidungsnetzen NP-hart ist. Zielsetzung dieser Dissertation ist die Transformation entscheidungstheoretischer Modelle in Fuzzy-Regelbasen als Zielsprache. Fuzzy-Regelbasen lassen sich effizient auswerten, eignen sich zur Approximation nichtlinearer funktionaler Beziehungen und garantieren die Interpretierbarkeit des resultierenden Handlungsmodells. Die Übersetzung eines Entscheidungsmodells in eine Fuzzy-Regelbasis wird durch einen neuen Transformationsprozess unterstützt.
Ein Agent kann zunächst ein Bayes\u27sches Netz durch Anwendung eines in dieser Arbeit neu vorgestellten parametrisierten Strukturlernalgorithmus generieren lassen. Anschließend lässt sich durch Anwendung von Präferenzlernverfahren und durch Präzisierung der Wahrscheinlichkeitsinformation ein entscheidungstheoretisches Modell erstellen. Ein Transformationsalgorithmus kompiliert daraus eine Regelbasis, wobei ein Approximationsmaß den erwarteten Nutzenverlust als Gütekriterium berechnet. Anhand eines Beispiels zur Zustandsüberwachung einer Rotationsspindel wird die Praxistauglichkeit des Konzeptes gezeigt.Human experts are able to flexible adjust their decision behaviour with regard to the respective situation. This capability pays in situations under limited resources like time restrictions. It is particularly advantageous to adapt the underlying knowledge representation and to make use of decision models at different levels of abstraction. Furthermore human experts have the ability to include uncertain information and vague perceptions in decision making.
Classical decision-theoretic models are based directly on the concept of rationality, whereby the decision behaviour prescribed by the principle of maximum expected utility. For each observation some optimal decision function prescribes an action that maximizes expected utility. Modern graph-based methods like Bayesian networks or influence diagrams make use of modelling. One disadvantage of decision-theoretic methods concerns the issue of complexity. Finding an optimal decision might become very expensive. Inference in decision networks is known to be NP-hard. This dissertation aimed at combining the advantages of decision-theoretic models with rule-based systems by transforming a decision-theoretic model into a fuzzy rule-based system. Fuzzy rule bases are an efficient implementation from a computational point of view, they can approximate non-linear functional dependencies and they are also intelligible. There was a need for establishing a new transformation process to generate rule-based representations from decision models, which provide an efficient implementation architecture and represent knowledge in an explicit, intelligible way. At first, an agent can apply the new parameterized structure learning algorithm to identify the structure of the Bayesian network. The use of learning approaches to determine preferences and the specification of probability information subsequently enables to model decision and utility nodes and to generate a consolidated decision-theoretic model. Hence, a transformation process compiled a rule base by measuring the utility loss as approximation measure. The transformation process concept has been successfully applied to the problem of representing condition monitoring results for a rotation spindle
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