771 resultados para Rutschungen, Massenbewegungen, Gefahrenanalyse, Risikoanalyse, Fuzzy-Logik
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Mode of access: Internet.
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Mode of access: Internet.
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Mode of access: Internet.
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Mode of access: Internet.
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Thesis (doctoral)--Universitat Bern.
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Thesis (doctoral)--Universitat Bern.
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Thesis (doctoral)--Universitat Leipzig.
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Thesis (doctoral)--Vereinigte Friedrichs-Universitat, Halle-Wittenberg.
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Mode of access: Internet.
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This paper proposes a novel application of fuzzy logic to web data mining for two basic problems of a website: popularity and satisfaction. Popularity means that people will visit the website while satisfaction refers to the usefulness of the site. We will illustrate that the popularity of a website is a fuzzy logic problem. It is an important characteristic of a website in order to survive in Internet commerce. The satisfaction of a website is also a fuzzy logic problem that represents the degree of success in the application of information technology to the business. We propose a framework of fuzzy logic for the representation of these two problems based on web data mining techniques to fuzzify the attributes of a website.
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THE STORY OF HOW FEATHERS EVOLVED IS FAR FROM OVER. IN 1868, THOMAS HUXLEY declared that dinosaurs gave rise to birds. He based his claim on Compsognathus, a 150-million-year-old dinosaur fossil from Solnhofen, Germany, whose delicate hind legs were remarkably similar to those of table fowl. The discovery seven years earlier of Archaeopteryx, a fossil bird with a long bony tail, toothed jaws and clawed fingers, had convinced many people that birds were somehow related to reptiles. But Compsognathus was the fossil that placed dinosaurs firmly in the middle of this complex evolutionary equation. Wings, claimed Huxley, must have grown out of rudimentary forelimbs. And feathers? Whether Compsognathus had them, Huxley could only guess. Nevertheless, his theory clearly required that scales had somehow transformed into feathers. The question was not just how, but why?
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The integration of geo-information from multiple sources and of diverse nature in developing mineral favourability indexes (MFIs) is a well-known problem in mineral exploration and mineral resource assessment. Fuzzy set theory provides a convenient framework to combine and analyse qualitative and quantitative data independently of their source or characteristics. A novel, data-driven formulation for calculating MFIs based on fuzzy analysis is developed in this paper. Different geo-variables are considered fuzzy sets and their appropriate membership functions are defined and modelled. A new weighted average-type aggregation operator is then introduced to generate a new fuzzy set representing mineral favourability. The membership grades of the new fuzzy set are considered as the MFI. The weights for the aggregation operation combine the individual membership functions of the geo-variables, and are derived using information from training areas and L, regression. The technique is demonstrated in a case study of skarn tin deposits and is used to integrate geological, geochemical and magnetic data. The study area covers a total of 22.5 km(2) and is divided into 349 cells, which include nine control cells. Nine geo-variables are considered in this study. Depending on the nature of the various geo-variables, four different types of membership functions are used to model the fuzzy membership of the geo-variables involved. (C) 2002 Elsevier Science Ltd. All rights reserved.
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Fault diagnosis has become an important component in intelligent systems, such as intelligent control systems and intelligent eLearning systems. Reiter's diagnosis theory, described by first-order sentences, has been attracting much attention in this field. However, descriptions and observations of most real-world situations are related to fuzziness because of the incompleteness and the uncertainty of knowledge, e. g., the fault diagnosis of student behaviors in the eLearning processes. In this paper, an extension of Reiter's consistency-based diagnosis methodology, Fuzzy Diagnosis, has been proposed, which is able to deal with incomplete or fuzzy knowledge. A number of important properties of the Fuzzy diagnoses schemes have also been established. The computing of fuzzy diagnoses is mapped to solving a system of inequalities. Some special cases, abstracted from real-world situations, have been discussed. In particular, the fuzzy diagnosis problem, in which fuzzy observations are represented by clause-style fuzzy theories, has been presented and its solving method has also been given. A student fault diagnostic problem abstracted from a simplified real-world eLearning case is described to demonstrate the application of our diagnostic framework.