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Content available remote The First Step Toward Processor for Rough Set Methods
EN
In this paper we propose a combination of capabilities of the FPGA based device and PC computer for data processing using rough set methods. Presented architecture has been tested on the exemplary data sets. Obtained results confirm the significant acceleration of the computation time using hardware supporting rough set operations in comparison to software implementation.
EN
In the paper, we focus on ant-based clustering time series data represented by means of the so-called delta episode information systems. A clustering process is made on the basis of delta representation of time series, i.e., we are interested in characters of changes between two consecutive data points in time series instead of original data points. Most algorithms use similarity measures to compare time series. In the paper, we propose to use a measure based on temporal rough set flow graphs. This measure has a probabilistic character and it is considered in terms of the Decision Theoretic Rough Set (DTRS) model. To perform ant-based clustering, the algorithm based on the versions proposed by J. Deneubourg, E. Lumer and B. Faieta as well as J. Handl et al. is used.
PL
W artykule przedstawiono propozycję projektową komponentu wyszukiwania systemu wnioskowania na podstawie przypadków zdarzeń (ang. case based reasoning). Komponent ten bazuje na opracowanej ontologii dziedzinowej wspierającej proces wyszukiwania przypadków zdarzeń. Opracowana ontologia jest wynikiem m.in. przeprowadzonej przez autora analizy dokumentacji opisujących akcje ratowniczo-gaśnicze.
EN
This paper describes problems of designing the search module of case based reasoning system. In the first part of this article the author describes a review of solutions available in fire service such as decision support system with implements reasoning solution – case based reasoning CBR. Second part of this article describes a search component of this CBR system. The author proposes on ontology layer to support the searching process of case in this module. This ontology layer is a result of the conducted, by the author's analysis of the documentation describing the rescue actions. In the last section the author summarizes proposed project of this component and presents, a new developed way to construct, refactorize and extend the proposed ontology.
4
Content available A survey of methods for 3D model feature extraction
EN
This paper deals with problems that are related to a feature extraction from 3D objects. The main aim of the feature extraction is to describe a shape of 3D object by a feature vector. Then the elements of this feature vector characterize the shape of the own 3D objects and they can serve as a key in searching for similar models. In this paper are introduced current methods for the feature extraction of 3D models and their classification. These methods are based on different mathematical background and according to that they are separated into several groups.
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