This paper proposes the MDMA (Multi-dimensional Distributed Mining Association rules) algorithm based on advanced SQL query. The algorithm works on star-style structure networks. It uses CUBE operator in new standard ...
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In allusion to the boundary leaking problem of traditional active contours when used to extract facial features, two kinds of local information are introduced to enhance the weak boundary, then to build new external f...
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Over the last decade, partial differential equations (PDEs) have been justified as effective tools for image smoothing;they are able to achieve a good trade-off between noise removal and edge-preserving. Among various...
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Over the last two decades, many image processing problems have been modeled by partial differential equations (PDEs), such as restoration and segmentation. Due to good performance at controlling the trade-off between ...
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In this study, a novel method is presented for segmentation of the endocardium and epicardium of the left ventricle in cardiac magnetic resonance images using snake models. We first generalize the DDGVF snake model by...
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For active application of many cases, context-aware computing with uncertainty includes forming model, fusing of aware context, managing context information and so on. Approach research on computing of aware context w...
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For active application of many cases, context-aware computing with uncertainty includes forming model, fusing of aware context, managing context information and so on. Approach research on computing of aware context with uncertainty for making dynamic decision is focused in this paper. We compare dynamic context-aware computing with improved Random Set Theory (RST) and extended D-S Evidence Theory (EDS). We give new modeling mode based on RST for aware context and our computing approach of modeled aware context, extend classic D-S Evidence Theory after considering context's feature. Then compare relative computing methods, enumerate experimental examples and give the evaluation. By comparisons, the more validity of new context-aware computing approach based on RST than EDS with uncertainty information has been tested successfully.
During decision making procedure of active application, the primary signals with noise, which got from each sensor, were sent to a decision making center, the fusion center, through the distributed or centralized comp...
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During decision making procedure of active application, the primary signals with noise, which got from each sensor, were sent to a decision making center, the fusion center, through the distributed or centralized computer network. The fusion center then formed a final decision based on the primary decisions received from low-level sensors. But multi-sensor heterogeneous information fusion is an extremely challenging problem because of the complex and highly irregular nature of environment. We have designed a kind of new decision fusion method based on sensor evidence for active application in this paper. A fusion model, approach and relative lemma for multi-sensor heterogeneous information has been presented. For decision fusion, we have utilized the approaching relative efficiency performance measure. This method has been tested successfully by our active application projects.
Apparently, this function of nomadic service is suitable for mobile services. But when nomadic service for computing task is realized among PC, laptop, or PDA, there are several difficult problems to be solved, such a...
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Apparently, this function of nomadic service is suitable for mobile services. But when nomadic service for computing task is realized among PC, laptop, or PDA, there are several difficult problems to be solved, such as how to supply the continuity and adaptability. In order to realize nomadic service, we design and improve relative approach. In this paper, firstly, formal description of task and its type have been given, then, classification of Agent and transferring granularity of task have been suggested, and then efficient mechanism of nomadic service based on agent for pervasive computing has been presented and designed, the description of suggested management platform supporting nomadic service.
Knowledge engineering stems from E. A. Figenbaum＇s proposal in 1977, but it will enter a new decade with the new challenges. This paper first summarizes three knowledge engineering experiments we have undertaken to s...
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Knowledge engineering stems from E. A. Figenbaum＇s proposal in 1977, but it will enter a new decade with the new challenges. This paper first summarizes three knowledge engineering experiments we have undertaken to show possibility of separating knowledge development from intelligentsoftware development. We call it the ICAX mode of intelligent application software generation. The key of this mode is to generate knowledge base, which is the source of intelligence of ICAX software, independently and parallel to intelligentsoftware development. That gives birth to a new and more general concept ＂knowware＂. Knowware is a commercialized knowledge module with documentation and intellectual property, which is computer operable, but free of any built-in control mechanism, meeting some industrial standards and embeddable in software/hardware. The process of development, application and management of knowware is called knowware engineering. Two different knowware life cycle models are discussed： the furnace model and the crystallization model. Knowledge middleware is a class of software functioning in all aspects of knowware life cycle models. Finally, this paper also presents some examples of building knowware in the domain of information system engineering.
An algebraic multi-class classification method AHSC, i.e. Algebraic Hyper Surface Classification, is proposed. The separating algebraic hyper surface of two-class data may be directly constructed by a single polynomia...
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ISBN:
(纸本)0780384032
An algebraic multi-class classification method AHSC, i.e. Algebraic Hyper Surface Classification, is proposed. The separating algebraic hyper surface of two-class data may be directly constructed by a single polynomial in theory, but it is too difficult to separate multi-class data by a single polynomial even though the polynomial is multivalued. AHSC can be used for classifying multi-class data by integrating a series of polynomial networks based on binary numbers which are used for labeling the classes of samples. The problem that multi-class data can not always be separated by a single polynomial is solved by AHSC. Moreover, the order of polynomial can be chosen by using an adaptive method. The experiment results show that the new method can efficiently and accurately classify multi-class and high dimension data.
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