In this paper, an adaptive fuzzy control system with supervisory controller is proposed to improve dynamic performance of three-phase active power filter (APF). The proposed adaptive fuzzy controller for APF does not ...
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We introduce a new logic programming paradigm-answer set programming with uncertain facts (LPuF for short). A LPuF program is an extension of answer set programs (ASP for short). We first define the syntax of LPuF pro...
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MicroRNA (miRNA in short) is a kind of small RNAs that acts as an important post-transcriptional regulator with the Argonaute family of proteins to regulate target mRNAs in animals and plants etc. Since its first reco...
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A new scheme of object tracking with mean shift is put forward in this paper. At first, texture feature is fused in the processing by Local Ternary Pattern (LTP). Since LTP is sensitive to local noise, least median of...
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ISBN:
(纸本)9781467321969
A new scheme of object tracking with mean shift is put forward in this paper. At first, texture feature is fused in the processing by Local Ternary Pattern (LTP). Since LTP is sensitive to local noise, least median of squares (LMedS) algorithm is used to adaptively calculate the noise threshold for accurate estimation of the LTP texture information. Furthermore, target scale and orientation is estimated in case of partial occlusion or rotation, so as to realize robust object tracking. Experimental results show that the proposed algorithm can acquire robust tracking performance under complex background .
Opportunistic networks are sparse wireless networks which have no complete path from the source to the destination most of the time. Many applications require the support of delay constrained routing mechanism which c...
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To explore the association relations among disease, pathogenesis, physician, symptoms and drug, we adapt a variational Apriori algorithm for discovering association rules on a dataset of the Qing Court Medical Records...
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Link Detection (abbr. LDT) is to determine whether two stories discuss the same topic in Topic Detection and Tracking (abbr. TDT) track. The key issue is to correctly measure the relevance between two stories. Most re...
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Link Detection (abbr. LDT) is to determine whether two stories discuss the same topic in Topic Detection and Tracking (abbr. TDT) track. The key issue is to correctly measure the relevance between two stories. Most researches on LDT use a series of independent words to describe stories (each story is a text specially discussing news), and the relevance between two stories is determined based on the percentage and weight of overlapping words between them. Although substantial improvement has been achieved, inadequate descriptions of word sense and semantics still have negative influences on the accuracy of LDT. In this paper we propose an online semantic tree, which is hierarchically constructed by the most relevant words extracted from previous story streams. In online semantic tree, word sense is described by a series of words in a sense closed-loop, and semantic relation among words is measured by depth and width of level that words locate in. In LDT, online semantic tree is built for each story, and the relevance between two stories is determined by measuring the KL divergence between their online semantic trees. The method performs quite well on TDT4 corpus. The Min Norm CDet of the method in testing is 0.2274 lower than that of the baseline.
Independent component analysis (ICA), instead of the traditional discrete cosine transform (DCT), is often used to project log Mel spectrum in robust speech feature extraction. The paper proposed using symmetric ortho...
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Multiple criteria decision making (MCDM) has received increasing attentions in both engineering and economic fields. Weights of the criteria directly affect decision results in MCDM, so it is important for us to acqui...
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Image segmentation evaluation is still a hotspot problem. Various methods of image segmentation evaluation have been proposed. Amongst all the evaluation approaches, Segmentation Entropy Quantitative Assessment (SEQA)...
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Image segmentation evaluation is still a hotspot problem. Various methods of image segmentation evaluation have been proposed. Amongst all the evaluation approaches, Segmentation Entropy Quantitative Assessment (SEQA) is one of the most popular methods. In this paper, segmentation entropy is proposed. In experiments, some standard images which are segmented by multi-level thresholds are tested and used to conclude the characteristics of SEQA, including its application conditions, advantages and disadvantages in image segmentation evaluation.
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