Deriving evidence theoretical functions in multivariate data spaces: a systematic approach.

The mathematical theory of evidence is a generalization of the Bayesian theory of probability. It is one of the primary tools for knowledge representation and uncertainty and probabilistic reasoning and has found many applications. Using this theory to solve a specific problem is critically dependent on the availability of a mass function (or basic belief assignment). In this paper, we consider the important problem of how to systematically derive mass functions from the common multivariate data spaces and also the ensuing problem of how to compute the various forms of belief function efficiently. We also consider how such a systematic approach can be used in practical pattern recognition problems. More specifically, we propose a novel method in which a mass function can be systematically derived from multivariate data and present new methods that exploit the algebraic structure of a multivariate data space to compute various belief functions including the belief, plausibility, and commonality functions in polynomial-time. We further consider the use of commonality as an equality check. We also develop a plausibility-based classifier. Experiments show that the equality checker and the classifier are comparable to state-of-the-art algorithms.     

A Regression Approach to Music Emotion Recognition

Content-based retrieval has emerged in the face of content explosion as a promising approach to information access. In this paper, we focus on the challenging issue of recognizing the emotion content of music signals, or music emotion recognition (MER). Specifically, we formulate MER as a regression problem to predict the arousal and valence values (AV values) of each music sample directly. Associated with the AV values, each music sample becomes a point in the arousal-valence plane, so the users can efficiently retrieve the music sample by specifying a desired point in the emotion plane. Because no categorical taxonomy is used, the regression approach is free of the ambiguity inherent to conventional categorical approaches. To improve the performance, we apply principal component analysis to reduce the correlation between arousal and valence, and RReliefF to select important features. An extensive performance study is conducted to evaluate the accuracy of the regression approach for predicting AV values. The best performance evaluated in terms of the R ² statistics reaches 58.3% for arousal and 28.1% for valence by employing support vector machine as the regressor. We also apply the regression approach to detect the emotion variation within a music selection and find the prediction accuracy superior to existing works. A group-wise MER scheme is also developed to address the subjectivity issue of emotion perception.     

基于Windows Media的网络媒体服务系统的实现

介绍了Windows Media系统的体系结构和通信协议,构建了采用Windows Media技术的网络媒体服务系统,详细叙述了系统的连接、配置和技术要点,以较低的代价实现了媒体服务系统的应用。     

Direct manipulation of FFD: efficient explicit solutions and decomposible multiple point constraints

Published online: 25 July 2001     

ProxyServer中Cache的管理和使用

使用Ｃａｃｈｅ可以减少Ｂｒｏｗｅｒ的等待时间和降低代理服务器和Ｉｎｔｅｒｎｅｔ的通信量,本文主要介绍了Ｐｒｏｘｙ对Ｃａｃｈｅ管理和使用。     

Polycrystalline diamond MESFETs by Au-mask technology for RF applications

Metal-semiconductor field effect transistors (MESFETs) were fabricated on H-terminated polycrystalline diamond.The DC characteristics of the p-channel MESFET showed a maximum drain current density of 204 mA/mm at a gate-source voltage of 6 V,and a maximum transconductance of 20 mS/mm at a gate-source voltage of 1.5 V.The small signal S-parameters of MESFET with 2 100 m gate width and 2 m gate length were measured.An extrinsic cut-off frequency (fT) of 1.7 GHz and the maximum oscillation frequency (fmax) of 2.5 GHz were obtained,which was the first report on diamond MESFETs with RF characteristics in China.     

基于改进免疫遗传算法的电网故障诊断研究

针对电网故障的特点,应用一种改进的免疫遗传算法对电网故障进行研究。该算法能够较好地解决传统遗传算法的不足。通过引入新的交叉和变异率,更多考虑了种群的全局特征,采用动态自适应方式提取疫苗,避免了传统遗传算法收敛速度较慢的缺点。改进的算法本着优胜劣汰的思想,删除适值较低的抗体群,取而代之的是随机生成的部分新抗体,保持种群的多样性。建立一个新的目标函数,通过对一个电网的分析,验证了方法的有效性。     

Node trust evaluation in mobile ad hoc networks based on multi-dimensional fuzzy and Markov SCGM(1, 1) model

Due to the nature of distribution and self-organization, mobile ad hoc networks rely on cooperation between nodes to transfer information. One of the key factors to ensure high communication quality is an efficient assessment scheme for risks and trust of choosing next potential cooperative nodes. Trust model, an abstract psychological cognitive process, is one of the most complex concepts in social relationships, involving factors such as assumptions, expectations and behaviors. All of the above make it difficult to quantify and forecast trust accurately. In this paper, based on the theories of fuzzy recognition with feedback, SCGM(1, 1) model and Markov chain, we present a pattern of prediction making. The analysis and experimental computation show that this scheme is efficient in trust prediction for ad hoc networks.