一个具有拟神经组织的边缘检测模型

基于视皮层简单细胞的Gabor函数模型,仿照视皮层的朝向柱结构,并辅以模式增 强和处理网络,构造了一个具有拟神经组织的边缘检测模型.模拟结果显示,其检测能力与 平滑-空间导数型边缘检测算法相当.分析表明,这个基于早期边缘检测器概念的模型与平 滑-空间导数型算法在检测机理上是一致的.模型为用光学-电子方法和人工神经网络实现 边缘检测提供了一个原型.     

基于knee points的改进多目标人工蜂群算法

传统的人工蜂群算法（Artificial Bee Colony algorithm,ABC）及其在多目标上的扩展（Multi Objective Artificial Bee Colony algorithm,MOABC）存在着在高维、多峰函数情况下收敛速度变慢、后期容易陷入局部最优以及寻优精度丢失等问题。基于knee points提高收敛性和分布性的特点,设计了一种快速识别knee point的算法并将其应用到多目标人工蜂群算法中,提出了一种基于knee points的改进多目标人工蜂群算法（KnMOABC）。算法在迭代过程中考虑pareto支配关系的同时,优先选择knee point作为下一代个体,极大地增强了算法的收敛速度,同时,在knee point识别算法中加入自适应的策略以保持良好的分布性。实验结果表明,KnMOABC的性能优于三个最新的多目标人工蜂群对比算法。     

一种构造任意类三次三角曲线的方法

在自由曲线曲面造型中,一般多以多项式为基函数构造参数曲线曲面,而在三角函数空间中也能构造参数曲线曲面.给出了一种构造任意类三次三角参数曲线的方法,该法以三次多项式曲线的基本性质为基础,从而构造出的曲线与对应的三次多项式曲线具有几乎完全相似的性质,而且所构造的曲线能精确表示圆弧、椭圆弧、抛物线弧等二次曲线,为曲线曲面造型提供了一种新方法.     

谐波检测中频谱泄漏问题的研究

研究了基于FFT的电力系统谐波检测中的频谱泄漏现象,分析了产生频谱泄漏的原因,提出了利用窗函数来解决频谱泄漏现象的方法。仿真结果表明,汉宁窗可以很好地减小频谱泄漏,满足谐波检测的精度要求。     

Free binary decision diagrams for the computation of EAR n

Free binary decision diagrams (FBDDs) are graph-based data structures representing Boolean functions with the constraint (additional to binary decision diagram) that each variable is tested at most once during the computation. The function EAR_n is the following Boolean function defined for n × n Boolean matrices: EAR_n(M) = 1 iff the matrix M contains two equal adjacent rows. We prove that each FBDD computing EAR_n has size at least and we present a construction of such diagrams of size approximately .     

输出概率密度函数形状的多步预测控制

A predictive control strategy is proposed for the shaping of the output probability density function (PDF) of linear stochastic systems. The B-spline neural network is used to set up the output PDF model and therefore converts the PDF-shaping into the control of B-spline weights vector. The Diophantine equation is then introduced to formulate the predictive PDF model, based on which a moving-horizon control algorithm is developed so as to realize the predictive PDF tracking performance.     

基于EM-EBF模型的遥感影像分类方法研究

椭球径向基函数神经网络(EBF)是在径向基函数(RBF)映射理论基础上的改进。在保留RBF3层网络结构基础上,EBF采用了最大期望算法来估计特征空间的混合密度分布参数,用椭球体集合来分解混合密度分布,从而构造了神经网络的中间层基函数的状态。由于遥感数据在特征空间中通常表现为混合密度分布,EBF模型能够充分利用期望最大(EM)算法获得的最大似然参数估计得到更合理的特征空间的密度分解模型,从而使得EBF模型能够保留RBF非线性复杂映射能力的同时,获得更合理的分类结果。为此提出了基于EBF的遥感分类方法,试验结果表明EBF方法比RBF方法网络连接更简单、分类精度更高。     

"Worth a thousand words": Absolute and relative decoding of nonlinguistic affect vocalizations.

The authors compared the accuracy of emotion decoding for nonlinguistic affect vocalizations, speech-embedded vocal prosody, and facial cues representing 9 different emotions. Participants (N = 121) decoded 80 stimuli from 1 of the 3 channels. Accuracy scores for nonlinguistic affect vocalizations and facial expressions were generally equivalent, and both were higher than scores for speech-embedded prosody. In particular, affect vocalizations showed superior decoding over the speech stimuli for anger, contempt, disgust, fear, joy, and sadness. Further, specific emotions that were decoded relatively poorly through speech-embedded prosody were more accurately identified through affect vocalizations, suggesting that emotions that are difficult to communicate in running speech can still be expressed vocally through other means. Affect vocalizations also showed superior decoding over faces for anger, contempt, disgust, fear, sadness, and surprise. Facial expressions showed superior decoding scores over both types of vocal stimuli for joy, pride, embarrassment, and “neutral” portrayals. Results are discussed in terms of the social functions served by various forms of nonverbal emotion cues and the communicative advantages of expressing emotions through particular channels. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Dependence tree structure estimation via copula

We propose an approach for dependence tree structure learning via copula. A nonparametric algorithm for copula estimation is presented. Then a Chow-Liu like method based on dependence measure via copula is proposed to estimate maximum spanning bivariate copula associated with bivariate dependence relations. The main advantage of the approach is that learning with empirical copula focuses on dependence relations among random variables, without the need to know the properties of individual variables as well as without the requirement to specify parametric family of entire underlying distribution for individual variables. Experiments on two real-application data sets show the effectiveness of the proposed method.     

Combination of sources of evidence with different discounting factors based on a new dissimilarity measure

The sources of evidence may have different reliability and importance in real applications for decision making. The estimation of the discounting (weighting) factors when the prior knowledge is unknown have been regularly studied until recently. In the past, the determination of the weighting factors focused only on reliability discounting rule and it was mainly dependent on the dissimilarity measure between basic belief assignments (bba's) represented by an evidential distance. Nevertheless, it is very difficult to characterize efficiently the dissimilarity only through an evidential distance. Thus, both a distance and a conflict coefficient based on probabilistic transformations BetP are proposed to characterize the dissimilarity. The distance represents the difference between bba's, whereas the conflict coefficient reveals the divergence degree of the hypotheses that two belief functions strongly support. These two aspects of dissimilarity are complementary in a certain sense, and their fusion is used as the dissimilarity measure. Then, a new estimation method of weighting factors is presented by using the proposed dissimilarity measure. In the evaluation of weight of a source, both its dissimilarity with other sources and their weighting factors are considered. The weighting factors can be applied in the both importance and reliability discounting rules, but the selection of the adapted discounting rule should depend on the actual application. Simple numerical examples are given to illustrate the interest of the proposed approach.