一种从马尔可夫聚类簇发现潜在WEB社区特征的方法

在分析了目前一些典型的社区发现算法的基础上,通过对无主题条件下的隐含社区发现算法的研究,提出将基于流的社区特征和马尔可夫图形聚类算法(MCL)的簇结合起来寻找Web隐含社区的方法.将镜像或近似镜像页面的删除放在图形聚类之后,大大减少了比较的代价.然后,在聚类簇的基础上,使用判定每个簇内元素的筛选算法产生可能的社区候选集合.实验表明,该方法是可行的,可以发现许多存在的社区.     

基于星凸形随机超曲面模型多扩展目标多伯努利滤波器

针对复杂不确定性环境下具有不规则形状的多扩展目标跟踪问题, 提出了一种基于星凸形随机超曲面模型(Star-convex RHM)的多扩展目标多伯努利滤波算法.首先, 在有限集统计(Finite set statistics, FISST)理论框架下, 采用多伯努利随机有限集(MBer-RFS)和泊松RFS (Possion-RFS)分别描述多扩展目标的状态和观测, 并给出扩展目标势均衡多目标多伯努利(ET-CBMeMBer)滤波器.其次, 利用RHM去描述任意星凸形扩展目标的量测源分布, 提出了容积卡尔曼高斯混合星凸形多扩展目标多伯努利滤波器.此外, 本文给出了一种多扩展目标不规则形状估计性能的评价指标.最后, 通过多扩展目标和具有形状突变的多群目标的跟踪仿真实验验证了本文方法的有效性.     

随机中文汉字验证码的生成及应用

介绍了验证码的应用优势和生成原理.分析了汉字的编码和处理原理.详述了还处于发展阶段的中文验证码以及噪音图片的生成和应用.     

Information properties of the simplest feedback operator and certain conclusions

A study of the response to a random input signal of the simplest operator with constant transfer factor in a direct channel where there is negative feedback present is carried out. It is shown that feedback in the channel exerts a positive influence on the information properties of the output quantity. It is suggested that the results of the study can be recommended for the construction of reliable computations in future computers and other information systems.     

一种鲁棒性的少样本学习方法

少样本学习是目前机器学习研究领域的一个热点,它能在少量的标记样本中学习到较好的分类模型.但是,在噪声的不确定环境中,传统的少样本学习模型泛化能力弱.针对这一问题,提出一种鲁棒性的少样本学习方法RFSL(Robust Few-Shot Learning).首先,使用核密度估计(Kernel Density Estimat...     

基于离散小波变换和随机森林的轴承故障诊断研究

针对不同工况下数据特征选择困难和单一分类器在滚动轴承故障诊断中识别率较低等问题,提出了一种基于离散小波变换和随机森林相结合的滚动轴承故障诊断方法。该方法首先利用离散小波变换分解振动信号,得到n层近似系数;然后创新性地采用sigmoid熵构造出n维特征向量,sigmoid熵能较好地提取非平稳信号的特征,提高诊断准确率;最后采用随机森林对滚动轴承不同故障信号进行分类。实验采用西储凯斯大学轴承数据中心网站提供的轴承数据,与传统分类器(KNN和SVM)以及单个分类回归树CART进行对比分析,结果表明该方法具有更好的诊断效果。     

基于Matlab的加权随机点名提问系统设计

该文从教学需求出发,设计一套具有权重的随机点名课堂提问系统,其目的是提高课堂教学质量与学生参与的积极性。该系统具有两个方面的功能,第一,可以避免教师在课堂教学中的带有个人感情色彩的提问偏重,给予每个学生相对均等的回答课堂提问的机会;第二,可以根据学生知识掌握情况以及课堂问题难易程度,适度调节随机点名的权重分配,提高学生问题回答质量与课堂效率,将课堂提问在教学环节中发挥到最大的作用。     

Random errors caused by temperature in magnitude of principal strains evaluated ith 3-element strain gauge rosettes

The effects of the uncertainties associated ith the apparent strain and gauge factor data, given by the manufacturer, in the evaluation of principal strains are examined. Principal strains are obtained by a reduction of the strains measured ith electrical resistance strain gauge rectangular or delta rosettes. The theoretical analysis points out the relevance of the effects caused.     

On Measuring the Root-Mean-Square Value of a Finite Record Length Periodic Waveform

An analysis of the uncertainty in measuring the root-mean-square, rms or R_q, value of a periodic waveform which results from the use of a finite record length is presented. Even though the results of the analysis are somewhat as expected, i.e., that the uncertainty is inversely proportional to the number of periods in the record, the explicit relationship between the magnitude of the uncertainty and properties of the waveform does not appear to be available in the literature. The paper first presents an introductory example in terms of the reasonably well known case of bandwidth limited Gaussian waveform to introduce definitions. Following this is an analysis of the periodic waveform using the same approach. It is shown that for a large number of periods, n, in the record length, the normalized three standard deviation of the rms value is given by 3/(8πn).     

Selection of optimized features for fusion of palm print and finger knuckle‐based person authentication

The impact of digital technology in biometrics is much more efficient at interpreting data than humans, which results in completely replacement of manual identification procedures in forensic science. Because the single modality‐based biometric frameworks limit performance in terms of accuracy and anti‐spoofing capabilities due to the presence of low quality data, therefore, information fusion of more than one biometric characteristic in pursuit of high recognition results can be beneficial. In this article, we present a multimodal biometric system based on information fusion of palm print and finger knuckle traits, which are least associated to any criminal investigation as evidence yet. The proposed multimodal biometric system might be useful to identify the suspects in case of physical beating or kidnapping and establish supportive scientific evidences, when no fingerprint or face information is present in photographs. The first step in our work is data preprocessing, in which region of interest of palm and finger knuckle images have been extracted. To minimize nonuniform illumination effects, we first normalize the detected circular palm or finger knuckle and then apply line ordinal pattern (LOP)‐based encoding scheme for texture enrichment. The nondecimated quaternion wavelet provides denser feature representation at multiple scales and orientations when extracted over proposed LOP encoding and increases the discrimination power of line and ridge features. To best of our knowledge, this first attempt is a combination of backtracking search algorithm and 2D²LDA has been employed to select the dominant palm and knuckle features for classification. The classifiers output for two modalities are combined at unsupervised rank level fusion rule through Borda count method, which shows an increase in performance in terms of recognition and verification, that is, 100% (correct recognition rate), 0.26% (equal error rate), 3.52 (discriminative index), and 1,262 m (speed).