CIMSCLASS─—面向CIM的零件分类软件开发工具

介绍了一个能为工厂设置零件编码系统和开发零件分类系统的工具系统。该系统能适应用户的具体环境，能对零件的特征信息做较详细的描述和建立相应目标的零件分类系统，从而能在ＧＴ（成组技术）基础上为机械加工零件开发ＣＡＤ／ＣＡＰＰ／ＣＡＭ集成系统提供支持，以满足ＣＩＭＳ的需要。     

手势语言识别的神经网络方法

提供了一种用于人机交互（HCI）的手势语言可视化识别方法。该方法包括用于几种控制命令的手势的探测、分割、特征提取及识别，第一步的处理都用到了神经网络方法，像肤色探测、主元分析（PCA）以及在编码识别。实验结果显示正确识别率高达94％。     

Group technology based adaptive cell formation using predator-prey genetic algorithm

In recent years, there has been a considerable growth of application of group technology in cellular manufacturing. This has led to investigation of the primary cell formation problem (CFP), both in classical and soft-computing domain. Compared to more well-known and analytical techniques like mathematical programming which have been used rigorously to solve CFPs, heuristic approaches have yet gained the same level of acceptance. In the last decade we have seen some fruitful attempts to use evolutionary techniques like genetic algorithm (GA) and Ant Colony Optimization to find solutions of the CFP. The primary aim of this study is to investigate the applicability of a fine grain variant of the predator-prey GA (PPGA) in CFPs. The algorithm has been adapted to emphasize local selection strategy and to maintain a reasonable balance between prey and predator population, while avoiding premature convergence. The results show that the algorithm is competitive in identifying machine-part clusters from the initial CFP matrix with significantly less number of iterations. The algorithm scaled efficiently for large size problems with competitive performance. Optimal cluster identification is then followed by removal of the bottleneck elements to give a final solution with minimum inter-cluster transition cost. The results give considerable impetus to study similar NP-complete combinatorial problems using fine-grain GAs in future.     

基于社会网络的分组机制在P2P中的应用

分析了现有P2P存在的安全问题,提出了一种基于社会网络的分组机制。该机制基于社会网络的成员管理把节点分成不同的组,组内节点具有相同的资源。可以有效地避免不必要的资源请求,减少了相互恶评的可能;组间节点的请求可以更加高效。     

基于内容分组与能力匹配的邻居选择算法

邻居选择算法是影响P2P文件共享系统的整体吞吐量和带宽利用率的关键技术之一.目前BT类P2P文件共享系统中的一些常用邻居选择算法一般存在着邻居节点间的内容可交换性差和带宽利用率低等问题.提出一种新的邻居选择算法,将节点按内容分组,由节点上报的上传、下载的字节数计算出带宽能力,让能力匹配的节点成为邻居.计算机仿真实验表明,新算法显著地提高文件共享系统的整体吞吐量,减少用户的平均下载时间,从而有效地改善P2P文件共享系统的整体性能.     

独立光伏系统的应用及控制策略探讨

本文分析了独立光伏系统在解决边防及海岛部队供电难题和生态营区建设等方面的应用,讨论了独立光伏系统存在的主要问题,分析了能量控制策略的现状和蓄电池分组控制策略,给出了分组原则,研究了控制电路的结构和原理。实践证明,分组充放电对于提高供电可靠性,延长蓄电池寿命有一定的现实意义。     

A web-based e-learning system for increasing study efficiency by stimulating learner’s motivation

Due to the opportunities provided by the Internet, more and more people are taking advantage of distance learning courses and during the last few years enormous research efforts have been dedicated to the development of distance learning systems. So far, many e-learning systems are proposed and used practically. However, in these systems the e-learning completion rate is about 30%. One of the reasons is the low study desire when the learner studies the learning materials. In this research, we propose an interactive Web-based e-learning system. The purpose of our system is to increase the e-learning completion rate by stimulating learner’s motivation. The proposed system has three subsystems: the learning subsystem, learner support subsystem, and teacher support subsystem. The learning subsystem improves the learner’s study desire. The learner support subsystem supports the learner during the study, and the teacher support subsystem supports the teacher to get the learner’s study state. To evaluate the proposed system, we developed several experiments and surveys. By using new features such as: display of learner’s study history, change of interface color, encourage function, ranking function, self-determination of the study materials, and grouping of learners, the proposed system can increase the learning efficiency.

多约束三维装箱问题的混合遗传算法

三维装箱问题提出至今已有很多研究成果,各种启发式算法配合遗传算法、蚁群算法和模拟退火算法的设计层出不穷。而针对于三维装箱问题的各种约束,虽然各自有相应的处理方法,但却没有一种方法可以整合各种约束条件,这是因为启发式算法往往容易满足部分约束却很难满足所有约束的特点。在前人研究的基础上,针对各种遗传算法的约束条件,设计可以相互组合的解决各种约束条件的算法,通过对这些算法规则组合,可以解决各种约束条件下的三维装箱问题。     

Cross grouping strategy based 2DPCA method for face recognition

Grouping strategy exactly specifies the form of covariance matrix, therefore it is very essential. Most 2DPCA methods use the original 2D image matrices to form the covariance matrix which actually means that the strategy is to group the random variables by row or column of the input image. Because of their grouping strategies these methods have two main drawbacks. Firstly, 2DPCA and some of its variants such as A2DPCA, DiaPCA and MatPCA preserve only the covariance information between the elements of these groups. This directly implies that 2DPCA and these variants eliminate some covariance information while PCA preserves such information that can be useful for recognition. Secondly, all the existing methods suffer from the relatively high intra-group correlation, since the random variables in a row, column, or a block are closely located and highly correlated. To overcome such drawbacks we propose a novel grouping strategy named cross grouping strategy. The algorithm focuses on reducing the redundancy among the row and the column vectors of the image matrix. While doing this the algorithm completely preserves the covariance information of PCA between local geometric structures in the image matrix which is partially maintained in 2DPCA and its variants. And also in the proposed study intra-group correlation is weak according to the 2DPCA and its variants because the random variables spread over the whole face image. These make the proposed algorithm superior to 2DPCA and its variants. In order to achieve this, image cross-covariance matrix is calculated from the summation of the outer products of the column and the row vectors of all images. The singular value decomposition (SVD) is then applied to the image cross-covariance matrix. The right and the left singular vectors of SVD of the image cross-covariance matrix are used as the optimal projective vectors. Further in order to reduce the dimension LDA is applied on the feature space of the proposed method that is proposed method + LDA. The exhaustive experimental results demonstrate that proposed grouping strategy for 2DPCA is superior to 2DPCA, its specified variants and PCA, and proposed method outperforms bi-directional PCA + LDA.     

Supporting comprehensible presentation of clone candidates through two-dimensional maximisation

Duplicated code detection has been an active research field for several decades. Although many algorithms have been proposed, only a few researches have focussed on the comprehensive presentation of the detected clones. During the evaluation of clone detectors developed by the authors, it was observed that the results of the clone detectors were hard to comprehend. Therefore, in this paper a broadly suitable grouping method with which clone pairs can be easily grouped together to provide a more compact result is presented. The grouping algorithm is examined and a more precise revised algorithm is proposed to present all of the candidates to the user.