Distributed memory systems for simulating artificial neural networks

In executing tasks involving intelligent information processing, the human brain performs better than the digital computer. The human brain derives its power from a large number [O(10¹¹)] of neurons which are interconnected by a dense interconnection network [O(10⁵) connections per neuron]. Artificial neural network (ANN) paradigms adopt the structure of the brain to try to emulate the intelligent information processing methods of the brain. ANN techniques are being employed to solve problems in areas such as pattern recognition, and robotic processing. Simulation of ANNs involves implementation of large number of neurons and a massive interconnection network. In this paper, we discuss various simulation models of ANNs and their implementation on distributed memory systems. Our investigations reveal that communication-efficient networks of distributed memory systems perform better than other topologies in implementing ANNs.     

一种分布式的洋葱路由新机制

洋葱路由技术在公网上提供双向、实时的匿名通信，其面向连接和严格的源路由方式导致效率低、扩展性差。利用分布式的概念，采用分段连接技术思想提出了一种安全、高效的分布式洋葱路由机制(DORS)，给出了DORS路由实现算法，并从方案实现效率、安全性方面对DORS进行了分析。分析结果表明DORS具有良好的扩展性、鲁棒性、简单性和有效性。     

选择性集成算法分类与比较

选择性集成是当前机器学习领域的研究热点之一。由于选择性集成属于NP"难"问题,人们多利用启发式方法将选择性集成转化为其他问题来求得近似最优解,因为各种算法的出发点和描述角度各不相同,现有的大量选择性集成算法显得繁杂而没有规律。为便于研究人员迅速了解和应用本领域的最新进展,本文根据选择过程中核心策略的特征将选择性集成算法分为四类,即迭代优化法、排名法、分簇法、模式挖掘法;然后利用UCI数据库的20个常用数据集,从预测性能、选择时间、结果集成分类器大小三个方面对这些典型算法进行了实验比较;最后总结了各类方法的优缺点,并展望了选择性集成的未来研究重点。     

A demerit-fuzzy rating system,monitoring scheme and classification for manufacturing processes

For monitoring online manufacturing processes, the proportion of weights imposed on each type of product’s defects (nonconformities or demerits) has a profoundly effective impact on control charts’ performance. Apparently, the demerit-chart approach is superior than the widely-used c-chart scheme, because it allows us to place relative precise weights (real numbers) on defects according to their distinctly inferior degrees affecting the product quality so that the abnormal variations of processes can be literally exposed. However, in many applications, the seriousness of defects is evaluated partially or entirely by the inspectors’ perceptive judgement or knowledge, so with the precise-weight assignment, the demerit rating mechanism is considered to be somewhat constrained and subjective which inevitably leads to the targeted manufacturing process with limited and possibly biased information for online surveillance. To cope with the drawback, a demerit-fuzzy rating system and monitoring scheme is proposed in this paper. We first incorporate fuzzy weights (fuzzy numbers) to properly reflect the severity measures of defects which are categorized linguistically. Then, based on properties of fuzzy set theory and proposed approaches for fuzzy-number ranking, we develop the demerit-fuzzy charting scheme which is capable of discriminating process conditions into multi-intermittent statuses between in-control and out-of-control. This approach improves the traditional process control techniques with the binary-classification restraint for the process conditions. Finally, the proposed demerit-fuzzy rating system, monitoring scheme, and classification is elucidated by an application in garment industry to monitor textile-stitching nonconformities conditions.     

Exploiting temporal information in Web search

Time plays important roles in Web search, because most Web pages contain temporal information and a lot of Web queries are time-related. How to integrate temporal information in Web search engines has been a research focus in recent years. However, traditional search engines have little support in processing temporal-textual Web queries. Aiming at solving this problem, in this paper, we concentrate on the extraction of the focused time for Web pages, which refers to the most appropriate time associated with Web pages, and then we used focused time to improve the search efficiency for time-sensitive queries. In particular, three critical issues are deeply studied in this paper. The first issue is to extract implicit temporal expressions from Web pages. The second one is to determine the focused time among all the extracted temporal information, and the last issue is to integrate focused time into a search engine. For the first issue, we propose a new dynamic approach to resolve the implicit temporal expressions in Web pages. For the second issue, we present a score model to determine the focused time for Web pages. Our score model takes into account both the frequency of temporal information in Web pages and the containment relationship among temporal information. For the third issue, we combine the textual similarity and the temporal similarity between queries and documents in the ranking process. To evaluate the effectiveness and efficiency of the proposed approaches, we build a prototype system called Time-Aware Search Engine (TASE). TASE is able to extract both the explicit and implicit temporal expressions for Web pages, and calculate the relevant score between Web pages and each temporal expression, and re-rank search results based on the temporal-textual relevance between Web pages and queries. Finally, we conduct experiments on real data sets. The results show that our approach has high accuracy in resolving implicit temporal expressions and extracting focused time, and has better ranking effectiveness for time-sensitive Web queries than its competitor algorithms.     

A heuristic biomarker selection approach based on professional tennis player ranking strategy

Extracting significant features from high-dimension and small sample size biological data is a challenging problem. Recently, Micha? Draminski proposed the Monte Carlo feature selection (MC) algorithm, which was able to search over large feature spaces and achieved better classification accuracies. However in MC the information of feature rank variations is not utilized and the ranks of features are not dynamically updated. Here, we propose a novel feature selection algorithm which integrates the ideas of the professional tennis players ranking, such as seed players and dynamic ranking, into Monte Carlo simulation. Seed players make the feature selection game more competitive and selective. The strategy of dynamic ranking ensures that it is always the current best players to take part in each competition. The proposed algorithm is tested on 8 biological datasets. Results demonstrate that the proposed method is computationally efficient, stable and has favorable performance in classification.     

基于分层的改进A＊算法在路径规划中的应用

智能交通中的路径诱导系统能够极大地提高人们的出行效率与出行体验。经典A＊算法只注重搜索精度而忽略了搜索效率,在城市道路网络分层的基础上,对高层道路使用的A＊算法进行了改进,对于道路网络中的不同节点,设置估价函数具有不同的权值,同时给定权值的一个上下限阈值,以平衡算法的搜索效率与搜索精度。实验表明,得到的最短路径虽然不是常规的距离最短却是实际行驶时间最优的。     

一种新型指纹锁电路设计

市场上主流的电子锁是基于密码设计的。密码锁的最大的缺陷是密码容易被他人窃取、猜测及遗忘。随着生物技术的发展,越来越多的活体技术应用到识别系统中,如指纹、掌纹、人脸、虹膜等。相对于其它的活体识别技术,指纹识别系统以其可实现性强,成本相对低廉,同时又具备较高的安全性,被越来越多的应用到各种场合。文章给出了一种新型的指纹锁架构,并详细论述了系统的各个组成部分以及指纹识别算法的实现流程。文章对降低系统功耗和增加保密性都提出了独特的方法。     

融合动态层次聚类和邻域区间重组的蚁群算法

针对蚁群算法搜索速度过慢以及解质量不足等问题,提出一种融合动态层次聚类和邻域区间重组的蚁群算法。在初始阶段,调整层次聚类阈值并按照类间距离最小合并的原则迭代至目标簇集,根据预合并系数进行簇间合并,通过蚁群系统得到小类路径并断开重组以加快算法整体收敛速度;接着使用蚁群系统对解空间进行优化,同时并行处理簇集与簇集邻域区间扩散重组,增加解的多样性,进一步固定迭代次数进行比较,若邻域区间重组解质量优于当前优化解则进行推荐处理,提高解的精度;当算法停滞时,引入调整因子降低各路径信息素之间差异以增强蚂蚁搜索能力,有助于算法跳出局部最优。实验结果表明,在面对大规模问题时,算法的精度在3%左右,该方法相比传统方法可以有效提高解的精度和收敛速度。     

基于颜色矩的图像检索算法研究

为克服基于文字的图像检索技术对大型图像数据库实施困难、描述不完整等弊端,对RGB颜色矩和Munsell颜色矩进行研究,RGB颜色矩只适用于简单图像,Munsell颜色矩无法判断图片具体信息。为此,采用信息融合技术进行检索判断层的融合,选择多颜色空间融合颜色矩,在分析传统融合算法基础上,提出一种改进融合算法NewcombMNZ。实验结果表明,该算法能提高检索精准度和排序值,对噪声具有良好的鲁棒性,且能减少检索性能较差的特征对融合结果的影响,适用于特征数量较少的情况。