网络机器人在数据库比对系统中的应用

网络机器人(Web Robo)t是一种自动化获取网页数据的程序。实践中使用对特定主题感兴趣的网络机器人获取数据,与数据库中数据进行比对,得出有用的结果。     

一种基于小波变换的MQAM信号调制识别方法

MQAM调制样式识别是非合作通信研究的重要内容之一。通过分析MQAM信号在小波域具备的特征,提出了一种基于小波变换的MQAM信号识别算法,该算法不需要预先知道载频、波特率等参数,步骤简单,可在低信噪比情况下实现对MQAM信号的分类。     

基于RTP与模板卷积法的视频无线传输技术

速率控制和差错控制是视频传输系统重点研究内容,在RTP实时传输协议和MPEG-4码流分包的基础上,提出了采用一种新的基于RTCP反馈的自适应分包技术和基于模板卷积的计算运动矢量的方法.该技术能有效解决高分辨率、大数据量视频图像在无线视频传输中的丢包率难题.通过实验结果表明,该技术能保证无线视频实时传输质量.     

图像映射技术在公共座椅元素设计中的应用

简要介绍了Image Map（图像映射）技术在工业设计中的运用方法,将该技术运用于产品设计元素提取中,使设计过程科学严谨。提取了数量众多的公共座椅实例作为研究对象,利用Image Map（图像映射图）技术进行全面分析,以提取较有说服力的设计元素。     

车辆动态监管系统的创新与设计

针对新时期国内道路运输车辆管理的现状,并根据道路交通安全建设的需求,提出把重点车辆(客车、出租车、校车、危化品运输车、工程车以及突出交通违法车辆)纳入交通安全重点管理的范畴,在交通管理信息系统中运用创新的管理体系,实现对重点车辆的动态、实时、联动监管,以科技手段及时侦测交通运行过程中重点车辆的异常情况,有效预防重特大道路交通事故发生,从而提升道路交通管理水平。该系统具有良好的应用前景。     

An algorithm for automatic 2D finite element mesh generation with line constraints

In this study, an algorithm is designed specifically for automatic finite element (FE) mesh generation on the transverse structure of hulls reinforced by stiffeners. Stiffeners attached to the transverse structure are considered as line constraints in the geometry boundary. For the FE mesh generation used in this study, the line constraints are treated as boundaries and by that means the geometry domain attached to the line constraints is decomposed into sub-domains, constrained only by the closed boundaries. Then, the mesh can be generated directly on those sub-domains by the traditional approach. The performance of the proposed algorithm is evaluated and the quality of the generated mesh meets expectations.     

Performance evaluation of GAIA supercomputer using NPB multi-zone benchmarks

GAIA is a recently developed IBM POWER6 supercomputer consisting of 24 SMP compute nodes with 64-way processors each, and it is currently ranked 393 on the Top500 supercomputer list published in November 2009. In this paper, we present the performance characteristics of GAIA evaluated by interconnecting the 24 computing nodes with low-latency InfiniBand network. We evaluate the performance of the new dual-core Power 595 system in terms of increased problem size using the multi-zone versions of the NAS Parallel Benchmarks.     

Data envelopment analysis classification machine

This paper establishes the equivalent relationship between the data classification machine and the data envelopment analysis (DEA) model, and thus build up a DEA based classification machine. A data is characterized by a set of values. Without loss of the generality, it is assumed that the data with a set of smaller values is preferred. The classification is to label if a particular data belongs to a specified group according to a set of predetermined characteristic or attribute values. We treat such a data as a decision making unit (DMU) with these given attribute values as input and an artificial output of identical value 1. Then classifying a data is equivalent to testing if the DMU is in the production possibility set, called acceptance domain, constructed by a sample training data set. The proposed DEA classification machine consists of an acceptance domain and a classification function. The acceptance domain is given by an explicit system of linear inequalities. This makes the classification process computationally convenient. We then discuss the preference cone restricted classification process. The method can be applied to classifying large amount of data. Furthermore, the research finds that DEA classification machines based on different DEA models have the same format. Input-oriented and output-oriented DEA classification machines have similar properties. The method developed has great potential in practice with its computational efficiency.     

Handling boundary constraints for particle swarm optimization in high-dimensional search space

Despite the fact that the popular particle swarm optimizer (PSO) is currently being extensively applied to many real-world problems that often have high-dimensional and complex fitness landscapes, the effects of boundary constraints on PSO have not attracted adequate attention in the literature. However, in accordance with the theoretical analysis in [11], our numerical experiments show that particles tend to fly outside of the boundary in the first few iterations at a very high probability in high-dimensional search spaces. Consequently, the method used to handle boundary violations is critical to the performance of PSO. In this study, we reveal that the widely used random and absorbing bound-handling schemes may paralyze PSO for high-dimensional and complex problems. We also explore in detail the distinct mechanisms responsible for the failures of these two bound-handling schemes. Finally, we suggest that using high-dimensional and complex benchmark functions, such as the composition functions in [19], is a prerequisite to identifying the potential problems in applying PSO to many real-world applications because certain properties of standard benchmark functions make problems inexplicit.     

Optimization for limited angle tomography in medical image processing

This paper aims to reduce the problems of incomplete data in computed tomography, which happens frequently in medical image process and analysis, e.g., when the high-density region of objects can only be penetrated by X-rays at a limited angular range. As the projection data are available only in an angular range, the incomplete data problem can be attributed to the limited angle problem, which is an ill-posed inverse problem. Image reconstruction based on total variation (TV) reduces the problem and gives better performance on edge-preserving reconstruction; however, the artificial parameter can only be determined through considerable experimentation. In this paper, an effective TV objective function is proposed to reduce the inverse problem in the limited angle tomography. This novel objective function provides a robust and effective reconstruction without any artificial parameter in the iterative processes, using the TV as a multiplicative constraint. The results demonstrate that this reconstruction strategy outperforms some previous ones.