结合相关规则和本体加权图的查询扩展

针对现有信息检索系统查询性能的不足,提出了一种结合相关规则和WordNet本体信息的查询扩展方法.该方法借助相关规则挖掘和WordNet本体信息构建加权词语关系图,并根据加权图的结构和权重信息计算扩展词的重要性.查询时,从这个图中取原查询词的最邻近词作为扩展词来源,选取其中权重最大的p个词返回并进行二次检索.在实现算法的基础上,通过Lucene全文检索器进行实验,将所得的结果值F1与其他算法的结果作比较.比较结果表明,该方法比不作扩展的检索有约16.93％的性能提升.     

酚醛胺(T-31)环氧树脂固化剂之浅谈

介绍了酚醛胺(T-31)系列环氧固化剂及原料的现状和特点,并对酚醛胺(T-31)系列环氧固化剂进行了分代,阐述了第三代酚醛胺(T-31)系列环氧固化剂的系列化、专用化、高档化。预测和展望了2005年的需求和方向。     

基于Hg~(2+)诱导DNA双链形成的汞离子检测试纸条研制

Hg2+能特异性地与2个胸腺嘧啶碱基(T)共价结合,介导T-T错配形成稳定的T-Hg2+-T结构。基于DNA修饰的纳米金探针研制了一种可检测水溶液中Hg2+浓度的层析试纸条。试纸条包含1条控制线和2条测试线,检测结果在5 min内可见,裸眼可见检测灵敏度为100 nmol/L。金标条阅读仪分析结果在10 nmol/L~10μmol/L范围内具有良好的线性关系,且对常见二价重金属离子Cu2+,Ni2+,Mg2+,Zn2+,Pb2+具有很好的选择性。该试纸条灵敏特异、快捷简单、操作方便、成本低廉,在环境监测和食品安全的现场快速检测中具有很好的应用前景。     

学科交叉融合对计算机基础教学的新挑战

学科交叉融合是现代世界一流大学注重培养的新思路,也越来越受到普通高校教学研究者们的重视。《大学计算机基础》课程则是高校教改的重点和难点。就如何在《大学计算机基础》课程中引入新的教学思路,让学科交叉融合能在高等教育的开展之初就能融入到大学生的学习和研究中,进行部分实践和探索。随着计算机在现代科学研究中的重要性越来越突出,计算机教学改革势在必行。     

两套过滤器反冲洗程序并行解决方案

该反冲洗系统是以艾默生DeltaV为控制系统,采用SFC（Sequential FunctionChart）顺序控制功能进行组态设计。介绍了＇过滤器反冲洗顺控流程,包含反冲洗联锁、反冲洗间隔时间、反冲洗时间、反冲洗跳过等,能提供两套过滤器反冲洗程序并行解决方案。     

基于Zigbee和GPRS的多参数水质监测系统设计

针对传统水质监测存在的实时性差、覆盖范围窄、网络布局困难等问题,设计出一种基于Zigbee无线传感网络和GPRS技术的远程实时水质监测系统.文中重点阐述该系统的硬件设计和软件实现过程.该系统将水质传感器采集的各项参数传送给CC2530芯片后,经Zigbee网络发送给网络协调器,再通过GPRS无线通信模块SIM300传送到Internet或移动终端,最后用户可通过PC机或手机终端查看水质参数信息,实现了水质信息的远程实时监测.实验应用表明,该系统监测范围广、使用方便、快捷,具有较为广阔的应用前景.     

HEGM: A hierarchical elastic graph matching for hand gesture recognition

A hierarchical scheme for elastic graph matching applied to hand gesture recognition is proposed. The proposed algorithm exploits the relative discriminatory capabilities of visual features scattered on the images, assigning the corresponding weights to each feature. A boosting algorithm is used to determine the structure of the hierarchy of a given graph. The graph is expressed by annotating the nodes of interest over the target object to form a bunch graph. Three annotation techniques, manual, semi-automatic, and automatic annotation are used to determine the position of the nodes. The scheme and the annotation approaches are applied to explore the hand gesture recognition performance. A number of filter banks are applied to hand gestures images to investigate the effect of using different feature representation approaches. Experimental results show that the hierarchical elastic graph matching (HEGM) approach classified the hand posture with a gesture recognition accuracy of 99.85% when visual features were extracted by utilizing the Histogram of Oriented Gradient (HOG) representation. The results also provide the performance measures from the aspect of recognition accuracy to matching benefits, node positions correlation and consistency on three annotation approaches, showing that the semi-automatic annotation method is more efficient and accurate than the other two methods.     

Telerobot-enabled HUB-CI model for collaborative lifecycle management of design and prototyping

The HUB-CI model is investigated in a telerobotic system in a client/server network to manage the lifecycle of engineering design and prototyping. The purpose of this platform is to support collaborative engineering design and proof of concept to enhance distributed team collaboration and resource utilization. The suggested platform is exemplified in two collaboration support tools and a physical prototyping platform. Structured Co-Insight Management is developed to support innovative idea exchanges and the consensus decision-making during the design process. Conflict/error detection management helps preventing conflicts and errors during the lifecycle of design and development. Physical collaboration over the network occurs when a team controls the telerobot operation during prototyping and testing in design cycles. A pilot system is implemented with a group project for the design of an electronic circuit (including both hardware and software designs). The functional assessment method is used to compare this platform to other collaborative design tools. The system presented offers unique qualitative advantages as an integrated collaboration support system.     

A multipurpose audio aggregation watermarking based on multistage vector quantization

Watermarking technology can achieve multipurpose such as copyright protection, copy protection, and integrity authentication. We propose a novel watermarking approach which involves robust watermark and fragile watermark in a two-stage quantization technique. Traditional watermarking algorithms mostly have poor performance in imperceptibility as the codeword selected from the modified codebook is not optimal. In our method, we select the codeword in the original codebook to ensure optimality. Furthermore, we use Huffman encoding to pick up property which is sensitive to many attacks in the entire aggregation. For copyright protection, the proposed scheme can resist attacks such as lossy compression, noise addition, and normalization. As for content authentication, the proposed scheme is sensitive to various attacks provided by Stirmark Benchmark for Audio. Experimental results show that the proposed method can be used respectively for protecting the copyright and authenticating the integrity of the audio aggregation.     

Using self-organizing maps with complex network topologies and coalitions for time series prediction

A Self-organizing Map (SOM) is a competitive learning neural network architecture that make available a certain amount of classificatory neurons, which self-organize spatially based on input patterns. In this paper we explore the use of complex network topologies, like small-world, scale-free or random networks; for connecting the neurons within a SOM, and apply them for Time Series Prediction. We follow the VQTAM model for function prediction, and consider several benchmarks to evaluate the quality of the predictions. Afterwards, we introduce the CASOM algorithm (Coalitions and SOM) that uses coalitions to temporarily extend the neighborhood of a neuron, and to provide more accuracy in prediction problems than classical SOM. The results presented in this work suggest that the most regular the network topology is, the better results it provides in prediction. Besides, we have found that not updating all the neurons at the same time provides much better results. Finally, we describe how the use of coalitions can enhance the capability of SOM for Time Series Prediction.