Proceedings. 2005 IEEE Networking, Sensing and Control, 2005.最新文献_第8页

Implementation of changing message migration using mobile agents 使用移动代理实现更改消息迁移

Proceedings. 2005 IEEE Networking, Sensing and Control, 2005. Pub Date : 2005-03-19 DOI: 10.1109/ICNSC.2005.1461249

Xuezhou Xu, Qiufang Zhan, Jing Yao

引用次数: 0

Research on architecture for reconfigurable underwater sensor networks 可重构水下传感器网络体系结构研究

Proceedings. 2005 IEEE Networking, Sensing and Control, 2005. Pub Date : 2005-03-19 DOI: 10.1109/ICNSC.2005.1461299

Shuo Wang, M. Tan

引用次数: 9

Two-layer competitive Hopfield neural network for wafer defect detection 基于两层竞争Hopfield神经网络的晶圆缺陷检测

Proceedings. 2005 IEEE Networking, Sensing and Control, 2005. Pub Date : 2005-03-19 DOI: 10.1109/ICNSC.2005.1461344

Chan-Yu Chang, Si-Yan Lin, M. Jeng

引用次数: 3

On hybrid flexure hinges 在混合柔性铰链上

Proceedings. 2005 IEEE Networking, Sensing and Control, 2005. Pub Date : 2005-03-19 DOI: 10.1109/ICNSC.2005.1461275

Guimin Chen, J. Jia, Zhiwu Li

引用次数: 20

Kansei modeling of the color design decision process in Web designs 感性造型在网页设计中的色彩设计决策过程

Proceedings. 2005 IEEE Networking, Sensing and Control, 2005. Pub Date : 2005-03-19 DOI: 10.1109/ICNSC.2005.1461261

E. Cooper, K. Kamei

引用次数: 4

Rough logic for building a landmine classifier 构建地雷分类器的粗略逻辑

Proceedings. 2005 IEEE Networking, Sensing and Control, 2005. Pub Date : 2005-03-19 DOI: 10.1109/ICNSC.2005.1461304

A. Agrawal, A. Agarwal

{"title":"Rough logic for building a landmine classifier","authors":"A. Agrawal, A. Agarwal","doi":"10.1109/ICNSC.2005.1461304","DOIUrl":"https://doi.org/10.1109/ICNSC.2005.1461304","url":null,"abstract":"Landmines are significant barriers to financial, economic and social development in various parts of this world. Metal detectors currently used by teams engaged in the decontamination of mines cannot differentiate a mine from metallic debris where the soil contains large quantities of metal scraps and cartridge cases. So what is required is a sensor that will reliably confirm that the ground being tested does not contain an explosive device with almost perfect reliability. The various sensors provide different attributes about the nature of the soil. Even human experts are unable to give belief and plausibility to their rules. Thus the conventional Dempster-Shafer theory cannot be applied to build an expert system. Thus rough sets are applied to classify the landmine data because here, any prior knowledge of rules is not needed; these rules are automatically discovered from the database. For the application of rough set theory, first, approximation space is to be identified. This can be done by a human expert, otherwise, principal component analysis can be used. In fact the problem of identifying application space is similar to that of identifying redundant attributes (which carry no useful information for the purpose of classification) and throwing them away. It is observed that aspect ratio, blob size, and grayscale are important features for landmine classification. Now, all data tuples which agree on all task relevant attributes, form one elementary set. Thus the whole of the database is divided into mutually exclusive elementary sets. As no crisp rules exist for classification, this essentially implies the decision sets (the sets formed by the presence or absence of landmines) are not definable (cannot be expressed as union or intersection of elementary sets). Thus lower and upper approximations of the decision sets are taken and the boundary set is found.","PeriodicalId":313251,"journal":{"name":"Proceedings. 2005 IEEE Networking, Sensing and Control, 2005.","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126255837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

An optimized approach to application of neural networks to classification of multispectral, remote sensing data 神经网络在多光谱遥感数据分类中的优化应用

Proceedings. 2005 IEEE Networking, Sensing and Control, 2005. Pub Date : 2005-03-19 DOI: 10.1109/ICNSC.2005.1461193

M. Toshniwal

{"title":"An optimized approach to application of neural networks to classification of multispectral, remote sensing data","authors":"M. Toshniwal","doi":"10.1109/ICNSC.2005.1461193","DOIUrl":"https://doi.org/10.1109/ICNSC.2005.1461193","url":null,"abstract":"Satellite image processing is one of the key research areas in the area of remote sensing. Remote sensing derives immense applications from this field like terrain analysis and generation, topographic mapping. Traditional statistical approaches provide reasonable success in this field, but the efficiency is limited with respect to the robustness of results. The statistical approaches are parametric, based on an assumed statistical distribution and hence the efficiency and correctness of results closely correlates to the proximity of data to the assumed distribution. Feed-forward neural networks can be trained to learn pixel classes and hence can be applied to the area of satellite image segmentation. This paper describes a technique developed to select training parameters and collection of training sets. An algorithm to accelerate the training process and reduce the time for classification is also explored. This paper provides a suitably developed neural network architecture with high accuracy. We obtained accuracy and efficiency in terms of standard parameters, and were able to achieve accurate image segmentation with kappa coefficient of 0.97. The time for classification was reduced by more than 70%.","PeriodicalId":313251,"journal":{"name":"Proceedings. 2005 IEEE Networking, Sensing and Control, 2005.","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116437350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 12

A new approach for detecting the best feature set 一种检测最佳特征集的新方法

Proceedings. 2005 IEEE Networking, Sensing and Control, 2005. Pub Date : 2005-03-19 DOI: 10.1109/ICNSC.2005.1461164

Shuang Gang, Hongnian Yu

{"title":"A new approach for detecting the best feature set","authors":"Shuang Gang, Hongnian Yu","doi":"10.1109/ICNSC.2005.1461164","DOIUrl":"https://doi.org/10.1109/ICNSC.2005.1461164","url":null,"abstract":"The efficiency and accuracy of training neural-net classifiers is typically improved by eliminating of features that are redundant and irrelevant. The objective is to reduce the size of the input feature set and at the same time retain as much as possible of the class discriminatory information. Such an input features set after reducing features from the original input features, which is called best feature space, would offer a reduction of both cost and complexity of feature collection as well as improve the efficiency and accuracy of the resultant classifier. In this paper, we develop and evaluate two composite feature selection algorithms: mutual information feature space forward selection (MIFSFS) and mutual information feature space backward selection (MTFSBS). These two algorithms use mutual information (MI), for both continuous-valued and discrete-valued features, between the feature space and the class in order to find the best feature space from the original input features. The most important output from the new algorithms is that we not only can identity the irrelevant features, but also identify the redundant features, which cannot be identified by the common feature selection algorithms (for example artificial neural networks, ANN). Empirical studies of both realistic new and previously published classification problems indicate that the proposed algorithms are robust, stable and efficient. One of real application is drug discovery. Knowledge discovery from gene expression data is a highly research topical area for the drug discovery. Finding a number of genes out of the thousands caused a certain types of diseases is a significant contribution for the preventing and fighting diseases. Here we use the myeloma disease as an example to demonstrate how to identify the genes caused the myeloma disease.","PeriodicalId":313251,"journal":{"name":"Proceedings. 2005 IEEE Networking, Sensing and Control, 2005.","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114997259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Agent-based dynamic scheduling for semiconductor wafer fab 基于agent的半导体晶圆厂动态调度

Proceedings. 2005 IEEE Networking, Sensing and Control, 2005. Pub Date : 2005-03-19 DOI: 10.1109/ICNSC.2005.1461180

Li Li, F. Qiao, Qidi Wu

引用次数: 5

Artificial neural network based macromodeling approach for two-port RF MEMS resonating structures 基于人工神经网络的双端口射频MEMS谐振结构宏观建模方法

Proceedings. 2005 IEEE Networking, Sensing and Control, 2005. Pub Date : 2005-03-19 DOI: 10.1109/ICNSC.2005.1461198

Yongjae Lee, Yong-hwa Park, F. Niu, D. Filipović

引用次数: 11