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2006 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications最新文献

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A Novel Ontology for Sensor Networks Data 一种新的传感器网络数据本体
2006 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications Pub Date : 2006-07-12 DOI: 10.1109/CIMSA.2006.250753
M. Eid, R. Liscano, A. El Saddik
{"title":"A Novel Ontology for Sensor Networks Data","authors":"M. Eid, R. Liscano, A. El Saddik","doi":"10.1109/CIMSA.2006.250753","DOIUrl":"https://doi.org/10.1109/CIMSA.2006.250753","url":null,"abstract":"Sensor networks have seen an exponential growth in the last few years. They involve deploying a large number of small sensing nodes for capturing environmental data. Searching such networks is limited by two major constraints: scalability and precision. We argue that the key to enabling scalable and precise sensor information search is to define an ontology that associates sensor information taxonomy for searching and interpreting raw data streams. We present the motivation and description of the development of the proposed ontology, partial evaluation of the early prototype ontology, a discussion of design and implementation issues, and directions for future research works","PeriodicalId":431033,"journal":{"name":"2006 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications","volume":"238 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116463799","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: 45
The Experience of Using a Neural Assistor to Enhance the Transient Characteristics of well-defined Control Systems 利用神经辅助器增强定义良好的控制系统暂态特性的经验
2006 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications Pub Date : 2006-07-12 DOI: 10.1109/CIMSA.2006.250764
Penchen Chou, TsiChow Chang, TsiChian Hwang
{"title":"The Experience of Using a Neural Assistor to Enhance the Transient Characteristics of well-defined Control Systems","authors":"Penchen Chou, TsiChow Chang, TsiChian Hwang","doi":"10.1109/CIMSA.2006.250764","DOIUrl":"https://doi.org/10.1109/CIMSA.2006.250764","url":null,"abstract":"Neural networks (NN) as controllers, are generally nonlinear ones in nature, the input command range in such a system is limited in advance. For different input, the controller may behave in different way. A NN can be used together with a conventional controller such as a PID one, in this condition; NN can further improve the transient of the control system. In this paper, a NN used as an assistor besides the conventional controllers can show the improvement of transient performances","PeriodicalId":431033,"journal":{"name":"2006 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129784868","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
Hall Effect Sensor and Artificial Neural Networks Applied on Diagnosis of Broken Rotor Bars in Large Induction Motors 霍尔效应传感器和人工神经网络在大型异步电动机转子断条诊断中的应用
2006 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications Pub Date : 2006-07-12 DOI: 10.1109/CIMSA.2006.250744
C. G. Dias, I. Chabu, M. A. Bussab
{"title":"Hall Effect Sensor and Artificial Neural Networks Applied on Diagnosis of Broken Rotor Bars in Large Induction Motors","authors":"C. G. Dias, I. Chabu, M. A. Bussab","doi":"10.1109/CIMSA.2006.250744","DOIUrl":"https://doi.org/10.1109/CIMSA.2006.250744","url":null,"abstract":"This paper presents the use of the neural networks techniques in order to help on diagnosis of broken bars in large induction motors in real time. The obtained signal of the Hall effect sensor is applied in an artificial neural network to identify a fault and to estimate the number of broken bars. Simulation results are presented from the model implemented in the SIMULINK/MATLAB tool","PeriodicalId":431033,"journal":{"name":"2006 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123781350","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: 8
Morphological Neural Networks for Localization and Mapping 用于定位和映射的形态学神经网络
2006 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications Pub Date : 2006-07-12 DOI: 10.1109/CIMSA.2006.250739
I. Villaverde, M. Graña, A. D'Anjou
{"title":"Morphological Neural Networks for Localization and Mapping","authors":"I. Villaverde, M. Graña, A. D'Anjou","doi":"10.1109/CIMSA.2006.250739","DOIUrl":"https://doi.org/10.1109/CIMSA.2006.250739","url":null,"abstract":"Morphological associative memories (MAM) have been proposed for image denoising and pattern recognition. We have shown that they can be applied to other domains, like image retrieval and hyperspectral image unsupervised segmentation. In both cases the key idea is that morphological auto associative memories (MAAM) selective sensitivity to erosive and dilative noise can be applied to detect the morphological independence between patterns. The convex coordinates obtained by linear unmixing based on the sets of morphological independent patterns define a feature extraction process. These features may be useful either for pattern classification. We present some results on the task of visual landmark recognition for a mobile robot self-localization task","PeriodicalId":431033,"journal":{"name":"2006 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications","volume":"133 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122437223","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: 6
An Intelligent System for Decentralized Load Management 一种分散负荷管理的智能系统
2006 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications Pub Date : 2006-07-12 DOI: 10.1109/CIMSA.2006.250752
A. Amato, M. Calabrese, V. Di Lecce, V. Piuri
{"title":"An Intelligent System for Decentralized Load Management","authors":"A. Amato, M. Calabrese, V. Di Lecce, V. Piuri","doi":"10.1109/CIMSA.2006.250752","DOIUrl":"https://doi.org/10.1109/CIMSA.2006.250752","url":null,"abstract":"This work proposes a model of an intelligent short term demand side management system based on a MAS. The system is designed to avoid peaks of power request greater than a given threshold and to give maximum comfort to user. The proposed system is composed of a distributed network of processing nodes (PN). Each PN hosts one agent and it is able to manage a single socket tap allowing or disallowing it to supply power. Each agent reacts to a new critical condition entering in competition with the others to gain the access at a shared limited resource. As the results shown the proposed agency can be the consumer's key to take advantage of a DSM program automatically","PeriodicalId":431033,"journal":{"name":"2006 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115187534","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
Pattern recognition for infrared profiles of steel strips based on fuzzy knowledge 基于模糊知识的钢带红外轮廓模式识别
2006 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications Pub Date : 2006-07-12 DOI: 10.1109/CIMSA.2006.250759
R. Usamentiaga, D. García, D. González, J. Molleda
{"title":"Pattern recognition for infrared profiles of steel strips based on fuzzy knowledge","authors":"R. Usamentiaga, D. García, D. González, J. Molleda","doi":"10.1109/CIMSA.2006.250759","DOIUrl":"https://doi.org/10.1109/CIMSA.2006.250759","url":null,"abstract":"The recent demand for extremely thin high-quality steel products makes temperature control an increasingly determining factor in the final quality. In fact, uneven temperature during thin steel production makes the steel fracture rate increase sharply. This work proposes a method to recognize a common uneven temperature pattern known as the hot-shoulders pattern. The proposed recognition method is carried out in three steps. Firstly, the infrared image obtained from the steel strip is processed in order to calculate the infrared profiles. Next, each of these profiles is processed in order to determine its shape. Finally, a fuzzy approach is used to determine the membership degree of each profile to the hot-shoulders temperature pattern","PeriodicalId":431033,"journal":{"name":"2006 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130014510","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: 0
Computational intelligence techniques to detect toxic gas presence 计算智能技术检测有毒气体的存在
2006 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications Pub Date : 2006-07-12 DOI: 10.1109/CIMSA.2006.250745
C. Alippi, G. Pelosi, M. Roveri
{"title":"Computational intelligence techniques to detect toxic gas presence","authors":"C. Alippi, G. Pelosi, M. Roveri","doi":"10.1109/CIMSA.2006.250745","DOIUrl":"https://doi.org/10.1109/CIMSA.2006.250745","url":null,"abstract":"The detection of toxic gas in industrial environments (performed by means of an array of low-cost on-chip chemical sensors) is a valuable approach to increase daily safety. The aim of this paper is to critically discuss the use of a-priori knowledge in the design of gas sensor systems implementing computational intelligence techniques for signal processing and gas presence detection. The availability of a-priori information about the probability density function of the considered classes as well as about the class separation boundary (Bayes boundary) allow the classifier designer for selecting appropriate condensing and editing techniques to keep under control the computational complexity","PeriodicalId":431033,"journal":{"name":"2006 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications","volume":"124 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122485065","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: 1
A Training Methodology for Neural Networks Noise-Filtering when no Training Sets are available for Supervised Learning 无监督学习训练集时神经网络噪声滤波的训练方法
2006 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications Pub Date : 2006-07-12 DOI: 10.1109/CIMSA.2006.250755
M. M. Luaces
{"title":"A Training Methodology for Neural Networks Noise-Filtering when no Training Sets are available for Supervised Learning","authors":"M. M. Luaces","doi":"10.1109/CIMSA.2006.250755","DOIUrl":"https://doi.org/10.1109/CIMSA.2006.250755","url":null,"abstract":"Noise filtering is considered one of the main applications of neural networks due to its importance in a wide range of scientific and technological areas. The standard methodology needs to obtain first an accurate measure of the desired signal, which is a must in supervised learning. Nevertheless, in some areas these data sets are rarely available, nor can be determined noise function although its distribution is usually known. In this paper, we propose a training methodology combining data simulation, modular neural networks and an interval-splitting strategy for noise-filtering where training data sets are not necessary. Method is explained step by step, and finally results are presented and conclusions done","PeriodicalId":431033,"journal":{"name":"2006 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129308727","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: 0
Adaptive Spatio-Spectral Hyperspectral Image Processing for Online Industrial Classification of Inhomogeneous Materials 非均匀材料在线工业分类的自适应空间光谱高光谱图像处理
2006 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications Pub Date : 2006-07-12 DOI: 10.1109/CIMSA.2006.250758
A. Prieto, F. Bellas, R. Duro, F. López-Peña
{"title":"Adaptive Spatio-Spectral Hyperspectral Image Processing for Online Industrial Classification of Inhomogeneous Materials","authors":"A. Prieto, F. Bellas, R. Duro, F. López-Peña","doi":"10.1109/CIMSA.2006.250758","DOIUrl":"https://doi.org/10.1109/CIMSA.2006.250758","url":null,"abstract":"An approach for considering spatio-spectral information when classifying inhomogeneous materials in industrial environments is proposed. Its main application would be in the inspection and quality control tasks. They system core is an ANN based hyperspectral processing unit able to perform the online determination of the quality of the material based on its composition and grain size. A training adviser is being implemented in the system in order to automate the determination of the optimal spatial window size, as well as to reduce the number of spectral bands used and for determining the optimal spectral combination function through the automatic extraction of the discriminating features. Several tests have been carried out on synthetic and real data sets. In particular, the proposed approach is used to discriminate samples of andalusite having different purities; the results obtained show an accuracy of better than 98%","PeriodicalId":431033,"journal":{"name":"2006 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116391649","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: 1
NN-based software tool for helping wireless communications components design 基于nn的软件工具，帮助设计无线通信组件
2006 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications Pub Date : 2006-07-12 DOI: 10.1109/CIMSA.2006.250756
Georgina Stegmayer, O. Chiotti
{"title":"NN-based software tool for helping wireless communications components design","authors":"Georgina Stegmayer, O. Chiotti","doi":"10.1109/CIMSA.2006.250756","DOIUrl":"https://doi.org/10.1109/CIMSA.2006.250756","url":null,"abstract":"This paper presents a support to the task of automatic neural network model generation for RF/microwave devices, directly from measurement data, which can also be automatically exported into a commercial RF simulator","PeriodicalId":431033,"journal":{"name":"2006 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications","volume":"119 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116199417","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: 0
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