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2008 IEEE International Conference on Computational Cybernetics最新文献

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Design of the modular production system by simulation software's cosimir® 利用仿真软件cosimir®进行模块化生产系统的设计
2008 IEEE International Conference on Computational Cybernetics Pub Date : 2008-11-01 DOI: 10.1109/ICCCYB.2008.4721410
J. Miháliková, S. Rovnakova, O. Liska, J. Živčák
{"title":"Design of the modular production system by simulation software's cosimir®","authors":"J. Miháliková, S. Rovnakova, O. Liska, J. Živčák","doi":"10.1109/ICCCYB.2008.4721410","DOIUrl":"https://doi.org/10.1109/ICCCYB.2008.4721410","url":null,"abstract":"The article describes utilisation of COSIMIRreg Professional, COSIMIRreg PLC and COSIMIRreg Robotics for designing of modular production system (MPS). It describes COSIMIRreg Professional, e.g. basic functions, 3D modeling, 3D simulation and robot programming. These programs were tried out on modular production system. This flexible system is composed of a conveyer and 6 stations.","PeriodicalId":320146,"journal":{"name":"2008 IEEE International Conference on Computational Cybernetics","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128142348","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
The use of entropy to measure structural diversity 利用熵来衡量结构多样性
2008 IEEE International Conference on Computational Cybernetics Pub Date : 2008-10-20 DOI: 10.1109/ICCCYB.2008.4721376
L. Masisi, F. Nelwamondo, T. Marwala
{"title":"The use of entropy to measure structural diversity","authors":"L. Masisi, F. Nelwamondo, T. Marwala","doi":"10.1109/ICCCYB.2008.4721376","DOIUrl":"https://doi.org/10.1109/ICCCYB.2008.4721376","url":null,"abstract":"In this paper entropy based methods are compared and used to measure structural diversity of an ensemble of 21 classifiers. This measure is mostly applied in ecology, whereby species counts are used as a measure of diversity. The measures used were Shannon entropy, Simpsons and the Berger Parker diversity indexes. As the diversity indexes increased so did the accuracy of the ensemble. An ensemble dominated by classifiers with the same structure produced poor accuracy. Uncertainty rule from information theory was also used to further define diversity. Genetic algorithms were used to find the optimal ensemble by using the diversity indices as the cost function. The method of voting was used to aggregate the decisions.","PeriodicalId":320146,"journal":{"name":"2008 IEEE International Conference on Computational Cybernetics","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126501642","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: 52
Finding Sparse Features for Face Detection Using Genetic Algorithms 利用遗传算法寻找稀疏特征用于人脸检测
2008 IEEE International Conference on Computational Cybernetics Pub Date : 2008-07-29 DOI: 10.1109/ICCCYB.2008.4721401
H. Sagha, M. Dehghani, E. Enayati
{"title":"Finding Sparse Features for Face Detection Using Genetic Algorithms","authors":"H. Sagha, M. Dehghani, E. Enayati","doi":"10.1109/ICCCYB.2008.4721401","DOIUrl":"https://doi.org/10.1109/ICCCYB.2008.4721401","url":null,"abstract":"Although Face detection is not a recent activity in the field of image processing, it is still an open area for research. The greatest step in this field is the work reported by Viola and the recent analogous one is proposed by Huang et al. Both of them use similar features and also similar training process. The former is just for detecting upright faces, but the latter can detect multi-view faces in still grayscale images using new features called 'sparse feature'. Finding these features is very time consuming and inefficient by proposed methods. Here, we propose a new approach for finding sparse features using a genetic algorithm system. This method requires less computational cost and gets more effective features in learning process for face detection that causes more accuracy.","PeriodicalId":320146,"journal":{"name":"2008 IEEE International Conference on Computational Cybernetics","volume":"483 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126166050","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
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