2010 IEEE Conference on Cybernetics and Intelligent Systems最新文献

Cascaded control of 3D path following for an unmanned helicopter 无人直升机三维路径跟踪的级联控制

2010 IEEE Conference on Cybernetics and Intelligent Systems Pub Date : 2010-06-28 DOI: 10.1109/ICCIS.2010.5518579

Biao Wang, Xiangxu Dong, Ben M. Chen

引用次数: 11

A load transfer scheme of radial distribution feeders considering distributed generation 考虑分布式发电的径向配电馈线负荷转移方案

2010 IEEE Conference on Cybernetics and Intelligent Systems Pub Date : 2010-06-28 DOI: 10.1109/ICCIS.2010.5518551

Wen-chih Yang, Wei-Tzer Huang

引用次数: 11

Machine Learning algorithms applied to the classification of robotic soccer formations and opponent teams 机器学习算法应用于机器人足球阵型和对手球队的分类

2010 IEEE Conference on Cybernetics and Intelligent Systems Pub Date : 2010-06-28 DOI: 10.1109/ICCIS.2010.5518540

B. Faria, Luis Paulo Reis, N. Lau, Gladys Castillo

{"title":"Machine Learning algorithms applied to the classification of robotic soccer formations and opponent teams","authors":"B. Faria, Luis Paulo Reis, N. Lau, Gladys Castillo","doi":"10.1109/ICCIS.2010.5518540","DOIUrl":"https://doi.org/10.1109/ICCIS.2010.5518540","url":null,"abstract":"Machine Learning (ML) and Knowledge Discovery (KD) are research areas with several different applications but that share a common objective of acquiring more and new information from data. This paper presents an application of several ML techniques in the identification of the opponent team and also on the classification of robotic soccer formations in the context of RoboCup international robotic soccer competition. RoboCup international project includes several distinct leagues were teams composed by different types of real or simulated robots play soccer games following a set of pre-established rules. The simulated 2D league uses simulated robots encouraging research on artificial intelligence methodologies like high-level coordination and machine learning techniques. The experimental tests performed, using four distinct datasets, enabled us to conclude that the Support Vector Machines (SVM) technique has higher accuracy than the k-Nearest Neighbor, Neural Networks and Kernel Naïve Bayes in terms of adaptation to a new kind of data. Also, the experimental results enable to conclude that using the Principal Component Analysis SVM achieves worse results than using simpler methods that have as primary assumption the distance between samples, like k-NN.","PeriodicalId":445473,"journal":{"name":"2010 IEEE Conference on Cybernetics and Intelligent Systems","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123919649","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

Vision aided motion estimation for unmanned helicopters in GPS denied environments 无GPS环境下无人直升机视觉辅助运动估计

2010 IEEE Conference on Cybernetics and Intelligent Systems Pub Date : 2010-06-28 DOI: 10.1109/ICCIS.2010.5518578

F. Lin, Ben M. Chen, Tong-heng Lee

引用次数: 11

Modeling and intelligent control design of car following behavior in real traffic flow 真实交通流中汽车跟随行为建模与智能控制设计

2010 IEEE Conference on Cybernetics and Intelligent Systems Pub Date : 2010-06-28 DOI: 10.1109/ICCIS.2010.5518546

A. Khodayari, R. Kazemi, A. Ghaffari, N. Manavizadeh

引用次数: 26

Electric load forecasting by SVR with chaotic ant swarm optimization 基于混沌蚁群优化的SVR电力负荷预测

2010 IEEE Conference on Cybernetics and Intelligent Systems Pub Date : 2010-06-28 DOI: 10.1109/ICCIS.2010.5518572

Wei‐Chiang Hong, Chien-Yuan Lai, Wei-Mou Hung, Yucheng Dong

{"title":"Electric load forecasting by SVR with chaotic ant swarm optimization","authors":"Wei‐Chiang Hong, Chien-Yuan Lai, Wei-Mou Hung, Yucheng Dong","doi":"10.1109/ICCIS.2010.5518572","DOIUrl":"https://doi.org/10.1109/ICCIS.2010.5518572","url":null,"abstract":"Support vector regression (SVR) has revealed the strong potential in accurate electric load forecasting, particularly by employing effective evolutionary algorithms to determine suitable values of its three parameters. Based on previous research results, these employed evolutionary algorithms themselves also have drawbacks, such as premature convergence, slowly reaching the global optimal solution, and trapping into a local optimum in parameters determination of a SVR model. This paper presents a short-term electric load forecasting model which applies a novel algorithm, namely chaotic ant swarm optimization (CAS), to improve the forecasting performance by searching suitable parameters combination in a SVR forecasting model. The proposed CAS combines with the chaotic behavior of single ant and self-organization behavior of ant colony in the foraging process to overcome premature local optimum. The empirical results indicate that the SVR model with CAS (SVRCAS) results in better forecasting performance than the other methods, namely SVRCPSO (SVR with chaotic PSO), SVRCGA (SVR with chaotic GA), regression model, and ANN model.","PeriodicalId":445473,"journal":{"name":"2010 IEEE Conference on Cybernetics and Intelligent Systems","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133176315","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

Comparison of EM algorithm and particle swarm optimisation for local model network training 局部模型网络训练中EM算法与粒子群算法的比较

2010 IEEE Conference on Cybernetics and Intelligent Systems Pub Date : 2010-06-28 DOI: 10.1109/ICCIS.2010.5518547

C. Hametner, S. Jakubek

引用次数: 2

Optimization and integration of electronic identity authentication using a biometric indicator and RFID 使用生物识别指示器和RFID的电子身份认证的优化和集成

2010 IEEE Conference on Cybernetics and Intelligent Systems Pub Date : 2010-06-28 DOI: 10.1109/ICCIS.2010.5518565

Narges Peyravi, S. Jafari

引用次数: 0

Football scientia- an automated tool for professional soccer coaches 足球科学-专业足球教练的自动化工具

2010 IEEE Conference on Cybernetics and Intelligent Systems Pub Date : 2010-06-28 DOI: 10.1109/ICCIS.2010.5518568

P. Abreu, José Moura, Daniel Castro Silva, Luis Paulo Reis, J. Garganta

{"title":"Football scientia- an automated tool for professional soccer coaches","authors":"P. Abreu, José Moura, Daniel Castro Silva, Luis Paulo Reis, J. Garganta","doi":"10.1109/ICCIS.2010.5518568","DOIUrl":"https://doi.org/10.1109/ICCIS.2010.5518568","url":null,"abstract":"A soccer game can be seen as a confrontation between two teams of agents where each player, communicating with his teammates, try to interpret in the best way as possible the game situations, achieving its main goals. Today the most important factors in a soccer clubs life and in its coach success are the game results they achieve. They represent the success of the club and in many cases the coachs future. Because of that club coaches usually use automated tools to measure their teams performance all over a soccer competition. Based only in Cartesian coordinates and in a sequential time frame analysis, this research work presents an automatic tool capable to calculate many technical aspects in a soccer match. For the validation tool process, games of simulation 2d RoboCup international competition were used. The results achieved were quite satisfactory. In what concerns to the set of statistics collected more than 92% of the total events were detected and only for the shot event this number dropped to between 74% and 85%. The future work will be concerned in incorporating this project with a real time tracking system and increasing the number of technical aspects calculated by the system.","PeriodicalId":445473,"journal":{"name":"2010 IEEE Conference on Cybernetics and Intelligent Systems","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124451616","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

Particle swarm optimization identification of IPMC actuator using fuzzy NARX model 基于模糊NARX模型的IPMC执行器粒子群优化辨识

2010 IEEE Conference on Cybernetics and Intelligent Systems Pub Date : 2010-06-28 DOI: 10.1109/ICCIS.2010.5518573

H. Anh

引用次数: 2