2010 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications最新文献

{"title":"Score normalization by Dynamic Time Warping","authors":"R. Modugno, G. Pirlo, D. Impedovo","doi":"10.1109/CIMSA.2010.5611775","DOIUrl":"https://doi.org/10.1109/CIMSA.2010.5611775","url":null,"abstract":"Multi-expert approach is a well-known paradigm to support complex decisions and several decision fusion techniques have been considered. In this field score normalization is a fundamental step to allow high performances and hence several techniques have been proposed so far. This paper addresses the problem of score normalization and presents a new technique based on Dynamic Time Warping. The experimental results, carried out in the field of pattern classification, show the superiority of the new technique with respect to other approaches in the literature, based on MIN-MAX, z-score and characteristics functions.","PeriodicalId":162890,"journal":{"name":"2010 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122463102","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}

{"title":"An Adaptive Network Based Fuzzy Inference System algorithm for assessment and improvement of job security among operators with respect to HSE-Ergonomics program","authors":"V. Nadimi, A. Azadeh, M. Rouzbahman, Morteza Saberi, S. Shabibi","doi":"10.1109/CIMSA.2010.5611772","DOIUrl":"https://doi.org/10.1109/CIMSA.2010.5611772","url":null,"abstract":"Researchers have been continuously trying to improve human performance with respect to Health, Safety and Environment (HSE) and ergonomics (hence HSEE). Performance measurement and assessment of operators are fundamental to management planning and control activities, and accordingly, have received considerable attention by both management practitioners and theorists. There has been several efficiency frontier analysis methods reported in the literature. However, each of these methodologies has its strength as well as major limitations. This study proposes a non-parametric efficiency frontier analysis methods based on Adaptive Network-Based Fuzzy Inference System (ANFIS) for measuring efficiency as a complementary tool for performance assessment and improvement of operators. The proposed ANFIS algorithm is able to find a stochastic frontier based on a set of input-output observational data and do not require explicit assumptions about the functional structure of the stochastic frontier. Furthermore, it uses a similar approach to econometric methods for calculating the efficiency scores. The proposed approach is applied to a set of operators in a petrochemical unit to show its applicability and superiority. In fact, this study proposes an adaptive intelligence algorithm for measuring and improving job security among operators with respect to HSE-Ergonomics in a petrochemical unit. To achieve the objectives of this study, standard questionnaires with respect to HSE-Ergonomics are completed by operators. The average results for each category of HSE-Ergonomics are used as inputs and work job security is used as output for the algorithm. Moreover, this algorithm is used to rank operators performance with respect to HSE-Ergonomics. Finally, normal probability technique is used to identify outlier operators. This is the first study that introduces an integrated intelligence algorithm for assessment and improvement of human performance with respect to HSE-Ergonomics program in complex systems.","PeriodicalId":162890,"journal":{"name":"2010 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications","volume":"2014 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126428303","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}

{"title":"A hybrid P2P and master-slave architecture for intelligent multi-agent reinforcement learning in a distributed computing environment: A case study","authors":"D. B. Megherbi, M. Madera","doi":"10.1109/CIMSA.2010.5611770","DOIUrl":"https://doi.org/10.1109/CIMSA.2010.5611770","url":null,"abstract":"In this paper, we propose a distributed architecture for reinforcement learning in a multi-agent environment, where agents share information learned via a distributed network. Here we propose a hybrid master/slave and peer-to-peer system architecture, where a master node effectively assigns a work load (a portion of the terrain) to each node. However, this master node also manages communications between all the other system nodes, and in that sense it is a peer-to-peer architecture. It is a loosely-coupled system in that node slaves only know about the existence of the master node, and are only concerned with their work load (portion of the terrain). As part of this architecture, we show how agents are allowed to communicate with other agents in the same or different nodes and share information that pertains to all agents, including the agent obstacle barriers. In particular, one main contribution of the paper is multi-agent reenforcement learning in a distributed system, where the agents do not have complete knowledge and information of their environment, other than what is available on the computing node, the particular agent (s) is (are) running on. We show how agents, running on same or different nodes, coordinate the sharing of their respective environment states/information to collaboratively perform their respective tasks.","PeriodicalId":162890,"journal":{"name":"2010 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114608269","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}

{"title":"The image auto-focusing method based on artificial neural networks","authors":"Chen Guojin, Ling Yongning, Zhu Miao-fen, Wang Wan-qiang","doi":"10.1109/CIMSA.2010.5611751","DOIUrl":"https://doi.org/10.1109/CIMSA.2010.5611751","url":null,"abstract":"According to the image feature extraction capacity based on wavelet transformation and the nonlinear, self-adaptive and pattern recognition capacity based on artificial neural networks, the image auto-focusing method based on artificial neural networks is put forward. The wavelet components' statistics obtained by the wavelet transform are taken as the inputs of the 5 layer BP neural network model. The model identifies the image definition applying the steepest descent method of the additional momentum in a variable step to adjust the network weights. The model is first trained by 75 images from a training set, and then is tested by 102 images from a testing set. The results show that it is a very effective identification method which can obtain a higher recognition rate.","PeriodicalId":162890,"journal":{"name":"2010 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications","volume":"221 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121719204","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}

{"title":"A dynamic URL assignment method for parallel web crawler","authors":"A. Guerriero, F. Ragni, Claudio Martines","doi":"10.1109/CIMSA.2010.5611764","DOIUrl":"https://doi.org/10.1109/CIMSA.2010.5611764","url":null,"abstract":"A web crawler is a relatively simple automated program or script that methodically scans or “crawls” through Internet pages to retrieval information from data. Alternative names for a web crawler include web spider, web robot, bot, crawler, and automatic indexer. There are many different uses for a web crawler. Their primary purpose is to collect data so that when Internet surfers enter a search term on their site, they can quickly provide the surfer with relevant web sites. In this work we propose the model of a low cost web crawler for distributed environments based on an efficient URL assignment algorithm. The function of every module of the crawler is analyzed and main rules that crawlers must follow to maintain load balancing and robustness of system when they are searching on the web simultaneously, are discussed. The proposed a dynamic URL assignment method, based on grid computing technology and dynamic clustering, results efficient increasing web crawler performance.","PeriodicalId":162890,"journal":{"name":"2010 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127764403","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}

{"title":"Application of Honey Bee Mating Optimization algorithm to load profile clustering","authors":"M. Gavrilas, G. Gavrilas, C. Sfintes","doi":"10.1109/CIMSA.2010.5611759","DOIUrl":"https://doi.org/10.1109/CIMSA.2010.5611759","url":null,"abstract":"A broad range of intelligent metering solutions in the form of Automated Meter Reading (AMR) or Advanced Metering Infrastructure (AMI) are used today in electrical networks to meet the challenges posed by the development of electricity markets. In parallel, Load Profiling (LP-ing) techniques based on intelligent software solutions, can be used to support market access of small consumers who are not equipped with digital meters. This paper proposes a new approach to the LP clustering problem based on the Honey-Bee Mating Optimization (HBMO) algorithm. The results show a good behavior of the proposed algorithm in terms of robustness and stability with respect to the structure of the database. The proposed approach requires fewer parameters to be calibrated, in comparison with other alternative methods.","PeriodicalId":162890,"journal":{"name":"2010 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131482792","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}

{"title":"Path planning combined with Fuzzy control for autonomous ships","authors":"Héctor Garcia de Marina, Fernando J. Pereda, J. Girón-Sierra","doi":"10.1109/CIMSA.2010.5611754","DOIUrl":"https://doi.org/10.1109/CIMSA.2010.5611754","url":null,"abstract":"This paper is linked to a research proposing autonomous ships for risky humanitarian operations, like sea demining or poisonous spill recovery on the sea. This mission is based upon placing waypoints and the autonomous ships are under a Fuzzy control. Since ships have limited turning radii, a specific path planning strategy is developed, so the ship trajectory turns around waypoints at a suitable distance. The system is being tested with scaled ships.","PeriodicalId":162890,"journal":{"name":"2010 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126976009","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}

{"title":"Failure prediction on railway turnouts using time delay neural networks","authors":"Halis Yilboga, O. Eker, Adem Guclu, F. Camci","doi":"10.1109/CIMSA.2010.5611756","DOIUrl":"https://doi.org/10.1109/CIMSA.2010.5611756","url":null,"abstract":"Turnout systems on railways play critical role on reliability of railway infrastructure. Identification and prediction of failures on mechanical systems have been attracting researchers and industry in recent years. Condition based maintenance focuses on failure identification and prediction using sensory information collected real-time from sensors embedded on electro-mechanical systems. This paper presents neural network based failure prediction algorithm on railway turnouts.","PeriodicalId":162890,"journal":{"name":"2010 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133136534","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}

{"title":"Particle swarm optimization based spectral transformation for radioactive material detection and classification","authors":"Wei Wei, Q. Du, N. Younan","doi":"10.1109/CIMSA.2010.5611753","DOIUrl":"https://doi.org/10.1109/CIMSA.2010.5611753","url":null,"abstract":"We investigate buried depleted uranium detection and classification using data collected with short sensor dwell time (i.e., less than or equal to 1s). Under this circumstance, the gamma spectroscope collected by a NaI detector can be sparse and random, and may be severely affected by energy counts from the background. Several spectral transformations using binned energy windows can help alleviate the negative effect from background spectral noisy variation. The simplest way for such spectral partition is to use a fixed bin-width for uniform partition. In this paper, we propose a particle swarm optimization (PSO)-based optimization method to automatically determine the varied bin-width for each energy window. The experimental result shows that the spectral transformation methods using PSO-selected bins with variable widths can outperform those with a fixed bin-width.","PeriodicalId":162890,"journal":{"name":"2010 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125116075","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}

{"title":"Amplitude-frequency classification of Power Quality transients using higher-order cumulants and Self-Organizing Maps","authors":"J. J. González de la Rosa, A. Aguera Perez, J. C. Palomares Salas, A. Moreno-Muñoz","doi":"10.1109/CIMSA.2010.5611749","DOIUrl":"https://doi.org/10.1109/CIMSA.2010.5611749","url":null,"abstract":"This paper deals with the automatic classification of Power Quality (PQ) transients according to their amplitudes and frequencies, and following the geometrical pattern established via higher-order statistical measurements. The clustering is achieved thanks to the third and fourth-order features associated to the electrical anomalies, which in turn are coupled to the 50-Hz power-line. The main contribution of the paper is the novel finding that the maxima and the minima of these higher-order cumulants distribute according to a family of curves, each of which associated to the transient's frequency. Given a statistical order, each point in a curve corresponds to a given initial amplitude of a transient, and to a couple of extreme values of the statistical estimator. The random grouping through each curve reveals the a priori hidden geometry, linked to the subjacent phenomenon. Once the geometry has been found, we show the computational intelligence modulus, based in Self-Organizing Maps, which performs satisfactory learning along each frequency curve. Performance of a six-neuron network with two different geometries is shown. The experience is a continuation of the research towards an automatic procedure for PQ event classification.","PeriodicalId":162890,"journal":{"name":"2010 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131755500","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}