Pub Date : 2014-12-01DOI: 10.1109/CIES.2014.7011828
Won-Chul Jang, Myeongsu Kang, Jaeyoung Kim, Jong-Myon Kim, Hung Nguyen Ngoc
Condition monitoring is a vital task in the maintenance of industry machines. This paper proposes a reliable condition monitoring method using a genetic algorithm (GA) which selects the most discriminate features by taking a transformation matrix. Experimental results show that the features selected by the GA outperforms original and randomly selected features using the same k-nearest neighbor (k-NN) classifier in terms of convergence rate, the number of features, and classification accuracy. The GA-based feature selection method improves the classification accuracy from 3% to 100% and from 30% to 100% over the original and randomly selected features, respectively.
{"title":"Reliable condition monitoring of an induction motor using a genetic algorithm based method","authors":"Won-Chul Jang, Myeongsu Kang, Jaeyoung Kim, Jong-Myon Kim, Hung Nguyen Ngoc","doi":"10.1109/CIES.2014.7011828","DOIUrl":"https://doi.org/10.1109/CIES.2014.7011828","url":null,"abstract":"Condition monitoring is a vital task in the maintenance of industry machines. This paper proposes a reliable condition monitoring method using a genetic algorithm (GA) which selects the most discriminate features by taking a transformation matrix. Experimental results show that the features selected by the GA outperforms original and randomly selected features using the same k-nearest neighbor (k-NN) classifier in terms of convergence rate, the number of features, and classification accuracy. The GA-based feature selection method improves the classification accuracy from 3% to 100% and from 30% to 100% over the original and randomly selected features, respectively.","PeriodicalId":287779,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129402039","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}
Pub Date : 2014-12-01DOI: 10.1109/CIES.2014.7011846
C. Marcelino, E. Wanner, P. E. M. Almeida
The consumption of electric energy for general supply of a country is increasing over the years. In Brazil, energy demand grows, on average, 5% per year and the power source is predominantly hydroelectric. Many of the power plants installed in Brazil do not operate efficiently, from the water consumption point of view. The normal mode of operation (NMO) equally divides power demand between existing generation units of a power plant, regardless if this individual demand represents or not a good operation point for each unit. The unit dispatch problem is defined as the attribution of operational values to each unit inside a power plant, given some criteria to be met. In this context, an optimal solution for the dispatch problem means production of electricity with minimal water consumption. This work proposes a multi-objective approach to solve the electric dispatch problem in which the objective functions considered are: maximization of hydroelectric productivity function and minimization of the distance between NMO and optimized control mode (OCM). The proposed approach is applied to a large hydroelectric plant operating in Brazil. Results indicate that it is possible to identify operating points near NMO that present productivity efficiency, saving in one month about 14.6 million m3 of water. Moreover, higher productivity can be achieved with smaller differences between NMO and OCM in lower power demands. Finally, it is worth to mention that the simplicity and the nature of the proposed approach indicate that it can be easily applied to studies of similar power plants, and thus can potentially be used to provide further economy on water consumption to larger extents of the hydroelectric production.
{"title":"An evolutionary approach to improve efficiency for solving the electric dispatch problem","authors":"C. Marcelino, E. Wanner, P. E. M. Almeida","doi":"10.1109/CIES.2014.7011846","DOIUrl":"https://doi.org/10.1109/CIES.2014.7011846","url":null,"abstract":"The consumption of electric energy for general supply of a country is increasing over the years. In Brazil, energy demand grows, on average, 5% per year and the power source is predominantly hydroelectric. Many of the power plants installed in Brazil do not operate efficiently, from the water consumption point of view. The normal mode of operation (NMO) equally divides power demand between existing generation units of a power plant, regardless if this individual demand represents or not a good operation point for each unit. The unit dispatch problem is defined as the attribution of operational values to each unit inside a power plant, given some criteria to be met. In this context, an optimal solution for the dispatch problem means production of electricity with minimal water consumption. This work proposes a multi-objective approach to solve the electric dispatch problem in which the objective functions considered are: maximization of hydroelectric productivity function and minimization of the distance between NMO and optimized control mode (OCM). The proposed approach is applied to a large hydroelectric plant operating in Brazil. Results indicate that it is possible to identify operating points near NMO that present productivity efficiency, saving in one month about 14.6 million m3 of water. Moreover, higher productivity can be achieved with smaller differences between NMO and OCM in lower power demands. Finally, it is worth to mention that the simplicity and the nature of the proposed approach indicate that it can be easily applied to studies of similar power plants, and thus can potentially be used to provide further economy on water consumption to larger extents of the hydroelectric production.","PeriodicalId":287779,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133750423","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}
Pub Date : 2014-12-01DOI: 10.1109/CIES.2014.7011844
Ronaldo Aquino, O. N. Neto, R. B. Souza, M. Lira, Manoel A. Carvalho, Teresa B Ludermir, A. Ferreira
This paper presents the results of models created for prediction of wind power generation using Echo State Networks (ESN). An echo state network consist of a large, randomly connected neural network, the reservoir, which is driven by an input signal and projects to output units. ESN offer an intuitive methodology for using the temporal processing power of recurrent neural networks without the hassle of training them. The models perform forecasting of wind power generation with 6 hours ahead, discretized by 10 minutes and with 5 days ahead, discretized by 30 minutes. These models use ESNs with spectral radius greater than 1 and even then they can make predictions with good results. The forecast horizons presented here fall in medium-term forecasts, up to five days ahead, which is an appropriate horizon to subsidize the operation planning of power systems. Models that directly predict the wind power generation with ESNs showed promising results.
{"title":"Investigating the use of Echo State Networks for prediction of wind power generation","authors":"Ronaldo Aquino, O. N. Neto, R. B. Souza, M. Lira, Manoel A. Carvalho, Teresa B Ludermir, A. Ferreira","doi":"10.1109/CIES.2014.7011844","DOIUrl":"https://doi.org/10.1109/CIES.2014.7011844","url":null,"abstract":"This paper presents the results of models created for prediction of wind power generation using Echo State Networks (ESN). An echo state network consist of a large, randomly connected neural network, the reservoir, which is driven by an input signal and projects to output units. ESN offer an intuitive methodology for using the temporal processing power of recurrent neural networks without the hassle of training them. The models perform forecasting of wind power generation with 6 hours ahead, discretized by 10 minutes and with 5 days ahead, discretized by 30 minutes. These models use ESNs with spectral radius greater than 1 and even then they can make predictions with good results. The forecast horizons presented here fall in medium-term forecasts, up to five days ahead, which is an appropriate horizon to subsidize the operation planning of power systems. Models that directly predict the wind power generation with ESNs showed promising results.","PeriodicalId":287779,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127428585","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}
Pub Date : 2014-12-01DOI: 10.1109/CIES.2014.7011838
Seth J. Meyerson, William E. Whiteside, T. Drapela, W. Potter
Since the problem's formulation by Kautz in 1958 as an error detection tool, diverse applications for long snakes and coils have been found. These include coding theory, electrical engineering, and genetics. Over the years, the problem has been explored by many researchers in different fields using varied approaches, and has taken on additional meaning. The problem has become a benchmark for evaluating search techniques in combinatorially expansive search spaces (NP-complete Optimizations). We present an effective process for searching for long achordal open paths (snakes) and achordal closed paths (coils) in n-dimensional hypercube graphs. Stochastic Beam Search provides the overall structure for the search while graph theory based techniques are used in the computation of a generational fitness value. This novel fitness value is used in guiding the search. We show that our approach is likely to work in all dimensions of the SIB problem and we present new lower bounds for a snake in dimension 11 and coils in dimensions 10, 11, and 12. The best known solutions of the unsolved dimensions of this problem have improved over the years and we are proud to make a contribution to this problem as well as the continued progress in combinatorial search techniques.
{"title":"Finding longest paths in hypercubes, snakes and coils","authors":"Seth J. Meyerson, William E. Whiteside, T. Drapela, W. Potter","doi":"10.1109/CIES.2014.7011838","DOIUrl":"https://doi.org/10.1109/CIES.2014.7011838","url":null,"abstract":"Since the problem's formulation by Kautz in 1958 as an error detection tool, diverse applications for long snakes and coils have been found. These include coding theory, electrical engineering, and genetics. Over the years, the problem has been explored by many researchers in different fields using varied approaches, and has taken on additional meaning. The problem has become a benchmark for evaluating search techniques in combinatorially expansive search spaces (NP-complete Optimizations). We present an effective process for searching for long achordal open paths (snakes) and achordal closed paths (coils) in n-dimensional hypercube graphs. Stochastic Beam Search provides the overall structure for the search while graph theory based techniques are used in the computation of a generational fitness value. This novel fitness value is used in guiding the search. We show that our approach is likely to work in all dimensions of the SIB problem and we present new lower bounds for a snake in dimension 11 and coils in dimensions 10, 11, and 12. The best known solutions of the unsolved dimensions of this problem have improved over the years and we are proud to make a contribution to this problem as well as the continued progress in combinatorial search techniques.","PeriodicalId":287779,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130334792","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}
Pub Date : 2014-12-01DOI: 10.1109/CIES.2014.7011832
Kailiang Xu, Gang Zheng, Sha Liu
This paper concerns on a two-machine flow-shop scheduling problem with controllable processing times modeled by a non-linear convex resource consumption function. The objective is to minimize the resource consumption that is needed to control the makespan not to exceed the given deadline. A tabu-search algorithm is designed, which searches for the optimal or near optimal job-processing sequence, while the processing times of the operations are determined by an optimal resource allocation algorithm thereafter. Numerical experiment shows the tabu-search algorithm is able to provide optimal or near-optimal solutions for medium or large-scaled problems.
{"title":"A tabu-search algorithm for two-machine flow-shop with controllable processing times","authors":"Kailiang Xu, Gang Zheng, Sha Liu","doi":"10.1109/CIES.2014.7011832","DOIUrl":"https://doi.org/10.1109/CIES.2014.7011832","url":null,"abstract":"This paper concerns on a two-machine flow-shop scheduling problem with controllable processing times modeled by a non-linear convex resource consumption function. The objective is to minimize the resource consumption that is needed to control the makespan not to exceed the given deadline. A tabu-search algorithm is designed, which searches for the optimal or near optimal job-processing sequence, while the processing times of the operations are determined by an optimal resource allocation algorithm thereafter. Numerical experiment shows the tabu-search algorithm is able to provide optimal or near-optimal solutions for medium or large-scaled problems.","PeriodicalId":287779,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122286078","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}
Pub Date : 2014-12-01DOI: 10.1109/CIES.2014.7011841
C. Roberts-Thomson, A. Lokshin, V. Kuzkin
Jump detection and measurement is of particular interest in a wide range of sports, including snowboarding, skiing, skateboarding, wakeboarding, motorcycling, biking, gymnastics, and the high jump, among others. However, determining jump duration and height is often difficult and requires expert knowledge or visual analysis either in real-time or using video. Recent advances in low-cost MEMS inertial sensors enable a data-driven approach to jump detection and measurement. Today, inertial and GPS sensors attached to an athlete or to his or her equipment, e.g. snowboard, skateboard, or skis, can collect data during sporting activities. In these real life applications, effects such as vibration, sensor noise and bias, and various athletic maneuvers make jump detection difficult even using multiple sensors. This paper presents a fuzzy logic-based algorithm for jump detection in sport using accelerometer data. Fuzzy logic facilitates conversion of human intuition and vague linguistic descriptions of jumps to algorithmic form. The fuzzy algorithm described here was applied to snowboarding and ski jumping data, and successfully detected 92% of snowboarding jumps identified visually (rejecting 8% of jumps identified visually), with only 8% of detected jumps being false positives. In ski jumping, it successfully detected 100% of jumps identified visually, with no false positives. The fuzzy algorithm presented here has successfully been applied to automate jump detection in ski and snowboarding on a large scale, and as the basis of the AlpineReplay ski and snowboarding smartphone app, has identified 6370971 jumps from August 2011 through June 2014.
{"title":"Jump detection using fuzzy logic","authors":"C. Roberts-Thomson, A. Lokshin, V. Kuzkin","doi":"10.1109/CIES.2014.7011841","DOIUrl":"https://doi.org/10.1109/CIES.2014.7011841","url":null,"abstract":"Jump detection and measurement is of particular interest in a wide range of sports, including snowboarding, skiing, skateboarding, wakeboarding, motorcycling, biking, gymnastics, and the high jump, among others. However, determining jump duration and height is often difficult and requires expert knowledge or visual analysis either in real-time or using video. Recent advances in low-cost MEMS inertial sensors enable a data-driven approach to jump detection and measurement. Today, inertial and GPS sensors attached to an athlete or to his or her equipment, e.g. snowboard, skateboard, or skis, can collect data during sporting activities. In these real life applications, effects such as vibration, sensor noise and bias, and various athletic maneuvers make jump detection difficult even using multiple sensors. This paper presents a fuzzy logic-based algorithm for jump detection in sport using accelerometer data. Fuzzy logic facilitates conversion of human intuition and vague linguistic descriptions of jumps to algorithmic form. The fuzzy algorithm described here was applied to snowboarding and ski jumping data, and successfully detected 92% of snowboarding jumps identified visually (rejecting 8% of jumps identified visually), with only 8% of detected jumps being false positives. In ski jumping, it successfully detected 100% of jumps identified visually, with no false positives. The fuzzy algorithm presented here has successfully been applied to automate jump detection in ski and snowboarding on a large scale, and as the basis of the AlpineReplay ski and snowboarding smartphone app, has identified 6370971 jumps from August 2011 through June 2014.","PeriodicalId":287779,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES)","volume":"190 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116646269","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}
Pub Date : 2014-01-15DOI: 10.1109/CIES.2014.7011836
Sansiri Tarnpradab, K. Mehrotra, C. Mohan, D. Chandler
This study aims to improve stream-ow forecast at Reynolds Mountain East watersheds, which is located at the southernmost of all watersheds in Reynolds Creek Experimental Watershed Idaho, USA. Two separate models, one for the annual data and the other for the seasonal (April-June) data from 1983-1995 are tested for their predictability. Due to the difficulties in collecting data during winter months, in particular the snow water equivalent (SWE), this study evaluates the impact of excluding this variable. Our results show that multilayer perceptrons (MLP) and support vector machines (SVM) are more suitable for modeling the data. The results also reveal that the difference between stream-ow forecast via annual and seasonal models is insignificant and for longer term predictions SWE is a strong driver in the stream-ow forecast. Principal Component Analysis (PCA) and Particle Swarm Optimization (PSO) are also used in this study to identify useful features. The results from PCA derived models show that PCA helps reduce prediction error and the results are more stable than using models without PCA. PSO also improved results; however, the set of selected attributes by PSO is less believable than given by PCA. The best prediction is achieved when MLP model is implemented with attributes generated by PCA.
{"title":"Neural networks for prediction of stream flow based on snow accumulation","authors":"Sansiri Tarnpradab, K. Mehrotra, C. Mohan, D. Chandler","doi":"10.1109/CIES.2014.7011836","DOIUrl":"https://doi.org/10.1109/CIES.2014.7011836","url":null,"abstract":"This study aims to improve stream-ow forecast at Reynolds Mountain East watersheds, which is located at the southernmost of all watersheds in Reynolds Creek Experimental Watershed Idaho, USA. Two separate models, one for the annual data and the other for the seasonal (April-June) data from 1983-1995 are tested for their predictability. Due to the difficulties in collecting data during winter months, in particular the snow water equivalent (SWE), this study evaluates the impact of excluding this variable. Our results show that multilayer perceptrons (MLP) and support vector machines (SVM) are more suitable for modeling the data. The results also reveal that the difference between stream-ow forecast via annual and seasonal models is insignificant and for longer term predictions SWE is a strong driver in the stream-ow forecast. Principal Component Analysis (PCA) and Particle Swarm Optimization (PSO) are also used in this study to identify useful features. The results from PCA derived models show that PCA helps reduce prediction error and the results are more stable than using models without PCA. PSO also improved results; however, the set of selected attributes by PSO is less believable than given by PCA. The best prediction is achieved when MLP model is implemented with attributes generated by PCA.","PeriodicalId":287779,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116535013","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}
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