2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES)最新文献_第3页

Reliable condition monitoring of an induction motor using a genetic algorithm based method 基于遗传算法的异步电动机状态可靠监测方法

2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES) Pub Date : 2014-12-01 DOI: 10.1109/CIES.2014.7011828

Won-Chul Jang, Myeongsu Kang, Jaeyoung Kim, Jong-Myon Kim, Hung Nguyen Ngoc

引用次数: 2

An evolutionary approach to improve efficiency for solving the electric dispatch problem 一种改进电力调度问题求解效率的演化方法

2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES) Pub Date : 2014-12-01 DOI: 10.1109/CIES.2014.7011846

C. Marcelino, E. Wanner, P. E. M. Almeida

{"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}

引用次数: 0

Investigating the use of Echo State Networks for prediction of wind power generation 研究利用回声状态网络预测风力发电

2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES) Pub Date : 2014-12-01 DOI: 10.1109/CIES.2014.7011844

Ronaldo Aquino, O. N. Neto, R. B. Souza, M. Lira, Manoel A. Carvalho, Teresa B Ludermir, A. Ferreira

引用次数: 7

Finding longest paths in hypercubes, snakes and coils 在超立方体，蛇和线圈中寻找最长的路径

2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES) Pub Date : 2014-12-01 DOI: 10.1109/CIES.2014.7011838

Seth J. Meyerson, William E. Whiteside, T. Drapela, W. Potter

{"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}

引用次数: 10

A tabu-search algorithm for two-machine flow-shop with controllable processing times 加工时间可控的双机流水车间禁忌搜索算法

2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES) Pub Date : 2014-12-01 DOI: 10.1109/CIES.2014.7011832

Kailiang Xu, Gang Zheng, Sha Liu

引用次数: 0

Jump detection using fuzzy logic 使用模糊逻辑的跳跃检测

2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES) Pub Date : 2014-12-01 DOI: 10.1109/CIES.2014.7011841

C. Roberts-Thomson, A. Lokshin, V. Kuzkin

{"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}

引用次数: 7

Neural networks for prediction of stream flow based on snow accumulation 基于积雪量的水流预测神经网络

2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES) Pub Date : 2014-01-15 DOI: 10.1109/CIES.2014.7011836

Sansiri Tarnpradab, K. Mehrotra, C. Mohan, D. Chandler

{"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}

引用次数: 2