2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES)最新文献

A graph-based signal processing approach for low-rate energy disaggregation 基于图的低速率能量分解信号处理方法

2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES) Pub Date : 2014-12-09 DOI: 10.1109/CIES.2014.7011835

V. Stanković, J. Liao, L. Stanković

{"title":"A graph-based signal processing approach for low-rate energy disaggregation","authors":"V. Stanković, J. Liao, L. Stanković","doi":"10.1109/CIES.2014.7011835","DOIUrl":"https://doi.org/10.1109/CIES.2014.7011835","url":null,"abstract":"Graph-based signal processing (GSP) is an emerging field that is based on representing a dataset using a discrete signal indexed by a graph. Inspired by the recent success of GSP in image processing and signal filtering, in this paper, we demonstrate how GSP can be applied to non-intrusive appliance load monitoring (NALM) due to smoothness of appliance load signatures. NALM refers to disaggregating total energy consumption in the house down to individual appliances used. At low sampling rates, in the order of minutes, NALM is a difficult problem, due to significant random noise, unknown base load, many household appliances that have similar power signatures, and the fact that most domestic appliances (for example, microwave, toaster), have usual operation of just over a minute. In this paper, we proposed a different NALM approach to more traditional approaches, by representing the dataset of active power signatures using a graph signal. We develop a regularization on graph approach where by maximizing smoothness of the underlying graph signal, we are able to perform disaggregation. Simulation results using publicly available REDD dataset demonstrate potential of the GSP for energy disaggregation and competitive performance with respect to more complex Hidden Markov Model-based approaches.","PeriodicalId":287779,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121459396","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: 51

Why Ricker wavelets are successful in processing seismic data: Towards a theoretical explanation 为什么Ricker小波能成功处理地震数据:走向理论解释

2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES) Pub Date : 2014-12-01 DOI: 10.1109/CIES.2014.7011824

Afshin Gholamy, V. Kreinovich

引用次数: 34

Visualizing uncertainty with fuzzy rose diagrams 用模糊玫瑰图可视化不确定性

2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES) Pub Date : 2014-12-01 DOI: 10.1109/CIES.2014.7011827

A. Buck, J. Keller

引用次数: 5

If we take into account that constraints are soft, then processing constraints becomes algorithmically solvable 如果我们考虑到约束是软的，那么处理约束就变成了算法可解的

2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES) Pub Date : 2014-12-01 DOI: 10.1109/CIES.2014.7011823

Quentin Brefort, L. Jaulin, M. Ceberio, V. Kreinovich

引用次数: 6

Performance comparison of classifiers in the detection of short circuit incipient fault in a three-phase induction motor 分类器在三相感应电动机短路初期故障检测中的性能比较

2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES) Pub Date : 2014-12-01 DOI: 10.1109/CIES.2014.7011829

D. N. Coelho, G. Barreto, Cláudio M. S. Medeiros, J. Santos

引用次数: 22

Risk profiler in automated human authentication 自动人工身份验证中的风险分析器

2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES) Pub Date : 2014-12-01 DOI: 10.1109/CIES.2014.7011843

Shawn C. Eastwood, S. Yanushkevich

引用次数: 10

Solar irradiance forecasting by using wavelet based denoising 基于小波去噪的太阳辐照度预报

2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES) Pub Date : 2014-12-01 DOI: 10.1109/CIES.2014.7011839

Lingyu Lyu, M. Kantardzic, Elaheh Arabmakki

引用次数: 19

Compressive sensing based power spectrum estimation from incomplete records by utilizing an adaptive basis 基于压缩感知的不完全记录功率谱估计方法

2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES) Pub Date : 2014-12-01 DOI: 10.1109/CIES.2014.7011840

Liam A. Comerford, M. Beer, I. Kougioumtzoglou

{"title":"Compressive sensing based power spectrum estimation from incomplete records by utilizing an adaptive basis","authors":"Liam A. Comerford, M. Beer, I. Kougioumtzoglou","doi":"10.1109/CIES.2014.7011840","DOIUrl":"https://doi.org/10.1109/CIES.2014.7011840","url":null,"abstract":"A compressive sensing (CS) based approach is developed in conjunction with an adaptive basis reweighting procedure for stochastic process power spectrum estimation. In particular, the problem of sampling gaps in stochastic process records, occurring for reasons such as sensor failures, data corruption, and bandwidth limitations, is addressed. Specifically, due to the fact that many stochastic process records such as wind, sea wave and earthquake excitations can be represented with relative sparsity in the frequency domain, a CS framework can be applied for power spectrum estimation. To this aim, an ensemble of stochastic process realizations is often assumed to be available. Relying on this attribute an adaptive data mining procedure is introduced to modify harmonic basis coefficients, vastly improving on standard CS reconstructions. The procedure is shown to perform well with stationary and non-stationary processes even with up to 75% missing data. Several numerical examples demonstrate the effectiveness of the approach when applied to noisy, gappy signals.","PeriodicalId":287779,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES)","volume":"06 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":"129709482","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: 17

Fuzzy local linear approximation-based sequential design 基于模糊局部线性近似的序列设计

2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES) Pub Date : 2014-12-01 DOI: 10.1109/CIES.2014.7011825

J. Herten, D. Deschrijver, T. Dhaene

引用次数: 8

From offline to onboard system solution for a control sequence optimization problem 从离线到板载系统解决了一个控制序列优化问题

2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES) Pub Date : 2014-12-01 DOI: 10.1109/CIES.2014.7011833

Jin Huang, Xibin Zhao, Xinjie Chen, Jiaguang Sun, Qinwen Yang

{"title":"From offline to onboard system solution for a control sequence optimization problem","authors":"Jin Huang, Xibin Zhao, Xinjie Chen, Jiaguang Sun, Qinwen Yang","doi":"10.1109/CIES.2014.7011833","DOIUrl":"https://doi.org/10.1109/CIES.2014.7011833","url":null,"abstract":"The control sequence optimization problem is difficult to solve due to its high nonlinearity, various constraints and the possible changes in the sequence of comprising elements at any instant of time. The optimization of train trip running profile is a typical control sequence optimization problem, whose optimization object is to minimize the energy consumption as well as the time deviation under various constraints. Engineers always have to face the trade-off between the optimization performance and calculation time for an onboard control system for such problems. This paper mainly proposed a framework of an offline to onboard system solution for control sequence optimization problems, specifically using on the train trip profile optimization problems. The framework choose the parameter-decision tree solution for the onboard control system, and then a series of offline procedures including sequence mining, optimal computation, and machine learning is proposed for getting the parameter-decision tree. The framework inherits the good optimization performance of offline systems, as well as guaranteed the onboard calculation time for real-time control. Performance on using such a framework for solving train trip profile optimization problems is shown in the literature, which shows the potentials of using such frameworks on solving related control sequence optimization 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":"130699689","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