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10th Symposium on Neural Network Applications in Electrical Engineering最新文献

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Speech recognition using neural networks 使用神经网络的语音识别
10th Symposium on Neural Network Applications in Electrical Engineering Pub Date : 2010-11-29 DOI: 10.1109/NEUREL.2010.5644073
G. Tsenov, V. Mladenov
{"title":"Speech recognition using neural networks","authors":"G. Tsenov, V. Mladenov","doi":"10.1109/NEUREL.2010.5644073","DOIUrl":"https://doi.org/10.1109/NEUREL.2010.5644073","url":null,"abstract":"This paper presents investigation on speech recognition classification performance when using different standard neural networks structures as a classifier. Those cases include usage of a Feed-forward Neural Network (NN) with back propagation algorithm and a Radial Basis Functions (RBF) Neural Network.","PeriodicalId":227890,"journal":{"name":"10th Symposium on Neural Network Applications in Electrical Engineering","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128215226","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: 9
Nonnegative contraction/averaging tensor factorization 非负收缩/平均张量分解
10th Symposium on Neural Network Applications in Electrical Engineering Pub Date : 2010-11-29 DOI: 10.1109/NEUREL.2010.5644083
M. Jankovic, B. Reljin
{"title":"Nonnegative contraction/averaging tensor factorization","authors":"M. Jankovic, B. Reljin","doi":"10.1109/NEUREL.2010.5644083","DOIUrl":"https://doi.org/10.1109/NEUREL.2010.5644083","url":null,"abstract":"Nonnegative tensor factorization (NTF) is a recent multiway (multilinear) extension of negative matrix factorization (NMF), where nonnegativity constraints are mainly imposed on CANDECOMP/PARAFAC model and recently, also, on Tucker model. Nonnegative tensor factorization algorithms have many potential applications, including multiway clustering, multi-sensory or multidimensional data analysis and nonnegative neural sparse coding. In this paper we will present new approach to NTF which is based on CANDENCOMP/PARAFAC model. The proposed method is simple, computationally effective, easily extensible to higher dimensional tensors, can handle some problems related to rank-deficient tensors and can be used for analysis of the higher dimensional tensors than most of the known algorithms for NTF.","PeriodicalId":227890,"journal":{"name":"10th Symposium on Neural Network Applications in Electrical Engineering","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131698448","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
On the neural approach for FET small-signal modelling up to 50GHz 50GHz以下场效应管小信号建模的神经方法研究
10th Symposium on Neural Network Applications in Electrical Engineering Pub Date : 2010-11-29 DOI: 10.1109/NEUREL.2010.5644101
Z. Marinković, G. Crupi, A. Caddemi, V. Markovic
{"title":"On the neural approach for FET small-signal modelling up to 50GHz","authors":"Z. Marinković, G. Crupi, A. Caddemi, V. Markovic","doi":"10.1109/NEUREL.2010.5644101","DOIUrl":"https://doi.org/10.1109/NEUREL.2010.5644101","url":null,"abstract":"The aim of this paper is to discuss and compare two neural approaches applied in small-signal modelling of microwave FETs. One of them is completely based on artificial neural networks, while the other is a hybrid model putting together artificial neural networks and an equivalent circuit representation of a microwave transistor. Devices with different gate width are considered in this paper. Different modelling aspects are compared, with special emphasis on the model development procedure and model accuracy.","PeriodicalId":227890,"journal":{"name":"10th Symposium on Neural Network Applications in Electrical Engineering","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122583168","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: 9
Neural network for single phoneme recognition based on mel-frequency cepstral coefficients coding 基于mel-frequency倒谱系数编码的单音素识别神经网络
10th Symposium on Neural Network Applications in Electrical Engineering Pub Date : 2010-11-29 DOI: 10.1109/NEUREL.2010.5644071
Dino Kosic
{"title":"Neural network for single phoneme recognition based on mel-frequency cepstral coefficients coding","authors":"Dino Kosic","doi":"10.1109/NEUREL.2010.5644071","DOIUrl":"https://doi.org/10.1109/NEUREL.2010.5644071","url":null,"abstract":"This paper proposes novel approach in coding single phonemes based on mel-frequency cepstral coefficients (MFCC) in order to simplify the neural network used to recognize those phonemes. The efficiency and effectiveness of proposed algorithm are demonstrated for both male and female speakers.","PeriodicalId":227890,"journal":{"name":"10th Symposium on Neural Network Applications in Electrical Engineering","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121308239","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
Improved neural network for checking the stability of multidimensional systems 用于检测多维系统稳定性的改进神经网络
10th Symposium on Neural Network Applications in Electrical Engineering Pub Date : 2010-11-29 DOI: 10.1109/NEUREL.2010.5644086
N. Mastorakis, V. Mladenov, M. Swamy
{"title":"Improved neural network for checking the stability of multidimensional systems","authors":"N. Mastorakis, V. Mladenov, M. Swamy","doi":"10.1109/NEUREL.2010.5644086","DOIUrl":"https://doi.org/10.1109/NEUREL.2010.5644086","url":null,"abstract":"In this paper, the author's previous work is extended and a new neural network is utilized to solve the stability problem of multidimensional systems. In the original authors work the problem is transformed into an optimization problem. Using the DeCarlo-Strintzis Theorem one has to check if |B(Z<inf>1</inf>,…, 1, Z<inf>m</inf>)| ≠ 0 for |Z<inf>1</inf> = … = |Z<inf>m</inf>| = 1 or equivalently if the min |B(Z<inf>1</inf>, …, 1, Z<inf>m</inf>)| is 0 or not, where B(Z<inf>1</inf>, Z<inf>2</inf>, …, Z<inf>m</inf>) is the denominator of the discrete transfer funcion. Then, the problem is reduced to a minimization problem and a neural network is proposed for solving it. To improve the chance of convergence towards the global minimum, an extension of this neural network based on random noise terms is proposed in this contribution. The numerical examples illustrate the validity and the efficiency of the new neural network.","PeriodicalId":227890,"journal":{"name":"10th Symposium on Neural Network Applications in Electrical Engineering","volume":"216 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127151567","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
An enhanced ANN wind power forecast model based on a fuzzy representation of wind direction 基于风向模糊表示的增强型人工神经网络风电预测模型
10th Symposium on Neural Network Applications in Electrical Engineering Pub Date : 2010-11-29 DOI: 10.1109/NEUREL.2010.5644050
M. Gavrilas, G. Gavrilas
{"title":"An enhanced ANN wind power forecast model based on a fuzzy representation of wind direction","authors":"M. Gavrilas, G. Gavrilas","doi":"10.1109/NEUREL.2010.5644050","DOIUrl":"https://doi.org/10.1109/NEUREL.2010.5644050","url":null,"abstract":"Due to high penetration of wind generation in modern power systems, the influence of wind power production over the efficient operation of the power system is increasingly complex. Hence, an increasing interest is shown by different actors in the wind energy market to develop and enhance existent forecasting methods for power generated by wind farms. This paper presents the experience with wind power prediction of a small size wind power producer in Romania. The model was designed using components from Artificial Neural Networks and Fuzzy System theory.","PeriodicalId":227890,"journal":{"name":"10th Symposium on Neural Network Applications in Electrical Engineering","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125372164","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
ANN output energy control for laser surgical ophthalmic microscopes 激光外科眼科显微镜的神经网络输出能量控制
10th Symposium on Neural Network Applications in Electrical Engineering Pub Date : 2010-11-29 DOI: 10.1109/NEUREL.2010.5644079
S. Stanescu, Bujor Pavaloiu, Vasile Sava, M. Udrea, Paul Dan Cristea
{"title":"ANN output energy control for laser surgical ophthalmic microscopes","authors":"S. Stanescu, Bujor Pavaloiu, Vasile Sava, M. Udrea, Paul Dan Cristea","doi":"10.1109/NEUREL.2010.5644079","DOIUrl":"https://doi.org/10.1109/NEUREL.2010.5644079","url":null,"abstract":"This work presents the technical aspects for the development of a firmware for a Nd:YAG laser based ophthalmic surgery microscope. The software designed for a Microchip® PIC18F4550 microcontroller interfaces the electronic blocks of the device and allows the calibration of the laser energy attenuator. This paper mainly describes the experimental setup and presents the experimental data for an innovative energy attenuator which can be used for ophthalmic microscopes. The main advantage of the system is that the physician can operate with any value of energy of the laser beam and the system does not contain moving parts. Several methods to compute the coefficients for energy control are proposed and their performances are evaluated.","PeriodicalId":227890,"journal":{"name":"10th Symposium on Neural Network Applications in Electrical Engineering","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125570161","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
An efficient ECG modeling for heartbeat classification 一种用于心跳分类的高效心电模型
10th Symposium on Neural Network Applications in Electrical Engineering Pub Date : 2010-11-29 DOI: 10.1109/NEUREL.2010.5644105
S. Jokic, S. Krco, V. Delić, D. Sakac, I. Jokic, Z. Lukic
{"title":"An efficient ECG modeling for heartbeat classification","authors":"S. Jokic, S. Krco, V. Delić, D. Sakac, I. Jokic, Z. Lukic","doi":"10.1109/NEUREL.2010.5644105","DOIUrl":"https://doi.org/10.1109/NEUREL.2010.5644105","url":null,"abstract":"In this paper, an efficient heart beat classification algorithm suitable for implementation on mobile devices is presented. A simplified ECG model is used for feature extraction in the time domain. The QRS complex is modeled using straight lines, while P and T waves are modeled using parabolas. The model parameters are estimated by minimizing the root mean square (RMS) of the model error. Heart beats are classified as one of the following: normal (N), supraventricular (S) and Ventricular (V) ectopic beats using a feed-forward neural network. A series of tests have been performed to evaluate the classification algorithm using the MIT-BIH arrhythmia database ECG signals subset and expressed in the terms of sensitivity (Se), specificity (Sp) and accuracy (Acc). The best results were achieved when the classification algorithm was applied on the third model set. The proposed algorithm has been implemented as a J2ME mobile application. It has been tested on signals recorded by a telemedicine health care system and have achieved an average accuracy above 93%.","PeriodicalId":227890,"journal":{"name":"10th Symposium on Neural Network Applications in Electrical Engineering","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126027674","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: 11
ANN versus Grey theory based forecasting methods implemented on short time series 基于神经网络和灰色理论的短时间序列预测方法
10th Symposium on Neural Network Applications in Electrical Engineering Pub Date : 2010-11-29 DOI: 10.1109/NEUREL.2010.5644094
J. Milojković, Vaneo Litovski
{"title":"ANN versus Grey theory based forecasting methods implemented on short time series","authors":"J. Milojković, Vaneo Litovski","doi":"10.1109/NEUREL.2010.5644094","DOIUrl":"https://doi.org/10.1109/NEUREL.2010.5644094","url":null,"abstract":"Two modern concepts implemented for forecasting based on reduced time series are contrasted. Results obtained by use of artificial neural nets (ANNs), already discussed at this conference, are compared with the ones obtained by implementation of the so called Grey theory or Grey Model (GM). Particularly, feed-forward accommodated for prediction (FFAP) and time controlled recurrent (TCR) ANNs are used along with the GM(1,1) algorithm for one- and two-steps-ahead forecasting of various quantities (obsolete computers, electricity loads, number of fixed telephones etc). Advantages of the ANN concept are observed. The GM(1,1) was studied in the appendix and compared with no advantages against the least-mean-squares approximation by an exponential.","PeriodicalId":227890,"journal":{"name":"10th Symposium on Neural Network Applications in Electrical Engineering","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132864528","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
Independent Component Analysis (ICA) methods for neonatal EEG artifact extraction: Sensitivity to variation of artifact properties 新生儿脑电图伪影提取的独立分量分析方法:对伪影特性变化的敏感性
10th Symposium on Neural Network Applications in Electrical Engineering Pub Date : 2010-11-29 DOI: 10.1109/NEUREL.2010.5644041
N. Miljković, V. Matic, S. Van Huffel, M. Popovic
{"title":"Independent Component Analysis (ICA) methods for neonatal EEG artifact extraction: Sensitivity to variation of artifact properties","authors":"N. Miljković, V. Matic, S. Van Huffel, M. Popovic","doi":"10.1109/NEUREL.2010.5644041","DOIUrl":"https://doi.org/10.1109/NEUREL.2010.5644041","url":null,"abstract":"Independent Component Analysis (ICA) is becoming an accepted technique for artifact removal. Nevertheless, there is no consensus about appropriate methods for different applications. This study presents a comparison of common ICA methods: RobustICA, SOBI, JADE, and BSS-CCA, for extraction of ECG artifacts from EEG signal. Algorithms were applied to the data created by superimposing artifact free real-life neonatal EEG and synthetic ECG. Their sensitivity to variation of noise property was compared: we examined variability of Spearman correlation coefficients (SCC) for various Heart Rates (HR) in each of ICA methods. Results show that SOBI and BSS-CCA methods were less sensitive than RobustICA and JADE to artifact alterations (mean SCCs were 0.85 and 0.85 compared to 0.80 and 0.73, respectively) being quite successful in source signal extraction.","PeriodicalId":227890,"journal":{"name":"10th Symposium on Neural Network Applications in Electrical Engineering","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132270622","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: 11
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