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11th Symposium on Neural Network Applications in Electrical Engineering最新文献

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FPGA implementation of neural network as processing element in ice detector 以神经网络为处理单元的冰探测器FPGA实现
11th Symposium on Neural Network Applications in Electrical Engineering Pub Date : 2012-09-01 DOI: 10.1109/NEUREL.2012.6419970
M. Marouf, J. Popović-Božović, I. Popović
{"title":"FPGA implementation of neural network as processing element in ice detector","authors":"M. Marouf, J. Popović-Božović, I. Popović","doi":"10.1109/NEUREL.2012.6419970","DOIUrl":"https://doi.org/10.1109/NEUREL.2012.6419970","url":null,"abstract":"In this paper we propose usage of neural networks in the field of meteorology especially to detect ice formation on roads. Used algorithm for building and training network is based on the road and air conditions which define ice formation on road's surface. The ability of self and supervised learning are both used to solve this problem. We used VHDL to build proposed neural network as a part of the ice detector. FPGA implementation of the neural network is done using Xilinx Spartan-3 device.","PeriodicalId":343718,"journal":{"name":"11th Symposium on Neural Network Applications in Electrical Engineering","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124844615","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
Joint blind source separation: Applications in medical image analysis 联合盲源分离:在医学图像分析中的应用
11th Symposium on Neural Network Applications in Electrical Engineering Pub Date : 2012-09-01 DOI: 10.1109/NEUREL.2012.6419942
T. Adalı
{"title":"Joint blind source separation: Applications in medical image analysis","authors":"T. Adalı","doi":"10.1109/NEUREL.2012.6419942","DOIUrl":"https://doi.org/10.1109/NEUREL.2012.6419942","url":null,"abstract":"Summary form only given. Blind source separation (BSS) is based on a simple generative model and hence minimizes the assumptions on the nature of data. It provides a promising alternative to the traditional model-based approaches in many applications where the underlying dynamics are hard to characterize. Independent component analysis (ICA), in particular, has been a popular BSS approach and an active area of research. By imposing the constraint of statistical independence on the underlying components, ICA recovers linearly mixed components subject to only a scaling and permutation ambiguity, and has been successfully applied to numerous problems in areas as diverse as biomedicine, communications, finance, geophysics, and remote sensing. Blind separation of multiple datasets simultaneously, i.e., joint BSS, is becoming increasingly important in most of these application areas, for example in medical image analysis where data from multiple subjects need to be analyzed for subject level or group inferences.","PeriodicalId":343718,"journal":{"name":"11th Symposium on Neural Network Applications in Electrical Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129136814","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
H-reflex recorded by multi-pad EMG electrodes 多垫肌电电极记录h反射
11th Symposium on Neural Network Applications in Electrical Engineering Pub Date : 2012-09-01 DOI: 10.1109/NEUREL.2012.6419981
V. Kojić, N. Miljković, N. Malešević, D. Popović
{"title":"H-reflex recorded by multi-pad EMG electrodes","authors":"V. Kojić, N. Miljković, N. Malešević, D. Popović","doi":"10.1109/NEUREL.2012.6419981","DOIUrl":"https://doi.org/10.1109/NEUREL.2012.6419981","url":null,"abstract":"We recorded H-reflex using 4 × 4 multi-pad EMG electrodes on muscle Soleus from 4 healthy subjects. The results indicate that the new proposed method of recording H-reflex provides more information than standard bipolar or unipolar EMG recording, since it enables spatial and temporal parameters assessment. We have developed an algorithm which can automatically select the H-reflex with maximum peak to peak amplitude. We also examined the H-reflex propagation by the use of EMG topography map based on the latency.","PeriodicalId":343718,"journal":{"name":"11th Symposium on Neural Network Applications in Electrical Engineering","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128542364","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: 3
A modified adaptive retraining procedure for data forecasting 一种改进的数据预测自适应再训练程序
11th Symposium on Neural Network Applications in Electrical Engineering Pub Date : 2012-09-01 DOI: 10.1109/NEUREL.2012.6419995
D. Năstac, P. Cristea
{"title":"A modified adaptive retraining procedure for data forecasting","authors":"D. Năstac, P. Cristea","doi":"10.1109/NEUREL.2012.6419995","DOIUrl":"https://doi.org/10.1109/NEUREL.2012.6419995","url":null,"abstract":"The paper presents a further improvement of the adaptive retraining procedure of Artificial Neural Networks (ANNs) used for time series predictions. An important advantage of this approach is that the model is periodically adapted to the changes of the non-stationary environment. The retraining starts from proportionally reduced values of the parameters used in the previous version of the ANN model. As usual, variously delayed versions of the time series to be predicted and of the previous outputs are applied at the input of the ANN. In addition, the newly developed model also uses as inputs the averaged seasonal values from the previous years, obtained for the desired target variables in some specified time windows.","PeriodicalId":343718,"journal":{"name":"11th Symposium on Neural Network Applications in Electrical Engineering","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116687414","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
Personalized TV program guide based on neural network 基于神经网络的个性化电视节目导播
11th Symposium on Neural Network Applications in Electrical Engineering Pub Date : 2012-09-01 DOI: 10.1109/NEUREL.2012.6420017
M. Krstić, M. Bjelica
{"title":"Personalized TV program guide based on neural network","authors":"M. Krstić, M. Bjelica","doi":"10.1109/NEUREL.2012.6420017","DOIUrl":"https://doi.org/10.1109/NEUREL.2012.6420017","url":null,"abstract":"As digital TV providers today offer hundreds of channels, TV viewers do not have problem with content availability, but with finding an interesting content in a reasonable time instead. In a situation like this, both the providers and the viewers would benefit from personalized TV program guides, the tools that would track and learn the viewers' preferences and then recommend them the content they would like. In this paper, we propose one such guide which is based on artificial neural network. We examine and compare several learning algorithms, with recommendation accuracy and neural network training time as performance metrics.","PeriodicalId":343718,"journal":{"name":"11th Symposium on Neural Network Applications in Electrical Engineering","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129436800","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: 8
GA-based optimization of fuzzy rule bases for pattern classification 基于遗传算法的模式分类模糊规则库优化
11th Symposium on Neural Network Applications in Electrical Engineering Pub Date : 1900-01-01 DOI: 10.1109/neurel.2012.6419987
G. Schaefer
{"title":"GA-based optimization of fuzzy rule bases for pattern classification","authors":"G. Schaefer","doi":"10.1109/neurel.2012.6419987","DOIUrl":"https://doi.org/10.1109/neurel.2012.6419987","url":null,"abstract":"Many problems can be cast as pattern classification problems. Consequently, developing effective classifiers has become an important research area. Various techniques have been proposed to produce classifiers, however many of these appear to the user as “black boxes” which merely give a decision without any additional insight. In this lecture, the focus will be on fuzzy rule-based classification systems which generate simple if-then rules that can thus also be interpreted by the user. Since rule-based classifiers are prone to rule explosion, It will be presented, in particular, optimization approaches to rule base generation that are based on genetic algorithms and a shown to result in a compact yet effective set of rules. In addition, through a simple modification, the resulting classifier can be made cost-sensitive which is in particular useful for applications in medical diagnosis. Example applications will include the classification of gene expression data and the use of classifiers for breast cancer diagnosis.","PeriodicalId":343718,"journal":{"name":"11th Symposium on Neural Network Applications in Electrical Engineering","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126004382","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
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