2007 International Joint Conference on Neural Networks最新文献_第10页

Classification of Infrasound Surf Events Using Parallel Neural Network Banks 基于并行神经网络库的次声冲浪事件分类

2007 International Joint Conference on Neural Networks Pub Date : 2007-10-29 DOI: 10.1109/IJCNN.2007.4371046

F. Ham, R. Acharyya, Young-Chan Lee, M. Garcés, D. Fee, Chelsea Whitten, Eric Rivera

引用次数: 5

Power Transformers Diagnosis Using Neural Networks 基于神经网络的电力变压器诊断

2007 International Joint Conference on Neural Networks Pub Date : 2007-10-29 DOI: 10.1109/IJCNN.2007.4371253

Marcela P. Moreira, L. T. B. Santos, M. Vellasco

{"title":"Power Transformers Diagnosis Using Neural Networks","authors":"Marcela P. Moreira, L. T. B. Santos, M. Vellasco","doi":"10.1109/IJCNN.2007.4371253","DOIUrl":"https://doi.org/10.1109/IJCNN.2007.4371253","url":null,"abstract":"Power transformers are one of the most used and expensive equipments in many substations of electric energy. This fact justifies the application of predictive techniques of diagnosis, with the objective to minimize possible failures and to increase the trustworthiness of the system. Amongst these techniques, one of the most distinguished are the analysis of gases dissolved in the oil (gaseous chromatography) and the physical-chemical analysis of the isolating oil. Although their generalized use, the diagnosis made by these techniques presents deficiencies, demanding the presence of specialists to complete the diagnosis. A great contribution for the electric sector would be a decision support tool capable of providing a correct and automatic diagnosis, to improve the monitoring process of power transformers. This article presents a diagnosis system, based on two artificial neural networks, each dedicated to the analysis of gaseous chromatography and physical-chemical of the isolating oil, respectively. The idea to enclose these two techniques is to accomplish a more complete diagnosis of the equipment, as well as a reduction of specialists' participation, creating a more automatic diagnosis system. The obtained results with the proposed system are compared with traditional methods. The resultant system represents a more complete decision support tool in the determination of the diagnosis of power transformers.","PeriodicalId":350091,"journal":{"name":"2007 International Joint Conference on Neural Networks","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126500619","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: 4

Bayesian Signal Classifier 贝叶斯信号分类器

2007 International Joint Conference on Neural Networks Pub Date : 2007-10-29 DOI: 10.1109/IJCNN.2007.4370956

C. Chow, S. Y. Yuen

引用次数: 0

Incremental Learning for Classification of Protein Sequences 蛋白质序列分类的增量学习

2007 International Joint Conference on Neural Networks Pub Date : 2007-10-29 DOI: 10.1109/IJCNN.2007.4370924

S. Mohamed, D. Rubin, T. Marwala

引用次数: 10

Just-in-time Adaptive Classifiers in Non-Stationary Conditions 非平稳条件下的即时自适应分类器

2007 International Joint Conference on Neural Networks Pub Date : 2007-10-29 DOI: 10.1109/IJCNN.2007.4371097

C. Alippi, M. Roveri

引用次数: 18

Portable Biomimetic Retina for Learning, Perception-based Image Acquisition 用于学习的便携式仿生视网膜，基于感知的图像采集

2007 International Joint Conference on Neural Networks Pub Date : 2007-10-29 DOI: 10.1109/IJCNN.2007.4371340

R. Eckmiller, R. Schatten, O. Baruth

{"title":"Portable Biomimetic Retina for Learning, Perception-based Image Acquisition","authors":"R. Eckmiller, R. Schatten, O. Baruth","doi":"10.1109/IJCNN.2007.4371340","DOIUrl":"https://doi.org/10.1109/IJCNN.2007.4371340","url":null,"abstract":"We developed a portable biomimetic retina for blind subjects with retinal defects. 1-To simplify the visual prosthetic function, image segmentation of input patterns PI was provided by a set of line elements with specific lengths and orientations. 2-The segmented images were mapped by a filter module (FM: array of tunable spatio-temporal (ST) filters) onto a data stream as future stimulation signals for the human central visual system. 3-The foveal region of the central visual system was simulated by an inverter module (IM) to test the generation of a visual percept P2 of a given PI before the entire system will be applied to blind humans. 4-The parameter vector (PV) of FM could be modified interactively by the human user with evolutionary algorithms (EA) based on a perceptual comparison. 5-Two small displays for separate presentation of PI and the simulated percept P2 were integrated in a lightweight head mount and were combined with a 3-D acceleration sensor (AS) for head movement detection. 6-Subjects with normal vision were able to tune FM in a perception-based dialog exclusively by means of specific small head movements and to iteratively select the best three out of six possible percepts P2 until the output of IM, P2 became identical to a given PI.","PeriodicalId":350091,"journal":{"name":"2007 International Joint Conference on Neural Networks","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122313356","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

Neural Network Control of Robot Formations using RISE Feedback 基于RISE反馈的机器人编队神经网络控制

2007 International Joint Conference on Neural Networks Pub Date : 2007-10-29 DOI: 10.1109/IJCNN.2007.4371402

T. Dierks, S. Jagannathan

引用次数: 1

2007 International Joint Conference on Neural Networks Pub Date : 2007-10-29 DOI: 10.1109/IJCNN.2007.4371376

Kouhei Nagashima, Masao Nakada, Y. Osana

引用次数: 14

Acoustic Modeling using Vector Quantization in Kernel Feature Space and Classification using String Kernel based Support Vector Machines 基于核特征空间的矢量量化声学建模和基于字符串核的支持向量机分类

2007 International Joint Conference on Neural Networks Pub Date : 2007-10-29 DOI: 10.1109/IJCNN.2007.4371182

R. Anitha, C. Sekhar

{"title":"Acoustic Modeling using Vector Quantization in Kernel Feature Space and Classification using String Kernel based Support Vector Machines","authors":"R. Anitha, C. Sekhar","doi":"10.1109/IJCNN.2007.4371182","DOIUrl":"https://doi.org/10.1109/IJCNN.2007.4371182","url":null,"abstract":"In this paper, we propose an approach to acoustic modeling using vector quantization in a Mercer kernel feature space to obtain a sequence of codebook indices, and then use a support vector machine based classifier to classify the sequence of codebook indices. Clustering and vector quantization in the kernel feature space induced by a nonlinear innerproduct kernel is helpful in proper separation of nonlinearly separable clusters in the input acoustic feature space. Effectiveness of the proposed approach to acoustic modeling is demonstrated for recognition of spoken letters in E-set of English alphabet, and for recognition of a large number of consonant-vowel type subword units in continuous speech of three Indian languages. Performance of the proposed approach to acoustic modeling is compared with that of a continuous density hidden Markov model based classifier in the input acoustic feature space. Though there is a significant loss of information due to discretization involved in vector quantization, the proposed approach gives a performance better than that of classifiers using the continuous valued acoustic feature representation.","PeriodicalId":350091,"journal":{"name":"2007 International Joint Conference on Neural Networks","volume":"42 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120987236","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

Automated Abstraction of Dynamic Neural Systems for Natural Language Processing 用于自然语言处理的动态神经系统的自动抽象

2007 International Joint Conference on Neural Networks Pub Date : 2007-10-29 DOI: 10.1109/IJCNN.2007.4371171

H. Jacobsson, S. Frank, D. Federici

引用次数: 3