{"title":"Performance of the DBF neural controller for transient stability enhancement of the power system","authors":"Wenming Cao, Mingjun Cheng, H. Feng","doi":"10.1109/ICNNSP.2003.1279273","DOIUrl":"https://doi.org/10.1109/ICNNSP.2003.1279273","url":null,"abstract":"To enhance the first swing stability a DBF neural network fastvalving controller is proposed. The solution approach is based on a recent fuzzy fastvalving control scheme. Disturbances in the PS are used for training the NN controller. The performance of the DBF neural controller is simulated in a single machine to an infinite bus power system.","PeriodicalId":336216,"journal":{"name":"International Conference on Neural Networks and Signal Processing, 2003. Proceedings of the 2003","volume":"27 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":"114534604","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}
{"title":"ARMA lattice model for speech analysis and synthesis","authors":"H. Kwan, M. Wang","doi":"10.1109/ICNNSP.2003.1280748","DOIUrl":"https://doi.org/10.1109/ICNNSP.2003.1280748","url":null,"abstract":"In this paper, the use of the ARMA lattice model for glottal source excited pitch synchronous speech analysis and synthesis is presented. By modeling the vocal tract with the ARMA lattice model, the effect of the nasal cavity is modeled by the introduced zeros. Consequently, a higher quality speech could be synthesized. In so doing, the limitation due to the absence of zeros for modeling a nasal sound in the LP model is overcome. The ARMA lattice model under study is computational efficient and numerically stable, which is desirable for robust speech modeling applications. Simulation results are presented and compared with those of the LP model.","PeriodicalId":336216,"journal":{"name":"International Conference on Neural Networks and Signal Processing, 2003. Proceedings of the 2003","volume":"77 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":"116176180","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}
{"title":"Adaptive blind equalization of the time-and frequency-selective OFDM system based on the local approximation","authors":"X. Dai, Xiaodong Xie","doi":"10.1109/ICNNSP.2003.1281135","DOIUrl":"https://doi.org/10.1109/ICNNSP.2003.1281135","url":null,"abstract":"Time-and frequency-selective fades will largely degrade the performance of orthogonal frequency-division multiplexing (OFDM) systems because the former introduces the inter-carrier interference (ICI) and the latter attenuates the amplitude and blurs the phase of transmitted sequence. In this paper, by dividing a long OFDM block into a series of short segments in which the time variation is significantly small and can be neglected, the time-and frequency-selective channel is approximated as a set of linear time invariant (LTI) systems, and thus can be further equalized using the blind equalization schemes of LTI systems. The study shows the new blind equalization has an improved performance in canceling the effects of the time-and frequency selective fades.","PeriodicalId":336216,"journal":{"name":"International Conference on Neural Networks and Signal Processing, 2003. Proceedings of the 2003","volume":"71 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":"116322008","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}
{"title":"Faults diagnosis in industrial reheating furnace using principal component analysis","authors":"Jun Liang, Ning Wang","doi":"10.1109/ICNNSP.2003.1281190","DOIUrl":"https://doi.org/10.1109/ICNNSP.2003.1281190","url":null,"abstract":"The fault detection and identification based upon multivariate statistical projection methods (such as principal component analysis, PCA) have attracted more and more interest in academic research and engineering practice. In this paper, PCA and statistical control chart have been used to detect and isolate process operating faults on an industrial rolling mill reheating furnace. The diagnosing results to single fault (fuel-gas pipe control valve failure or furnace temperature sensor failure alone) and multiple faults (control valve failure and temperature sensor failure simultaneously) were presented after establishing the operating PCA model. The calculating result indicates that the method is effective and available.","PeriodicalId":336216,"journal":{"name":"International Conference on Neural Networks and Signal Processing, 2003. Proceedings of the 2003","volume":"13 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":"122130239","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}
{"title":"Simplified beamforming on phase-only arrays","authors":"Shengxian Sun, Huiyong Li","doi":"10.1109/ICNNSP.2003.1281107","DOIUrl":"https://doi.org/10.1109/ICNNSP.2003.1281107","url":null,"abstract":"In this paper, a novel, simplified beamforming on phase-only antenna arrays is presented, which stemmed from the odd symmetric characteristic construction of phasor after persymmetry when optimum beamforming. By the odd symmetric characteristic construction of phasor, the actual freedom of phase-only antenna arrays was lessened by half. The phase-only gradient vector and Hessian matrix of the weight were formulated by using some proper matrix transforms and mathematical analysis. The optimal numerical phasor was obtained by phase-only conjugate gradient algorithm and Newton algorithm. The simulations demonstrated that the new approach can null interferences efficiently, but with dramatic reduced computational complexity.","PeriodicalId":336216,"journal":{"name":"International Conference on Neural Networks and Signal Processing, 2003. Proceedings of the 2003","volume":"52 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":"117203643","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}
{"title":"A novel edge detection method based on fractal theory","authors":"Li. Qiong, Gao Jun, Gan Long, Dong Huo-ming","doi":"10.1109/ICNNSP.2003.1281062","DOIUrl":"https://doi.org/10.1109/ICNNSP.2003.1281062","url":null,"abstract":"The gray image of nature objects surrounding us satisfies the fractional Brownian motion (fBm) model. In this paper, the limitations and disadvantages of available methods based on fBm model are pointed out and a novel edge detection method based on fractal theory is proposed. Moreover, an edge evaluation method is introduced to analyze its performance. The experiment results show that the proposed method not only can detect abundant edge details but also is computationally economical. Furthermore, the general edge evaluation of the proposed method is much better than that of the traditional method.","PeriodicalId":336216,"journal":{"name":"International Conference on Neural Networks and Signal Processing, 2003. Proceedings of the 2003","volume":"51 17","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120808852","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}
{"title":"A novel motion model and tracking algorithm","authors":"Dang Jianwu, H. Jianguo","doi":"10.1109/ICNNSP.2003.1279346","DOIUrl":"https://doi.org/10.1109/ICNNSP.2003.1279346","url":null,"abstract":"A novel motion model and adaptive algorithm for tracking maneuvering target are proposed, in which the acceleration of maneuvering targets is considered as a time-correlation random process with non-zero mean values and the probability density of the acceleration is assumed by Gaussian distribution. The mean value of the distribution function is the optimal estimation of the target acceleration at present and its variance is directly proportional to the square of the differential coefficient of the optimal estimations of the target acceleration at present. The Monte Carlo simulation results show that the model and adaptive algorithm proposed in this paper can estimate the position, velocity and acceleration of a target well and requires less computation than the others, no matter what the target is maneuvering at any form.","PeriodicalId":336216,"journal":{"name":"International Conference on Neural Networks and Signal Processing, 2003. Proceedings of the 2003","volume":"54 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":"125745460","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}
Ao Li, Tao Wang, Yun Zhou, Minghui Wang, Huan-qing Feng
{"title":"An efficient structure learning method in gene prediction","authors":"Ao Li, Tao Wang, Yun Zhou, Minghui Wang, Huan-qing Feng","doi":"10.1109/ICNNSP.2003.1279336","DOIUrl":"https://doi.org/10.1109/ICNNSP.2003.1279336","url":null,"abstract":"This paper proposes an efficient structure learning method to simplify Bayesian network for detecting splice junction site in gene sequences. In this method, nodes in Bayesian networks are selected as features by feature selection algorithm for structure learning. This algorithm is based on genetic algorithm and uses a MAP (maximum a posterior) classifier for this purpose. The result shows that this method can greatly simplify the network while maintains the high accuracy of prediction. The architecture of the optimized network also indicates that the nucleotides close to Donor site are the key elements in the expression of genes.","PeriodicalId":336216,"journal":{"name":"International Conference on Neural Networks and Signal Processing, 2003. Proceedings of the 2003","volume":"87 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":"128215721","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}
{"title":"Architecture of wavelet-based frame skipping transcoder","authors":"K. Fung, W. Siu","doi":"10.1109/ICNNSP.2003.1281092","DOIUrl":"https://doi.org/10.1109/ICNNSP.2003.1281092","url":null,"abstract":"A new architecture of wavelet based frame skipping transcoding process for multipoint video conferencing is proposed in this paper. The work involves new structures for a video coder and a transcoder which are wavelet-based. The video transcoder extracts information of motion activities from the video bitstream produced by a wavelet-based video coder. Using the proposed frame skipping process in the wavelet domain, temporal scalability can be achieved. The video quality of inactive sub-sequences can be easily adjusted using the video combiner. This is achieved by discarding fine details of the bitstream or performing frame skipping in the wavelet domain. In other words, more bits can be reallocated to active sub-sequences to achieve a good visual quality with smooth motion. In addition, the video coder is region-based so that different wavelet kernels can be used for the foreground and background. This setting can, on one hand, reduce the computational complexity significantly. On the other hand, by considering unequal importance of various regions, a high video quality for foreground objects is always guaranteed whilst an acceptable background quality can also be maintained even under low bitrate environments. Since the video transcoder only needs to rearrange the video quality level according to their motion activities or performing frame skipping in wavelet domain, a significant saving in computational complexity can be achieved as compared to the conventional video combiner using transcoding approach. The new video coder and transcoder are then used to realize a multipoint video conferencing system. Experimental results are included at the end of the paper, which show a good improvement in performance due to the proposed architecture.","PeriodicalId":336216,"journal":{"name":"International Conference on Neural Networks and Signal Processing, 2003. Proceedings of the 2003","volume":"24 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":"128226007","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}
{"title":"Combining multiple neural networks for classification based on rough set reduction","authors":"D. Yu, Qinghua Hu, W. Bao","doi":"10.1109/ICNNSP.2003.1279331","DOIUrl":"https://doi.org/10.1109/ICNNSP.2003.1279331","url":null,"abstract":"Generalization ability is a measure of performance of neural networks. Multiple neural networks combination based on the combination of a set of networks is used to achieve high pattern recognition performance. In our work rough set theory is introduced to reduce high dimensional data and get multiple concise representations (reducts) of a single sample set. Multiple neural networks classifiers are built based on different reducts. Average strategy and majority voting strategy are introduced to combine the outputs from different classifiers. The experimental results show the combined system outperforms a single classifier.","PeriodicalId":336216,"journal":{"name":"International Conference on Neural Networks and Signal Processing, 2003. Proceedings of the 2003","volume":"55 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":"127084008","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}