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2012 IEEE Fifth International Conference on Advanced Computational Intelligence (ICACI)最新文献

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A subpixel registration algorithm for low PSNR images 低PSNR图像的亚像素配准算法
2012 IEEE Fifth International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2018-03-31 DOI: 10.1109/ICACI.2012.6463241
Song Feng, L. Deng, G. Shu, Feng Wang, H. Deng, Kaifan Ji
{"title":"A subpixel registration algorithm for low PSNR images","authors":"Song Feng, L. Deng, G. Shu, Feng Wang, H. Deng, Kaifan Ji","doi":"10.1109/ICACI.2012.6463241","DOIUrl":"https://doi.org/10.1109/ICACI.2012.6463241","url":null,"abstract":"This paper presents a fast algorithm for obtaining high-accuracy subpixel translation of low PSNR images. Instead of locating the maximum point on the up-sampled images or fitting the peak of correlation surface, the proposed algorithm is based on the measurement of centroid on the cross correlation surface by Modified Moment method. Synthetic images, real solar images and standard testing images with white Gaussian noise added were tested, and the results show that the accuracies of our algorithm are comparable with other subpixel registration techniques and the processing speed is higher. The drawback is also discussed at the end of this paper.","PeriodicalId":404759,"journal":{"name":"2012 IEEE Fifth International Conference on Advanced Computational Intelligence (ICACI)","volume":"107 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133575133","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: 29
Constrained batch-to-batch optimal control for batch process based on kernel principal component regression model 基于核主成分回归模型的批处理约束批间最优控制
2012 IEEE Fifth International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2012-10-01 DOI: 10.1109/ICACI.2012.6463335
Ganping Li, Tao Huang, Jun Zhao
{"title":"Constrained batch-to-batch optimal control for batch process based on kernel principal component regression model","authors":"Ganping Li, Tao Huang, Jun Zhao","doi":"10.1109/ICACI.2012.6463335","DOIUrl":"https://doi.org/10.1109/ICACI.2012.6463335","url":null,"abstract":"A batch-to-batch optimal control method is presented in the paper for batch process control under input constraints. Generally it is very difficult to acquire an accurate mechanistic model for a batch process. Kernel principal component regression (KPCR) technique is a nonlinear modeling method that has a better ability to deal with nonlinear data. A KPCR model based batch-to-batch optimal control strategy is developed for end-point quality control of batch process. On the basis of the linearized KPCR model, the control input is obtained by minimising a quadratic objective function concerning the end-point product quality. To ensure the safe, smooth operations of batch process, certain input constraints are taken into considered. Furthermore, the KPCR model is updated from batch-to-batch to overcome the process variations or disturbances. Numerical simulation shows that the method can improve the end-point product qualities from batch to batch under input constraints. Based on updated KPCR model, the approach has better adaptability for process variations or disturbances than the policy based on updated PCR model has.","PeriodicalId":404759,"journal":{"name":"2012 IEEE Fifth International Conference on Advanced Computational Intelligence (ICACI)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115627146","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 improved distance regularized level set evolution without re-initialization 一种改进的距离正则化水平集进化，无需重新初始化
2012 IEEE Fifth International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2012-10-01 DOI: 10.1109/ICACI.2012.6463242
Weifeng Wu, Yuan Wu, Qian Huang
{"title":"An improved distance regularized level set evolution without re-initialization","authors":"Weifeng Wu, Yuan Wu, Qian Huang","doi":"10.1109/ICACI.2012.6463242","DOIUrl":"https://doi.org/10.1109/ICACI.2012.6463242","url":null,"abstract":"Level set methods have been widely used in image processing and computer vision. The re-initialization problem of level set limits its application. Recently proposed distance regularized level set evolution (DRLSE) can avoid level set re-initializations, the DRLSE formulation allows the use of more general and efficient initialization of the level set function and provides a simple narrowband implementation to greatly reduce computational cost. However the diffusion rate may incur undesirable side effect in some circumstances, and thus influence the distance regularization. An improved diffusion rate model is proposed in this paper, and experiment results show that our model performs better in distance regularization, and moreover the example of applying our model in image segmentation task indicates it has more widely applications in other image processing tasks.","PeriodicalId":404759,"journal":{"name":"2012 IEEE Fifth International Conference on Advanced Computational Intelligence (ICACI)","volume":"220 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115652011","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
Fault diagnosis for locomotive bearings based on IPSO-BP neural network 基于IPSO-BP神经网络的机车轴承故障诊断
2012 IEEE Fifth International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2012-10-01 DOI: 10.1109/ICACI.2012.6463279
Bin Lei, HaiLong Tao, Lijuan Xing
{"title":"Fault diagnosis for locomotive bearings based on IPSO-BP neural network","authors":"Bin Lei, HaiLong Tao, Lijuan Xing","doi":"10.1109/ICACI.2012.6463279","DOIUrl":"https://doi.org/10.1109/ICACI.2012.6463279","url":null,"abstract":"This paper presents a BP network model based on improved PSO for bearing fault diagnosis. Combining PSO algorithm for global optimization ability with BP neural network advantages of local search, the model effectively prevents the network from a local minimum, and at the same time guarantees the accuracy of diagnosis. Simulation results show that the locomotive bearings have been effectively diagnosed. Compared with the conventional BP neural network model, this method not only improves the convergence speed, but also improves the fault diagnosis accuracy.","PeriodicalId":404759,"journal":{"name":"2012 IEEE Fifth International Conference on Advanced Computational Intelligence (ICACI)","volume":"10 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116776140","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
Research and application of chaotic time series prediction based on Empirical Mode Decomposition 基于经验模态分解的混沌时间序列预测研究与应用
2012 IEEE Fifth International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2012-10-01 DOI: 10.1109/ICACI.2012.6463160
Yin Xu, G. Ji, Shuliang Zhang
{"title":"Research and application of chaotic time series prediction based on Empirical Mode Decomposition","authors":"Yin Xu, G. Ji, Shuliang Zhang","doi":"10.1109/ICACI.2012.6463160","DOIUrl":"https://doi.org/10.1109/ICACI.2012.6463160","url":null,"abstract":"Time series that composed of disperse observation like climatic time series have nonlinear and nonstationary features. Because of the superiority of Support Vector Machine in solving nonlinear problem and the advantage of Empirical Mode Decomposition in handling nonstationary signal, this paper combined the two methods in the research on chaotic time series prediction, and applied it to the seasonal precipitation forecast in Guangxi Zhuang Autonomous Region. Apart from this, this paper compares this result with RBF neural network algorithm and Support Vector Machine algorithm neither with the Empirical Mode Decomposition algorithm. Results show that relative to the directly predict methods, algorithm in this paper has the higher precision in prediction and better generalization ability.","PeriodicalId":404759,"journal":{"name":"2012 IEEE Fifth International Conference on Advanced Computational Intelligence (ICACI)","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116897746","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
Global robust exponential stability for Cohen-Grossberg neural networks with time-varying delays 时变时滞Cohen-Grossberg神经网络的全局鲁棒指数稳定性
2012 IEEE Fifth International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2012-10-01 DOI: 10.1109/ICACI.2012.6463285
Xiaolin Li, Jia Jia
{"title":"Global robust exponential stability for Cohen-Grossberg neural networks with time-varying delays","authors":"Xiaolin Li, Jia Jia","doi":"10.1109/ICACI.2012.6463285","DOIUrl":"https://doi.org/10.1109/ICACI.2012.6463285","url":null,"abstract":"Global robust exponential stability problems for Cohen-Grossberg neural networks are investigated in this paper. New sufficient conditions are derived to ensure the global robust exponential stability of the equilibrium point by using a new inequality and linear matrix inequality technique. A numerical example is given to show the effectiveness of the theoretical results.","PeriodicalId":404759,"journal":{"name":"2012 IEEE Fifth International Conference on Advanced Computational Intelligence (ICACI)","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127496682","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 extended car-following model with the dynamical collaborative between two consecutive vehicles 一个具有两辆连续车辆之间动态协作的扩展汽车跟随模型
2012 IEEE Fifth International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2012-10-01 DOI: 10.1109/ICACI.2012.6463304
Ying Zhou, Zhi-peng Li
{"title":"An extended car-following model with the dynamical collaborative between two consecutive vehicles","authors":"Ying Zhou, Zhi-peng Li","doi":"10.1109/ICACI.2012.6463304","DOIUrl":"https://doi.org/10.1109/ICACI.2012.6463304","url":null,"abstract":"An extended car-following model is proposed in this paper based on the idea of the dynamical cooperation between nearest-neighbor vehicles. The stability condition of the new model is derived by using the linear stability theory. By using the reductive perturbation method and nonlinear analysis, the modified Korteweg-de Vries (mKdV) equation are derived to describe the traffic density waves in the unstable region. And the corresponding kink-antikink solution is used to describe the traffic congestions. It is found that the dynamical collaboration between two consecutive vehicles can further stabilize traffic flow and suppress traffic jams effectively, which is verified by direct numerical simulations.","PeriodicalId":404759,"journal":{"name":"2012 IEEE Fifth International Conference on Advanced Computational Intelligence (ICACI)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115017688","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
Infer the probability of read in microblogs 推断微博被阅读的概率
2012 IEEE Fifth International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2012-10-01 DOI: 10.1109/ICACI.2012.6463166
Zhaoyun Ding, Bingying Xu, Lei Deng, H Zhao, Yan Jia, Bin Zhou
{"title":"Infer the probability of read in microblogs","authors":"Zhaoyun Ding, Bingying Xu, Lei Deng, H Zhao, Yan Jia, Bin Zhou","doi":"10.1109/ICACI.2012.6463166","DOIUrl":"https://doi.org/10.1109/ICACI.2012.6463166","url":null,"abstract":"In microblogs contexts like Twitter, a large number of users follow others. In case the author is not protecting his tweets, they appear in the so-called public timeline and his followers will receive all the messages from him. However, if followers of the author do not browse the personal page of the author, or they do not browse the timeline of themselves, they will not read messages of the author. So, followers of the author could not read all messages of the author. In this paper, we will infer the probability of read in microblogs according to the daily time-series model of posting and the similarity of personal interest. Experiments were conducted on a real dataset from Twitter containing about 0.26 million users and 2.7 million tweets. Experimental results indicate that out method is effective to infer the probability of read in microblogs.","PeriodicalId":404759,"journal":{"name":"2012 IEEE Fifth International Conference on Advanced Computational Intelligence (ICACI)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115073695","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
Proximal support vector machine based pavement image classification 基于近端支持向量机的路面图像分类
2012 IEEE Fifth International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2012-10-01 DOI: 10.1109/ICACI.2012.6463255
Wei Na, Wang Tao
{"title":"Proximal support vector machine based pavement image classification","authors":"Wei Na, Wang Tao","doi":"10.1109/ICACI.2012.6463255","DOIUrl":"https://doi.org/10.1109/ICACI.2012.6463255","url":null,"abstract":"Pavement cracking is one of the most important distress types. This paper provids an approach for achieving an automatic classification for pavement surface images. First, image enhancement is performed by mathematical morphological operator. secondly, pavement image segmentation is performed to separate the cracks from the background. Projection features are then extracted. The proximal support vector machine(PSVM) is used for pavement surface images classification, which is more efficient and easier to be implemented than the traditional support vector machine. The experimental results prove that the proposed method not only improves the computation efficiency but also preserves the classification performance.","PeriodicalId":404759,"journal":{"name":"2012 IEEE Fifth International Conference on Advanced Computational Intelligence (ICACI)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116232522","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: 16
An improved Particle Swarm Optimization algorithm for speaker recognition 一种改进的粒子群算法用于说话人识别
2012 IEEE Fifth International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2012-10-01 DOI: 10.1109/ICACI.2012.6463244
Ruiling Luo, Wenqing Cai, Min Chen, D. Zhu
{"title":"An improved Particle Swarm Optimization algorithm for speaker recognition","authors":"Ruiling Luo, Wenqing Cai, Min Chen, D. Zhu","doi":"10.1109/ICACI.2012.6463244","DOIUrl":"https://doi.org/10.1109/ICACI.2012.6463244","url":null,"abstract":"Considering the Particle Swam Optimization (PSO) is easily relapsing into local extremum, an improved PSO(IPSO) is proposed in this paper. In the new algorithm, we apply the evolution speed factor as the trigger conditions to stochastically disturb the local optimal solution. The IPSO algorithm can not only improve extraordinarily the convergence velocity in the evolutionary optimization, but also can adjust the balance between global and local exploration suitably. Then a speaker recognition approach using this improved algorithm to train Support vector machine (SVM) is presented. The experimental results show that the SVM optimized by IPSO achieves higher classification accuracy than the standard SVM and effectively improves the speaker identification speed and accuracy.","PeriodicalId":404759,"journal":{"name":"2012 IEEE Fifth International Conference on Advanced Computational Intelligence (ICACI)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128326931","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
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