Pub Date : 2016-12-01DOI: 10.1109/ICICIP.2016.7885899
Jinwu Gao, T. Shen
Calibration in combustion phase control is an effective way to get sophisticated engine performance, but is only workable by analyzing offline data or running engine in test mode. When engine is aged or runs at unfamiliar situations, traditional calibration method cannot promise the same performance as before. To improve calibration technique, an online calibration method for combustion phase control is presented, which also works when engine is running in driving operating condition. Based on bilinear interpolation algorithm, online calibration problem is converted to parameters estimation issue, then stochastic gradient descent algorithm is utilized to estimate parameters by iteratively updates. Finally, the proposed strategy is verified on a gasoline spark ignition engine.
{"title":"Online calibration of spark advance for combustion phase control of gasoline SI engines","authors":"Jinwu Gao, T. Shen","doi":"10.1109/ICICIP.2016.7885899","DOIUrl":"https://doi.org/10.1109/ICICIP.2016.7885899","url":null,"abstract":"Calibration in combustion phase control is an effective way to get sophisticated engine performance, but is only workable by analyzing offline data or running engine in test mode. When engine is aged or runs at unfamiliar situations, traditional calibration method cannot promise the same performance as before. To improve calibration technique, an online calibration method for combustion phase control is presented, which also works when engine is running in driving operating condition. Based on bilinear interpolation algorithm, online calibration problem is converted to parameters estimation issue, then stochastic gradient descent algorithm is utilized to estimate parameters by iteratively updates. Finally, the proposed strategy is verified on a gasoline spark ignition engine.","PeriodicalId":226381,"journal":{"name":"2016 Seventh International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129664673","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}
Pub Date : 2016-12-01DOI: 10.1109/ICICIP.2016.7885904
Hua Yang, Jianguo Liu, Feng Jiang
Stochastic jump systems have great potential in finance engineering and stochastic control. In this paper, we mainly consider stability of stochastic jump-diffusion systems with parameter. We establish the criteria of locally exponential stability of mild solutions of the systems by using stochastic integral inequalities technique. We extend some existing results to more general cases. Finally, we use an example to show our result.
{"title":"Stability analysis for a class of jump-diffusion systems with parameter","authors":"Hua Yang, Jianguo Liu, Feng Jiang","doi":"10.1109/ICICIP.2016.7885904","DOIUrl":"https://doi.org/10.1109/ICICIP.2016.7885904","url":null,"abstract":"Stochastic jump systems have great potential in finance engineering and stochastic control. In this paper, we mainly consider stability of stochastic jump-diffusion systems with parameter. We establish the criteria of locally exponential stability of mild solutions of the systems by using stochastic integral inequalities technique. We extend some existing results to more general cases. Finally, we use an example to show our result.","PeriodicalId":226381,"journal":{"name":"2016 Seventh International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132654586","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}
Pub Date : 2016-12-01DOI: 10.1109/ICICIP.2016.7885876
Jian Hou, Hongxia Cui
The recently proposed clustering algorithm based on density peaks is reported to generate very good clustering results. This algorithm is simple and efficient, and can be used to generate clusters of arbitrary shapes. However, the performance of this algorithm relies on the selection of the kernel in local density calculation. The original density peak based algorithm uses the cutoff kernel and Gaussian kernel to calculate the local density, and the clustering results are found to be influenced by the cutoff distance, which can only be determined empirically so far. In this paper we use a different kernel in density calculation, and evaluate the influence of related parameter on the clustering results. Our work is helpful in understanding the clustering mechanism of this algorithm.
{"title":"Experimental evaluation of a density kernel in clustering","authors":"Jian Hou, Hongxia Cui","doi":"10.1109/ICICIP.2016.7885876","DOIUrl":"https://doi.org/10.1109/ICICIP.2016.7885876","url":null,"abstract":"The recently proposed clustering algorithm based on density peaks is reported to generate very good clustering results. This algorithm is simple and efficient, and can be used to generate clusters of arbitrary shapes. However, the performance of this algorithm relies on the selection of the kernel in local density calculation. The original density peak based algorithm uses the cutoff kernel and Gaussian kernel to calculate the local density, and the clustering results are found to be influenced by the cutoff distance, which can only be determined empirically so far. In this paper we use a different kernel in density calculation, and evaluate the influence of related parameter on the clustering results. Our work is helpful in understanding the clustering mechanism of this algorithm.","PeriodicalId":226381,"journal":{"name":"2016 Seventh International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131247792","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}
Pub Date : 2016-12-01DOI: 10.1109/ICICIP.2016.7885877
Wei-Xue Liu, Jian Hou
The density peak based clustering algorithm is a recently proposed clustering approach. It uses the local density of each data and the distance to the nearest neighbor with higher density to isolate and identify the cluster centers. After the cluster centers are identified, the other data are assigned labels equaling to those of their nearest neighbors with higher density. This algorithm is simple and efficient. On condition that the cluster centers are identified correctly, it can generate very good clustering results. However, the results of this algorithm depend on a parameter in the local density calculation. In this paper we investigate the influence of the parameter on the clustering results through extensive experiments on several datasets. Our work can be useful in applying the density peak based clustering algorithm to practical tasks.
{"title":"Study on a density peak based clustering algorithm","authors":"Wei-Xue Liu, Jian Hou","doi":"10.1109/ICICIP.2016.7885877","DOIUrl":"https://doi.org/10.1109/ICICIP.2016.7885877","url":null,"abstract":"The density peak based clustering algorithm is a recently proposed clustering approach. It uses the local density of each data and the distance to the nearest neighbor with higher density to isolate and identify the cluster centers. After the cluster centers are identified, the other data are assigned labels equaling to those of their nearest neighbors with higher density. This algorithm is simple and efficient. On condition that the cluster centers are identified correctly, it can generate very good clustering results. However, the results of this algorithm depend on a parameter in the local density calculation. In this paper we investigate the influence of the parameter on the clustering results through extensive experiments on several datasets. Our work can be useful in applying the density peak based clustering algorithm to practical tasks.","PeriodicalId":226381,"journal":{"name":"2016 Seventh International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115402025","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}
Pub Date : 2016-12-01DOI: 10.1109/ICICIP.2016.7885882
Chengyuan Sun, Jian Hou, Aihua Zhang, Zhiyong She
Particle filter (PF) serves as an effective method applied to the fault diagnosis of nonlinear and non-Gaussian systems. However, the result of state estimation is influenced by the particle impoverishment problem which is common in the typical PF algorithm. Based on the analysis of the PF algorithm, the general particle impoverishment problem is attributed to the deficiency of particle diversity. In this paper a novel intelligent particle filter (NIPF) is designed to deal with the problem of particle impoverishment by means of the genetic and adaptive strategy. The general PF can be regarded as a particular instance of NIPF. The experiment on the vertically falling body model shows that the NIPF can increase the particles diversity and improve the results of state estimation.
{"title":"A novel intelligent particle filter for process monitoring","authors":"Chengyuan Sun, Jian Hou, Aihua Zhang, Zhiyong She","doi":"10.1109/ICICIP.2016.7885882","DOIUrl":"https://doi.org/10.1109/ICICIP.2016.7885882","url":null,"abstract":"Particle filter (PF) serves as an effective method applied to the fault diagnosis of nonlinear and non-Gaussian systems. However, the result of state estimation is influenced by the particle impoverishment problem which is common in the typical PF algorithm. Based on the analysis of the PF algorithm, the general particle impoverishment problem is attributed to the deficiency of particle diversity. In this paper a novel intelligent particle filter (NIPF) is designed to deal with the problem of particle impoverishment by means of the genetic and adaptive strategy. The general PF can be regarded as a particular instance of NIPF. The experiment on the vertically falling body model shows that the NIPF can increase the particles diversity and improve the results of state estimation.","PeriodicalId":226381,"journal":{"name":"2016 Seventh International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123261550","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}
Pub Date : 2016-12-01DOI: 10.1109/ICICIP.2016.7885914
Ao Chen, Honpeng Zhou, Jian Jiao, Tianyi Gao
Modern industrial system is becoming more and more complex in order to produce the goods with high quality or achieve the functional requirements set by human beings. However, once the faults occur in the system, it's highly possible that the financial losses and even the operators' death may be caused. Therefore, it's necessary to improve the reliability of the system. The data-based fault diagnosis scheme is an important approach to realize the fault-tolerant control to further secure the system operation in the normal condition. This paper concentrates on the multivariate statistical analyses included in the framework of data-based scheme, more specifically, Total Projection to Latent Structures (TPLS). Although the traditional TPLS has achieved effective monitoring results in some practical applications, it should be noted that the decomposition principle of process variables is not appropriate. Furthermore, the test statistic it chooses can not reflect the subspaces they monitored. Both of the weaknesses make TPLS useless in some circumstances. To solve the problems, this paper proposes a Modified TPLS (MTPLS) based on TPLS, statistics theory and matrix analysis. Compared with TPLS, MTPLS has better fault diagnosis performance. A numerical example is used to validate the effectiveness of TPLS.
{"title":"SVD and statistic theory based modified TPLS","authors":"Ao Chen, Honpeng Zhou, Jian Jiao, Tianyi Gao","doi":"10.1109/ICICIP.2016.7885914","DOIUrl":"https://doi.org/10.1109/ICICIP.2016.7885914","url":null,"abstract":"Modern industrial system is becoming more and more complex in order to produce the goods with high quality or achieve the functional requirements set by human beings. However, once the faults occur in the system, it's highly possible that the financial losses and even the operators' death may be caused. Therefore, it's necessary to improve the reliability of the system. The data-based fault diagnosis scheme is an important approach to realize the fault-tolerant control to further secure the system operation in the normal condition. This paper concentrates on the multivariate statistical analyses included in the framework of data-based scheme, more specifically, Total Projection to Latent Structures (TPLS). Although the traditional TPLS has achieved effective monitoring results in some practical applications, it should be noted that the decomposition principle of process variables is not appropriate. Furthermore, the test statistic it chooses can not reflect the subspaces they monitored. Both of the weaknesses make TPLS useless in some circumstances. To solve the problems, this paper proposes a Modified TPLS (MTPLS) based on TPLS, statistics theory and matrix analysis. Compared with TPLS, MTPLS has better fault diagnosis performance. A numerical example is used to validate the effectiveness of TPLS.","PeriodicalId":226381,"journal":{"name":"2016 Seventh International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128530036","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}
Pub Date : 2016-12-01DOI: 10.1109/ICICIP.2016.7885885
Junnan Shen, Zepeng Ning, B. Cai, Rui Han, Lixian Zhang
The paper addresses the design problem of piecewise-affine output-feedback controllers for a family of piecewise-affine systems against signal quantization. The quantization is considered to occur in both measurement outputs and control inputs. By constructing a novel quantization-dependent Lyapunov function, the stability and H∞ performance criteria are developed for the closed-loop system with the usage of S-procedure that involves the region partition information. Then, the desired controller gains are obtained in order to guarantee that the resulting closed-loop control system is asymptotically stable with a guaranteed H∞ performance index. Finally, a numerical example is provided to show the effectiveness of the proposed control method.
{"title":"A novel control approach for piecewise-affine systems with quantization in both measurement outputs and control inputs","authors":"Junnan Shen, Zepeng Ning, B. Cai, Rui Han, Lixian Zhang","doi":"10.1109/ICICIP.2016.7885885","DOIUrl":"https://doi.org/10.1109/ICICIP.2016.7885885","url":null,"abstract":"The paper addresses the design problem of piecewise-affine output-feedback controllers for a family of piecewise-affine systems against signal quantization. The quantization is considered to occur in both measurement outputs and control inputs. By constructing a novel quantization-dependent Lyapunov function, the stability and H∞ performance criteria are developed for the closed-loop system with the usage of S-procedure that involves the region partition information. Then, the desired controller gains are obtained in order to guarantee that the resulting closed-loop control system is asymptotically stable with a guaranteed H∞ performance index. Finally, a numerical example is provided to show the effectiveness of the proposed control method.","PeriodicalId":226381,"journal":{"name":"2016 Seventh International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121901704","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}
Pub Date : 2016-12-01DOI: 10.1109/ICICIP.2016.7885909
Lijie Wang, Qi Zhou, A. Zhang, Hongyi Li
This paper focuses on adaptive neural control for nonlinear system in nonstrict feedback form in the presence of output constraint. Since the backstepping control can not be directly employed to nonstrict feedback structure during controller design. Using the variable separation method, the above obstacle has been overcome. Then, by utilizing barrier Lyapunov function, the issue of output constraint is handled. Combing neural networks (NNs) with the adaptive backstepping technique, it is not only guaranteed that all variables remain bounded in the closed-loop system, but the tracking error is made around the zero with an adjustable small neighborhood. A numerical simulation is provided to demonstrate the control scheme.
{"title":"Adaptive neural control of nonstrict system with output constriant","authors":"Lijie Wang, Qi Zhou, A. Zhang, Hongyi Li","doi":"10.1109/ICICIP.2016.7885909","DOIUrl":"https://doi.org/10.1109/ICICIP.2016.7885909","url":null,"abstract":"This paper focuses on adaptive neural control for nonlinear system in nonstrict feedback form in the presence of output constraint. Since the backstepping control can not be directly employed to nonstrict feedback structure during controller design. Using the variable separation method, the above obstacle has been overcome. Then, by utilizing barrier Lyapunov function, the issue of output constraint is handled. Combing neural networks (NNs) with the adaptive backstepping technique, it is not only guaranteed that all variables remain bounded in the closed-loop system, but the tracking error is made around the zero with an adjustable small neighborhood. A numerical simulation is provided to demonstrate the control scheme.","PeriodicalId":226381,"journal":{"name":"2016 Seventh International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130497735","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}
Pub Date : 2016-12-01DOI: 10.1109/ICICIP.2016.7885894
Jian Wu, Liwei Ma, Xiaolin Hu
Convolutional Neural Networks (CNNs) have achieved great successes in many computer vision tasks and have been applied to pose regression for camera relocalization. Traditional Simultaneously Localization and Mapping (SLAM) approaches use correspondences between camera coordinates and world coordinates to estimate camera pose. In this paper, we present a new camera relocalization method including pixels' world coordinates regression with CNNs and camera pose optimization. We also explore the different characteristics of CNNs and SCoRe Forests on world coordinates regression. Experiments show that our approach has larger camera relocalization error but better performance on predicting world coordinates of pixels compared to SCoRe Forests.
{"title":"Predicting world coordinates of pixels in RGB images using Convolutional Neural Network for camera relocalization","authors":"Jian Wu, Liwei Ma, Xiaolin Hu","doi":"10.1109/ICICIP.2016.7885894","DOIUrl":"https://doi.org/10.1109/ICICIP.2016.7885894","url":null,"abstract":"Convolutional Neural Networks (CNNs) have achieved great successes in many computer vision tasks and have been applied to pose regression for camera relocalization. Traditional Simultaneously Localization and Mapping (SLAM) approaches use correspondences between camera coordinates and world coordinates to estimate camera pose. In this paper, we present a new camera relocalization method including pixels' world coordinates regression with CNNs and camera pose optimization. We also explore the different characteristics of CNNs and SCoRe Forests on world coordinates regression. Experiments show that our approach has larger camera relocalization error but better performance on predicting world coordinates of pixels compared to SCoRe Forests.","PeriodicalId":226381,"journal":{"name":"2016 Seventh International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"708 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123839280","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}
Pub Date : 2016-12-01DOI: 10.1109/ICICIP.2016.7885902
Chengyuan Sun, Jian Hou, Aihua Zhang, Zhiyong She
With the continuous development of computer science and control science, the complexity of the system also increased rapidly. Accordingly, people began to improve the security and stability of the systems, and fault diagnosis in time is an effectively method to reduce the loss of property. The reality systems are invariably more complex, nonlinear and non-Gaussian. The previous method cannot solve the problem very well. However, as an unique technology, the particle filter (PF) can apply to nonlinear and non-Gaussian systems effectively. The accuracy of state estimation is influenced by the particle impoverishment problem in the quintessential PF algorithm. In order to deal with the particle impoverishment, we propose an intelligent particle filter (IPF) algorithm which based on genetic algorithm optimization after analyzed the particle filter algorithm. The common PF is a special circumstances of IPF that relieves the particular parameters. Results of these two experimental applications of the IPF are given to illustrate it can increase the particles diversity and improve the state estimation results of fault diagnosis.
{"title":"An intelligent particle filter for state estimation and fault detection","authors":"Chengyuan Sun, Jian Hou, Aihua Zhang, Zhiyong She","doi":"10.1109/ICICIP.2016.7885902","DOIUrl":"https://doi.org/10.1109/ICICIP.2016.7885902","url":null,"abstract":"With the continuous development of computer science and control science, the complexity of the system also increased rapidly. Accordingly, people began to improve the security and stability of the systems, and fault diagnosis in time is an effectively method to reduce the loss of property. The reality systems are invariably more complex, nonlinear and non-Gaussian. The previous method cannot solve the problem very well. However, as an unique technology, the particle filter (PF) can apply to nonlinear and non-Gaussian systems effectively. The accuracy of state estimation is influenced by the particle impoverishment problem in the quintessential PF algorithm. In order to deal with the particle impoverishment, we propose an intelligent particle filter (IPF) algorithm which based on genetic algorithm optimization after analyzed the particle filter algorithm. The common PF is a special circumstances of IPF that relieves the particular parameters. Results of these two experimental applications of the IPF are given to illustrate it can increase the particles diversity and improve the state estimation results of fault diagnosis.","PeriodicalId":226381,"journal":{"name":"2016 Seventh International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121135942","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}
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