Pub Date : 2014-12-01DOI: 10.1109/CCSSE.2014.7224517
You-zhi Zeng, Ning Zhang
This paper reviews the traffic flow lattice model for road vehicle traffic flow, and describes its advantages and disadvantages; based on the research carried out by the authors, discusses the reality conformity of some model assumptions and puts forward some new insights and views.
{"title":"Review and new insights of the traffic flow lattice model for road vehicle traffic flow","authors":"You-zhi Zeng, Ning Zhang","doi":"10.1109/CCSSE.2014.7224517","DOIUrl":"https://doi.org/10.1109/CCSSE.2014.7224517","url":null,"abstract":"This paper reviews the traffic flow lattice model for road vehicle traffic flow, and describes its advantages and disadvantages; based on the research carried out by the authors, discusses the reality conformity of some model assumptions and puts forward some new insights and views.","PeriodicalId":251022,"journal":{"name":"2014 IEEE International Conference on Control Science and Systems Engineering","volume":"22 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123273001","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 : 2014-12-01DOI: 10.1109/CCSSE.2014.7224529
Mu Fu-lin, Li Hong-yang
In the light of the one-sidedness of commonly used algorithms of power load classification caused by single similarity function, and the defects of these algorithm which have special requirements to the data space distribution and are easy to fall into local optimal solution, proposes a new electric power load classification algorithm. The algorithm first proposed a dual-scale similarity function base on the combination of Euclidean distance and the shape of the curve, thus to describe the similarity between the power load curves more accurately. Then cluster load curves according to the principle of spectral clustering, thus to make the algorithm not sensitive to the data distribution and data dimension, and to ensure the convergence to the global optimal solution. This algorithm can make more performance on classification of different power users, and has great significance to the implementation of the power user load control.
{"title":"Power load classification based on spectral clustering of dual-scale","authors":"Mu Fu-lin, Li Hong-yang","doi":"10.1109/CCSSE.2014.7224529","DOIUrl":"https://doi.org/10.1109/CCSSE.2014.7224529","url":null,"abstract":"In the light of the one-sidedness of commonly used algorithms of power load classification caused by single similarity function, and the defects of these algorithm which have special requirements to the data space distribution and are easy to fall into local optimal solution, proposes a new electric power load classification algorithm. The algorithm first proposed a dual-scale similarity function base on the combination of Euclidean distance and the shape of the curve, thus to describe the similarity between the power load curves more accurately. Then cluster load curves according to the principle of spectral clustering, thus to make the algorithm not sensitive to the data distribution and data dimension, and to ensure the convergence to the global optimal solution. This algorithm can make more performance on classification of different power users, and has great significance to the implementation of the power user load control.","PeriodicalId":251022,"journal":{"name":"2014 IEEE International Conference on Control Science and Systems Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114420421","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 : 2014-12-01DOI: 10.1109/CCSSE.2014.7224513
Chenghong Zhou, Weiping Qian
Particle-beam weapon is a concept of directed energy weapons and used for attacking targets utilizing particle-beam with high energy. It is a scientific idea that attracts some attention and encourages of people to research in theory and experiment. Particle-Beam weapons can be classified into charged and neutral particle-beam weapons according the electrical property of particles. In this article, the basic principle is introduced firstly, including the acceleration, propagation, and interaction, which provides a theoretical support for design and construction of particle-beam weapon system. Besides, the target tracking system is focused on and discussed in detail, in which the estimation of trajectory is based on the model-filter process to adjust shoot direction in real-time. Actually, it is hard to be realized because of the tough technology limitation.
{"title":"Particle-beam weapons system","authors":"Chenghong Zhou, Weiping Qian","doi":"10.1109/CCSSE.2014.7224513","DOIUrl":"https://doi.org/10.1109/CCSSE.2014.7224513","url":null,"abstract":"Particle-beam weapon is a concept of directed energy weapons and used for attacking targets utilizing particle-beam with high energy. It is a scientific idea that attracts some attention and encourages of people to research in theory and experiment. Particle-Beam weapons can be classified into charged and neutral particle-beam weapons according the electrical property of particles. In this article, the basic principle is introduced firstly, including the acceleration, propagation, and interaction, which provides a theoretical support for design and construction of particle-beam weapon system. Besides, the target tracking system is focused on and discussed in detail, in which the estimation of trajectory is based on the model-filter process to adjust shoot direction in real-time. Actually, it is hard to be realized because of the tough technology limitation.","PeriodicalId":251022,"journal":{"name":"2014 IEEE International Conference on Control Science and Systems Engineering","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114586233","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 : 2014-12-01DOI: 10.1109/CCSSE.2014.7224530
Yuming Hua, Junhai Guo, Hua Zhao
Micro motion is a common phenomenon in radar detection. Although In ISAR imaging, micro-motion parts attached to target always have an impact on the quantity of the image. In this passage we put forward a method to remove the effect of micro-Doppler phenomenon in the range profile sequences. This method is based on Inverse-Radon transformations and can achieve clear image of rigid body of target. In the end, we test this method by simulation in Matlab.
{"title":"The usage of inverse-radon transformation in ISAR imaging","authors":"Yuming Hua, Junhai Guo, Hua Zhao","doi":"10.1109/CCSSE.2014.7224530","DOIUrl":"https://doi.org/10.1109/CCSSE.2014.7224530","url":null,"abstract":"Micro motion is a common phenomenon in radar detection. Although In ISAR imaging, micro-motion parts attached to target always have an impact on the quantity of the image. In this passage we put forward a method to remove the effect of micro-Doppler phenomenon in the range profile sequences. This method is based on Inverse-Radon transformations and can achieve clear image of rigid body of target. In the end, we test this method by simulation in Matlab.","PeriodicalId":251022,"journal":{"name":"2014 IEEE International Conference on Control Science and Systems Engineering","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121936003","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 : 2014-12-01DOI: 10.1109/CCSSE.2014.7224506
Yan Cang, Weijin Sun, Di Chen
Since the Markov transition probability matrix (MTPM) in the interactive multiple model (IMM) based on the unscented Kalman filter (UKF) is a constant value, the IMMUKF algorithm can't exactly describe the transition probability of each model and produce lots of error in the result. Taking account of this situation, in this paper, a novel method which combines the posterior Cramer-Rao lower bound (PCRLB) with the likelihood ratio is proposed to improve tracking accuracy. PCRLB is calculated by mean and covariance of the estimated online state. The residual covariance that can be used to calculate the likelihood function of each model is updated by substituting PCRLB for the filtering error covariance matrix of UKF. Real-time estimation of MTPM can be obtained according to updated likelihood function and likelihood ratio, and then applied in IMMUKF. An adaptive MTPM IMMUKF algorithm can be obtained. Finally, to verify the correctness and validity, the proposed method is applied to a missile trajectory tracking. The root-mean-square (RMS) error is used as a performance evaluation index. The simulation results show that the proposed algorithm outperforms the IMMUKF algorithm and achieves a RMS tracking performance which is quite close to the PCRLB.
{"title":"Online estimation of transition probabilities for nonlinear discrete time systems","authors":"Yan Cang, Weijin Sun, Di Chen","doi":"10.1109/CCSSE.2014.7224506","DOIUrl":"https://doi.org/10.1109/CCSSE.2014.7224506","url":null,"abstract":"Since the Markov transition probability matrix (MTPM) in the interactive multiple model (IMM) based on the unscented Kalman filter (UKF) is a constant value, the IMMUKF algorithm can't exactly describe the transition probability of each model and produce lots of error in the result. Taking account of this situation, in this paper, a novel method which combines the posterior Cramer-Rao lower bound (PCRLB) with the likelihood ratio is proposed to improve tracking accuracy. PCRLB is calculated by mean and covariance of the estimated online state. The residual covariance that can be used to calculate the likelihood function of each model is updated by substituting PCRLB for the filtering error covariance matrix of UKF. Real-time estimation of MTPM can be obtained according to updated likelihood function and likelihood ratio, and then applied in IMMUKF. An adaptive MTPM IMMUKF algorithm can be obtained. Finally, to verify the correctness and validity, the proposed method is applied to a missile trajectory tracking. The root-mean-square (RMS) error is used as a performance evaluation index. The simulation results show that the proposed algorithm outperforms the IMMUKF algorithm and achieves a RMS tracking performance which is quite close to the PCRLB.","PeriodicalId":251022,"journal":{"name":"2014 IEEE International Conference on Control Science and Systems Engineering","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122351424","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 : 2014-12-01DOI: 10.1109/CCSSE.2014.7224525
Wirote Sangtungtong, Arthit Kongthai
This paper addresses the discrete-time interconnected observers that jointly estimate the DC voltage drops across each capacitor at the DC side of every H-bridge inverter connected together into a leg of multilevel STATCOM. The Heun's method is adopted in order to discretize their counterparts concerning continuous-time into such a discrete-time form. Beneath this manner the discrete-time observers are all introduced into one prediction and one correction stages for each iteration. Once their sampling period becomes very small, their stability condition is analogous to that of the continuous-time observers. Some simulations are carried out for the purpose of verification on performance in their estimations. In comparison with the actual voltages, the results of test confirm effectiveness of the discrete-time observers offered.
{"title":"Discrete-time interconnected observer for dc voltage estimations in multilevel STATCOM","authors":"Wirote Sangtungtong, Arthit Kongthai","doi":"10.1109/CCSSE.2014.7224525","DOIUrl":"https://doi.org/10.1109/CCSSE.2014.7224525","url":null,"abstract":"This paper addresses the discrete-time interconnected observers that jointly estimate the DC voltage drops across each capacitor at the DC side of every H-bridge inverter connected together into a leg of multilevel STATCOM. The Heun's method is adopted in order to discretize their counterparts concerning continuous-time into such a discrete-time form. Beneath this manner the discrete-time observers are all introduced into one prediction and one correction stages for each iteration. Once their sampling period becomes very small, their stability condition is analogous to that of the continuous-time observers. Some simulations are carried out for the purpose of verification on performance in their estimations. In comparison with the actual voltages, the results of test confirm effectiveness of the discrete-time observers offered.","PeriodicalId":251022,"journal":{"name":"2014 IEEE International Conference on Control Science and Systems Engineering","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131591957","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 : 2014-12-01DOI: 10.1109/CCSSE.2014.7224536
Tao Han, Yuqing Lan, Limin Xiao, Binyang Huang, Kai Zhang
Event detection through sensors data recording human activities is an aspect to learn human behaviors. In this paper, counted numbers from a sensor installed on a building entrance recording the number of people entering the building, will be processed to find the anomaly time interval when there are more people going through the entrance, which is viewed as event. An approach is adopted having two steps: first, the counted numbers over time is processed by Fourier Transformation and we get the parameter of a vector (ReX[k], ImX[k]) representing kth point in the data set; second, the vectors of (ReX[k], ImX[k]) are classified by KNN algorithm in two dimensions, categorizing the data in the same time interval in 70 days and the data in 48 intervals in one day. The results show that the proposed method works well.
{"title":"Event detection with vector similarity based on fourier transformation","authors":"Tao Han, Yuqing Lan, Limin Xiao, Binyang Huang, Kai Zhang","doi":"10.1109/CCSSE.2014.7224536","DOIUrl":"https://doi.org/10.1109/CCSSE.2014.7224536","url":null,"abstract":"Event detection through sensors data recording human activities is an aspect to learn human behaviors. In this paper, counted numbers from a sensor installed on a building entrance recording the number of people entering the building, will be processed to find the anomaly time interval when there are more people going through the entrance, which is viewed as event. An approach is adopted having two steps: first, the counted numbers over time is processed by Fourier Transformation and we get the parameter of a vector (ReX[k], ImX[k]) representing kth point in the data set; second, the vectors of (ReX[k], ImX[k]) are classified by KNN algorithm in two dimensions, categorizing the data in the same time interval in 70 days and the data in 48 intervals in one day. The results show that the proposed method works well.","PeriodicalId":251022,"journal":{"name":"2014 IEEE International Conference on Control Science and Systems Engineering","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114402976","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 : 2014-12-01DOI: 10.1109/CCSSE.2014.7224535
Chunyu Chen, Y. Shao
In this paper, we focus on the problem of detection and localization of crowd escape anomalous behaviors in video surveillance systems. The scheme proposed can not only detect the abnormal events which have been studied, but also detect the possible location of abnormal events. People usually instinctively escape from a place where abnormal or dangerous events occur. Based on this inference, a novel algorithm of detecting the divergent center is proposed: The divergent center indicates possible place where abnormal events occur. The model of crowd motion in both the normal and abnormal situations has been made according to the proposed method. Intersections of vector are obtained through solving the straight line equation sets, where the straight line Equation sets are determined by the location and direction of motion vector which are calculated by the optical flow. Then the dense regions of intersection sets, i.e., the divergent center, are obtained by using the distance segmentation method, the threshold method and the graphical method. Escape detection is finally judged according to the speed and energy of motion and the divergent center. Experiments on UMN datasets and other real videos show that the proposed method is valid on crowd escape behavior detection.
{"title":"Anomalous crowd behavior detection and localization in video surveillance","authors":"Chunyu Chen, Y. Shao","doi":"10.1109/CCSSE.2014.7224535","DOIUrl":"https://doi.org/10.1109/CCSSE.2014.7224535","url":null,"abstract":"In this paper, we focus on the problem of detection and localization of crowd escape anomalous behaviors in video surveillance systems. The scheme proposed can not only detect the abnormal events which have been studied, but also detect the possible location of abnormal events. People usually instinctively escape from a place where abnormal or dangerous events occur. Based on this inference, a novel algorithm of detecting the divergent center is proposed: The divergent center indicates possible place where abnormal events occur. The model of crowd motion in both the normal and abnormal situations has been made according to the proposed method. Intersections of vector are obtained through solving the straight line equation sets, where the straight line Equation sets are determined by the location and direction of motion vector which are calculated by the optical flow. Then the dense regions of intersection sets, i.e., the divergent center, are obtained by using the distance segmentation method, the threshold method and the graphical method. Escape detection is finally judged according to the speed and energy of motion and the divergent center. Experiments on UMN datasets and other real videos show that the proposed method is valid on crowd escape behavior detection.","PeriodicalId":251022,"journal":{"name":"2014 IEEE International Conference on Control Science and Systems Engineering","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116596035","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 : 2014-12-01DOI: 10.1109/CCSSE.2014.7224534
Jie Jin, Qingle Pang
Optical incremental encoder is extensively used in motion control to obtain position or/and velocity information. The calculation of velocity from finite discrete position pulses inherently will produce lots of noise that seriously affects the performance of servo derive system. Based on the analysis of mechanism of velocity measurement, a novel acceleration estimation algorithm is proposed by combining the Kalman filter (KF) and adaptive windowing (AW) technology together. Firstly, a revised single-dimensional KF is used to estimate the instantaneous velocity. Secondly, an AW technology is used to estimate the rotor acceleration according to the output of KF. During the estimation of acceleration, a first-order function is adopted to fit the input velocity. Accurate acceleration information is obtained by minimizing the estimation error of estimated velocity and instantaneous velocity based on AW algorithm. Simulation results are shown to demonstrate the effectiveness of the proposed methods.
{"title":"A novel accelaration estimation algorithm based on Kalman filter and adaptive windowing using low-resolution optical encoder","authors":"Jie Jin, Qingle Pang","doi":"10.1109/CCSSE.2014.7224534","DOIUrl":"https://doi.org/10.1109/CCSSE.2014.7224534","url":null,"abstract":"Optical incremental encoder is extensively used in motion control to obtain position or/and velocity information. The calculation of velocity from finite discrete position pulses inherently will produce lots of noise that seriously affects the performance of servo derive system. Based on the analysis of mechanism of velocity measurement, a novel acceleration estimation algorithm is proposed by combining the Kalman filter (KF) and adaptive windowing (AW) technology together. Firstly, a revised single-dimensional KF is used to estimate the instantaneous velocity. Secondly, an AW technology is used to estimate the rotor acceleration according to the output of KF. During the estimation of acceleration, a first-order function is adopted to fit the input velocity. Accurate acceleration information is obtained by minimizing the estimation error of estimated velocity and instantaneous velocity based on AW algorithm. Simulation results are shown to demonstrate the effectiveness of the proposed methods.","PeriodicalId":251022,"journal":{"name":"2014 IEEE International Conference on Control Science and Systems Engineering","volume":"440 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123618469","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 : 2014-12-01DOI: 10.1109/CCSSE.2014.7224498
Falin Wu, Linjie Yu, Yan Zhao, Haibo Zhong
The tracking loop is an important part of GPS receiver, and its performance has great influence upon the receiver. So the tracking performance of tracking loop is underlined in this paper. To improve the performance of traditional tracking loop, three typical structures of Kalman filter based tracking loops are investigated, which are carrier and code combined Kalman filtering loop, carrier and code separated Kalman filtering loop and carrier only Kalman filtering loop. The performances of these three typical tracking loop structures are compared and analyzed in tracking and navigation domains, respectively. The results show that the performances of these three Kalman filter based tracking loop are better than the traditional non Kalman filter based tracking loop, especially in signal weak environment, and the performance of the carrier and code separated Kalman filtering loop is the best in the three typical Kalman filter based tracking loops. Furthermore with the decreasing of carrier-to-noise ratio, the advantage of Kalman filter becomes greater.
{"title":"Performance analysis of typical Kalman filter based GPS tracking loop","authors":"Falin Wu, Linjie Yu, Yan Zhao, Haibo Zhong","doi":"10.1109/CCSSE.2014.7224498","DOIUrl":"https://doi.org/10.1109/CCSSE.2014.7224498","url":null,"abstract":"The tracking loop is an important part of GPS receiver, and its performance has great influence upon the receiver. So the tracking performance of tracking loop is underlined in this paper. To improve the performance of traditional tracking loop, three typical structures of Kalman filter based tracking loops are investigated, which are carrier and code combined Kalman filtering loop, carrier and code separated Kalman filtering loop and carrier only Kalman filtering loop. The performances of these three typical tracking loop structures are compared and analyzed in tracking and navigation domains, respectively. The results show that the performances of these three Kalman filter based tracking loop are better than the traditional non Kalman filter based tracking loop, especially in signal weak environment, and the performance of the carrier and code separated Kalman filtering loop is the best in the three typical Kalman filter based tracking loops. Furthermore with the decreasing of carrier-to-noise ratio, the advantage of Kalman filter becomes greater.","PeriodicalId":251022,"journal":{"name":"2014 IEEE International Conference on Control Science and Systems Engineering","volume":"147 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126187969","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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