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2018 International Conference on Control, Automation and Information Sciences (ICCAIS)最新文献

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Aircraft 3D Trajectory Estimation with a Single Nonlinear Filter Using Two 2D Radars 基于二维雷达的单非线性滤波飞机三维轨迹估计
2018 International Conference on Control, Automation and Information Sciences (ICCAIS) Pub Date : 2018-10-01 DOI: 10.1109/ICCAIS.2018.8570535
M. Mallick, Yanjun Yan, S. Arulampalam
{"title":"Aircraft 3D Trajectory Estimation with a Single Nonlinear Filter Using Two 2D Radars","authors":"M. Mallick, Yanjun Yan, S. Arulampalam","doi":"10.1109/ICCAIS.2018.8570535","DOIUrl":"https://doi.org/10.1109/ICCAIS.2018.8570535","url":null,"abstract":"We consider 3D trajectory estimation of an aircraft using two air traffic control 2D radars with Cartesian state vector. The assumed motion of the aircraft is nearly constant velocity with nearly constant altitude. We use the cubature Kalman filter (CKF) for the nonlinear filtering problem and present three filter initialization algorithms. Using Monte Carlo simulations, we compare results from our proposed algorithms with those from existing height-parametrized unscented Kalman filter and bias-compensated pseudolinear estimator based CKF, and associated posterior Cramér-Rao lower bound (PCRLB). CKF using two single-point filter initialization algorithms achieves accurate state estimation with low computational complexity.","PeriodicalId":223618,"journal":{"name":"2018 International Conference on Control, Automation and Information Sciences (ICCAIS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122454362","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
A Sliding Mode Control Method for Manipulator Based on GA Optimize Neural Network 基于遗传算法优化神经网络的机械臂滑模控制方法
2018 International Conference on Control, Automation and Information Sciences (ICCAIS) Pub Date : 2018-10-01 DOI: 10.1109/ICCAIS.2018.8570345
Jing Liu, J. Pu, Chi Zhang
{"title":"A Sliding Mode Control Method for Manipulator Based on GA Optimize Neural Network","authors":"Jing Liu, J. Pu, Chi Zhang","doi":"10.1109/ICCAIS.2018.8570345","DOIUrl":"https://doi.org/10.1109/ICCAIS.2018.8570345","url":null,"abstract":"In this paper, a neural network sliding mode control method based on genetic algorithm (GA) is proposed, considering the control accuracy reduction problem of the manipulators caused by the external interference and modeling error. The system is divided into the nominal part and the uncertain part, using state feedback control and improved neural network sliding mode control respectively. Neural network used to approximate the upper bound of uncertainty and serves as an input for the dynamic compensation of the sliding mode controller. At the same time, genetic algorithm is used to optimize the structural parameters of the neural network to ensure effective mapping. The global stability of the system is proved by Lyapunov function. The simulation results show that the proposed method can effectively track the desired trajectory with small error and has good robustness to the external uncertainty.","PeriodicalId":223618,"journal":{"name":"2018 International Conference on Control, Automation and Information Sciences (ICCAIS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130905115","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
Sparse Pointcloud Map Fusion of Multi-Robot System 稀疏点云地图融合的多机器人系统
2018 International Conference on Control, Automation and Information Sciences (ICCAIS) Pub Date : 2018-10-01 DOI: 10.1109/ICCAIS.2018.8570467
Shiqin Sun, Benlian Xu, Yidan Sun, Zhen Sun
{"title":"Sparse Pointcloud Map Fusion of Multi-Robot System","authors":"Shiqin Sun, Benlian Xu, Yidan Sun, Zhen Sun","doi":"10.1109/ICCAIS.2018.8570467","DOIUrl":"https://doi.org/10.1109/ICCAIS.2018.8570467","url":null,"abstract":"Map building is a crucial function of multi-robot system, and many available autonomous navigations in multi-robot systems assume high precision of the environmental map. Thus, poor performance in map building can heavily affect the navigation results in a large-scale environment. The core issue that needs to be addressed of multi-robot mapping is how to integrate the data of the different robots into a single global map. In this paper, a matching search strategy based on the co-viewing relationship between key frames is proposed to reduce matching time. The key frames to be matched are selected from the maps according to a certain condition instead of being matched individually. Thus, a considerable amount of match time can be saved. After a set of matched map points are obtained, the motion estimation between matched points is solved by nonlinear optimization and an error compensation technique is employed to obtain more accurate camera posture. Finally, the redundant map points after fusion are removed, and both the connection between the key frames and the map points in the two maps are established. The algorithm is tested in an indoor environment and the experiment results show the validness of the proposed method.","PeriodicalId":223618,"journal":{"name":"2018 International Conference on Control, Automation and Information Sciences (ICCAIS)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128377164","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: 3
Word Semantic Similarity Calculation Based on Word2vec 基于Word2vec的词语义相似度计算
2018 International Conference on Control, Automation and Information Sciences (ICCAIS) Pub Date : 2018-10-01 DOI: 10.1109/ICCAIS.2018.8570612
Xiaolin Jin, Shuwu Zhang, Jie Liu
{"title":"Word Semantic Similarity Calculation Based on Word2vec","authors":"Xiaolin Jin, Shuwu Zhang, Jie Liu","doi":"10.1109/ICCAIS.2018.8570612","DOIUrl":"https://doi.org/10.1109/ICCAIS.2018.8570612","url":null,"abstract":"In order to solve the problem of poor universality and the absence of contextual information in word similarity calculation based on dictionary, this paper proposes a semantic similarity computation method based on Word2vec. This method improves HowNet and Tongyici Cilin, and also adds the word vector model as a weighing parameter to calculate the word similarity, after compares the similarity of the words by assigning different weights to the three methods. Through experimental comparison, the Pearson coefficient of the algorithm and the artificial value is 0.892, and the method can cover most words so that it can effectively solve the problem of the similarity of the word calculation in the dictionary.","PeriodicalId":223618,"journal":{"name":"2018 International Conference on Control, Automation and Information Sciences (ICCAIS)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116083809","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: 18
A New Panoramic Visual Tracking Method 一种新的全景视觉跟踪方法
2018 International Conference on Control, Automation and Information Sciences (ICCAIS) Pub Date : 2018-10-01 DOI: 10.1109/ICCAIS.2018.8570674
Long Liu, Jie Ding, Qing Liu
{"title":"A New Panoramic Visual Tracking Method","authors":"Long Liu, Jie Ding, Qing Liu","doi":"10.1109/ICCAIS.2018.8570674","DOIUrl":"https://doi.org/10.1109/ICCAIS.2018.8570674","url":null,"abstract":"Panoramic vision has developed greatly in the field of computer vision during these years. The tracking of moving objects is one of the most classic subjects, which has a wide application prospect. Due to the influence of panorama image distortion, it brings a serious error to the target feature extraction, and it is hard to track the target precisely. The existing method of panoramic vision tracking still has some limitations. Therefore, based on the particle filter tracking, this paper proposes a panoramic visual tracking algorithm based on adaptive extraction of the fused features of color and shape features, and included the particle weights into the tracking process. The target tracking accuracy is significantly improved. Experimental results show that the proposed algorithm can solve the problem of change in object's appearance, which caused by the translation, rotation, external illumination and occlusion of the target object, effectively improve the robustness of panoramic visual tracking.","PeriodicalId":223618,"journal":{"name":"2018 International Conference on Control, Automation and Information Sciences (ICCAIS)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123949120","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
Multi-Frame Multi-Sensor Multi-Target Task Allocation Method Based on Improved Wolf Colony Algorithm 基于改进狼群算法的多帧多传感器多目标任务分配方法
2018 International Conference on Control, Automation and Information Sciences (ICCAIS) Pub Date : 2018-10-01 DOI: 10.1109/ICCAIS.2018.8570419
Feng Yang, Pengxiang Wang, Yizhai Zhang
{"title":"Multi-Frame Multi-Sensor Multi-Target Task Allocation Method Based on Improved Wolf Colony Algorithm","authors":"Feng Yang, Pengxiang Wang, Yizhai Zhang","doi":"10.1109/ICCAIS.2018.8570419","DOIUrl":"https://doi.org/10.1109/ICCAIS.2018.8570419","url":null,"abstract":"With the increasing number of targets, limited sensor resources cannot meet the requirements of reasonable management. Therefore, how to solve the problem effectively has been widely concerned by scholars. The traditional allocation method only considers single frame allocation, and ignores the relationship between the results of consecutive several frame. Thinking about the sequentiality for continuous time frames, a multi-frame multi-sensor multi-target task allocation method based on improved Wolf Colony Algorithm (WCA) is proposed. In the paper, the influence of the allocation results of the previous frames on at current frame is considered. The threat degree of the next frame of the target which has not been assigned sensor monitoring for several consecutive frames is updated based on the pervious allocation results in multi frames. The multi-sensor multi-target task allocation problem is modeled as a constrained optimization problem, which is solved by the Improved WCA. Simulation results show that the result of allocation of sensor resources is more reasonable, with increase slightly of the allocation time cost, compared with Improved WCA without the method.","PeriodicalId":223618,"journal":{"name":"2018 International Conference on Control, Automation and Information Sciences (ICCAIS)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121453432","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
Multi-Kernel Learning Based Autonomous Fault Diagnosis for Centrifugal Pumps* 基于多核学习的离心泵故障自主诊断*
2018 International Conference on Control, Automation and Information Sciences (ICCAIS) Pub Date : 2018-10-01 DOI: 10.1109/ICCAIS.2018.8570322
Haoran Feng, Yongcai Wang, Jiajun Zhu, Deying Li
{"title":"Multi-Kernel Learning Based Autonomous Fault Diagnosis for Centrifugal Pumps*","authors":"Haoran Feng, Yongcai Wang, Jiajun Zhu, Deying Li","doi":"10.1109/ICCAIS.2018.8570322","DOIUrl":"https://doi.org/10.1109/ICCAIS.2018.8570322","url":null,"abstract":"Centrifugal pumps are fundamental instruments in many industry plants, whose continuously operation plays an important role in the production cycle. For improving production efficiency, autonomous pump fault diagnosis has been widely adopted by many enterprises and has also attracted great research attentions. Existing studies exploit machine learning algorithms for autonomous pump fault diagnosis, which generally needs human knowledge to select distinctive data features. To avoid the bias of human selection, this paper proposes a multi-kernel learning (MKL) based autonomous pump fault diagnosis method. It trains basic classifiers (BCs) by each feature and weightily combines the basic classifiers to form the combined classifier for fault diagnosis. An autonomous BC weighting algorithm is proposed, which trains the combination weights of the BCs autonomously. We show the MKL based fault diagnosis method provide more accurate fault detection than the existing methods and without the need of human experts' interventions.","PeriodicalId":223618,"journal":{"name":"2018 International Conference on Control, Automation and Information Sciences (ICCAIS)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121899665","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: 5
Research on Optimization of Chiller Based on Adaptive Weight Particle Swarm Algorithm 基于自适应权重粒子群算法的制冷机优化研究
2018 International Conference on Control, Automation and Information Sciences (ICCAIS) Pub Date : 2018-10-01 DOI: 10.1109/ICCAIS.2018.8570511
Anping Lu, Qiang Ding, Aipeng Jiang
{"title":"Research on Optimization of Chiller Based on Adaptive Weight Particle Swarm Algorithm","authors":"Anping Lu, Qiang Ding, Aipeng Jiang","doi":"10.1109/ICCAIS.2018.8570511","DOIUrl":"https://doi.org/10.1109/ICCAIS.2018.8570511","url":null,"abstract":"To study the parameters of minimum energy consumption and optimal performance of the system under specific load, the refrigeration mainframe of the cold water system is analyzed and selected, the performance model of the main engine and the pumps are built according to the actual situation. The load limitation of the constant frequency compressor is analyzed, the load ratio can be adjusted in a larger range by frequency conversion compressor, the adaptive weight particle swarm optimization algorithm is used to optimize the solution, and the comparison with the traditional constant flow scheme is made. The maximum energy saving is 11.25%, which shows the advantage of the intelligent algorithm.","PeriodicalId":223618,"journal":{"name":"2018 International Conference on Control, Automation and Information Sciences (ICCAIS)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128933951","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
Radar Emitter Identification Based on Deep Convolutional Neural Network 基于深度卷积神经网络的雷达辐射源识别
2018 International Conference on Control, Automation and Information Sciences (ICCAIS) Pub Date : 2018-10-01 DOI: 10.1109/ICCAIS.2018.8570480
Mingxin Kong, Jing Zhang, Weifeng Liu, Guilin Zhang
{"title":"Radar Emitter Identification Based on Deep Convolutional Neural Network","authors":"Mingxin Kong, Jing Zhang, Weifeng Liu, Guilin Zhang","doi":"10.1109/ICCAIS.2018.8570480","DOIUrl":"https://doi.org/10.1109/ICCAIS.2018.8570480","url":null,"abstract":"Aiming at the identification and classification of radar radiation sources, this paper proposes a classification method based on the Convolutional Neural Network(CNN) for radar signal classification. Firstly, this paper sets the appropriate learning rate, batch size, iteration number, momentum and weight decay coefficient. Secondly, the time domain real part waveform signal is modeled and the network structure is selected for analysis. Finally, according to the spectrogram of the time domain waveform by Short Time Fourier Transform(STFT), design two different convolutional neural network models. The results show that the network learns more distinguishing feature representations and has better generalization capabilities after STFT. The Deep Learning of CNN has a greater advantage in extracting the feature representations in the spectrogram of the radar signal.","PeriodicalId":223618,"journal":{"name":"2018 International Conference on Control, Automation and Information Sciences (ICCAIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129163449","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: 19
Stacked Sparse Auto Encoder Network Based Multimode Process Monitoring 基于堆叠稀疏自动编码器网络的多模式进程监控
2018 International Conference on Control, Automation and Information Sciences (ICCAIS) Pub Date : 2018-10-01 DOI: 10.1109/ICCAIS.2018.8570618
Feiya Lv, Xiaonan Fan, Chenglin Wen, Z. Bao
{"title":"Stacked Sparse Auto Encoder Network Based Multimode Process Monitoring","authors":"Feiya Lv, Xiaonan Fan, Chenglin Wen, Z. Bao","doi":"10.1109/ICCAIS.2018.8570618","DOIUrl":"https://doi.org/10.1109/ICCAIS.2018.8570618","url":null,"abstract":"It is difficult for conventional multivariate statistical process control (MSPC) methods to choose the optimal control limits in multimode online monitoring. This may not only ignore some potential relationships within raw mode data, but also cover the impact of incipient faults. Thus a stacked sparse auto encoder (SSAE) based multimode monitoring method is proposed in this paper to pursuit deeper architecture information and essential characteristics of raw measurements. It is put forward by incorporating real-time mode identification with fault discrimination online within a global model. For incipient faults that cannot be detected by conventional statistical methods, the proposed framework shows a remarkable high efficiency. Experiments on Tennessee Eastman Process (TEP) have shown its efficiency for not only multimode identification, but also fault detection.","PeriodicalId":223618,"journal":{"name":"2018 International Conference on Control, Automation and Information Sciences (ICCAIS)","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116358904","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: 3
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