2018 International Conference on Control, Automation and Information Sciences (ICCAIS)最新文献_第9页

Modeling and Simulation Analysis of Hybrid Energy Storage System Based on Wind Power Generation System 基于风力发电系统的混合储能系统建模与仿真分析

2018 International Conference on Control, Automation and Information Sciences (ICCAIS) Pub Date : 2018-10-01 DOI: 10.1109/ICCAIS.2018.8570660

Jiancheng Wang, Yonghua Xu, Meilei Lv

{"title":"Modeling and Simulation Analysis of Hybrid Energy Storage System Based on Wind Power Generation System","authors":"Jiancheng Wang, Yonghua Xu, Meilei Lv","doi":"10.1109/ICCAIS.2018.8570660","DOIUrl":"https://doi.org/10.1109/ICCAIS.2018.8570660","url":null,"abstract":"Hybrid energy storage system(HESS) based on wind power generation system(WPGS) plays an important role in smoothing wind power fluctuations. In consideration of limited advantage of single energy storing dielectric, the hybrid energy storage system has a good application prospect with its merits complementation. In this paper, take super-capacitors(SC) and batteries for example, the advantages and disadvantages of different topology structures of power type and energy type are analyzed. Firstly, The HESS topology is built. Then, the model of wind power and principle of bidirectional DC/DC converters for charging and discharging is introduced and a current control is designed. Lastly, fuzzy sliding control strategy of hybrid energy storage system was set. Fuzzy controller can eliminate chattering of sliding controller, and it is used to smooth the power fluctuations and optimize power distribution among each energy unit, according to their different characteristics. A simulation model of the system is built in MATLAB/SIMULINK for WPGS. The results show that the proposed energy management strategy can be used to smooth power fluctuations, and optimize power distribution effectively.","PeriodicalId":223618,"journal":{"name":"2018 International Conference on Control, Automation and Information Sciences (ICCAIS)","volume":"43 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":"114992323","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: 4

Fault Detection Method Based on Improved Isomap and SVM in Noise-Containing Nonlinear Process 基于改进等高图和支持向量机的含噪非线性过程故障检测方法

2018 International Conference on Control, Automation and Information Sciences (ICCAIS) Pub Date : 2018-10-01 DOI: 10.1109/ICCAIS.2018.8570478

Yankun Han, Qianshuai Cheng, Yandong Hou

{"title":"Fault Detection Method Based on Improved Isomap and SVM in Noise-Containing Nonlinear Process","authors":"Yankun Han, Qianshuai Cheng, Yandong Hou","doi":"10.1109/ICCAIS.2018.8570478","DOIUrl":"https://doi.org/10.1109/ICCAIS.2018.8570478","url":null,"abstract":"In order to solve the problem of high dimension and nonlinearity of monitoring data in chemical process, a fault detection method based on the combination of improved isometric mapping (Isomap) and Support Vector Machines (SVM) is proposed. First of all, a new method of Isomap improvement is proposed in this paper, called Standardized Residuals-Isomap (SR-Isomap), to solve the problem that Isomap algorithm is easily affected by noise. Then based on the statistic-proximity ratio r, the residuals are analyzed and the noise is separated within the confidence intervals [-2, 2] to accurately extract the low-dimensional principal components in the high-dimensional and Nonlinear manifold under the noisy environment, the robustness of Isomap algorithm to noise is enhanced. Finally, based on the feature of minimizing the structural risk of support vector machines, an SR-Isomap-SVM fault detection model is constructed and the radial basis function suitable for process monitoring signal is chosen to train and learn the low-dimensional clustering data to realize the fault detection of nonlinear monitoring data with noise. The simulation results of Tennessee Eastman(TE) Process show that this method can effectively realize the fault detection of non-linear chemical process with noise.","PeriodicalId":223618,"journal":{"name":"2018 International Conference on Control, Automation and Information Sciences (ICCAIS)","volume":"2 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":"114264981","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

Multi-Dimensional Observation Characteristic Function Filtering Based On Fixed Point Equation 基于不动点方程的多维观测特征函数滤波

2018 International Conference on Control, Automation and Information Sciences (ICCAIS) Pub Date : 2018-10-01 DOI: 10.1109/ICCAIS.2018.8570557

Weijie Chen, Chenglin Wen, Yi Ren

引用次数: 2

Fault Diagnosis for Rotating Machinery with Scarce Labeled Samples: A Deep CNN Method Based on Knowledge-Transferring from Shallow Models 基于浅模型知识传递的深度CNN旋转机械故障诊断

2018 International Conference on Control, Automation and Information Sciences (ICCAIS) Pub Date : 2018-10-01 DOI: 10.1109/ICCAIS.2018.8570515

Jing Zhang, De-wei Zhang, Mingyue Yang, Xiaobin Xu, Weifeng Liu, Chenglin Wen

{"title":"Fault Diagnosis for Rotating Machinery with Scarce Labeled Samples: A Deep CNN Method Based on Knowledge-Transferring from Shallow Models","authors":"Jing Zhang, De-wei Zhang, Mingyue Yang, Xiaobin Xu, Weifeng Liu, Chenglin Wen","doi":"10.1109/ICCAIS.2018.8570515","DOIUrl":"https://doi.org/10.1109/ICCAIS.2018.8570515","url":null,"abstract":"Early and accurately detecting faults in rotating machineries is crucial for operation safety of modern manufacturing system. In this paper, we proposed a novel deep CNN method based on knowledge-transferring from shallow models for rotating machinery fault diagnosis with scarce labeled samples. It is based on the idea that shallow models trained with different hand-crafted features can reveal the latent prior knowledge or diagnostic expertise and have good generalization ability even with scarce labeled samples. First, The raw vibration signal is transformed into time-frequency domain by applying the short-time Fourier transform (STFT) to extract integral features accordingly. Then, we train the SVM model with scarce labeled samples and make predictions on unlabeled samples. The predicted labels can be regarded as the data format of expert knowledge learned by the SVM model, which are combined together with the scarce fine labeled samples. Finally, they are used to train a deep CNN model of better discriminative ability. Experimental results demonstrate the effectiveness the proposed method that it achieves better performance than SVM model and original deep CNN model trained with only scarce labeled samples. Moreover, it is computational efficient and is promising for real-time rotating machinery fault diagnosis.","PeriodicalId":223618,"journal":{"name":"2018 International Conference on Control, Automation and Information Sciences (ICCAIS)","volume":"34 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":"131617748","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

Multiple Cell Tracking by Ripple Spreading Optimization 基于纹波扩展优化的多单元跟踪

2018 International Conference on Control, Automation and Information Sciences (ICCAIS) Pub Date : 2018-10-01 DOI: 10.1109/ICCAIS.2018.8570642

Jian Shi, Benlian Xu, Mingli Lu, Peiyi Zhu, Jihong Zhu

引用次数: 0

Operational Network Topology Reeonfiguration Based on Foeus of Guarantee 基于保证原则的业务网络拓扑重构

2018 International Conference on Control, Automation and Information Sciences (ICCAIS) Pub Date : 2018-10-01 DOI: 10.1109/ICCAIS.2018.8570657

Jianfeng Hou, Ruicheng Yan, Ye He, Wangqi Miao, Xucfeng Wang, Dongsheng Li

引用次数: 0

Observer Based Tracking Control for the Isothermal CSTR 基于观测器的等温CSTR跟踪控制

2018 International Conference on Control, Automation and Information Sciences (ICCAIS) Pub Date : 2018-10-01 DOI: 10.1109/iccais.2018.8570641

Sana Bzioui, R. Channa

引用次数: 2

Weighted Evidence Combination with Ranking Distance 基于排序距离的加权证据组合

2018 International Conference on Control, Automation and Information Sciences (ICCAIS) Pub Date : 2018-10-01 DOI: 10.1109/ICCAIS.2018.8570508

Yi Yang, Yongqing Zhou, Deqiang Han, Wei Ai

引用次数: 0

RFIDCam: a RFID-Aided Visual Tracking System RFIDCam: rfid辅助视觉跟踪系统

2018 International Conference on Control, Automation and Information Sciences (ICCAIS) Pub Date : 2018-10-01 DOI: 10.1109/ICCAIS.2018.8570565

Yan Guo, Xingfa Shen, Ning Chen

引用次数: 1

Spatial Clutter Measurement Density Estimation with the Clutter Probability for Improving Multi-Target Tracking Performance in Cluttered Environments 基于杂波概率的空间杂波测量密度估计提高杂波环境下多目标跟踪性能

2018 International Conference on Control, Automation and Information Sciences (ICCAIS) Pub Date : 2018-10-01 DOI: 10.1109/ICCAIS.2018.8570331

Seung-Hyo Park, Yifan Xie, T. Song, Du Hee Hant

{"title":"Spatial Clutter Measurement Density Estimation with the Clutter Probability for Improving Multi-Target Tracking Performance in Cluttered Environments","authors":"Seung-Hyo Park, Yifan Xie, T. Song, Du Hee Hant","doi":"10.1109/ICCAIS.2018.8570331","DOIUrl":"https://doi.org/10.1109/ICCAIS.2018.8570331","url":null,"abstract":"The measurements obtained from a target tracking sensor include the clutter measurements as well as measurements of the targets being tracked. In order to perform target tracking in this environment, a data association method that can effectively distinguish the target measurement from the clutter measurements is required. Target tracking algorithms with data association are designed based on the assumption that the spatial probability distribution of the clutter measurements is uniform in general. However, in real environments, since the prior information about the clutter measurement is unknown, target tracking may not perform properly if this assumption is violated. Therefore, the spatial clutter measurement density estimator (SCMDE) for estimating the clutter measurement density as a non-parametric variable has been proposed. As the SCMDE estimates the sparsity by considering that the adjacent measurements are generated from clutter, the sparsity may be biased if the target measurement is located close to the measurement of interest. Moreover, target tracking performance may be degraded in the situation where multiple targets intersect. In this paper, we propose a clutter measurement density estimation method that can more accurately estimate the clutter measurement density by calculating the clutter probabilities of the adjacent measurements. The proposed method is applied to multiple target tracking to verify the performance improvement by a series of Monte Carlo simulation.","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":"117246865","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