2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS)最新文献_第7页

Iterative Learning Reliable Control for A Kind of Discrete-time Nonlinear Systems with Stochastic Transmission Attenuation and Offset Fault in Actuator 一类具有随机传输衰减和执行器偏移故障的离散非线性系统的迭代学习可靠控制

2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2021-05-14 DOI: 10.1109/DDCLS52934.2021.9455500

Xuan Yang, Xiaoe Ruan, Yan Geng

引用次数: 0

Model Predictive Control-based Stability Performance Recovery 基于模型预测控制的稳定性性能恢复

2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2021-05-14 DOI: 10.1109/DDCLS52934.2021.9455688

Jia Wang, Ying Yang

引用次数: 1

Fault Diagnosis of Satellites under Variable Conditions based on Domain Adaptive Adversarial Deep Neural Network 基于领域自适应对抗深度神经网络的变工况卫星故障诊断

2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2021-05-14 DOI: 10.1109/DDCLS52934.2021.9455711

Yuxing Gu, Zehui Mao, Xing-gang Yan, Hanyu Liang, Wenjing Liu, Chengrui Liu

{"title":"Fault Diagnosis of Satellites under Variable Conditions based on Domain Adaptive Adversarial Deep Neural Network","authors":"Yuxing Gu, Zehui Mao, Xing-gang Yan, Hanyu Liang, Wenjing Liu, Chengrui Liu","doi":"10.1109/DDCLS52934.2021.9455711","DOIUrl":"https://doi.org/10.1109/DDCLS52934.2021.9455711","url":null,"abstract":"Fault diagnosis of satellite attitude control system is an important task to ensure the safe and reliable operation of on-orbit satellites. At present, most fault diagnosis methods are to diagnose independent identically distributed(i.i.d) task objects. However, even if the same device works under different working conditions, the distribution domain of the collected data almost always changes. At the same time, the training of fault diagnosis model under full working conditions can increase the model complexity and training time, and there may unknown working conditions. In view of the above situation, this paper proposed a domain adaptive adversarial deep neural network based fault diagnosis method. By combining the feature extractor, label classifier and domain classifier with the convolutional neural network and gradient inversion layer (GRL), the effective label classification can be achieved while the resolution of different domains can be reduced. We achieved feature extraction of the classification learning task in the source domain and transfer of the classification task between the two domains. The effectiveness of the diagnosis model is verified in the ground simulation data of a certain satellite under different conditions.","PeriodicalId":325897,"journal":{"name":"2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127903395","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

Remote Operation with Haptic Force and Virtual Proxy for an Underwater Vehicle-Manipulator System 基于触觉力和虚拟代理的水下机器人操纵系统远程操作

2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2021-05-14 DOI: 10.1109/DDCLS52934.2021.9455502

Jin Ma, Yu Wang, Rui Wang, Shuo Wang

引用次数: 0

EFAG-CNN: Effectively fused attention guided convolutional neural network for WCE image classification EFAG-CNN:有效融合注意引导卷积神经网络用于WCE图像分类

2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2021-05-14 DOI: 10.1109/DDCLS52934.2021.9455575

Jing Cao, Jiafeng Yao, Zhibo Zhang, Shan Cheng, Sheng Li, Jinhui Zhu, Xiongxiong He, Qianru Jiang

{"title":"EFAG-CNN: Effectively fused attention guided convolutional neural network for WCE image classification","authors":"Jing Cao, Jiafeng Yao, Zhibo Zhang, Shan Cheng, Sheng Li, Jinhui Zhu, Xiongxiong He, Qianru Jiang","doi":"10.1109/DDCLS52934.2021.9455575","DOIUrl":"https://doi.org/10.1109/DDCLS52934.2021.9455575","url":null,"abstract":"Wireless capsule endoscopy (WCE) has been widely used in the detection of digestive tract diseases because of its painlessness and convenience. Accurate classification of WCE abnormal images is very crucial for the diagnosis and treatment of early gastrointestinal tumors, while it remains challenging due to the ambiguous boundary between lesions and normal tissues. In order to overcome the above limitations, a three-branch effectively fused attention guided convolutional neural network (EFAG-CNN) which imitates the practical diagnosis process is proposed. Specifically, global features and local images with suppressed background noise are generated by branch1 and local features are extracted by branch2 based on the local images. What's more, an effective attention feature fusion (EAFF) module is devised and inserted into branch3 to make the final prediction, which helps adaptively capture more discriminative features for classification. EAFF can integrate the representative features from branch1 and branch2 better than other methods. Furthermore, we propose a joint loss function to enhance the classification performance of branch2. Extensive experimental results demonstrate that the overall classification accuracy of the proposed method on the public Kvasir dataset reaches 96.50%, which is superior to the state-of-the-art deep learning methods.","PeriodicalId":325897,"journal":{"name":"2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132408526","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

A Gear Fault Diagnosis Method Based on EEMD Cloud Model and PSO_SVM 基于EEMD云模型和PSO_SVM的齿轮故障诊断方法

2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2021-05-14 DOI: 10.1109/DDCLS52934.2021.9455486

Yunhui Ou, Darong Huang, Chengchong Hu, Haiyang Hao, J. Gong, Ling Zhao

引用次数: 0

Time Delayed Feedback Control for a Class of Hyper-chaotic Systems 一类超混沌系统的时滞反馈控制

2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2021-05-14 DOI: 10.1109/DDCLS52934.2021.9455367

Bin Lu, Zunshui Cheng

引用次数: 0

A Practical Hybrid Automatic Transmission Model for Commercial Vehicles 实用的商用车混合动力自动变速器模型

2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2021-05-14 DOI: 10.1109/DDCLS52934.2021.9455674

Haiyang Hao, Haoxing Chen, Darong Huang, Zhenyuan Zhang

引用次数: 0

Pattern Recognition of Traction Energy Consumption for Urban Rail Transit by Using Symbolic Aggregate Approximation 基于符号聚合逼近的城市轨道交通牵引能耗模式识别

2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2021-05-14 DOI: 10.1109/DDCLS52934.2021.9455709

Licheng Zhang, J. Xun, Wei Zhang, Xi Li, Yanlong Zhang

{"title":"Pattern Recognition of Traction Energy Consumption for Urban Rail Transit by Using Symbolic Aggregate Approximation","authors":"Licheng Zhang, J. Xun, Wei Zhang, Xi Li, Yanlong Zhang","doi":"10.1109/DDCLS52934.2021.9455709","DOIUrl":"https://doi.org/10.1109/DDCLS52934.2021.9455709","url":null,"abstract":"In the urban rail transit system, the traction energy consumption accounts for 40%-60% of the total energy consumption. There is a large amount of traction energy consumption data in time series format recorded by energy meters. Accurate analysis of traction energy consumption based on time series is in urgent demand for energy saving. In order to analyze the law of traction energy consumption, this paper proposes a pattern recognition method for traction energy consumption based on SAX (Symbolic Aggregate approXimation). The original time series of traction energy consumption is transformed by SAX and the sub-patterns are obtained. The traction energy consumption patterns are recognized by using K-means algorithm. To show the effectiveness and efficiency, we apply the proposed method to a data set from Beijing Subway, and find 3 representative patterns. We find that the recognized patterns of traction energy consumption appears coherence with the major services prescribed in the rolling stock scheduling plan. By calculating the similarity and comparing with these representative patterns, the days that differ from the typical patterns are judged as anomalies.","PeriodicalId":325897,"journal":{"name":"2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128485527","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

Bearing Health Monitoring Based on the Improved BiISTM-CRF 基于改进biist - crf的轴承健康监测

2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2021-05-14 DOI: 10.1109/ddcls52934.2021.9455471

Zhiqiang Geng, Xin Zhang, Yongming Han, Chengmei Zhang, Kai Chen, Feng Xie

{"title":"Bearing Health Monitoring Based on the Improved BiISTM-CRF","authors":"Zhiqiang Geng, Xin Zhang, Yongming Han, Chengmei Zhang, Kai Chen, Feng Xie","doi":"10.1109/ddcls52934.2021.9455471","DOIUrl":"https://doi.org/10.1109/ddcls52934.2021.9455471","url":null,"abstract":"Bearing Remaining Useful Life (RUL) prediction has important meaning in the mechanical maintenance. However, the existing RUL algorithms cannot achieve stable prediction. Therefore, an improved bearing health monitoring algorithm based on Bidirectional Long Short-Term Memory (BiLSTM) integrating Conditional Random Field (BiLSTM-CRF) is proposed. The empirical mode decomposition (EMD) algorithm is used to decompose the bearing diagnostic signal into several intrinsic mode function (IMF) components. Moreover, the effective IMF component is selected to reconstruct the signal by combining the crosscorrelation coefficient and kurtosis criterion. Through the reconstructed signal extracting the time-frequency features into a feature vector, the feature data with lower dimension can be got. Then, the feature with lower dimension as inputs and RUL status as the output are used to train the BiLSTM-CRF model, which can achieve more accurate predictions. Finally, the XJTU-SY bearing data is used to verify the effectiveness of the proposed algorithm. Experiments show that this proposed method can get the best performance comparing with the convolutional neural networks and the Long Short-Term Memory.","PeriodicalId":325897,"journal":{"name":"2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131663741","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