Pub Date : 2021-05-14DOI: 10.1109/DDCLS52934.2021.9455500
Xuan Yang, Xiaoe Ruan, Yan Geng
This paper focuses on the reliability of the iterative learning control strategy for a kind of repeatable discrete-time models subject to transmission attenuation and offset fault produced in actuator. The attenuation is a random multiplier with respect to both time and iteration index and the fault is an additive stochastic disturbance. So, the real control input is modelled by multiplying a stochastic variable with the original control signal and adding a random bounded-disturbance function. By resorting to the time-weighted norm technique, the tracking performance is analyzed in the statistical sense and the sufficiency of convergence is established. To illustrate the effectiveness and reliability of the proposed results, numerical experiments are carried out.
{"title":"Iterative Learning Reliable Control for A Kind of Discrete-time Nonlinear Systems with Stochastic Transmission Attenuation and Offset Fault in Actuator","authors":"Xuan Yang, Xiaoe Ruan, Yan Geng","doi":"10.1109/DDCLS52934.2021.9455500","DOIUrl":"https://doi.org/10.1109/DDCLS52934.2021.9455500","url":null,"abstract":"This paper focuses on the reliability of the iterative learning control strategy for a kind of repeatable discrete-time models subject to transmission attenuation and offset fault produced in actuator. The attenuation is a random multiplier with respect to both time and iteration index and the fault is an additive stochastic disturbance. So, the real control input is modelled by multiplying a stochastic variable with the original control signal and adding a random bounded-disturbance function. By resorting to the time-weighted norm technique, the tracking performance is analyzed in the statistical sense and the sufficiency of convergence is established. To illustrate the effectiveness and reliability of the proposed results, numerical experiments are carried out.","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":"122270990","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 : 2021-05-14DOI: 10.1109/DDCLS52934.2021.9455688
Jia Wang, Ying Yang
This paper studies the stability performance recovery for linear systems with input and output constraints. In particular, the model predictive controller is formulated based on the nominal model to cope with constraints. The multiplicative fault-induced performance degradation is detected by the stability margin. For the purpose to recover the stability performance, the model of the faulty plant is identified with the aid of the process data, then, the model predictive controller is reconfigured based on the identified model.
{"title":"Model Predictive Control-based Stability Performance Recovery","authors":"Jia Wang, Ying Yang","doi":"10.1109/DDCLS52934.2021.9455688","DOIUrl":"https://doi.org/10.1109/DDCLS52934.2021.9455688","url":null,"abstract":"This paper studies the stability performance recovery for linear systems with input and output constraints. In particular, the model predictive controller is formulated based on the nominal model to cope with constraints. The multiplicative fault-induced performance degradation is detected by the stability margin. For the purpose to recover the stability performance, the model of the faulty plant is identified with the aid of the process data, then, the model predictive controller is reconfigured based on the identified model.","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":"123803329","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}
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.
{"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}
Pub Date : 2021-05-14DOI: 10.1109/DDCLS52934.2021.9455502
Jin Ma, Yu Wang, Rui Wang, Shuo Wang
This paper aims to investigate a smooth teleoperation method for the underwater vehicle-manipulator system. First, a coordinated mapping control method for the vehicle is presented. The haptic force is considered to help assist the operation. Then, two mapping modes are used to teleoperate the manipulator: when the end-effector needs to move in a large area, two virtual points and a spring-damping system are implemented to filter the operator's hand jitter and limit the manipulator's speed; when the end-effector needs to move in a small area, a position increment control method with a small proportional coefficient is used to improve the precision. Finally, the simulation demonstrates the effectiveness of the proposed teleoperation method.
{"title":"Remote Operation with Haptic Force and Virtual Proxy for an Underwater Vehicle-Manipulator System","authors":"Jin Ma, Yu Wang, Rui Wang, Shuo Wang","doi":"10.1109/DDCLS52934.2021.9455502","DOIUrl":"https://doi.org/10.1109/DDCLS52934.2021.9455502","url":null,"abstract":"This paper aims to investigate a smooth teleoperation method for the underwater vehicle-manipulator system. First, a coordinated mapping control method for the vehicle is presented. The haptic force is considered to help assist the operation. Then, two mapping modes are used to teleoperate the manipulator: when the end-effector needs to move in a large area, two virtual points and a spring-damping system are implemented to filter the operator's hand jitter and limit the manipulator's speed; when the end-effector needs to move in a small area, a position increment control method with a small proportional coefficient is used to improve the precision. Finally, the simulation demonstrates the effectiveness of the proposed teleoperation method.","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":"127990478","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}
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.
{"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}
Aiming at the difficulty in identifying small fault of gear, a gear diagnosis method was proposed based on integrated empirical mode decomposition (EEMD), cloud model, support vector machine, and particle swarm optimization (PSO-SVM). Firstly, the vibration signal was decomposed into several IMF components by EEMD, and the backward cloud generator calculation was performed on the IMF components to obtain the digital characteristics of the cloud model. Then, the digital features obtained and the frequency domain features and time-domain features obtained after linear reconstruction were constructed as feature vectors, which were dimensionalized by principal component analysis. Finally, the features after dimensionality reduction are input into PSO-SVM for classification training and testing. The results show that this method can effectively complete gear fault diagnosis and has a higher recognition rate.
{"title":"A Gear Fault Diagnosis Method Based on EEMD Cloud Model and PSO_SVM","authors":"Yunhui Ou, Darong Huang, Chengchong Hu, Haiyang Hao, J. Gong, Ling Zhao","doi":"10.1109/DDCLS52934.2021.9455486","DOIUrl":"https://doi.org/10.1109/DDCLS52934.2021.9455486","url":null,"abstract":"Aiming at the difficulty in identifying small fault of gear, a gear diagnosis method was proposed based on integrated empirical mode decomposition (EEMD), cloud model, support vector machine, and particle swarm optimization (PSO-SVM). Firstly, the vibration signal was decomposed into several IMF components by EEMD, and the backward cloud generator calculation was performed on the IMF components to obtain the digital characteristics of the cloud model. Then, the digital features obtained and the frequency domain features and time-domain features obtained after linear reconstruction were constructed as feature vectors, which were dimensionalized by principal component analysis. Finally, the features after dimensionality reduction are input into PSO-SVM for classification training and testing. The results show that this method can effectively complete gear fault diagnosis and has a higher recognition rate.","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":"122212072","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 : 2021-05-14DOI: 10.1109/DDCLS52934.2021.9455367
Bin Lu, Zunshui Cheng
We study the dynamic behavior of a new type of hyper-chaotic system and use the method of time delay control to achieve the purpose of the control system. This paper analyzes the stability and existence of the equilibrium point and discusses the cross-sectional conditions under which the balance point has Hopf bifurcation. Then we give the time delay value of the periodic solution generated by the system equilibrium point. Numerical examples are given to verify the theoretical results.
{"title":"Time Delayed Feedback Control for a Class of Hyper-chaotic Systems","authors":"Bin Lu, Zunshui Cheng","doi":"10.1109/DDCLS52934.2021.9455367","DOIUrl":"https://doi.org/10.1109/DDCLS52934.2021.9455367","url":null,"abstract":"We study the dynamic behavior of a new type of hyper-chaotic system and use the method of time delay control to achieve the purpose of the control system. This paper analyzes the stability and existence of the equilibrium point and discusses the cross-sectional conditions under which the balance point has Hopf bifurcation. Then we give the time delay value of the periodic solution generated by the system equilibrium point. Numerical examples are given to verify the theoretical results.","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":"130807251","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}
This study proposes a practical hybrid automatic transmission model for commercial vehicles based on the first-principle modelling approach. The developed plant model consists of three base elements, i.e. hydraulic circuit, multi-plate wet clutches and planetary gear sets. In today's intelligent transmission control system development framework, plant model plays an important role. It can be used to valid the control algorithm as well as control system in an early stage of the development process, thus shortening development process and improving software quality.
{"title":"A Practical Hybrid Automatic Transmission Model for Commercial Vehicles","authors":"Haiyang Hao, Haoxing Chen, Darong Huang, Zhenyuan Zhang","doi":"10.1109/DDCLS52934.2021.9455674","DOIUrl":"https://doi.org/10.1109/DDCLS52934.2021.9455674","url":null,"abstract":"This study proposes a practical hybrid automatic transmission model for commercial vehicles based on the first-principle modelling approach. The developed plant model consists of three base elements, i.e. hydraulic circuit, multi-plate wet clutches and planetary gear sets. In today's intelligent transmission control system development framework, plant model plays an important role. It can be used to valid the control algorithm as well as control system in an early stage of the development process, thus shortening development process and improving software quality.","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":"128189513","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 : 2021-05-14DOI: 10.1109/DDCLS52934.2021.9455709
Licheng Zhang, J. Xun, Wei Zhang, Xi Li, Yanlong Zhang
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.
{"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}
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.
{"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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：