Pub Date : 2022-10-13DOI: 10.1109/PHM-Yantai55411.2022.9941843
Zhongyuan Lu, Zhongxun Wang, B. Dan
At present, the progress in broadband and polarimetric measurement technology contributes to a significant advancement of the target identification technology based on HRRP characteristics. This paper focuses on the method used to extract the polarimetric HRRP characteristic of target ships according to the Cloude decomposition theory which is based on the eigenvalue and eigenvector analysis of target coherence matrixes and the Cameron decomposition theory based on the decomposition of Sinclair scattering matrixes. With a clear physical significance, the extracted characteristics can be applied to characterize the target from different angles. By analyzing the divisibility value of them, the characteristics with a high level of divisibility are selected to construct the vectors of the target characteristics. The divisibility and robustness of these characteristics are demonstrated through the simulation of five ship targets, while the effectiveness of the method is verified by the identification results.
{"title":"Ship Target Identification Method based on the Characteristic of Target Polarimetric HRRP of Radars","authors":"Zhongyuan Lu, Zhongxun Wang, B. Dan","doi":"10.1109/PHM-Yantai55411.2022.9941843","DOIUrl":"https://doi.org/10.1109/PHM-Yantai55411.2022.9941843","url":null,"abstract":"At present, the progress in broadband and polarimetric measurement technology contributes to a significant advancement of the target identification technology based on HRRP characteristics. This paper focuses on the method used to extract the polarimetric HRRP characteristic of target ships according to the Cloude decomposition theory which is based on the eigenvalue and eigenvector analysis of target coherence matrixes and the Cameron decomposition theory based on the decomposition of Sinclair scattering matrixes. With a clear physical significance, the extracted characteristics can be applied to characterize the target from different angles. By analyzing the divisibility value of them, the characteristics with a high level of divisibility are selected to construct the vectors of the target characteristics. The divisibility and robustness of these characteristics are demonstrated through the simulation of five ship targets, while the effectiveness of the method is verified by the identification results.","PeriodicalId":315994,"journal":{"name":"2022 Global Reliability and Prognostics and Health Management (PHM-Yantai)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126638850","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 : 2022-10-13DOI: 10.1109/PHM-Yantai55411.2022.9942097
Q. Liu, Zhiqiang Wang, Nan Wang, Di Tian
The current classroom evaluation in colleges and universities is mostly qualitative analysis, lacking of necessary quantitative means. Although the traditional analytic hierarchy process can deal with this problem quantitatively, it is difficult to test whether the judgment matrix has consistency. A multi-mode teaching quality evaluation model design technique based on an upgraded particle swarm optimization algorithm is suggested as a solution to these issues. An appropriate assessment system index is created by studying the evaluation system of teaching quality in colleges and universities using enhanced particle swarm optimization algorithm. To deal with the qualitative problem quantitatively, we use the enhanced particle swarm optimization approach, the multi-mode teaching model of education course is established. The experimental results show that the multi-mode teaching quality evaluation model based on improved particle swarm optimization algorithm is easy to operate, and can solve the subjectivity and randomness of teaching quality evaluation.
{"title":"Multimode Teaching Quality Evaluation Model of Higher Education Course Based on Improved Particle Swarm Optimization","authors":"Q. Liu, Zhiqiang Wang, Nan Wang, Di Tian","doi":"10.1109/PHM-Yantai55411.2022.9942097","DOIUrl":"https://doi.org/10.1109/PHM-Yantai55411.2022.9942097","url":null,"abstract":"The current classroom evaluation in colleges and universities is mostly qualitative analysis, lacking of necessary quantitative means. Although the traditional analytic hierarchy process can deal with this problem quantitatively, it is difficult to test whether the judgment matrix has consistency. A multi-mode teaching quality evaluation model design technique based on an upgraded particle swarm optimization algorithm is suggested as a solution to these issues. An appropriate assessment system index is created by studying the evaluation system of teaching quality in colleges and universities using enhanced particle swarm optimization algorithm. To deal with the qualitative problem quantitatively, we use the enhanced particle swarm optimization approach, the multi-mode teaching model of education course is established. The experimental results show that the multi-mode teaching quality evaluation model based on improved particle swarm optimization algorithm is easy to operate, and can solve the subjectivity and randomness of teaching quality evaluation.","PeriodicalId":315994,"journal":{"name":"2022 Global Reliability and Prognostics and Health Management (PHM-Yantai)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122964248","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}
Aim at the problem of lack of samples in machine learning-based fault diagnosis of suspension system of high-speed train, this study introduces the model-agnostic meta-learning (MAML) algorithm to train the two dimension (2D) convolutional neural network (CNN). A sample reconstruction method is proposed to convert the raw vibration signals of the suspension system into feature matrices containing more fault information, and the feature matrices are used as the training samples of 2D CNN. The results show that the 2D CNN achieve the fault diagnosis accuracy of exceeding 90.41% with one training sample. It means that this study has important potential for real-time fault diagnosis of suspension system under few-shot condition.
{"title":"Fault diagnosis of suspension system of high-speed train based on model-agnostic meta-learning","authors":"Funing Yang, Lumei Lv, Chunrong Hua, Libo Xiong, Dawei Dong","doi":"10.1109/PHM-Yantai55411.2022.9941960","DOIUrl":"https://doi.org/10.1109/PHM-Yantai55411.2022.9941960","url":null,"abstract":"Aim at the problem of lack of samples in machine learning-based fault diagnosis of suspension system of high-speed train, this study introduces the model-agnostic meta-learning (MAML) algorithm to train the two dimension (2D) convolutional neural network (CNN). A sample reconstruction method is proposed to convert the raw vibration signals of the suspension system into feature matrices containing more fault information, and the feature matrices are used as the training samples of 2D CNN. The results show that the 2D CNN achieve the fault diagnosis accuracy of exceeding 90.41% with one training sample. It means that this study has important potential for real-time fault diagnosis of suspension system under few-shot condition.","PeriodicalId":315994,"journal":{"name":"2022 Global Reliability and Prognostics and Health Management (PHM-Yantai)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126106895","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 : 2022-10-13DOI: 10.1109/PHM-Yantai55411.2022.9942021
Linghong Lai
A k-means clustering based anomaly detection method for software test data is proposed to enhance the detection ability of software test anomaly data. The distribution document model of abnormal teaching data is established based on portable multi-dimensional control software; Identifying software parameters based on semantic features and extracting feature quantities of software related information; Clustering of abnormal data by feature combination analysis according to feature distribution; Through the fusion of abnormal feature distribution, the joint detection of multi-dimensional features is completed; K-means clustering is used to obtain the optimal data combination and complete data anomaly detection. The experimental results show that the advantages of this method are good performance and accuracy.
{"title":"Abnormal Data Detection Method of Web Database Based on Improved K-Means Algorithm","authors":"Linghong Lai","doi":"10.1109/PHM-Yantai55411.2022.9942021","DOIUrl":"https://doi.org/10.1109/PHM-Yantai55411.2022.9942021","url":null,"abstract":"A k-means clustering based anomaly detection method for software test data is proposed to enhance the detection ability of software test anomaly data. The distribution document model of abnormal teaching data is established based on portable multi-dimensional control software; Identifying software parameters based on semantic features and extracting feature quantities of software related information; Clustering of abnormal data by feature combination analysis according to feature distribution; Through the fusion of abnormal feature distribution, the joint detection of multi-dimensional features is completed; K-means clustering is used to obtain the optimal data combination and complete data anomaly detection. The experimental results show that the advantages of this method are good performance and accuracy.","PeriodicalId":315994,"journal":{"name":"2022 Global Reliability and Prognostics and Health Management (PHM-Yantai)","volume":"48 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113987484","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 : 2022-10-13DOI: 10.1109/PHM-Yantai55411.2022.9941788
Huimin Gao, Zhijun Chen, Fanhao Zhou, Dayang Li, Kun Yang, Xinfa Shi
In order to accurately understand the operating state of the equipment, monitor the abnormality of the oil state data in time, and effectively extract the abnormal data information in the oil monitoring data, this paper established a classification-driven SAE oil monitoring data abnormality recognition model. The nonlinear characteristics of the data predicted the state of the oil data. The label information is introduced into the collected oil monitoring data, and then the data is preprocessed. The deep features in the oil monitoring data are extracted by the stacked autoencoder (SAE). In the coding stage, the oil monitoring data training network with labels is used to realize the identification of abnormal data. The experimental results showed that: Compared with the Back Propagation Neural Network (BPNN) and the Support Vector Machine (SVM) classifier, the classification-driven stacked autoencoder had higher anomaly identification accuracy and could effectively detect abnormal data in oil monitoring data, so as to identified the abnormal monitoring of equipment status.
{"title":"Abnormal Identification of Oil monitoring Data Based on Classification-Driven SAE","authors":"Huimin Gao, Zhijun Chen, Fanhao Zhou, Dayang Li, Kun Yang, Xinfa Shi","doi":"10.1109/PHM-Yantai55411.2022.9941788","DOIUrl":"https://doi.org/10.1109/PHM-Yantai55411.2022.9941788","url":null,"abstract":"In order to accurately understand the operating state of the equipment, monitor the abnormality of the oil state data in time, and effectively extract the abnormal data information in the oil monitoring data, this paper established a classification-driven SAE oil monitoring data abnormality recognition model. The nonlinear characteristics of the data predicted the state of the oil data. The label information is introduced into the collected oil monitoring data, and then the data is preprocessed. The deep features in the oil monitoring data are extracted by the stacked autoencoder (SAE). In the coding stage, the oil monitoring data training network with labels is used to realize the identification of abnormal data. The experimental results showed that: Compared with the Back Propagation Neural Network (BPNN) and the Support Vector Machine (SVM) classifier, the classification-driven stacked autoencoder had higher anomaly identification accuracy and could effectively detect abnormal data in oil monitoring data, so as to identified the abnormal monitoring of equipment status.","PeriodicalId":315994,"journal":{"name":"2022 Global Reliability and Prognostics and Health Management (PHM-Yantai)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131172493","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}
Based on the theory of queuing theory and the characteristics of aircraft logistics support maintenance, this paper studies the needs of logistics support personnel in aircraft operation. The demand model of airline maintenance personnel is established. χ2 fit is applied to test the maintenance data, the input flow follows the Poisson distribution, and the aircraft maintenance time follows the negative exponential distribution. In the simulation part, the effects of service order, system load, and the completeness of system maintenance on the performance of the queuing system are studied, it is concluded that the service sequence and system load have a significant impact on the system performance.
{"title":"Modeling of Aircraft Maintenance Personnel Requirements Based on Queuing Theory with Experimental Validation","authors":"Shentianyu Zhou, Qichao Duan, Yudong Qiang, Fangyi Wan, Guanghui Liu, Hao Wei","doi":"10.1109/PHM-Yantai55411.2022.9942152","DOIUrl":"https://doi.org/10.1109/PHM-Yantai55411.2022.9942152","url":null,"abstract":"Based on the theory of queuing theory and the characteristics of aircraft logistics support maintenance, this paper studies the needs of logistics support personnel in aircraft operation. The demand model of airline maintenance personnel is established. χ2 fit is applied to test the maintenance data, the input flow follows the Poisson distribution, and the aircraft maintenance time follows the negative exponential distribution. In the simulation part, the effects of service order, system load, and the completeness of system maintenance on the performance of the queuing system are studied, it is concluded that the service sequence and system load have a significant impact on the system performance.","PeriodicalId":315994,"journal":{"name":"2022 Global Reliability and Prognostics and Health Management (PHM-Yantai)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131554160","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 : 2022-10-13DOI: 10.1109/PHM-Yantai55411.2022.9941903
Yong Li, Wei Huang
Three-dimensional virtual image reconstruction is a research hotspot in the field of image processing. Aiming at improving the effect of three-dimensional image reconstruction, this paper proposes a three-dimensional virtual image reconstruction algorithm based on visual communication. The proposed algorithm firstly constructs a three-dimensional virtual image visual communication model, and uses the calibrated camera equipment to collect the initial images from multiple dimensions. Secondly, through the steps of image degradation, distortion correction, denoising and segmentation, the preprocessing of the initial image is completed. Finally, the edge information of 3D virtual image is extracted, and the 3D virtual image is reconstructed by feature reconstruction. The experimental results show that the signal-to-noise ratio of the three-dimensional virtual image reconstructed by the proposed algorithm is improved by 1.466, and the matching coefficient with the actual scene is significantly improved, that is, the quality of the three-dimensional virtual image reconstructed by the optimized algorithm is higher.
{"title":"Research on 3D Virtual Image Robust Reconstruction Algorithm Based on Visual Communication","authors":"Yong Li, Wei Huang","doi":"10.1109/PHM-Yantai55411.2022.9941903","DOIUrl":"https://doi.org/10.1109/PHM-Yantai55411.2022.9941903","url":null,"abstract":"Three-dimensional virtual image reconstruction is a research hotspot in the field of image processing. Aiming at improving the effect of three-dimensional image reconstruction, this paper proposes a three-dimensional virtual image reconstruction algorithm based on visual communication. The proposed algorithm firstly constructs a three-dimensional virtual image visual communication model, and uses the calibrated camera equipment to collect the initial images from multiple dimensions. Secondly, through the steps of image degradation, distortion correction, denoising and segmentation, the preprocessing of the initial image is completed. Finally, the edge information of 3D virtual image is extracted, and the 3D virtual image is reconstructed by feature reconstruction. The experimental results show that the signal-to-noise ratio of the three-dimensional virtual image reconstructed by the proposed algorithm is improved by 1.466, and the matching coefficient with the actual scene is significantly improved, that is, the quality of the three-dimensional virtual image reconstructed by the optimized algorithm is higher.","PeriodicalId":315994,"journal":{"name":"2022 Global Reliability and Prognostics and Health Management (PHM-Yantai)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115135679","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 : 2022-10-13DOI: 10.1109/PHM-Yantai55411.2022.9941757
Ying Lu, Jing Zhang
Power grid stochastic disturbance is very harmful for Power Quality. The paper proposed a Winger-Ville time- frequency distribution (SPWVD) to analyze power grid harmonic wave based on time domain wave, FFT and time-frequency distribution diagram, the short-term power quality can be monitored in real time. In terms of showing voltage frequency mutation, voltage sags, Voltage surge and power outage, the effectiveness and precision of the harmonic interference and the distribution of harmonic energy are demonstrated.
{"title":"Power Harmonic Wave Analysis Using SPWVD","authors":"Ying Lu, Jing Zhang","doi":"10.1109/PHM-Yantai55411.2022.9941757","DOIUrl":"https://doi.org/10.1109/PHM-Yantai55411.2022.9941757","url":null,"abstract":"Power grid stochastic disturbance is very harmful for Power Quality. The paper proposed a Winger-Ville time- frequency distribution (SPWVD) to analyze power grid harmonic wave based on time domain wave, FFT and time-frequency distribution diagram, the short-term power quality can be monitored in real time. In terms of showing voltage frequency mutation, voltage sags, Voltage surge and power outage, the effectiveness and precision of the harmonic interference and the distribution of harmonic energy are demonstrated.","PeriodicalId":315994,"journal":{"name":"2022 Global Reliability and Prognostics and Health Management (PHM-Yantai)","volume":"776 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132760324","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}
A reasonable and accurate assessment of the reliability of integrated energy system helps to realize the collaborative planning and optimal regulation of smart energy. In this paper, we propose a multi-level fuzzy evaluation model based on combined empowerment for the reliability evaluation of Integrated Energy System (IES). The evaluation indicators are reasonably graded and scientifically integrated to produce assessment conclusions reflecting the overall system condition. The comparative analysis shows that the proposed method can effectively assess the reliability of IES.
{"title":"A multi-level fuzzy evaluation method for the reliability of integrated energy systems based on combined empowerment","authors":"Pei He, Xiaodong Wang, Yangming Guo, Sheng Xu, Qian Yang, Shaoquan Wang","doi":"10.1109/phm-yantai55411.2022.9941833","DOIUrl":"https://doi.org/10.1109/phm-yantai55411.2022.9941833","url":null,"abstract":"A reasonable and accurate assessment of the reliability of integrated energy system helps to realize the collaborative planning and optimal regulation of smart energy. In this paper, we propose a multi-level fuzzy evaluation model based on combined empowerment for the reliability evaluation of Integrated Energy System (IES). The evaluation indicators are reasonably graded and scientifically integrated to produce assessment conclusions reflecting the overall system condition. The comparative analysis shows that the proposed method can effectively assess the reliability of IES.","PeriodicalId":315994,"journal":{"name":"2022 Global Reliability and Prognostics and Health Management (PHM-Yantai)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132104964","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 : 2022-10-13DOI: 10.1109/PHM-Yantai55411.2022.9941950
Wenjuan Xie, Feng Liu
The currently used resource recommendation algorithm mainly recommends resources according to the user's preference for a tag class, ignoring the relationship between user preferences and needs and learning scenarios under mobile learning, resulting in poor efficiency and accuracy of recommended resources. In order to improve the shortcomings of the algorithm, this paper studies the personalized recommendation algorithm of Ideological and political teaching multimedia resources based on mobile learning. By constructing the map of Ideological and political teaching knowledge, this paper analyzes the correlation between resources. The diagnosis result of students' cognitive level is one of the characteristics of personalized recommendation. Mobile learning devices are used to collect data, calculate and perceive mobile learning scenarios. By improving the collaborative filtering technology, the teaching resources of Ideological and political courses can be personalized recommended. In the algorithm experiment, the average absolute error of the algorithm recommendation is relatively reduced by about 14.67%, the recommendation efficiency is higher, and the personalized recommendation effect is better.
{"title":"Personalized Accurate Recommendation Algorithm of Ideological and Political Teaching Multimedia Resources Based on Mobile Learning","authors":"Wenjuan Xie, Feng Liu","doi":"10.1109/PHM-Yantai55411.2022.9941950","DOIUrl":"https://doi.org/10.1109/PHM-Yantai55411.2022.9941950","url":null,"abstract":"The currently used resource recommendation algorithm mainly recommends resources according to the user's preference for a tag class, ignoring the relationship between user preferences and needs and learning scenarios under mobile learning, resulting in poor efficiency and accuracy of recommended resources. In order to improve the shortcomings of the algorithm, this paper studies the personalized recommendation algorithm of Ideological and political teaching multimedia resources based on mobile learning. By constructing the map of Ideological and political teaching knowledge, this paper analyzes the correlation between resources. The diagnosis result of students' cognitive level is one of the characteristics of personalized recommendation. Mobile learning devices are used to collect data, calculate and perceive mobile learning scenarios. By improving the collaborative filtering technology, the teaching resources of Ideological and political courses can be personalized recommended. In the algorithm experiment, the average absolute error of the algorithm recommendation is relatively reduced by about 14.67%, the recommendation efficiency is higher, and the personalized recommendation effect is better.","PeriodicalId":315994,"journal":{"name":"2022 Global Reliability and Prognostics and Health Management (PHM-Yantai)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115427146","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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