Pub Date : 2022-05-01DOI: 10.1109/PHM2022-London52454.2022.00041
Mingjie Chen, Jin Yan, Tieying Li, Chengzhi Chi
In order to solve the problem of real-time fault diagnosis of UAV flight control system, a fault diagnosis method based on hybrid diagnosis engine is proposed. Aiming at the multiple fault modes and cross-linking relationships of each node in the flight control system, the system reference model is established by qualitative and quantitative methods, and then a corresponding domain model is established according to the flight control system of a specific model. Finally, the fault diagnosis reasoning engine based on the model and the hybrid diagnosis engine realizes the diagnosis of the current fault of the system. The results show that this method can determine the time and location of the fault in real time and accurately, which provides an effective guarantee for improving the efficiency of UAV fault diagnosis and improving the flight safety of UAV.
{"title":"Research on Fault Diagnosis Technology of UAV Flight Control System Based on Hybrid Diagnosis Engine","authors":"Mingjie Chen, Jin Yan, Tieying Li, Chengzhi Chi","doi":"10.1109/PHM2022-London52454.2022.00041","DOIUrl":"https://doi.org/10.1109/PHM2022-London52454.2022.00041","url":null,"abstract":"In order to solve the problem of real-time fault diagnosis of UAV flight control system, a fault diagnosis method based on hybrid diagnosis engine is proposed. Aiming at the multiple fault modes and cross-linking relationships of each node in the flight control system, the system reference model is established by qualitative and quantitative methods, and then a corresponding domain model is established according to the flight control system of a specific model. Finally, the fault diagnosis reasoning engine based on the model and the hybrid diagnosis engine realizes the diagnosis of the current fault of the system. The results show that this method can determine the time and location of the fault in real time and accurately, which provides an effective guarantee for improving the efficiency of UAV fault diagnosis and improving the flight safety of UAV.","PeriodicalId":269605,"journal":{"name":"2022 Prognostics and Health Management Conference (PHM-2022 London)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131723701","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 : 2022-05-01DOI: 10.1109/PHM2022-London52454.2022.00052
Jing He, Lingxiao Li, Chuyi Wang
The ionosphere is an important part of geospatial environment, in the community of GNSS-based safety-critical systems, the GNSS error caused by the ionosphere is the error source second only to the satellite ephemeris error. To ensure that the difference between the unknown real position and the system-derived position estimate has a very high degree of confidence, it is necessary to determine the error caused by the ionosphere or the discontinuity of the GNSS signal. We sorted out the existing ionospheric threat model and confirmed the effectiveness of the threat model. In addition, the research status and progress of the existing GNSS space environment technology to deal with this ionospheric threat are also pointed out, including the basic assumptions and delay corrections of the 2D and 3D simulations of the ionosphere. We hope that the relevant description of this article can promote the comparison of ionospheric monitoring and mitigation technologies in GNSS space environment science.
{"title":"Monitoring and Mitigating Ionosphere threats in GNSS Space Environment Science","authors":"Jing He, Lingxiao Li, Chuyi Wang","doi":"10.1109/PHM2022-London52454.2022.00052","DOIUrl":"https://doi.org/10.1109/PHM2022-London52454.2022.00052","url":null,"abstract":"The ionosphere is an important part of geospatial environment, in the community of GNSS-based safety-critical systems, the GNSS error caused by the ionosphere is the error source second only to the satellite ephemeris error. To ensure that the difference between the unknown real position and the system-derived position estimate has a very high degree of confidence, it is necessary to determine the error caused by the ionosphere or the discontinuity of the GNSS signal. We sorted out the existing ionospheric threat model and confirmed the effectiveness of the threat model. In addition, the research status and progress of the existing GNSS space environment technology to deal with this ionospheric threat are also pointed out, including the basic assumptions and delay corrections of the 2D and 3D simulations of the ionosphere. We hope that the relevant description of this article can promote the comparison of ionospheric monitoring and mitigation technologies in GNSS space environment science.","PeriodicalId":269605,"journal":{"name":"2022 Prognostics and Health Management Conference (PHM-2022 London)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114344903","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 : 2022-05-01DOI: 10.1109/PHM2022-London52454.2022.00106
Huahua Zhang, Chuan Li, Yun Bai, shuai Yang
Lithium battery safety accidents occur frequently, and its lifespan prognostics has become a research focus at home and abroad, but it is still very challenging to accurately predict the lifespan of lithium battery. Approaches, which using machine learning techniques, are becoming more and more attractive to predict lifespan. In this study, a method based on a sparse autoencoder (SAE) and a long short term memory (LSTM) is developed for improving lifespan prognostics performance, using only previous capacity measurements. The SAE was firstly used to extract temporal features within a fragment of previous capacity measurements. LSTM was then used to fuse the extracted information with the previous input information and the current input and output ones, so as to obtain accurate lifespan prognostics. The proposed method’s performance is tested on a benchmark lithium-ion battery degradation dataset. The results show that it can accurately predict lifespan of batteries.
{"title":"Lifespan prognostics for lithium-ion batteries using Long Short Term Memory","authors":"Huahua Zhang, Chuan Li, Yun Bai, shuai Yang","doi":"10.1109/PHM2022-London52454.2022.00106","DOIUrl":"https://doi.org/10.1109/PHM2022-London52454.2022.00106","url":null,"abstract":"Lithium battery safety accidents occur frequently, and its lifespan prognostics has become a research focus at home and abroad, but it is still very challenging to accurately predict the lifespan of lithium battery. Approaches, which using machine learning techniques, are becoming more and more attractive to predict lifespan. In this study, a method based on a sparse autoencoder (SAE) and a long short term memory (LSTM) is developed for improving lifespan prognostics performance, using only previous capacity measurements. The SAE was firstly used to extract temporal features within a fragment of previous capacity measurements. LSTM was then used to fuse the extracted information with the previous input information and the current input and output ones, so as to obtain accurate lifespan prognostics. The proposed method’s performance is tested on a benchmark lithium-ion battery degradation dataset. The results show that it can accurately predict lifespan of batteries.","PeriodicalId":269605,"journal":{"name":"2022 Prognostics and Health Management Conference (PHM-2022 London)","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114641966","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 : 2022-05-01DOI: 10.1109/PHM2022-London52454.2022.00061
Lu Han
In this paper, I mainly focus on real-time pedestrian detection, which is a critical part of robot vision and autonomous driving cars. In recent, convolutional neural networks and deep learning have received so many reputations due to their enormous ability and wide use. For example, image classification, understanding climate, analyzing documents, advertising, etc. Object detection is different from image classification, which is a relatively new area where are waiting for more researchers to dedicate themselves. In the first part, I introduce the appliance of real-time object detection, and in the second part, I introduce some related works of real-time object detection, In the third part, where my work is, I indicate the method to increase the performance of pedestrian detection. I delete the y1 layer of the output of YOLOv3 and magnify the upsampling rate. At the last, I regulate the anchors to achieve more accuracy and better performance. Finally, I explain my experiments and give my research conclusion.
{"title":"Pedestrian Detection for Vehicle-borne Image Based on Two-level YOLOv3","authors":"Lu Han","doi":"10.1109/PHM2022-London52454.2022.00061","DOIUrl":"https://doi.org/10.1109/PHM2022-London52454.2022.00061","url":null,"abstract":"In this paper, I mainly focus on real-time pedestrian detection, which is a critical part of robot vision and autonomous driving cars. In recent, convolutional neural networks and deep learning have received so many reputations due to their enormous ability and wide use. For example, image classification, understanding climate, analyzing documents, advertising, etc. Object detection is different from image classification, which is a relatively new area where are waiting for more researchers to dedicate themselves. In the first part, I introduce the appliance of real-time object detection, and in the second part, I introduce some related works of real-time object detection, In the third part, where my work is, I indicate the method to increase the performance of pedestrian detection. I delete the y1 layer of the output of YOLOv3 and magnify the upsampling rate. At the last, I regulate the anchors to achieve more accuracy and better performance. Finally, I explain my experiments and give my research conclusion.","PeriodicalId":269605,"journal":{"name":"2022 Prognostics and Health Management Conference (PHM-2022 London)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125249937","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 : 2022-05-01DOI: 10.1109/PHM2022-London52454.2022.00019
Pengfei Yu, G. Wang, Yun Huang, Longjun Wang, G. Lu
Supercapacitors are a new type of energy storage device, and their ageing characteristics are of great importance to quality evaluation, life evaluation, and maintenance. This paper focused on the degradation process of supercapacitors in their early life. High-temperature accelerated calendar ageing test of supercapacitors was carried out, parameters of RC model were calculated by domain characterization method. Experiment results showed that its ESR and C increase and decrease with ageing respectively, and the rate of change both gradually decrease. Simultaneously, a possible physical explanation was given.
{"title":"Supercapacitor Early Degradation Behavior under High Temperature Accelerated Calendar Ageing Test","authors":"Pengfei Yu, G. Wang, Yun Huang, Longjun Wang, G. Lu","doi":"10.1109/PHM2022-London52454.2022.00019","DOIUrl":"https://doi.org/10.1109/PHM2022-London52454.2022.00019","url":null,"abstract":"Supercapacitors are a new type of energy storage device, and their ageing characteristics are of great importance to quality evaluation, life evaluation, and maintenance. This paper focused on the degradation process of supercapacitors in their early life. High-temperature accelerated calendar ageing test of supercapacitors was carried out, parameters of RC model were calculated by domain characterization method. Experiment results showed that its ESR and C increase and decrease with ageing respectively, and the rate of change both gradually decrease. Simultaneously, a possible physical explanation was given.","PeriodicalId":269605,"journal":{"name":"2022 Prognostics and Health Management Conference (PHM-2022 London)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125489486","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 : 2022-05-01DOI: 10.1109/PHM2022-London52454.2022.00013
Zhenwei Zhou, Tong Li, Tao Liu, Kaiwei Wang, Yun Huang, Linlin Shi
This paper presents a health evaluation of S700K turnout system based on Mahalanobis distance. The main implementation steps of this method consist of obtaining a time-domain statistical index data matrix of the normal-state turnout system, performing principal component analysis on the time-domain statistical index data matrix, establishing baseline Mahalanobis distance for the turnout system normal, determining the health threshold for the safe operation, evaluating the turnout system in an unknown health state. If the Mahalanobis distance of health assessment threshold is greater than the threshold, then it is suggested that condition-based maintenance be taken. An experiment case is demonstrated to verify the efficiency of the proposed method.
{"title":"Health Assessment of Turnout System Based on Mahalanobis Distance","authors":"Zhenwei Zhou, Tong Li, Tao Liu, Kaiwei Wang, Yun Huang, Linlin Shi","doi":"10.1109/PHM2022-London52454.2022.00013","DOIUrl":"https://doi.org/10.1109/PHM2022-London52454.2022.00013","url":null,"abstract":"This paper presents a health evaluation of S700K turnout system based on Mahalanobis distance. The main implementation steps of this method consist of obtaining a time-domain statistical index data matrix of the normal-state turnout system, performing principal component analysis on the time-domain statistical index data matrix, establishing baseline Mahalanobis distance for the turnout system normal, determining the health threshold for the safe operation, evaluating the turnout system in an unknown health state. If the Mahalanobis distance of health assessment threshold is greater than the threshold, then it is suggested that condition-based maintenance be taken. An experiment case is demonstrated to verify the efficiency of the proposed method.","PeriodicalId":269605,"journal":{"name":"2022 Prognostics and Health Management Conference (PHM-2022 London)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126785339","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 : 2022-05-01DOI: 10.1109/PHM2022-London52454.2022.00040
Rukhshinda Wasif, M. Tokhi, J. Rudlin, R. Marks, G. Shirkoohi, Zhangfang Zhao, Fang-wei Duan
The detection capability of magnetic eddy current and magnetic flux leakage sensors depends on the magnetization level in the test specimen. While low magnetization field intensity makes it difficult to detect defects, higher magnetization levels increase background noise as well as the size and weight of the sensors. Moreover, powerful magnets are used in the magnetization circuit that is difficult to handle and pose potential health and safety hazards. Finite element modelling is widely used for the optimization of the design of magnetization yokes. Modelling softwares are limited in their ability to conduct artificial intelligence-based optimization and require a large number of iterations. This can be time-consuming and computationally expensive. An optimization technique using particle swarm optimization algorithm for designing the excitation system for magnetic eddy current sensors is presented in this paper. Numerical simulation is used to determine the objective function and input variables for the algorithm. A comparative study is carried out to evaluate the algorithm's performance against genetic and artificial bee colony algorithms. The sensor design parameters obtained using the algorithm results are validated through experiments. The results show that the PSO is a fast and computationally efficient algorithm for optimizing the yoke design.
{"title":"Particle swarm optimization of excitation system design of magnetic eddy current sensor","authors":"Rukhshinda Wasif, M. Tokhi, J. Rudlin, R. Marks, G. Shirkoohi, Zhangfang Zhao, Fang-wei Duan","doi":"10.1109/PHM2022-London52454.2022.00040","DOIUrl":"https://doi.org/10.1109/PHM2022-London52454.2022.00040","url":null,"abstract":"The detection capability of magnetic eddy current and magnetic flux leakage sensors depends on the magnetization level in the test specimen. While low magnetization field intensity makes it difficult to detect defects, higher magnetization levels increase background noise as well as the size and weight of the sensors. Moreover, powerful magnets are used in the magnetization circuit that is difficult to handle and pose potential health and safety hazards. Finite element modelling is widely used for the optimization of the design of magnetization yokes. Modelling softwares are limited in their ability to conduct artificial intelligence-based optimization and require a large number of iterations. This can be time-consuming and computationally expensive. An optimization technique using particle swarm optimization algorithm for designing the excitation system for magnetic eddy current sensors is presented in this paper. Numerical simulation is used to determine the objective function and input variables for the algorithm. A comparative study is carried out to evaluate the algorithm's performance against genetic and artificial bee colony algorithms. The sensor design parameters obtained using the algorithm results are validated through experiments. The results show that the PSO is a fast and computationally efficient algorithm for optimizing the yoke design.","PeriodicalId":269605,"journal":{"name":"2022 Prognostics and Health Management Conference (PHM-2022 London)","volume":"89 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125973763","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}
In some applications of the power grid, there are problems that the volume of real data is small and the security of real data is difficult to guarantee, which poses a challenge to the data governance model. This paper proposes a parallel convolutional neural network structure based on approximate calculation of test data, constructs test data through approximate calculation, and uses parallel convolutional neural network structure to learn the corresponding data model, which can solve the problems of data resources, computing resources and problems in data governance. Calculate the cost problem. Experiments based on existing data sets show the unique advantages of this network structure for approximately computing test data.
{"title":"Research on Neural Network Construction Method Based on Approximate Computational Test Data","authors":"Lutao Wang, Lisha Wu, Jinlong Hao, Zhenyu Chen, Cui-Lan Jia","doi":"10.1109/PHM2022-London52454.2022.00081","DOIUrl":"https://doi.org/10.1109/PHM2022-London52454.2022.00081","url":null,"abstract":"In some applications of the power grid, there are problems that the volume of real data is small and the security of real data is difficult to guarantee, which poses a challenge to the data governance model. This paper proposes a parallel convolutional neural network structure based on approximate calculation of test data, constructs test data through approximate calculation, and uses parallel convolutional neural network structure to learn the corresponding data model, which can solve the problems of data resources, computing resources and problems in data governance. Calculate the cost problem. Experiments based on existing data sets show the unique advantages of this network structure for approximately computing test data.","PeriodicalId":269605,"journal":{"name":"2022 Prognostics and Health Management Conference (PHM-2022 London)","volume":"25 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125164411","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 : 2022-05-01DOI: 10.1109/PHM2022-London52454.2022.00046
Sheng Li, Jiangyun Deng, Yu-xiao Li, Feiyang Xu
Intermittent fault is one of the main causes of the degradation of electronic systems. Accurately evaluating the severity of intermittent faults is a key issue for electronic system fault prediction and health management (PHM). Traditional machine learning methods are difficult to effectively extract the characteristics of intermittent faults. In response to this problem, this paper proposes a method for evaluating the severity of intermittent faults based on LSTM network. This method preprocesses the original data and does not require the process of extracting fault features, then the pre-processed data can be used for the training and testing of the LSTM network. Finally, the paper uses the intermittent fault injector to inject intermittent faults into the key circuits of the electronic system to obtain sufficient fault data to train the LSTM network. The test results show that the proposal are effective and feasible.
{"title":"An Intermittent Fault Severity Evaluation Method for Electronic Systems Based on LSTM Network","authors":"Sheng Li, Jiangyun Deng, Yu-xiao Li, Feiyang Xu","doi":"10.1109/PHM2022-London52454.2022.00046","DOIUrl":"https://doi.org/10.1109/PHM2022-London52454.2022.00046","url":null,"abstract":"Intermittent fault is one of the main causes of the degradation of electronic systems. Accurately evaluating the severity of intermittent faults is a key issue for electronic system fault prediction and health management (PHM). Traditional machine learning methods are difficult to effectively extract the characteristics of intermittent faults. In response to this problem, this paper proposes a method for evaluating the severity of intermittent faults based on LSTM network. This method preprocesses the original data and does not require the process of extracting fault features, then the pre-processed data can be used for the training and testing of the LSTM network. Finally, the paper uses the intermittent fault injector to inject intermittent faults into the key circuits of the electronic system to obtain sufficient fault data to train the LSTM network. The test results show that the proposal are effective and feasible.","PeriodicalId":269605,"journal":{"name":"2022 Prognostics and Health Management Conference (PHM-2022 London)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131754454","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 : 2022-05-01DOI: 10.1109/PHM2022-London52454.2022.00097
Yingshun Li, Na Li, Zhannan Guo, Haiyang Liu
The fault prediction and health management (PHM) technology has the functions of fault diagnosis, fault prediction and health management, and plays an important role in reducing the maintenance cost of fire control equipment, improving the integrity of the fire control system, and enhancing the management efficiency of the fire control system. According to the development status and application requirements of general fire control system, PHM technology is introduced into the fire control system. This paper firstly introduces the principle of PHM technology and the development status at home and abroad, and focuses on the key technology of PHM and the PHM architecture of the general fire control system. Finally, the development trend of fire control system PHM technology is prospected.
{"title":"Fire Control System Failure Prediction and Health Management Technology","authors":"Yingshun Li, Na Li, Zhannan Guo, Haiyang Liu","doi":"10.1109/PHM2022-London52454.2022.00097","DOIUrl":"https://doi.org/10.1109/PHM2022-London52454.2022.00097","url":null,"abstract":"The fault prediction and health management (PHM) technology has the functions of fault diagnosis, fault prediction and health management, and plays an important role in reducing the maintenance cost of fire control equipment, improving the integrity of the fire control system, and enhancing the management efficiency of the fire control system. According to the development status and application requirements of general fire control system, PHM technology is introduced into the fire control system. This paper firstly introduces the principle of PHM technology and the development status at home and abroad, and focuses on the key technology of PHM and the PHM architecture of the general fire control system. Finally, the development trend of fire control system PHM technology is prospected.","PeriodicalId":269605,"journal":{"name":"2022 Prognostics and Health Management Conference (PHM-2022 London)","volume":"146 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123380313","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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