Pub Date : 2019-08-01DOI: 10.1109/QR2MSE46217.2019.9021166
Lu Wang, X. Ye, G. Zhai, Cen Chen, Han Wang
The power supply is an important part of an electronic system, whose reliability determines the reliability of the entire system. The secondary power supply in the servo system is prone to failure during its operational life cycle, resulting in no power output to the system. The electrical stress of a series of physical nodes is analyzed using the power supply model so as to clarify the potential failures in the supply. The practical measurement of the real power supply is also used to verify these given root causes. Moreover, the design defects are eliminated by modifying the circuit topology, so that the reliability of the power supply can be significantly improved.
{"title":"Failure Analysis and Optimization of Secondary Power Supply in Servo System Ba sed on Simulation","authors":"Lu Wang, X. Ye, G. Zhai, Cen Chen, Han Wang","doi":"10.1109/QR2MSE46217.2019.9021166","DOIUrl":"https://doi.org/10.1109/QR2MSE46217.2019.9021166","url":null,"abstract":"The power supply is an important part of an electronic system, whose reliability determines the reliability of the entire system. The secondary power supply in the servo system is prone to failure during its operational life cycle, resulting in no power output to the system. The electrical stress of a series of physical nodes is analyzed using the power supply model so as to clarify the potential failures in the supply. The practical measurement of the real power supply is also used to verify these given root causes. Moreover, the design defects are eliminated by modifying the circuit topology, so that the reliability of the power supply can be significantly improved.","PeriodicalId":233855,"journal":{"name":"2019 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE)","volume":"159 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115996949","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}
Many imported AC to DC power supply modules have been used in a certain nuclear power station. The failure rate of these power supply modules has been increasing after five years’ operation. If applying new power supply modules to replace the failure modules, the cost will be enormous. In order to reduce the cost, work has been done to invest manpower in failure analysis and maintenance of the failure modules and collect lots of data in continuous repairing and preventive maintenance work. This paper analyses the failure data and maintenance data of power supply module, uses Minitab tool to fit the analysis data, obtains the parameters needed for the reliability model, and then calculates the reliability and failure rate of power supply modules. Finally, the cost of new spare parts was calculated according to the failure analysis data if replacing failed modules with new modules each year, and the annual economic returns which base on failure modules repairing and preventive maintenance.
{"title":"Failure Distribution of Nuclear Power Station DC Power Supply Modules and Maintenance Benefit Analysis","authors":"Guixia Zhu, Feng Qin, Tianmi Zhou, Xiao-Xiao Shang, Yugang Qian","doi":"10.1109/QR2MSE46217.2019.9021266","DOIUrl":"https://doi.org/10.1109/QR2MSE46217.2019.9021266","url":null,"abstract":"Many imported AC to DC power supply modules have been used in a certain nuclear power station. The failure rate of these power supply modules has been increasing after five years’ operation. If applying new power supply modules to replace the failure modules, the cost will be enormous. In order to reduce the cost, work has been done to invest manpower in failure analysis and maintenance of the failure modules and collect lots of data in continuous repairing and preventive maintenance work. This paper analyses the failure data and maintenance data of power supply module, uses Minitab tool to fit the analysis data, obtains the parameters needed for the reliability model, and then calculates the reliability and failure rate of power supply modules. Finally, the cost of new spare parts was calculated according to the failure analysis data if replacing failed modules with new modules each year, and the annual economic returns which base on failure modules repairing and preventive maintenance.","PeriodicalId":233855,"journal":{"name":"2019 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114982589","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 : 2019-08-01DOI: 10.1109/QR2MSE46217.2019.9021119
B. Bai, Ze Li, Junyi Zhang
Based on fuzzy mathematics thought, a methodology combining the expert evaluation and multilevel hierarchy analysis (EE-MHA) is proposed, meanwhile, the non-electronic product reliability data, NPRD) of non-key parts is used to predict the reliability of RV reducer in six-axis industrial robots. First, the proportion of every component of RV reducer in the reliability prediction was calculated via expert scoring. Then the failures rates of main parts and RV reducer are obtained by the non-key part. Based on this, the reliability assessment is investigated. This method can quantify the cognition of engineers on RV reducer under the condition of processing and production, besides, the failure rate of RV reducer can be calculated, which provide theoretical basis for requirements of spare parts for manufacturers of industrial robots who is using RV reducer.
{"title":"Failure Rate Prediction and Reliability Assessment of RV Reducer","authors":"B. Bai, Ze Li, Junyi Zhang","doi":"10.1109/QR2MSE46217.2019.9021119","DOIUrl":"https://doi.org/10.1109/QR2MSE46217.2019.9021119","url":null,"abstract":"Based on fuzzy mathematics thought, a methodology combining the expert evaluation and multilevel hierarchy analysis (EE-MHA) is proposed, meanwhile, the non-electronic product reliability data, NPRD) of non-key parts is used to predict the reliability of RV reducer in six-axis industrial robots. First, the proportion of every component of RV reducer in the reliability prediction was calculated via expert scoring. Then the failures rates of main parts and RV reducer are obtained by the non-key part. Based on this, the reliability assessment is investigated. This method can quantify the cognition of engineers on RV reducer under the condition of processing and production, besides, the failure rate of RV reducer can be calculated, which provide theoretical basis for requirements of spare parts for manufacturers of industrial robots who is using RV reducer.","PeriodicalId":233855,"journal":{"name":"2019 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121059480","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}
After analyzing the shortcomings of traditional fault tree analysis methods, a fuzzy Bayesian network reliability analysis method based on fault tree is proposed. This method of modeling uses the Bayesian method, the event polymorphism of complex systems is described by the node polymorphism expression feature of Bayesian network theory, and the uncertain logical relationship between events is described by the conditional probability table of Bayesian network. Based on the Bayesian model, the fuzzy set theory is introduced, and the experts fuzzy evaluation of event probability is described by triangular fuzzy numbers. In the evaluation information of the experts with uncertain weights, the expert evaluation information of the uncertain weights is calculated by using the uncertainty-ordered weighted average operator to calculate the expert weights, and finally the exact value of the occurrence probability of different states is obtained. Substituting it into the Bayesian network to calculate the probability of occurrence of different states of the leaf nodes, and then calculating the posterior probability of each root node and its importance.
{"title":"Reliability Analysis of Fuzzy Bayesian Networks Based on Uncertain Ordered Weighted Operators","authors":"Chunwei Li, Honghua Sun, Qing-yang Li, Xudong Chen","doi":"10.1109/QR2MSE46217.2019.9021264","DOIUrl":"https://doi.org/10.1109/QR2MSE46217.2019.9021264","url":null,"abstract":"After analyzing the shortcomings of traditional fault tree analysis methods, a fuzzy Bayesian network reliability analysis method based on fault tree is proposed. This method of modeling uses the Bayesian method, the event polymorphism of complex systems is described by the node polymorphism expression feature of Bayesian network theory, and the uncertain logical relationship between events is described by the conditional probability table of Bayesian network. Based on the Bayesian model, the fuzzy set theory is introduced, and the experts fuzzy evaluation of event probability is described by triangular fuzzy numbers. In the evaluation information of the experts with uncertain weights, the expert evaluation information of the uncertain weights is calculated by using the uncertainty-ordered weighted average operator to calculate the expert weights, and finally the exact value of the occurrence probability of different states is obtained. Substituting it into the Bayesian network to calculate the probability of occurrence of different states of the leaf nodes, and then calculating the posterior probability of each root node and its importance.","PeriodicalId":233855,"journal":{"name":"2019 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125977791","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 : 2019-08-01DOI: 10.1109/QR2MSE46217.2019.9021135
Yingchun Xu, Wen Yao, Xiaohu Zheng, Xiaoqian Chen
In recent years, there often exist multiple priors from experienced experts or historical experiments with the rapid development of system structure in engineering fields. Bayesian Melding Method is commonly used for integrating multiple priors, which is based on the deterministic system structure. However, if the system model cannot be described by an explicit expression, the traditional Bayesian Melding Method is not feasible for system reliability analysis anymore. In order to describe the structure relationship clearly, Bayesian Network is applied in this paper to construct the complex system structure model and the system reliability is calculated by node probability tables rather than explicit expressions. Combining the advantages of the Bayesian Melding Method and Bayesian Network, a multi-prior integration and updating algorithm is developed for the system reliability analysis of complex system structures. Finally, a satellite attitude control system is used to demonstrate the proposed method. The system is established by the Bayesian Network and the comparison between natural prior and updated prior is discussed at length.
{"title":"Multi-Prior Integration Method for System Reliability Analysis Based on Bayesian Network and Bayesian Melding Method","authors":"Yingchun Xu, Wen Yao, Xiaohu Zheng, Xiaoqian Chen","doi":"10.1109/QR2MSE46217.2019.9021135","DOIUrl":"https://doi.org/10.1109/QR2MSE46217.2019.9021135","url":null,"abstract":"In recent years, there often exist multiple priors from experienced experts or historical experiments with the rapid development of system structure in engineering fields. Bayesian Melding Method is commonly used for integrating multiple priors, which is based on the deterministic system structure. However, if the system model cannot be described by an explicit expression, the traditional Bayesian Melding Method is not feasible for system reliability analysis anymore. In order to describe the structure relationship clearly, Bayesian Network is applied in this paper to construct the complex system structure model and the system reliability is calculated by node probability tables rather than explicit expressions. Combining the advantages of the Bayesian Melding Method and Bayesian Network, a multi-prior integration and updating algorithm is developed for the system reliability analysis of complex system structures. Finally, a satellite attitude control system is used to demonstrate the proposed method. The system is established by the Bayesian Network and the comparison between natural prior and updated prior is discussed at length.","PeriodicalId":233855,"journal":{"name":"2019 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125970681","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 : 2019-08-01DOI: 10.1109/QR2MSE46217.2019.9021117
Baiqiao Huang, Guodong Qin, Peng Zhang
It is the consensus of people that improving product quality by improving management, but the management standards adopted in different fields are not the same, which is easy to be confused. As to the current status of different quality management standards and methods used in different business areas, this paper analyzes the development history of the general quality management system in the production field and the core concept of total quality management(TQM), compares it with the CMMI standard of quality management in the system development field, analyzes each other’s strengths and weaknesses, and proposes suggestions for improving the deficiencies of CMMI. Finally, the relationship between TQM, CMMI and system engineering (SE) is analyzed, and concludes that the integration with model-based system engineering(MBSE) will be the new direction of CMMI’s future development.
{"title":"Comparative Analysis of TQM and CMMI","authors":"Baiqiao Huang, Guodong Qin, Peng Zhang","doi":"10.1109/QR2MSE46217.2019.9021117","DOIUrl":"https://doi.org/10.1109/QR2MSE46217.2019.9021117","url":null,"abstract":"It is the consensus of people that improving product quality by improving management, but the management standards adopted in different fields are not the same, which is easy to be confused. As to the current status of different quality management standards and methods used in different business areas, this paper analyzes the development history of the general quality management system in the production field and the core concept of total quality management(TQM), compares it with the CMMI standard of quality management in the system development field, analyzes each other’s strengths and weaknesses, and proposes suggestions for improving the deficiencies of CMMI. Finally, the relationship between TQM, CMMI and system engineering (SE) is analyzed, and concludes that the integration with model-based system engineering(MBSE) will be the new direction of CMMI’s future development.","PeriodicalId":233855,"journal":{"name":"2019 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127267122","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 : 2019-08-01DOI: 10.1109/QR2MSE46217.2019.9021224
Qidong You, S. Zeng, Jianbin Guo, Honghong Lv
The development of man-machine system poses a challenge to human’s information processing ability. Therefore, the human cognitive characteristics and the dynamic man-machine interaction (MMI) become the focus of the MMI research. This study takes the MMI process of complex system as the research object. According to multi-task and time-pressure scenarios, two kinds of MMI fault modes such as cognitive overload and cognitive confusion are proposed. In addition, this paper studies their failure mechanism and the uncertainty of MMI logic. And then a modeling method of the two faults based on Markov model are proposed. The corresponding quantitative calculation methods to complete the modeling and prediction of MMI reliability are introduced. At last, a case application proves the rationality and feasibility of the method.
{"title":"Man-Machine Interaction Reliability Modeling Method Based on Markov Model","authors":"Qidong You, S. Zeng, Jianbin Guo, Honghong Lv","doi":"10.1109/QR2MSE46217.2019.9021224","DOIUrl":"https://doi.org/10.1109/QR2MSE46217.2019.9021224","url":null,"abstract":"The development of man-machine system poses a challenge to human’s information processing ability. Therefore, the human cognitive characteristics and the dynamic man-machine interaction (MMI) become the focus of the MMI research. This study takes the MMI process of complex system as the research object. According to multi-task and time-pressure scenarios, two kinds of MMI fault modes such as cognitive overload and cognitive confusion are proposed. In addition, this paper studies their failure mechanism and the uncertainty of MMI logic. And then a modeling method of the two faults based on Markov model are proposed. The corresponding quantitative calculation methods to complete the modeling and prediction of MMI reliability are introduced. At last, a case application proves the rationality and feasibility of the method.","PeriodicalId":233855,"journal":{"name":"2019 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127562942","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}
Machine learning is nowadays one of the most efficient and popular tool and theory which has influenced many of the engineering fields. The traditional failure analysis is also based on statistical learning and reliability data, these methods can be used to assess characteristics over the design life, predict reliability, assess the exchange effect, product life prognosis and help to failure analysis. These two subjects have the natural connection, so this paper presents a very general overview on reliability and machine learning, which will demonstrate how the machine learning tools used for classical reliability system and failure analysis. We especially state some algorithms such as Bayesian networks and its’ method to reliability area. Then we can see how a typical engineering area can benefit from the machine learning.
{"title":"An Overview of Failure Analysis Expert System Based on Machine Learning","authors":"Hongjian Wang, Liyuan Liu, Youliang Wang, Zeya Peng","doi":"10.1109/QR2MSE46217.2019.9021177","DOIUrl":"https://doi.org/10.1109/QR2MSE46217.2019.9021177","url":null,"abstract":"Machine learning is nowadays one of the most efficient and popular tool and theory which has influenced many of the engineering fields. The traditional failure analysis is also based on statistical learning and reliability data, these methods can be used to assess characteristics over the design life, predict reliability, assess the exchange effect, product life prognosis and help to failure analysis. These two subjects have the natural connection, so this paper presents a very general overview on reliability and machine learning, which will demonstrate how the machine learning tools used for classical reliability system and failure analysis. We especially state some algorithms such as Bayesian networks and its’ method to reliability area. Then we can see how a typical engineering area can benefit from the machine learning.","PeriodicalId":233855,"journal":{"name":"2019 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122572477","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 : 2019-08-01DOI: 10.1109/QR2MSE46217.2019.9021212
Shuai Wu, Jianbin Guo, S. Zeng, Zhenping Lu
Situation awareness (SA) is a vital element for judging information and making decision in intense, dynamic working environments such as the aircraft landing process. The Situation Awareness Global Assessment Technique (SAGAT) can provide a subjective measurement method of the pilot’s situation awareness skill. During the aircraft’s actual landing process, some abnormal situations which will influence pilot’s decision-making and behavior may appear randomly with a potential probability. The pilot’s situation awareness has different characteristics in normal and abnormal situations. To measure the pilot’s SA in abnormal situations, we have injected faults into the aircraft system and used SAGAT to query pilots. This paper can provide a reference for the actual emergency’s treatment of the aircraft system in training the pilots.
{"title":"Direct Measurement of Situation Awareness in Abnormal Situation During Aircraft Landing","authors":"Shuai Wu, Jianbin Guo, S. Zeng, Zhenping Lu","doi":"10.1109/QR2MSE46217.2019.9021212","DOIUrl":"https://doi.org/10.1109/QR2MSE46217.2019.9021212","url":null,"abstract":"Situation awareness (SA) is a vital element for judging information and making decision in intense, dynamic working environments such as the aircraft landing process. The Situation Awareness Global Assessment Technique (SAGAT) can provide a subjective measurement method of the pilot’s situation awareness skill. During the aircraft’s actual landing process, some abnormal situations which will influence pilot’s decision-making and behavior may appear randomly with a potential probability. The pilot’s situation awareness has different characteristics in normal and abnormal situations. To measure the pilot’s SA in abnormal situations, we have injected faults into the aircraft system and used SAGAT to query pilots. This paper can provide a reference for the actual emergency’s treatment of the aircraft system in training the pilots.","PeriodicalId":233855,"journal":{"name":"2019 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126793452","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 : 2019-08-01DOI: 10.1109/QR2MSE46217.2019.9021252
Yang Ge, Jian Wu, Xiao-Mei Jiang
Remaining useful life prediction (RUL) is an important precondition for maintenance decision. Traditionally, Accurate RUL is very difficult to predict because of the diversity of operating mode and self-conditions between systems. A data-driven approach is proposed for RUL prediction using Bayesian theory. The Markov Chain Monte Carlo (MCMC) method is used for updating the parameters of the Bayesian model. Experiment on the C-MAPSS data shows that the proposed method has better performance and higher prediction accuracy than other popular methods. Additionally, an effective health indicator (HI) estimation method is employed to combine the multi-sensor HI into one, which makes HI value increase from 0 to 1 during the lifetime. The results of this study offer a new and effective approach for RUL prediction.
{"title":"A Prediction Method Using Bayesian Theory for Remaining Useful Life","authors":"Yang Ge, Jian Wu, Xiao-Mei Jiang","doi":"10.1109/QR2MSE46217.2019.9021252","DOIUrl":"https://doi.org/10.1109/QR2MSE46217.2019.9021252","url":null,"abstract":"Remaining useful life prediction (RUL) is an important precondition for maintenance decision. Traditionally, Accurate RUL is very difficult to predict because of the diversity of operating mode and self-conditions between systems. A data-driven approach is proposed for RUL prediction using Bayesian theory. The Markov Chain Monte Carlo (MCMC) method is used for updating the parameters of the Bayesian model. Experiment on the C-MAPSS data shows that the proposed method has better performance and higher prediction accuracy than other popular methods. Additionally, an effective health indicator (HI) estimation method is employed to combine the multi-sensor HI into one, which makes HI value increase from 0 to 1 during the lifetime. The results of this study offer a new and effective approach for RUL prediction.","PeriodicalId":233855,"journal":{"name":"2019 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126810223","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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