Wear particle segmentation is an important step in the analysis and processing of ferrographic images, and it is also a hot topic in the field of ferrographic images. At present, the acquisition of ferrographic images is mostly based on the principle of magnetic field deposition. Wear particles will be chained and accumulated during the deposition process. Therefore, an effective wear particle segmentation method is needed. In this paper, a wear particle segmentation method based on the nearest neighbor algorithm is proposed. The method first decomposes the captured video into images. Then, this method introduces the nearest neighbor algorithm to extract the deposition process of wear particles, uses the distance transformation to form markers, and uses the marker-controlled watershed to solve the segmentation of the wear particle chain.Compared with traditional watershed segmentation algorithm, the problem of over-segmentation and under-segmentation is solved. The experimental results show that the segmentation results of the ferrographic image are accurate and fast, which lays a foundation for the subsequent extraction of the wear particle features.
{"title":"Wear Particle Chain Segmentation Based on the Nearest Neighbor Method","authors":"Song Feng, M. Feng, Quan Chen, Kai Zheng, J. Mao","doi":"10.1109/SDPC.2019.00115","DOIUrl":"https://doi.org/10.1109/SDPC.2019.00115","url":null,"abstract":"Wear particle segmentation is an important step in the analysis and processing of ferrographic images, and it is also a hot topic in the field of ferrographic images. At present, the acquisition of ferrographic images is mostly based on the principle of magnetic field deposition. Wear particles will be chained and accumulated during the deposition process. Therefore, an effective wear particle segmentation method is needed. In this paper, a wear particle segmentation method based on the nearest neighbor algorithm is proposed. The method first decomposes the captured video into images. Then, this method introduces the nearest neighbor algorithm to extract the deposition process of wear particles, uses the distance transformation to form markers, and uses the marker-controlled watershed to solve the segmentation of the wear particle chain.Compared with traditional watershed segmentation algorithm, the problem of over-segmentation and under-segmentation is solved. The experimental results show that the segmentation results of the ferrographic image are accurate and fast, which lays a foundation for the subsequent extraction of the wear particle features.","PeriodicalId":403595,"journal":{"name":"2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)","volume":"7 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":"132081514","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}
C. He, Runze Wang, Li Ma, Xiaobo Li, Xiaofeng Jiao, Lei Song
The construction of Bayesian network (BN) model is a bottleneck problem that needs to be solved urgently in the field of fault diagnosis. Combining failure mode and effect analysis(FMEA) and fault tree analysis(FTA) can solve this problem well. In this paper, a BN model based on FMEA/FTA is proposed. The transformation methods of FMEA to BN and FTA to BN are analyzed. The structural matrix is used to realize the information transformation. The noisy-max model is used to determine the BN parameters. Taking the rub-impact fault between the high pressure rotor and the front shaft seal of a 600 MW turbogenerator unit as an example, the application of the BN model based on FMEA/FTA in fault diagnosis is realized from three aspects which are BN model construction, prior probability and conditional probability assignment, and diagnosis reasoning, respectively.
{"title":"Research on Fault Diagnosis Method Based on FMEA/FTA and Bayesian Network","authors":"C. He, Runze Wang, Li Ma, Xiaobo Li, Xiaofeng Jiao, Lei Song","doi":"10.1109/SDPC.2019.00039","DOIUrl":"https://doi.org/10.1109/SDPC.2019.00039","url":null,"abstract":"The construction of Bayesian network (BN) model is a bottleneck problem that needs to be solved urgently in the field of fault diagnosis. Combining failure mode and effect analysis(FMEA) and fault tree analysis(FTA) can solve this problem well. In this paper, a BN model based on FMEA/FTA is proposed. The transformation methods of FMEA to BN and FTA to BN are analyzed. The structural matrix is used to realize the information transformation. The noisy-max model is used to determine the BN parameters. Taking the rub-impact fault between the high pressure rotor and the front shaft seal of a 600 MW turbogenerator unit as an example, the application of the BN model based on FMEA/FTA in fault diagnosis is realized from three aspects which are BN model construction, prior probability and conditional probability assignment, and diagnosis reasoning, respectively.","PeriodicalId":403595,"journal":{"name":"2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)","volume":"26 Suppl 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":"131340003","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}
Chenhui Ren, Huajin Lei, Hai-ping Dong, Xue Dong, Yuxi Tao
Effective health status diagnosis of the aero-engine not only helps improve the safety and reliability of aero-engines, but also helps engineers and maintenance workers reduce engine maintenance and support costs. Firstly, this paper proposes integrating five different base learners based on the Stacking method to diagnose the health status of the aero-engine. Then, the deep neural network (DNN) is used to learn the complex nonlinear relationship between the base learners in Stacking ensemble (SE) learning. Finally, a case study shows that the established ensemble model has higher diagnostic stability, generalization ability and strong learning ability, and proves to be effective in health status diagnosis of aero-engines.
{"title":"Study on the Diagnosis Method of Aero-engine Health Status Based on Stacking Ensemble Learning","authors":"Chenhui Ren, Huajin Lei, Hai-ping Dong, Xue Dong, Yuxi Tao","doi":"10.1109/SDPC.2019.00078","DOIUrl":"https://doi.org/10.1109/SDPC.2019.00078","url":null,"abstract":"Effective health status diagnosis of the aero-engine not only helps improve the safety and reliability of aero-engines, but also helps engineers and maintenance workers reduce engine maintenance and support costs. Firstly, this paper proposes integrating five different base learners based on the Stacking method to diagnose the health status of the aero-engine. Then, the deep neural network (DNN) is used to learn the complex nonlinear relationship between the base learners in Stacking ensemble (SE) learning. Finally, a case study shows that the established ensemble model has higher diagnostic stability, generalization ability and strong learning ability, and proves to be effective in health status diagnosis of aero-engines.","PeriodicalId":403595,"journal":{"name":"2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)","volume":"131 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":"121307489","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}
The rolling attitude of Intelligent ammunition provides the necessary parameter information for the control of flight trajectory. Magnetometer is widely used to measure the attitude angle of intelligent ammunition because of its low cost, high measurement accuracy, no cumulative error, high output frequency and not easy to be interfered by the external environment. However, the simple-guided missile with lower cost will cause a certain angle measurement error due to imperfect parameter information of the missile flight state. In this paper, the angle measurement model of the magnetometer is established, and the angle measurement error under different flight conditions is analyzed. According to the principle of angle measurement error, the algorithm of magnetometer/GNSS combined measurement of missile roll angle is proposed, and the numerical simulation model of the algorithm is established. The numerical simulation results show that the method effectively improves the measurement accuracy of the roll angle of the guided missile.
{"title":"Algorithm for Measuring Attitude Angle of Intelligent Ammunition with Magnetometer/GNSS","authors":"Xiaolong Yan, Dunzhuo Bai, Fuchun Zhao, Lin Liu, Guoguang Chen, Xiaoli Tian","doi":"10.1109/SDPC.2019.00175","DOIUrl":"https://doi.org/10.1109/SDPC.2019.00175","url":null,"abstract":"The rolling attitude of Intelligent ammunition provides the necessary parameter information for the control of flight trajectory. Magnetometer is widely used to measure the attitude angle of intelligent ammunition because of its low cost, high measurement accuracy, no cumulative error, high output frequency and not easy to be interfered by the external environment. However, the simple-guided missile with lower cost will cause a certain angle measurement error due to imperfect parameter information of the missile flight state. In this paper, the angle measurement model of the magnetometer is established, and the angle measurement error under different flight conditions is analyzed. According to the principle of angle measurement error, the algorithm of magnetometer/GNSS combined measurement of missile roll angle is proposed, and the numerical simulation model of the algorithm is established. The numerical simulation results show that the method effectively improves the measurement accuracy of the roll angle of the guided missile.","PeriodicalId":403595,"journal":{"name":"2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)","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":"114223787","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}
Hongyu Zhou, Jiahui Feng, Jun Shen, Yang Chai, Qingyuan Wang
The trains of EMU are all electric locomotives. During the operation of EMU, many reasons such as bad weather, high voltage cable falling off, catenary failure, power supply system failure and so on will cause power outage of power supply network. The power of the train is lost, so it has to be passively parked for rescue or use its own on-board energy storage to carry out self-rescue to the nearest station. Once the train stops in the middle of the "V" terrain or in difficult rescue locations, the use of diesel Trailer rescue will consume a lot of energy and cause a lot of carbon emissions. To solve this problem, a BP neural network method based on Levenberg-Marquardt algorithm is proposed to determine the parking difficulties in train operation section using UAS simulation platform. Compared with UAS simulation data, the reliability of this method is verified.
{"title":"Determination of Difficult Parking Points in Train Running Section Based on UAS and BP Neural Network","authors":"Hongyu Zhou, Jiahui Feng, Jun Shen, Yang Chai, Qingyuan Wang","doi":"10.1109/SDPC.2019.00122","DOIUrl":"https://doi.org/10.1109/SDPC.2019.00122","url":null,"abstract":"The trains of EMU are all electric locomotives. During the operation of EMU, many reasons such as bad weather, high voltage cable falling off, catenary failure, power supply system failure and so on will cause power outage of power supply network. The power of the train is lost, so it has to be passively parked for rescue or use its own on-board energy storage to carry out self-rescue to the nearest station. Once the train stops in the middle of the \"V\" terrain or in difficult rescue locations, the use of diesel Trailer rescue will consume a lot of energy and cause a lot of carbon emissions. To solve this problem, a BP neural network method based on Levenberg-Marquardt algorithm is proposed to determine the parking difficulties in train operation section using UAS simulation platform. Compared with UAS simulation data, the reliability of this method is verified.","PeriodicalId":403595,"journal":{"name":"2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)","volume":"179 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":"120947528","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 this paper, the gray level co-occurrence matrix (GLCM) and histogram of oriented gradient (HOG) features fusion of time-frequency image are introduced into the reciprocating compressor fault diagnosis. Firstly, vibration signals are acquired from the reciprocating compressor in different states of head tile and the wavelet transform distributions of vibration signals were displayed in time-frequency images. Secondly, GLCM and HOG methods are used to extract features from time-frequency images, then GLCM feature and HOG feature are fused and input into support vector machine for recognition and classification. By this way, the fault diagnosis of time series signals of reciprocating compressor is transferred to the classification of time-frequency images. The results show that can accurately realize diagnosis of small-head wear fault of reciprocating compressor.
{"title":"Fault diagnosis for reciprocating compressor based on GLCM and HOG features fusion of time-frequency image","authors":"Hui Li, Haipeng Zhao, Zijia Wang, Zhiwei Mao","doi":"10.1109/SDPC.2019.00184","DOIUrl":"https://doi.org/10.1109/SDPC.2019.00184","url":null,"abstract":"In this paper, the gray level co-occurrence matrix (GLCM) and histogram of oriented gradient (HOG) features fusion of time-frequency image are introduced into the reciprocating compressor fault diagnosis. Firstly, vibration signals are acquired from the reciprocating compressor in different states of head tile and the wavelet transform distributions of vibration signals were displayed in time-frequency images. Secondly, GLCM and HOG methods are used to extract features from time-frequency images, then GLCM feature and HOG feature are fused and input into support vector machine for recognition and classification. By this way, the fault diagnosis of time series signals of reciprocating compressor is transferred to the classification of time-frequency images. The results show that can accurately realize diagnosis of small-head wear fault of reciprocating compressor.","PeriodicalId":403595,"journal":{"name":"2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)","volume":"207 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":"121438896","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 problems existing in the current prediction methods of aeroengine parameters, such as the difficulty in parameter selection, the slow training speed and the tendency to fall into local optimal solution of traditional BP neural network algorithm, this paper proposes the prediction method of aeroengine performance parameters based on mean influence value (MIV) algorithm and extreme learning machine (ELM). Firstly, we preprocess the sample data. Secondly, screening out the main parameters that affect the predicted parameters by MIV algorithm, attribute reduction is realized, the result of attribute reduction is taken as the input to train an ELM. Finally, using the test samples to do the test. The testing results show that the algorithm is faster and more accurate in parameter prediction.
{"title":"Case study of aeroengine parameter prediction based on MIV and ELM","authors":"Yingshun Li, Fuyang Wang, Ximing Sun, X. Yi","doi":"10.1109/SDPC.2019.00019","DOIUrl":"https://doi.org/10.1109/SDPC.2019.00019","url":null,"abstract":"Aiming at the problems existing in the current prediction methods of aeroengine parameters, such as the difficulty in parameter selection, the slow training speed and the tendency to fall into local optimal solution of traditional BP neural network algorithm, this paper proposes the prediction method of aeroengine performance parameters based on mean influence value (MIV) algorithm and extreme learning machine (ELM). Firstly, we preprocess the sample data. Secondly, screening out the main parameters that affect the predicted parameters by MIV algorithm, attribute reduction is realized, the result of attribute reduction is taken as the input to train an ELM. Finally, using the test samples to do the test. The testing results show that the algorithm is faster and more accurate in parameter prediction.","PeriodicalId":403595,"journal":{"name":"2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)","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":"122743588","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 monthly load forecasting method based on load trend is proposed for monthly load data, which has dual characteristics of long-term trend and periodic fluctuation. Taking the monthly power generation from August 2012 to July 2017 as the research object, the monthly load data are decomposed into long-term trend and cyclic variation sequence, seasonal factor sequence and error sequence by seasonal decomposition. This paper focuses on the monthly cycle component characteristics of the four high energy-consuming industries, and deep analyses the characteristics of the monthly cycle component of the sub-industries electricity consumption and its impact on the electricity consumption of the industry. The monthly power generation from August 2017 to July 2018 is predicted by ARIMA model. The results show that the seasonal fluctuation law of monthly power generation is significant, and the relative errors of forecasting results are less than 3%, which verifies the validity and applicability of this method.
{"title":"Monthly Load Forecasting Model and Seasonal Characteristic Effect Analysis under the Background of Energy Internet","authors":"Fangyuan Yang, Limin Xue, Tianmeng Yang, D. Xia","doi":"10.1109/SDPC.2019.00129","DOIUrl":"https://doi.org/10.1109/SDPC.2019.00129","url":null,"abstract":"A monthly load forecasting method based on load trend is proposed for monthly load data, which has dual characteristics of long-term trend and periodic fluctuation. Taking the monthly power generation from August 2012 to July 2017 as the research object, the monthly load data are decomposed into long-term trend and cyclic variation sequence, seasonal factor sequence and error sequence by seasonal decomposition. This paper focuses on the monthly cycle component characteristics of the four high energy-consuming industries, and deep analyses the characteristics of the monthly cycle component of the sub-industries electricity consumption and its impact on the electricity consumption of the industry. The monthly power generation from August 2017 to July 2018 is predicted by ARIMA model. The results show that the seasonal fluctuation law of monthly power generation is significant, and the relative errors of forecasting results are less than 3%, which verifies the validity and applicability of this method.","PeriodicalId":403595,"journal":{"name":"2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)","volume":"252 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":"133092350","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}
Online Sequential Extreme Learning Machine (OS-ELM) has high accuracy and fast update speed in the areas of classification, such as fault diagnosis and anomaly detection. However, OS-ELM selects hidden layer parameters randomly leads to unstable output, which reduces the reliability of OS-ELM seriously. In this paper, a ensemble method based on OS-ELM and Naive Bayes(Bayes-OS-ELM) has been developed. The ensemble model establishes parallel sub-classifiers with OS-ELM and a secondary classifier with Naive Bayes to fuse the results of the former sub-classifiers. Because of the parallel structure, the ensemble model can greatly reduce the disturbance caused by the random set of hidden layer parameters of OS-ELM and make the classification result more stable. Besides, as an accurate and stable algorithm, Naive Bayes effectively promote the accuracy and stability of the classification model. Several UCI data sets have been involved to verify the proposed classification model. Experimental results show that this method has high accuracy, stable result and great generalization performance compared with the existing approach.
{"title":"Bayes-OS-ELM :An Novel Ensemble Method For Classification Application","authors":"Qingyu Zhu, Rui Bai, Mengting Li, Shaowei Chen, Pengfei Wen","doi":"10.1109/SDPC.2019.00037","DOIUrl":"https://doi.org/10.1109/SDPC.2019.00037","url":null,"abstract":"Online Sequential Extreme Learning Machine (OS-ELM) has high accuracy and fast update speed in the areas of classification, such as fault diagnosis and anomaly detection. However, OS-ELM selects hidden layer parameters randomly leads to unstable output, which reduces the reliability of OS-ELM seriously. In this paper, a ensemble method based on OS-ELM and Naive Bayes(Bayes-OS-ELM) has been developed. The ensemble model establishes parallel sub-classifiers with OS-ELM and a secondary classifier with Naive Bayes to fuse the results of the former sub-classifiers. Because of the parallel structure, the ensemble model can greatly reduce the disturbance caused by the random set of hidden layer parameters of OS-ELM and make the classification result more stable. Besides, as an accurate and stable algorithm, Naive Bayes effectively promote the accuracy and stability of the classification model. Several UCI data sets have been involved to verify the proposed classification model. Experimental results show that this method has high accuracy, stable result and great generalization performance compared with the existing approach.","PeriodicalId":403595,"journal":{"name":"2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)","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":"131823101","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 problems of large subjectivity and inaccurate diagnosis results in the fault diagnosis of tank gun control system, the fault diagnosis method based on improved artificial bee colony is studied. Combined with the improved artificial bee colony algorithm and BP neural network, a BP neural network algorithm based on improved bee colony optimization algorithm is formed and the model of the algorithm is established. And through the use of MATLAB simulation of computer programs, compared with the BP neural network algorithm without optimization, the experiment is summarized. The results show that the system can give fault diagnosis results more accurately, which helps to improve the maintenance efficiency and reliability of the tank gun control system.
{"title":"Research on Fault Diagnosis of Neural Network Based on Bee Colony Algorithm Optimization in Gun Control System","authors":"Yingshun Li, Yongjian Liu, X. Yi","doi":"10.1109/SDPC.2019.00036","DOIUrl":"https://doi.org/10.1109/SDPC.2019.00036","url":null,"abstract":"Aiming at the problems of large subjectivity and inaccurate diagnosis results in the fault diagnosis of tank gun control system, the fault diagnosis method based on improved artificial bee colony is studied. Combined with the improved artificial bee colony algorithm and BP neural network, a BP neural network algorithm based on improved bee colony optimization algorithm is formed and the model of the algorithm is established. And through the use of MATLAB simulation of computer programs, compared with the BP neural network algorithm without optimization, the experiment is summarized. The results show that the system can give fault diagnosis results more accurately, which helps to improve the maintenance efficiency and reliability of the tank gun control system.","PeriodicalId":403595,"journal":{"name":"2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)","volume":"235 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":"131885962","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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