Pub Date : 2017-11-01DOI: 10.1109/ROPEC.2017.8261594
C. Morales-Perez, J. Rangel-Magdaleno, H. Peregrina-Barreto, J. Ramírez-Cortés
Currently, the Induction Motor is widely used in industry, due to its easy installation and operation. Induction motors require a more reliable monitoring due to constant operation increases the possibility of faults, for example, a broken rotor bar fault. Early stage, broken bar is not easy to detect, and its evolves is slow and quiet. In the most of cases, it is detected when the fault is critical and other faults have appeared. Many techniques have been proposed in the literature, but majority of these performs analysis in frequency domain, applying additional transformation or preprocessing methods. In this paper, a novel methodology to detect a half-broken bar fault is proposed, making use of the vibration signal from induction motor under two fault conditions: healthy and half-broken bar; and three load conditions: unloaded, half-loaded and three-fourths loaded. The detection is possible due to the sparse representation of the raw signal which is obtained and then evaluated by minimal decomposition error criterion. In this way, preprocessing methods are not needed, and the fault is detected early and directly. These tests were developed in Matlab software, with vibration signals from induction motors in steady state.
{"title":"Half-broken rotor bar detection on IM by using sparse representation under different load conditions","authors":"C. Morales-Perez, J. Rangel-Magdaleno, H. Peregrina-Barreto, J. Ramírez-Cortés","doi":"10.1109/ROPEC.2017.8261594","DOIUrl":"https://doi.org/10.1109/ROPEC.2017.8261594","url":null,"abstract":"Currently, the Induction Motor is widely used in industry, due to its easy installation and operation. Induction motors require a more reliable monitoring due to constant operation increases the possibility of faults, for example, a broken rotor bar fault. Early stage, broken bar is not easy to detect, and its evolves is slow and quiet. In the most of cases, it is detected when the fault is critical and other faults have appeared. Many techniques have been proposed in the literature, but majority of these performs analysis in frequency domain, applying additional transformation or preprocessing methods. In this paper, a novel methodology to detect a half-broken bar fault is proposed, making use of the vibration signal from induction motor under two fault conditions: healthy and half-broken bar; and three load conditions: unloaded, half-loaded and three-fourths loaded. The detection is possible due to the sparse representation of the raw signal which is obtained and then evaluated by minimal decomposition error criterion. In this way, preprocessing methods are not needed, and the fault is detected early and directly. These tests were developed in Matlab software, with vibration signals from induction motors in steady state.","PeriodicalId":260469,"journal":{"name":"2017 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120967463","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 : 2017-11-01DOI: 10.1109/ROPEC.2017.8261644
F. Martínez-Cárdenas, J. Correa-Gómez, J. A. Salazar-Torres, Jesús R. Alvarado-Sánchez, Salvador C. Jacobo-Cornejo
Multilevel converters are widely used with didactic purposes and for many applications where a CD-CA converter is required, such as: renewable energy systems, audio amplifiers and voltage amplifiers for electrical equipment testing. In this work, the implementation of a 9 Levels SPWM technique for a multilevel converter is shown. Three different platforms were implemented: discrete circuit, low cost microcontroller and a real time platform. The advantages and disadvantages of each platform are also described.
{"title":"SPWM for 9 levels converter implemented in three platforms: Discrete circuit, low cost microcontroller and real-time","authors":"F. Martínez-Cárdenas, J. Correa-Gómez, J. A. Salazar-Torres, Jesús R. Alvarado-Sánchez, Salvador C. Jacobo-Cornejo","doi":"10.1109/ROPEC.2017.8261644","DOIUrl":"https://doi.org/10.1109/ROPEC.2017.8261644","url":null,"abstract":"Multilevel converters are widely used with didactic purposes and for many applications where a CD-CA converter is required, such as: renewable energy systems, audio amplifiers and voltage amplifiers for electrical equipment testing. In this work, the implementation of a 9 Levels SPWM technique for a multilevel converter is shown. Three different platforms were implemented: discrete circuit, low cost microcontroller and a real time platform. The advantages and disadvantages of each platform are also described.","PeriodicalId":260469,"journal":{"name":"2017 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122357062","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 : 2017-11-01DOI: 10.1109/ROPEC.2017.8261617
Emilio Barocio O. C. Robles, J. Segundo, J. C. Olivares-Galvan, D. Guillen
In this paper, a Multi Scale Recurrence Quantification Analysis (MSRQA) method is proposed to clustering harmonics on microgrid systems. MSRQA is composed by the Variational Mode Decomposition algorithm and the Recurrence Quantification Analysis (RQA). MSRQA decomposes a signal into a finite number of Mono-Component Signals (MCSs), then a feature extraction is carry out by the RQA on each MCS. Finally, the identification of the optimal number of clusters based on the features extracted by RQA and the Davies-Bouldin index is carry out on the monitored microgrid system test signals. At the end an index based on the cluster information and the RQA measure is proposed to identify the harmonics present on the dynamic system behavior.
{"title":"Multi scale recurrence quantification analysis for clustering harmonics on microgrid systems","authors":"Emilio Barocio O. C. Robles, J. Segundo, J. C. Olivares-Galvan, D. Guillen","doi":"10.1109/ROPEC.2017.8261617","DOIUrl":"https://doi.org/10.1109/ROPEC.2017.8261617","url":null,"abstract":"In this paper, a Multi Scale Recurrence Quantification Analysis (MSRQA) method is proposed to clustering harmonics on microgrid systems. MSRQA is composed by the Variational Mode Decomposition algorithm and the Recurrence Quantification Analysis (RQA). MSRQA decomposes a signal into a finite number of Mono-Component Signals (MCSs), then a feature extraction is carry out by the RQA on each MCS. Finally, the identification of the optimal number of clusters based on the features extracted by RQA and the Davies-Bouldin index is carry out on the monitored microgrid system test signals. At the end an index based on the cluster information and the RQA measure is proposed to identify the harmonics present on the dynamic system behavior.","PeriodicalId":260469,"journal":{"name":"2017 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126946339","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 : 2017-11-01DOI: 10.1109/ROPEC.2017.8261680
Hugo E. Vega Ayala, Norberto García Barriga
This paper addresses the impact of the integration of plug-in electric vehicles (PEV) in a high-voltage (HV) electric grid located in the metropolitan area of the city of Morelia, Mexico. A penetration scenario of 10.5%, which corresponds to 31316 vehicles, is considered by allocating electric vehicles to substations. A time domain modelling of an electric vehicle (EV) and an AC Level-2 charger is implemented in a PSS/E-based simulation platform. Furthermore, an uncontrolled charging strategy is implemented in this paper, which is based on the assumption that EVs owners are free to connect and charge their vehicles whenever they want. Power flow solutions are reported in terms of nodal voltages, power losses and loading impacts in the 115 kV sub-transmission system, substation transformers and transmission system. Results show that the load due to the EVs can help to the nodal voltages regulation in the grid during certain times of the day. However, a large number of EVs can significantly increase the losses and exceed the capacity of the transformers depending on the load curve of each substation.
{"title":"Study of the impact of electric vehicles fleets in HV electric power grids based on an uncontrolled charging strategy","authors":"Hugo E. Vega Ayala, Norberto García Barriga","doi":"10.1109/ROPEC.2017.8261680","DOIUrl":"https://doi.org/10.1109/ROPEC.2017.8261680","url":null,"abstract":"This paper addresses the impact of the integration of plug-in electric vehicles (PEV) in a high-voltage (HV) electric grid located in the metropolitan area of the city of Morelia, Mexico. A penetration scenario of 10.5%, which corresponds to 31316 vehicles, is considered by allocating electric vehicles to substations. A time domain modelling of an electric vehicle (EV) and an AC Level-2 charger is implemented in a PSS/E-based simulation platform. Furthermore, an uncontrolled charging strategy is implemented in this paper, which is based on the assumption that EVs owners are free to connect and charge their vehicles whenever they want. Power flow solutions are reported in terms of nodal voltages, power losses and loading impacts in the 115 kV sub-transmission system, substation transformers and transmission system. Results show that the load due to the EVs can help to the nodal voltages regulation in the grid during certain times of the day. However, a large number of EVs can significantly increase the losses and exceed the capacity of the transformers depending on the load curve of each substation.","PeriodicalId":260469,"journal":{"name":"2017 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127186744","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 : 2017-11-01DOI: 10.1109/ROPEC.2017.8261667
Cesar E. Hernández-González, J. Ramírez-Cortés, P. Gómez-Gil, J. Rangel-Magdaleno, H. Peregrina-Barreto, Israel Cruz-Vega
A BCI system (Brain-Computer Interface) aims to the interpretation of brain signals perceived through electroencephalography (EEG) sensors in order to allow the user interaction with the environment through specific actions. In this paper we present an experiment of EEG signal classification under the motor imagery paradigm using two feature extraction methods for comparison purposes: discrete wavelet transform (DWT) and maximum overlap discrete wavelet transform (MODWT). The feature vectors are fed into a support vector machine (SVM) classification system. The results obtained show an accuracy of 98.81% in average.
{"title":"EEG motor imagery signals classification using maximum overlap wavelet transform and support vector machine","authors":"Cesar E. Hernández-González, J. Ramírez-Cortés, P. Gómez-Gil, J. Rangel-Magdaleno, H. Peregrina-Barreto, Israel Cruz-Vega","doi":"10.1109/ROPEC.2017.8261667","DOIUrl":"https://doi.org/10.1109/ROPEC.2017.8261667","url":null,"abstract":"A BCI system (Brain-Computer Interface) aims to the interpretation of brain signals perceived through electroencephalography (EEG) sensors in order to allow the user interaction with the environment through specific actions. In this paper we present an experiment of EEG signal classification under the motor imagery paradigm using two feature extraction methods for comparison purposes: discrete wavelet transform (DWT) and maximum overlap discrete wavelet transform (MODWT). The feature vectors are fed into a support vector machine (SVM) classification system. The results obtained show an accuracy of 98.81% in average.","PeriodicalId":260469,"journal":{"name":"2017 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC)","volume":"144 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127506453","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 : 2017-11-01DOI: 10.1109/ROPEC.2017.8261612
A. E. Choque-Rivero, Omar Fabián González Hernández
Let n be the dimension of the Brunovsky system. For n = 2m (respectively n = 2m + 1), we prove that every positive distribution on [0, ∞) that has at least n/2 points of increase on (0, ∞), (respectively (n + 1)/2 points of increase on [0, ∞) generates a positional control that stabilizes a family of Brunovsky systems of dimensions 1 ≤ k ≤ n.
{"title":"Stabilization via orthogonal polynomials","authors":"A. E. Choque-Rivero, Omar Fabián González Hernández","doi":"10.1109/ROPEC.2017.8261612","DOIUrl":"https://doi.org/10.1109/ROPEC.2017.8261612","url":null,"abstract":"Let n be the dimension of the Brunovsky system. For n = 2m (respectively n = 2m + 1), we prove that every positive distribution on [0, ∞) that has at least n/2 points of increase on (0, ∞), (respectively (n + 1)/2 points of increase on [0, ∞) generates a positional control that stabilizes a family of Brunovsky systems of dimensions 1 ≤ k ≤ n.","PeriodicalId":260469,"journal":{"name":"2017 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132755491","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 : 2017-11-01DOI: 10.1109/ROPEC.2017.8261630
Xavier Serrano-Guerrero, Ricardo Prieto-Galarza, Esteban Huilcatanda, Juan Cabrera-Zeas, G. Escrivá-Escrivá
Forecasting of electricity demand is a fundamental requirement for the energy sector since from its results important decisions are taken. The areas involved are maintenance of electrical networks, demand growth, increased installed capacity, among others, whose lack of precision can take high economic costs. In this work, we propose a method based on backpropagation neural networks and election of key variables as inputs. The number of neurons in the hidden layer was optimized. To avoid the overtraining the best time range of data was defined. The results show that the method works particularly well for short-term forecasting (24 or 48 hours).
{"title":"Election of variables and short-term forecasting of electricity demand based on backpropagation artificial neural networks","authors":"Xavier Serrano-Guerrero, Ricardo Prieto-Galarza, Esteban Huilcatanda, Juan Cabrera-Zeas, G. Escrivá-Escrivá","doi":"10.1109/ROPEC.2017.8261630","DOIUrl":"https://doi.org/10.1109/ROPEC.2017.8261630","url":null,"abstract":"Forecasting of electricity demand is a fundamental requirement for the energy sector since from its results important decisions are taken. The areas involved are maintenance of electrical networks, demand growth, increased installed capacity, among others, whose lack of precision can take high economic costs. In this work, we propose a method based on backpropagation neural networks and election of key variables as inputs. The number of neurons in the hidden layer was optimized. To avoid the overtraining the best time range of data was defined. The results show that the method works particularly well for short-term forecasting (24 or 48 hours).","PeriodicalId":260469,"journal":{"name":"2017 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128474764","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 : 2017-11-01DOI: 10.1109/ROPEC.2017.8261686
Bryan E. Martínez, J. C. Jacobo
The speaker emotional state recognition task in human-computer interaction will be one of the most common in the future. This task is known as Speech Emotion Recognition (SER). Previous works have developed some characterizations which heavily relies on some sort of feature selection method in order to choose the best subset of features. To our knowledge, no effort has been invested in working out the original features with the idea to improve the classification. In this work, a methodology for feature preprocessing is presented. To this end, our characterization method uses a speech signal from which different characteristics, as well as statistics, are extracted. Then, these characteristics go through a preprocessing phase which will enhance the classification efficiency. After this, a two-stage classification scheme is used. In the first stage k-Means is used for clustering and then in the second stage, we use several standard classifiers. This strategy shows consistently across the classifiers, except for SVM, a superior classification rate (91–100%) than those reported in previous works.
{"title":"An improved characterization methodology to efficiently deal with the speech emotion recognition problem","authors":"Bryan E. Martínez, J. C. Jacobo","doi":"10.1109/ROPEC.2017.8261686","DOIUrl":"https://doi.org/10.1109/ROPEC.2017.8261686","url":null,"abstract":"The speaker emotional state recognition task in human-computer interaction will be one of the most common in the future. This task is known as Speech Emotion Recognition (SER). Previous works have developed some characterizations which heavily relies on some sort of feature selection method in order to choose the best subset of features. To our knowledge, no effort has been invested in working out the original features with the idea to improve the classification. In this work, a methodology for feature preprocessing is presented. To this end, our characterization method uses a speech signal from which different characteristics, as well as statistics, are extracted. Then, these characteristics go through a preprocessing phase which will enhance the classification efficiency. After this, a two-stage classification scheme is used. In the first stage k-Means is used for clustering and then in the second stage, we use several standard classifiers. This strategy shows consistently across the classifiers, except for SVM, a superior classification rate (91–100%) than those reported in previous works.","PeriodicalId":260469,"journal":{"name":"2017 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC)","volume":"140 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134524455","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 : 2017-11-01DOI: 10.1109/ROPEC.2017.8261626
J. Fonseca-Campos, Leonardo Fonseca Ruiz, J. Mata-Machuca
Renewable energy has grown extensively in the last decades. The photovoltaic panels have been playing a significant role in the technologies used to produce this type of energy. This tendency is expected to continue in the future, because the cost of the solar panels has been gradually reduced. Systems providing information of the electric performance of the solar cells can be helpful for technicians and researchers. In this paper an inexpensive system that measures the I-V curves of photovoltaic cells is presented. With the experimental data the parameters of the single diode model of the solar cell were estimated implementing the simulated annealing algorithm. Three different solar modules were tested at various irradiation levels. A good agreement between the experimental data and the fitted curve was obtained.
{"title":"System for the electrical characterization of solar cells based on one data acquisition board and C#","authors":"J. Fonseca-Campos, Leonardo Fonseca Ruiz, J. Mata-Machuca","doi":"10.1109/ROPEC.2017.8261626","DOIUrl":"https://doi.org/10.1109/ROPEC.2017.8261626","url":null,"abstract":"Renewable energy has grown extensively in the last decades. The photovoltaic panels have been playing a significant role in the technologies used to produce this type of energy. This tendency is expected to continue in the future, because the cost of the solar panels has been gradually reduced. Systems providing information of the electric performance of the solar cells can be helpful for technicians and researchers. In this paper an inexpensive system that measures the I-V curves of photovoltaic cells is presented. With the experimental data the parameters of the single diode model of the solar cell were estimated implementing the simulated annealing algorithm. Three different solar modules were tested at various irradiation levels. A good agreement between the experimental data and the fitted curve was obtained.","PeriodicalId":260469,"journal":{"name":"2017 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133881877","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 : 2017-11-01DOI: 10.1109/ROPEC.2017.8261584
C. Vásquez, Cristhian Manuel Durán Acevedo
This article presents the analysis and design of a wireless system for the monitoring and detection of diseases through exhaled breath in people; This system comprises of a sampling device which contains a measuring chamber composed of MOS gas sensors, in order to detect the volatile compounds emitted from the breath for biomedical applications. A high-performance wireless data acquisition card sends the obtained signals to a remote terminal, which will incorporate a graphical set of pattern recognition algorithms (e.g PCA) and artificial intelligence for pre-processing and signal processing to find the patterns of the volatile compounds for discrimination and classification.
{"title":"Wireless system for the detection and monitoring of diseases through exhaled breath","authors":"C. Vásquez, Cristhian Manuel Durán Acevedo","doi":"10.1109/ROPEC.2017.8261584","DOIUrl":"https://doi.org/10.1109/ROPEC.2017.8261584","url":null,"abstract":"This article presents the analysis and design of a wireless system for the monitoring and detection of diseases through exhaled breath in people; This system comprises of a sampling device which contains a measuring chamber composed of MOS gas sensors, in order to detect the volatile compounds emitted from the breath for biomedical applications. A high-performance wireless data acquisition card sends the obtained signals to a remote terminal, which will incorporate a graphical set of pattern recognition algorithms (e.g PCA) and artificial intelligence for pre-processing and signal processing to find the patterns of the volatile compounds for discrimination and classification.","PeriodicalId":260469,"journal":{"name":"2017 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124192363","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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