Pub Date : 2021-12-16DOI: 10.1109/ITEC-India53713.2021.9932488
Fareed Ahmad, A. Iqbal, I. Ashraf, M. Marzband, Irfan Khan
Government organizations and the automotive industry are paying close attention to electric vehicles (EVs) because of their lower CO2 emissions, cheap maintenance, and low operating costs. EVs have a bright future ahead of them and have become increasingly popular as the primary source of new energy, due to which many countries are interested in developing the EV charging infrastructure. The optimal number of charging stations and placement has become a prominent research topic across the world, and these challenges are vital for government planning for EVs. This paper first built a mathematical model to predict the number of EV rapid charging stations (EVRCSs) in the given area. Moreover, a model has been developed to optimize the location of EVRCS in the power distribution network with V2G strategies at the charging stations. The power loss of the distribution network and the transportation cost of EVs from the demand point to EVRCS are considered objective functions for problem formulation. The proposed model for the placement of EVRCS has been tested on the IEEE34 bus system, and power flow was analyzed by using the backward forward sweep algorithm. Results are analyzed for the base case without EVRCS placement, the optimal location of EVRCS without V2G strategies, and the optimal location of EVRCS with V2G strategies.
{"title":"Optimal location of Electric Vehicle Rapid Charging Stations in Power Distribution Network and Transportation Network with V2G Strategies","authors":"Fareed Ahmad, A. Iqbal, I. Ashraf, M. Marzband, Irfan Khan","doi":"10.1109/ITEC-India53713.2021.9932488","DOIUrl":"https://doi.org/10.1109/ITEC-India53713.2021.9932488","url":null,"abstract":"Government organizations and the automotive industry are paying close attention to electric vehicles (EVs) because of their lower CO2 emissions, cheap maintenance, and low operating costs. EVs have a bright future ahead of them and have become increasingly popular as the primary source of new energy, due to which many countries are interested in developing the EV charging infrastructure. The optimal number of charging stations and placement has become a prominent research topic across the world, and these challenges are vital for government planning for EVs. This paper first built a mathematical model to predict the number of EV rapid charging stations (EVRCSs) in the given area. Moreover, a model has been developed to optimize the location of EVRCS in the power distribution network with V2G strategies at the charging stations. The power loss of the distribution network and the transportation cost of EVs from the demand point to EVRCS are considered objective functions for problem formulation. The proposed model for the placement of EVRCS has been tested on the IEEE34 bus system, and power flow was analyzed by using the backward forward sweep algorithm. Results are analyzed for the base case without EVRCS placement, the optimal location of EVRCS without V2G strategies, and the optimal location of EVRCS with V2G strategies.","PeriodicalId":162261,"journal":{"name":"2021 IEEE Transportation Electrification Conference (ITEC-India)","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117227267","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 : 2021-12-16DOI: 10.1109/ITEC-India53713.2021.9932522
Shraddha Gajbhiye, Navita Khatri
In DG unit operation, the Inverter plays a vital role in interfacing energy sources. Effective interfacing energy can successfully be accomplished by operating inverters with effective control techniques. Many researchers have worked on the inverters' control in a microgrid. This study discusses the control method for inverters for proper control of frequency, power sharing and voltage used in an isolated microgrid. The study introduces a control strategy made of the virtual impedance droop control with arctan function as a primary controller, and the current controller is used as a secondary controller in single phase microgrid. Comprehensive simulations have been carried out to approve the proposed control strategy's capability in terms of stabilization of frequency, voltage, and power proportionately among the micro sources in the isolated microgrid.
{"title":"Modified Robust Droop Control Based On Arctan Control Strategy For Proportional Load Sharing Between Parallel Operated Inverters","authors":"Shraddha Gajbhiye, Navita Khatri","doi":"10.1109/ITEC-India53713.2021.9932522","DOIUrl":"https://doi.org/10.1109/ITEC-India53713.2021.9932522","url":null,"abstract":"In DG unit operation, the Inverter plays a vital role in interfacing energy sources. Effective interfacing energy can successfully be accomplished by operating inverters with effective control techniques. Many researchers have worked on the inverters' control in a microgrid. This study discusses the control method for inverters for proper control of frequency, power sharing and voltage used in an isolated microgrid. The study introduces a control strategy made of the virtual impedance droop control with arctan function as a primary controller, and the current controller is used as a secondary controller in single phase microgrid. Comprehensive simulations have been carried out to approve the proposed control strategy's capability in terms of stabilization of frequency, voltage, and power proportionately among the micro sources in the isolated microgrid.","PeriodicalId":162261,"journal":{"name":"2021 IEEE Transportation Electrification Conference (ITEC-India)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121123116","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 : 2021-12-16DOI: 10.1109/ITEC-India53713.2021.9932481
Ahteshamul Haque, V. S. Kurukuru, Mohammed Ali Khan, Syed Mohammad Bilal
This paper proposes a cost-effective and user-oriented solution to the problem of smart charging of Electric Vehicles (EVs) in real-time. The proposed approach considers a decentralized framework where the EV user is autonomous to make their own charging decisions in order of minimizing their operating cost. To model the behavior of the EVs under different scenarios, the dynamic programming along with the Markov decision process is adapted. Further, to make the approach respond to a dynamic environment, and learn from historical time series data, the decision tree machine learning models are developed. The feasibility of the proposed smart charging approach is demonstrated by performing offline optimization and testing with the EV data from real-time and numerical simulation sources. The training process of the smart charging approach depicted 96.2% and the testing accuracy is identified to be 98.8%.
{"title":"Decision-Making Approach for Smart Charging of Electric Vehicles","authors":"Ahteshamul Haque, V. S. Kurukuru, Mohammed Ali Khan, Syed Mohammad Bilal","doi":"10.1109/ITEC-India53713.2021.9932481","DOIUrl":"https://doi.org/10.1109/ITEC-India53713.2021.9932481","url":null,"abstract":"This paper proposes a cost-effective and user-oriented solution to the problem of smart charging of Electric Vehicles (EVs) in real-time. The proposed approach considers a decentralized framework where the EV user is autonomous to make their own charging decisions in order of minimizing their operating cost. To model the behavior of the EVs under different scenarios, the dynamic programming along with the Markov decision process is adapted. Further, to make the approach respond to a dynamic environment, and learn from historical time series data, the decision tree machine learning models are developed. The feasibility of the proposed smart charging approach is demonstrated by performing offline optimization and testing with the EV data from real-time and numerical simulation sources. The training process of the smart charging approach depicted 96.2% and the testing accuracy is identified to be 98.8%.","PeriodicalId":162261,"journal":{"name":"2021 IEEE Transportation Electrification Conference (ITEC-India)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127542608","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 : 2021-12-16DOI: 10.1109/ITEC-India53713.2021.9932484
Sharanabasappa L. Paramoji, Basavaraj N. Pyati
Technology transformation in mobility solution has given electric motors higher attentions. So, it's essential to understand electric motor's thermal behavior to avoid failures and improve cycle efficiency. Its cumbersome to estimate inner components temperature with available testing & simulation methods. In this work, attempt was made to analyze the electric motor sensor data at various load conditions and build a correlation matrix of various parameters. This enabled a good understanding of dependent parameters to predict the rotor and stator temperature. Critical parameters in the data set were segregated and different regression models were investigated. The outcome of Machine Learning models was not satisfactory in terms of accuracy. Hence various Deep Learning models such as ANN, CNN and RNN were considered for further evaluation. Deep Learning Models with hyper parameter tuning technique yielded 95% regression score.
{"title":"Application of AI to Predict PMSM Temperature","authors":"Sharanabasappa L. Paramoji, Basavaraj N. Pyati","doi":"10.1109/ITEC-India53713.2021.9932484","DOIUrl":"https://doi.org/10.1109/ITEC-India53713.2021.9932484","url":null,"abstract":"Technology transformation in mobility solution has given electric motors higher attentions. So, it's essential to understand electric motor's thermal behavior to avoid failures and improve cycle efficiency. Its cumbersome to estimate inner components temperature with available testing & simulation methods. In this work, attempt was made to analyze the electric motor sensor data at various load conditions and build a correlation matrix of various parameters. This enabled a good understanding of dependent parameters to predict the rotor and stator temperature. Critical parameters in the data set were segregated and different regression models were investigated. The outcome of Machine Learning models was not satisfactory in terms of accuracy. Hence various Deep Learning models such as ANN, CNN and RNN were considered for further evaluation. Deep Learning Models with hyper parameter tuning technique yielded 95% regression score.","PeriodicalId":162261,"journal":{"name":"2021 IEEE Transportation Electrification Conference (ITEC-India)","volume":"57 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124088769","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 : 2021-12-16DOI: 10.1109/ITEC-India53713.2021.9932476
J. Kumar, S. Samanta
This paper addresses the bidirectional power flow capability of the current-fed inductive power transfer (IPT) converter. The proposed bi-directional IPT converter has Parallel-Series (P-S) topology during grid-to-vehicle (G2V) and Series-Parallel (S-P) topology during vehicle-to-grid (V2G) mode of operation. Mathematical modeling of both topologies is included that provides the detailed analysis of the converter. The selection of circuit parameters is made for the conventional mode of operation, G2V mode. The bidirectional power flow of the converter is controlled to meet the load requirements. The small-signal modeling and circuit frequency responses are also reported for both modes of operation. The viability of the technique is verified through the simulation results of the PowerSIM simulation platform.
{"title":"Modeling, Design, and Control of the Parallel-Series Compensated Bidirectional IPT Topology for EV Applications","authors":"J. Kumar, S. Samanta","doi":"10.1109/ITEC-India53713.2021.9932476","DOIUrl":"https://doi.org/10.1109/ITEC-India53713.2021.9932476","url":null,"abstract":"This paper addresses the bidirectional power flow capability of the current-fed inductive power transfer (IPT) converter. The proposed bi-directional IPT converter has Parallel-Series (P-S) topology during grid-to-vehicle (G2V) and Series-Parallel (S-P) topology during vehicle-to-grid (V2G) mode of operation. Mathematical modeling of both topologies is included that provides the detailed analysis of the converter. The selection of circuit parameters is made for the conventional mode of operation, G2V mode. The bidirectional power flow of the converter is controlled to meet the load requirements. The small-signal modeling and circuit frequency responses are also reported for both modes of operation. The viability of the technique is verified through the simulation results of the PowerSIM simulation platform.","PeriodicalId":162261,"journal":{"name":"2021 IEEE Transportation Electrification Conference (ITEC-India)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131571886","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 : 2021-12-16DOI: 10.1109/ITEC-India53713.2021.9932485
Tamilarasu S, Deivaraja Ramasamy
Electric vehicles (EVs) have the potential to replace conventional vehicles, but the short driving range and cost is currently limiting their market diffusion. Using analytical methods this paper looks at various losses in EV E/E architecture and proposes an architecture with improvements in efficiency and driving range.
{"title":"A Study of Electric Vehicle EE Power Network Architecture and Considerations to Improve Efficiency","authors":"Tamilarasu S, Deivaraja Ramasamy","doi":"10.1109/ITEC-India53713.2021.9932485","DOIUrl":"https://doi.org/10.1109/ITEC-India53713.2021.9932485","url":null,"abstract":"Electric vehicles (EVs) have the potential to replace conventional vehicles, but the short driving range and cost is currently limiting their market diffusion. Using analytical methods this paper looks at various losses in EV E/E architecture and proposes an architecture with improvements in efficiency and driving range.","PeriodicalId":162261,"journal":{"name":"2021 IEEE Transportation Electrification Conference (ITEC-India)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124380542","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 : 2021-12-16DOI: 10.1109/ITEC-India53713.2021.9932469
Debasish Mishra, Bhim Singh, B. K. Panigrahi
This paper describes a dual battery EV charging system with variable battery voltage operations. A wide range of EVs is now available based upon their operational flexibility and power availability. However, frequent modification in charging architecture and related components based upon the battery voltage diverseness can't be assured. To fulfill the charging requirements for multiple low power EVs, a dual battery charging system with minimal circuit components is described in this paper. A non-linear sliding mode control architecture with direct power control also ensures smooth operation during the bi-directional charging operation. In addition, the renewable and battery energy storage support is also depicted in the charging architecture to make it self-sufficient to operate in standalone mode. The control configuration is designed with MATLAB and the Simulink platform to validate the bi-directional charging operation with a Level-I charging prototype.
{"title":"Comprehensive Analysis of a Modular Dual Battery EV Charging System Architecture to Overcome Grid-Intermittency and Harmonics Distortion","authors":"Debasish Mishra, Bhim Singh, B. K. Panigrahi","doi":"10.1109/ITEC-India53713.2021.9932469","DOIUrl":"https://doi.org/10.1109/ITEC-India53713.2021.9932469","url":null,"abstract":"This paper describes a dual battery EV charging system with variable battery voltage operations. A wide range of EVs is now available based upon their operational flexibility and power availability. However, frequent modification in charging architecture and related components based upon the battery voltage diverseness can't be assured. To fulfill the charging requirements for multiple low power EVs, a dual battery charging system with minimal circuit components is described in this paper. A non-linear sliding mode control architecture with direct power control also ensures smooth operation during the bi-directional charging operation. In addition, the renewable and battery energy storage support is also depicted in the charging architecture to make it self-sufficient to operate in standalone mode. The control configuration is designed with MATLAB and the Simulink platform to validate the bi-directional charging operation with a Level-I charging prototype.","PeriodicalId":162261,"journal":{"name":"2021 IEEE Transportation Electrification Conference (ITEC-India)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128269448","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 : 2021-12-16DOI: 10.1109/ITEC-India53713.2021.9932516
Vineet Kumar Pant, Amitabh Das, M. R. Agrewale, Y. Bhateshvar, K. Vora
The need for obstacle detection is quintessential from the safety point of view for modern smart/autonomous vehicles. The implementation of such technology in farm equipment can lead to further improvements in efficient farming. This necessitates the requirement of a low cost and reliable method for obstacle detection and motion control. To suffice the need, this research work is focused on the development of a perception module using multiple sensors which can act harmoniously in a given scenario. To detect the obstacle, three different sensors are used, providing the distance and feature of the obstacle. The camera is used for object detection and distance measurement using OpenCV deep neural network. As the simultaneous distance measurement is relatively slow and dependent on the environmental conditions pertaining to visibility, a mini Lidar module is used. As the Lidar module has a limited field of view, ultrasonic sensors are used for the detection of obstacles at close range. Data obtained from the system is used to drive commands for the vehicle's motion using a set of actuators controlling the vehicle's motion in terms of acceleration, braking and steering.
{"title":"Obstacle Detection Using Sensor Fusion and Deep Neural Network for Motion Control of Smart Electric Tractor","authors":"Vineet Kumar Pant, Amitabh Das, M. R. Agrewale, Y. Bhateshvar, K. Vora","doi":"10.1109/ITEC-India53713.2021.9932516","DOIUrl":"https://doi.org/10.1109/ITEC-India53713.2021.9932516","url":null,"abstract":"The need for obstacle detection is quintessential from the safety point of view for modern smart/autonomous vehicles. The implementation of such technology in farm equipment can lead to further improvements in efficient farming. This necessitates the requirement of a low cost and reliable method for obstacle detection and motion control. To suffice the need, this research work is focused on the development of a perception module using multiple sensors which can act harmoniously in a given scenario. To detect the obstacle, three different sensors are used, providing the distance and feature of the obstacle. The camera is used for object detection and distance measurement using OpenCV deep neural network. As the simultaneous distance measurement is relatively slow and dependent on the environmental conditions pertaining to visibility, a mini Lidar module is used. As the Lidar module has a limited field of view, ultrasonic sensors are used for the detection of obstacles at close range. Data obtained from the system is used to drive commands for the vehicle's motion using a set of actuators controlling the vehicle's motion in terms of acceleration, braking and steering.","PeriodicalId":162261,"journal":{"name":"2021 IEEE Transportation Electrification Conference (ITEC-India)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132874521","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 : 2021-12-16DOI: 10.1109/ITEC-India53713.2021.9932511
Mudit Khandelwal, Satyajit Banerjee, Ankur Gupta
Model based approach for the design of E/E systems provides an effective and faster methodology in the development and validation of complex systems and algorithms. During the design phase, it is imperative to be able to test the algorithms and its robustness even before the proto ECU is available. In this paper, a rapid-prototyping system & device is presented to simulate the algorithms that require various external factors like road condition, traffic condition and accelerometer data etc. including a connectivity to server for processing. As an example of faster validation life-cycle, this test setup is used for EV range prediction in real time using Internet of things (IoT) and server connectivity. This setup has the potential to be utilized for any other application requiring IoT connectivity to achieve real time simulation or early software validation with faster algorithm development and iteration process.
{"title":"A System & Setup for Faster Model-Based Design Using ECU Rapid Prototyping and an External IoT Connectivity Device","authors":"Mudit Khandelwal, Satyajit Banerjee, Ankur Gupta","doi":"10.1109/ITEC-India53713.2021.9932511","DOIUrl":"https://doi.org/10.1109/ITEC-India53713.2021.9932511","url":null,"abstract":"Model based approach for the design of E/E systems provides an effective and faster methodology in the development and validation of complex systems and algorithms. During the design phase, it is imperative to be able to test the algorithms and its robustness even before the proto ECU is available. In this paper, a rapid-prototyping system & device is presented to simulate the algorithms that require various external factors like road condition, traffic condition and accelerometer data etc. including a connectivity to server for processing. As an example of faster validation life-cycle, this test setup is used for EV range prediction in real time using Internet of things (IoT) and server connectivity. This setup has the potential to be utilized for any other application requiring IoT connectivity to achieve real time simulation or early software validation with faster algorithm development and iteration process.","PeriodicalId":162261,"journal":{"name":"2021 IEEE Transportation Electrification Conference (ITEC-India)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126921160","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 : 2021-12-16DOI: 10.1109/ITEC-India53713.2021.9932536
Aritra Chaudhuri, Saptasrhi Pan, Athisiyaraj Albert, S. Basu
Electrochemical cells and their capacity to retain charge is fundamental in electric transportation. As cells undergoes use, they can hold lesser amount of charge as they age and degrade slowly. In this paper we present a novel machine learning/regression framework to estimate the state of health of a cell and remaining capacity at any time. We compute a partial capacity value for a standard battery dataset, and then build a machine learning based regression model.
{"title":"Battery State of Health Estimation using a Novel Regression Framework","authors":"Aritra Chaudhuri, Saptasrhi Pan, Athisiyaraj Albert, S. Basu","doi":"10.1109/ITEC-India53713.2021.9932536","DOIUrl":"https://doi.org/10.1109/ITEC-India53713.2021.9932536","url":null,"abstract":"Electrochemical cells and their capacity to retain charge is fundamental in electric transportation. As cells undergoes use, they can hold lesser amount of charge as they age and degrade slowly. In this paper we present a novel machine learning/regression framework to estimate the state of health of a cell and remaining capacity at any time. We compute a partial capacity value for a standard battery dataset, and then build a machine learning based regression model.","PeriodicalId":162261,"journal":{"name":"2021 IEEE Transportation Electrification Conference (ITEC-India)","volume":"44 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120897435","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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