Pub Date : 2020-11-02DOI: 10.1109/IGESSC50231.2020.9285009
N. Sockeel, Daniel Evans, M. Verlohner, J. Gafford, S. Essakiappan, M. Manjrekar, M. Mazzola
As awareness of human global footprint grows, solutions are investigated to reduce greenhouse gas emission. To accomplish this goal, electrification of our society with low carbon intensity has become a primary goal. Such target can be reached through a large deployment of renewable energy sources, as well as transportation electrification. To help this deployment, the development of economically viable, grid tied energy storage system has become crucial to balance energy production and consumption demands as well as reduce infrastructure upgrade costs to electrical grids. In this context, the main contribution of this paper is to evaluate an active cell balancing circuit for a new type of energy storage cells, called the Carbon-ion (C-ion) cells. Those cells have high longevity and high power density capabilities. They have been assembled into eight packs in series of five cells in parallel. Some cycling and charge-hold testing have been performed on those packs with and without the cell balancing circuit, and the performance of the cell balancing circuit has been evaluated. The active cell balancing circuit prevents the pack voltage from deviating more than ± 0.07 volt from the eight packs voltage mean value during dynamic conditions (cycling) as well as static conditions (charge-hold test). Moreover, it helps to reduce the standard deviation (from 180 F to 71 F) of the apparent capacitance from pack to pack.
{"title":"Evaluation of a cell balancing circuit for a new type of high-power density energy storage system","authors":"N. Sockeel, Daniel Evans, M. Verlohner, J. Gafford, S. Essakiappan, M. Manjrekar, M. Mazzola","doi":"10.1109/IGESSC50231.2020.9285009","DOIUrl":"https://doi.org/10.1109/IGESSC50231.2020.9285009","url":null,"abstract":"As awareness of human global footprint grows, solutions are investigated to reduce greenhouse gas emission. To accomplish this goal, electrification of our society with low carbon intensity has become a primary goal. Such target can be reached through a large deployment of renewable energy sources, as well as transportation electrification. To help this deployment, the development of economically viable, grid tied energy storage system has become crucial to balance energy production and consumption demands as well as reduce infrastructure upgrade costs to electrical grids. In this context, the main contribution of this paper is to evaluate an active cell balancing circuit for a new type of energy storage cells, called the Carbon-ion (C-ion) cells. Those cells have high longevity and high power density capabilities. They have been assembled into eight packs in series of five cells in parallel. Some cycling and charge-hold testing have been performed on those packs with and without the cell balancing circuit, and the performance of the cell balancing circuit has been evaluated. The active cell balancing circuit prevents the pack voltage from deviating more than ± 0.07 volt from the eight packs voltage mean value during dynamic conditions (cycling) as well as static conditions (charge-hold test). Moreover, it helps to reduce the standard deviation (from 180 F to 71 F) of the apparent capacitance from pack to pack.","PeriodicalId":437709,"journal":{"name":"2020 IEEE Green Energy and Smart Systems Conference (IGESSC)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134024856","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 : 2020-11-02DOI: 10.1109/igessc50231.2020.9285048
{"title":"IGESSC 2020 TOC","authors":"","doi":"10.1109/igessc50231.2020.9285048","DOIUrl":"https://doi.org/10.1109/igessc50231.2020.9285048","url":null,"abstract":"","PeriodicalId":437709,"journal":{"name":"2020 IEEE Green Energy and Smart Systems Conference (IGESSC)","volume":"32 12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131018239","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 : 2020-11-02DOI: 10.1109/IGESSC50231.2020.9285152
Lucas A. Gutierrez, Samnangdona An, S. Kwon, H. Yeh
6th Generation (6G) wireless communication seriously considers energy efficient systems aligned with the green energy technology. The novel spatial modulation (SM) scheme utilizing the polarization domain is proposed to improve SM demodulation error rate. The cross-polarization discrimination (XPD) can be estimated via incorporating multi-polarized antenna elements at the receiver (Rx). The XPD enhances the performance in classification of antenna indices at the Rx. Further, the practical orthogonal frequency division multiplexing (OFDM) system is considered for SM; subcarrier allocation algorithm utilizing the XPD is proposed with remarkable improvement of the system performance in terms of SM error rate.
{"title":"Novel Approach of Spatial Modulation: Polarization-Aware OFDM Subcarrier Allocation","authors":"Lucas A. Gutierrez, Samnangdona An, S. Kwon, H. Yeh","doi":"10.1109/IGESSC50231.2020.9285152","DOIUrl":"https://doi.org/10.1109/IGESSC50231.2020.9285152","url":null,"abstract":"6th Generation (6G) wireless communication seriously considers energy efficient systems aligned with the green energy technology. The novel spatial modulation (SM) scheme utilizing the polarization domain is proposed to improve SM demodulation error rate. The cross-polarization discrimination (XPD) can be estimated via incorporating multi-polarized antenna elements at the receiver (Rx). The XPD enhances the performance in classification of antenna indices at the Rx. Further, the practical orthogonal frequency division multiplexing (OFDM) system is considered for SM; subcarrier allocation algorithm utilizing the XPD is proposed with remarkable improvement of the system performance in terms of SM error rate.","PeriodicalId":437709,"journal":{"name":"2020 IEEE Green Energy and Smart Systems Conference (IGESSC)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116733833","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 : 2020-11-02DOI: 10.1109/IGESSC50231.2020.9284988
S. Krishnamoorthi, Aroma Macwan, Paul S. Oh, S. Kwon
Wireless technology has witnessed a massive amount of attention in communication domain, not only in industrial field, but in academia field as well. Multiple-input multiple-output (MIMO) systems among them has emerged itself as a promising future technology. Few of the properties of MIMO include diversity, spatial multiplexing, and beamforming. Exploiting these properties can lead us to improve the data rate. Among these properties lies polarization diversity which has large potential to improve the performance in wireless technologies. This paper contributes in investigating and implementing interference-awareness to multiple users with the help of dual polarized MIMO schemes at ±45º. Various factors such as Cross-polarization Discrimination (XPD), Signal to Interference noise ratio or SINR have been viewed that utilizes multi-polarization superposition beamforming (MPS-Beamforming) to optimize the system design. Simulation results are provided which shows improvement of the performance in terms of the Symbol Error Rate (SER) for the users. The polarization diversity with beamforming has high potential for the next generation wireless technologies.
{"title":"Interference-aware Multi-User, Multi-Polarization Superposition Beamforming (MPS-Beamforming)","authors":"S. Krishnamoorthi, Aroma Macwan, Paul S. Oh, S. Kwon","doi":"10.1109/IGESSC50231.2020.9284988","DOIUrl":"https://doi.org/10.1109/IGESSC50231.2020.9284988","url":null,"abstract":"Wireless technology has witnessed a massive amount of attention in communication domain, not only in industrial field, but in academia field as well. Multiple-input multiple-output (MIMO) systems among them has emerged itself as a promising future technology. Few of the properties of MIMO include diversity, spatial multiplexing, and beamforming. Exploiting these properties can lead us to improve the data rate. Among these properties lies polarization diversity which has large potential to improve the performance in wireless technologies. This paper contributes in investigating and implementing interference-awareness to multiple users with the help of dual polarized MIMO schemes at ±45º. Various factors such as Cross-polarization Discrimination (XPD), Signal to Interference noise ratio or SINR have been viewed that utilizes multi-polarization superposition beamforming (MPS-Beamforming) to optimize the system design. Simulation results are provided which shows improvement of the performance in terms of the Symbol Error Rate (SER) for the users. The polarization diversity with beamforming has high potential for the next generation wireless technologies.","PeriodicalId":437709,"journal":{"name":"2020 IEEE Green Energy and Smart Systems Conference (IGESSC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128287309","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 : 2020-11-02DOI: 10.1109/IGESSC50231.2020.9285082
Maurantonio Caprolu, Javier Hernandez Fernandez, Abdulrahman Alassi, Roberto Di Pietro
The amount of energy that can be fed into a network is limited by the physical capacity of the components that form the grid and the load it serves. Electrical utilities use Hosting Capacity (HC) methodologies to analyze and calculate the level of generation that a network can accommodate. Currently, HC analyses are based on conservative static models with the objective of ensuring that technical limitations are not exceeded, hence curbing the optimal network capacity. Demand Response (DR) programs seek to optimize energy resources by lowering or deferring consumption. As a result, technical solutions have focused on reducing demand. These solutions are efficient when there is no local generation of energy, or to reduce individual consumption, but they hamper with feed-in programs. To solve the above introduced issues, in this paper we propose a system with the objective to maximize the limit of the HC of each customer while ensuring stability of the grid. Each customer is assigned a nominal HC value, calculated using the existing methodologies, but has the option of requesting a temporary increase in the feed-in limit. The utility provider will grant or reject the request based on the current conditions of the grid by utilizing the existing smart meter infrastructure and the energy-balancing metering at the transformer substation. The architecture supporting the cited objectives is detailed, a PoC rooted on real experiments is showed, and future directions are also highlighted. Finally, the achieved experimental results show the viability of our proposal.
{"title":"Increasing Renewable Generation Feed-In Capacity Leveraging Smart Meters","authors":"Maurantonio Caprolu, Javier Hernandez Fernandez, Abdulrahman Alassi, Roberto Di Pietro","doi":"10.1109/IGESSC50231.2020.9285082","DOIUrl":"https://doi.org/10.1109/IGESSC50231.2020.9285082","url":null,"abstract":"The amount of energy that can be fed into a network is limited by the physical capacity of the components that form the grid and the load it serves. Electrical utilities use Hosting Capacity (HC) methodologies to analyze and calculate the level of generation that a network can accommodate. Currently, HC analyses are based on conservative static models with the objective of ensuring that technical limitations are not exceeded, hence curbing the optimal network capacity. Demand Response (DR) programs seek to optimize energy resources by lowering or deferring consumption. As a result, technical solutions have focused on reducing demand. These solutions are efficient when there is no local generation of energy, or to reduce individual consumption, but they hamper with feed-in programs. To solve the above introduced issues, in this paper we propose a system with the objective to maximize the limit of the HC of each customer while ensuring stability of the grid. Each customer is assigned a nominal HC value, calculated using the existing methodologies, but has the option of requesting a temporary increase in the feed-in limit. The utility provider will grant or reject the request based on the current conditions of the grid by utilizing the existing smart meter infrastructure and the energy-balancing metering at the transformer substation. The architecture supporting the cited objectives is detailed, a PoC rooted on real experiments is showed, and future directions are also highlighted. Finally, the achieved experimental results show the viability of our proposal.","PeriodicalId":437709,"journal":{"name":"2020 IEEE Green Energy and Smart Systems Conference (IGESSC)","volume":"261 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120985094","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 : 2020-11-02DOI: 10.1109/IGESSC50231.2020.9285079
Sokhom Sim, W. So, H. Yeh
In this paper, we presented the discrete Symlets combining with statistical methodology to extract the High Impedance Fault (HIF) characteristic in the 12 kV Residential Distribution Circuits (RDCs). Based on the experimental data conducted by Southern California Edison (SCE), the results show that the discrete Symlets strategy accompany by statistical methodology is an effective, fast, and accurate method in sensing the HIF behavior for real time applications without heavily rely on the heuristic threshold test as it has been chosen in the previous research projects.
{"title":"A Revisit to HIF Detection in the 12 kV RDCs Using Statistical Methodology and Symlets","authors":"Sokhom Sim, W. So, H. Yeh","doi":"10.1109/IGESSC50231.2020.9285079","DOIUrl":"https://doi.org/10.1109/IGESSC50231.2020.9285079","url":null,"abstract":"In this paper, we presented the discrete Symlets combining with statistical methodology to extract the High Impedance Fault (HIF) characteristic in the 12 kV Residential Distribution Circuits (RDCs). Based on the experimental data conducted by Southern California Edison (SCE), the results show that the discrete Symlets strategy accompany by statistical methodology is an effective, fast, and accurate method in sensing the HIF behavior for real time applications without heavily rely on the heuristic threshold test as it has been chosen in the previous research projects.","PeriodicalId":437709,"journal":{"name":"2020 IEEE Green Energy and Smart Systems Conference (IGESSC)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124217617","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 : 2020-11-02DOI: 10.1109/igessc50231.2020.9285013
{"title":"IGESSC 2020 Breaker Page","authors":"","doi":"10.1109/igessc50231.2020.9285013","DOIUrl":"https://doi.org/10.1109/igessc50231.2020.9285013","url":null,"abstract":"","PeriodicalId":437709,"journal":{"name":"2020 IEEE Green Energy and Smart Systems Conference (IGESSC)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114577417","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 : 2020-11-02DOI: 10.1109/IGESSC50231.2020.9284986
Ibrahim M Ali, Calvin J. Lee, H. Yeh, Sainesh Karan, Yu Yang, Wenlu Zhang, Emily N. Meese, C. Lowe
The aim of this paper is to enhance the performance of K-Nearest Neighbors (K-NN) used to classify data collected from an Acceleration Data Logger (ADL) into four shark behaviors, namely, Resting, Swimming, Feeding, and Non-Directed Motion (NDM). It is shown that using Ensemble Averaging (EA) to improve Signal-to-Noise Ratio (SNR), data resizing to reduce unbalanced samples distribution among behaviors, and other signal processing techniques enhance K-NN F1 Scores.
{"title":"Improvement of Classification of Shark Behaviors using K-Nearest Neighbors","authors":"Ibrahim M Ali, Calvin J. Lee, H. Yeh, Sainesh Karan, Yu Yang, Wenlu Zhang, Emily N. Meese, C. Lowe","doi":"10.1109/IGESSC50231.2020.9284986","DOIUrl":"https://doi.org/10.1109/IGESSC50231.2020.9284986","url":null,"abstract":"The aim of this paper is to enhance the performance of K-Nearest Neighbors (K-NN) used to classify data collected from an Acceleration Data Logger (ADL) into four shark behaviors, namely, Resting, Swimming, Feeding, and Non-Directed Motion (NDM). It is shown that using Ensemble Averaging (EA) to improve Signal-to-Noise Ratio (SNR), data resizing to reduce unbalanced samples distribution among behaviors, and other signal processing techniques enhance K-NN F1 Scores.","PeriodicalId":437709,"journal":{"name":"2020 IEEE Green Energy and Smart Systems Conference (IGESSC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130363567","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 : 2020-11-02DOI: 10.1109/IGESSC50231.2020.9285163
Yang Liu, Jelena Trajkovic, H. Yeh, Wenlu Zhang
There are many factors that affect performance of stock market, such as global and local economy, political events, supply and demand, and out of the ordinary events, as COVID-19 pandemic. The factors may not only influence the stock market movement, but also influence each other. We propose to observe the movement of Dow Jones Industrial Average in relations to daily news. We use top-5 news headlines from Reddit to create 1Day and 5-Day models to predict if Dow Jones Industrial Average movement will be in Down and Up direction from the moment the market opens till it closes. We propose use of shallow (traditional) Machine Learning algorithms and Deep Learning algorithms. Additionally, we explore the effect of word representation, using TF-IDF and GloVE approaches. Moreover, we evaluate our models in terms of accuracy of prediction on data sets containing data before pandemic and during pandemic. Our models show that Deep Learning models uniformly have higher accuracy than Machine Learning ones. Convolution Neural Network with TFIDF and 5 Days prediction performs the best for the dataset before the pandemic with accuracy of 59.6%. Gated Recurrent Unit (GRU), a class of Recurrent Neural Networks, with GloVe and 1 Day prediction outperforms the other models for dataset during the pandemic with the accuracy of 62.9%.
{"title":"Machine Learning for Predicting Stock Market Movement using News Headlines","authors":"Yang Liu, Jelena Trajkovic, H. Yeh, Wenlu Zhang","doi":"10.1109/IGESSC50231.2020.9285163","DOIUrl":"https://doi.org/10.1109/IGESSC50231.2020.9285163","url":null,"abstract":"There are many factors that affect performance of stock market, such as global and local economy, political events, supply and demand, and out of the ordinary events, as COVID-19 pandemic. The factors may not only influence the stock market movement, but also influence each other. We propose to observe the movement of Dow Jones Industrial Average in relations to daily news. We use top-5 news headlines from Reddit to create 1Day and 5-Day models to predict if Dow Jones Industrial Average movement will be in Down and Up direction from the moment the market opens till it closes. We propose use of shallow (traditional) Machine Learning algorithms and Deep Learning algorithms. Additionally, we explore the effect of word representation, using TF-IDF and GloVE approaches. Moreover, we evaluate our models in terms of accuracy of prediction on data sets containing data before pandemic and during pandemic. Our models show that Deep Learning models uniformly have higher accuracy than Machine Learning ones. Convolution Neural Network with TFIDF and 5 Days prediction performs the best for the dataset before the pandemic with accuracy of 59.6%. Gated Recurrent Unit (GRU), a class of Recurrent Neural Networks, with GloVe and 1 Day prediction outperforms the other models for dataset during the pandemic with the accuracy of 62.9%.","PeriodicalId":437709,"journal":{"name":"2020 IEEE Green Energy and Smart Systems Conference (IGESSC)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125655117","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 : 2020-11-02DOI: 10.1109/IGESSC50231.2020.9285011
A. Campos, Fair Aboshehwa, Lusi Li, Wenlu Zhang
In recent years, the advances in high-resolution satellite imagery have led to the popularity of automatic road extraction. However, most existing methods suffer from high computational cost and low efficiency. In this paper, we propose two novel encoder-decoder deep networks to tackle the automatic road extraction problem. The proposed methods integrate Atrous Spatial Pyramid Pooling (ASPP) and Dense Convolutional Network (DenseNet) on Unet. We implement our proposed models on DeepGlobe dataset and Massachusetts road extraction dataset. The experimental results show that our model is computationally efficient and able to effectively extract multi-scale global features and to preserve spatial information from deeper networks.
{"title":"Deep Convolutional Neural Networks for Road Extraction","authors":"A. Campos, Fair Aboshehwa, Lusi Li, Wenlu Zhang","doi":"10.1109/IGESSC50231.2020.9285011","DOIUrl":"https://doi.org/10.1109/IGESSC50231.2020.9285011","url":null,"abstract":"In recent years, the advances in high-resolution satellite imagery have led to the popularity of automatic road extraction. However, most existing methods suffer from high computational cost and low efficiency. In this paper, we propose two novel encoder-decoder deep networks to tackle the automatic road extraction problem. The proposed methods integrate Atrous Spatial Pyramid Pooling (ASPP) and Dense Convolutional Network (DenseNet) on Unet. We implement our proposed models on DeepGlobe dataset and Massachusetts road extraction dataset. The experimental results show that our model is computationally efficient and able to effectively extract multi-scale global features and to preserve spatial information from deeper networks.","PeriodicalId":437709,"journal":{"name":"2020 IEEE Green Energy and Smart Systems Conference (IGESSC)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115235342","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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