Pub Date : 2021-07-01DOI: 10.1109/ICNISC54316.2021.00107
Yajiao Lin, Weili Chen, Haonan Li
As an independent comprehensive system, Electrical Wiring Interconnection System (EWIS) has the characteristics of high degree of integration, complex installation environment, many components selection, huge system data and fast technology refresh and iteration. According to the distinguishing feature of EWIS development, this platform uses Dreamweaver to make the basic framework and modify the page, Apache + PHP + MySQL software architecture to build a dynamic website operation platform, build a life cycle EWIS data integration management platform with friendly visual interface, and integrate the design data such as input, scheme, design process data, design documents and design drawings in the EWIS design process, At the same time, we design a multi person collaborative data transfer module to realize the continuity and iteration of EWIS data.
{"title":"Conception of Building EWIS Data Integration Management Platform","authors":"Yajiao Lin, Weili Chen, Haonan Li","doi":"10.1109/ICNISC54316.2021.00107","DOIUrl":"https://doi.org/10.1109/ICNISC54316.2021.00107","url":null,"abstract":"As an independent comprehensive system, Electrical Wiring Interconnection System (EWIS) has the characteristics of high degree of integration, complex installation environment, many components selection, huge system data and fast technology refresh and iteration. According to the distinguishing feature of EWIS development, this platform uses Dreamweaver to make the basic framework and modify the page, Apache + PHP + MySQL software architecture to build a dynamic website operation platform, build a life cycle EWIS data integration management platform with friendly visual interface, and integrate the design data such as input, scheme, design process data, design documents and design drawings in the EWIS design process, At the same time, we design a multi person collaborative data transfer module to realize the continuity and iteration of EWIS data.","PeriodicalId":396802,"journal":{"name":"2021 7th Annual International Conference on Network and Information Systems for Computers (ICNISC)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127155239","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}
Suggesting personalized tags to the Pumped storage hydropower plants (PSHPs) towards purchase requirements forecasting plays a key role in achieving the smart power grids. However, current tag suggestion solutions only take single sequence into consideration, and predict single label for PSHPs, resulting in suboptimal forecasting accuracy. In this paper, we propose a novel Multi-Sequence Joint Regression (MSJR) model towards the task of PSHP tagging. In particular, MSJR exploits multi-sequence as input for collaborative perception purpose, and a multi-label regression module is built in the MSJR framework to predict tags describing the purchase requirements of PSHPs. Our encouraging experimental results on a real-world dataset, crawled from the ERP system of the State Grid Xin Yuan, validate the superiority of the our MSJR over several existing tagging suggestion methods.
{"title":"Profiling Pumped Storage Power Station via Multi-Sequence Joint Regression","authors":"Wancheng He, Xun Li, Kaitao Zhou, Junheng Huang, Shuang Tang","doi":"10.1109/ICNISC54316.2021.00106","DOIUrl":"https://doi.org/10.1109/ICNISC54316.2021.00106","url":null,"abstract":"Suggesting personalized tags to the Pumped storage hydropower plants (PSHPs) towards purchase requirements forecasting plays a key role in achieving the smart power grids. However, current tag suggestion solutions only take single sequence into consideration, and predict single label for PSHPs, resulting in suboptimal forecasting accuracy. In this paper, we propose a novel Multi-Sequence Joint Regression (MSJR) model towards the task of PSHP tagging. In particular, MSJR exploits multi-sequence as input for collaborative perception purpose, and a multi-label regression module is built in the MSJR framework to predict tags describing the purchase requirements of PSHPs. Our encouraging experimental results on a real-world dataset, crawled from the ERP system of the State Grid Xin Yuan, validate the superiority of the our MSJR over several existing tagging suggestion methods.","PeriodicalId":396802,"journal":{"name":"2021 7th Annual International Conference on Network and Information Systems for Computers (ICNISC)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114056081","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 : 2021-07-01DOI: 10.1109/ICNISC54316.2021.00092
Xiaozhi Du, Yurong Duan, Wei Huang
Extracting architectural elements from Industry Foundation Classes (IFC) files plays an important role on indoor air quality assessment. However, the traditional methods may extract useless instances and miss some necessary information, which results in poor air quality assessment. To address the above issues, this paper proposes an attribute extraction method for air quality assessment from IFC files, called as IFC-AAE. First the instances of the IFC file are preprocessed to remove the redundancies. Next the entity instances related to air quality assessment are extracted and then classified based on floors. Finally, the attribute information of these entities is extracted according to their reference relationship. The experimental results show that the IFC-AAE method is superior than the previous methods. Compared with the IFC file analyzer, the IFC-AEE method generates fewer invalid data. Compared with the Map-based extract method, the IFC-AEE method has an improvement by 4.78% on the precision rate on average.
{"title":"Attribute Information Extracting Method for Air Quality Assessment of Buildings","authors":"Xiaozhi Du, Yurong Duan, Wei Huang","doi":"10.1109/ICNISC54316.2021.00092","DOIUrl":"https://doi.org/10.1109/ICNISC54316.2021.00092","url":null,"abstract":"Extracting architectural elements from Industry Foundation Classes (IFC) files plays an important role on indoor air quality assessment. However, the traditional methods may extract useless instances and miss some necessary information, which results in poor air quality assessment. To address the above issues, this paper proposes an attribute extraction method for air quality assessment from IFC files, called as IFC-AAE. First the instances of the IFC file are preprocessed to remove the redundancies. Next the entity instances related to air quality assessment are extracted and then classified based on floors. Finally, the attribute information of these entities is extracted according to their reference relationship. The experimental results show that the IFC-AAE method is superior than the previous methods. Compared with the IFC file analyzer, the IFC-AEE method generates fewer invalid data. Compared with the Map-based extract method, the IFC-AEE method has an improvement by 4.78% on the precision rate on average.","PeriodicalId":396802,"journal":{"name":"2021 7th Annual International Conference on Network and Information Systems for Computers (ICNISC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122542733","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 : 2021-07-01DOI: 10.1109/ICNISC54316.2021.00174
Chunxia Zhang, Longxue Li, Xudong Li
Chinese character recognition has always attracted much attention in our country and is widely used in our lives and work. The combination of deep learning and neural networks and other algorithms has greatly improved the accuracy and speed. This article reviews the research status and related background of deep learning in the field of Chinese character recognition. First, it introduces the development process of Chinese character recognition and neural network algorithms. Secondly, it describes the Chinese character recognition architecture based on neural network, classifies and outlines the relevant methods of handwritten Chinese character recognition in simple scenarios and text recognition in complex scenarios, explains the construction and characteristics of network models, and analyzes and summarizes the characteristics of each network model. The shortcomings, and finally prospects for future research.
{"title":"A Survey of Chinese Character Recognition Research Based on Deep Learning","authors":"Chunxia Zhang, Longxue Li, Xudong Li","doi":"10.1109/ICNISC54316.2021.00174","DOIUrl":"https://doi.org/10.1109/ICNISC54316.2021.00174","url":null,"abstract":"Chinese character recognition has always attracted much attention in our country and is widely used in our lives and work. The combination of deep learning and neural networks and other algorithms has greatly improved the accuracy and speed. This article reviews the research status and related background of deep learning in the field of Chinese character recognition. First, it introduces the development process of Chinese character recognition and neural network algorithms. Secondly, it describes the Chinese character recognition architecture based on neural network, classifies and outlines the relevant methods of handwritten Chinese character recognition in simple scenarios and text recognition in complex scenarios, explains the construction and characteristics of network models, and analyzes and summarizes the characteristics of each network model. The shortcomings, and finally prospects for future research.","PeriodicalId":396802,"journal":{"name":"2021 7th Annual International Conference on Network and Information Systems for Computers (ICNISC)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121007877","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 : 2021-07-01DOI: 10.1109/ICNISC54316.2021.00137
Hang Li, Yi Lin, Yiyang Duan, K. Si, Peng Li
In complex marine or unpredictable combat environments, there may be unstable and even loss of GPS for the integrated navigation system of unmanned surface vehicles (USVs). This paper analyzes the error model of loose combination navigation, and proposes a method of the recurrent neural network (RNN) aided inertial navigation system (INS). When the GPS signal is interrupted, the RNN is used to correct the navigation error of the INS. Simulation results show that in case of unstable GPS, the RNN-aided navigation system can ensure rapid convergence of error, and the east position error can be kept within a satisfactory scope. Compared with the single inertial navigation system, the navigation accuracy is effectively improved.
{"title":"Integrated Navigation Algorithm for Intelligent USVs with Unstable GPS","authors":"Hang Li, Yi Lin, Yiyang Duan, K. Si, Peng Li","doi":"10.1109/ICNISC54316.2021.00137","DOIUrl":"https://doi.org/10.1109/ICNISC54316.2021.00137","url":null,"abstract":"In complex marine or unpredictable combat environments, there may be unstable and even loss of GPS for the integrated navigation system of unmanned surface vehicles (USVs). This paper analyzes the error model of loose combination navigation, and proposes a method of the recurrent neural network (RNN) aided inertial navigation system (INS). When the GPS signal is interrupted, the RNN is used to correct the navigation error of the INS. Simulation results show that in case of unstable GPS, the RNN-aided navigation system can ensure rapid convergence of error, and the east position error can be kept within a satisfactory scope. Compared with the single inertial navigation system, the navigation accuracy is effectively improved.","PeriodicalId":396802,"journal":{"name":"2021 7th Annual International Conference on Network and Information Systems for Computers (ICNISC)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133990565","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 : 2021-07-01DOI: 10.1109/ICNISC54316.2021.00182
Pengfei Shen, Luke Yan, Yanan Xu, Jiaqing Wu, Ting Cai
The continuous breakthrough of deep learning model in image processing, natural language processing and other fields is mainly due to the strong ability of deep neural network in feature extraction. Based on the idea of capsule neural network, this paper proposes a capsule neural network for general classification problems, and explores the learning ability of capsule network model for classification problems of discrete feature. In order to evaluate the capsule network model, this paper verifies the effect of the model on real datasets, and makes a comparative analysis with common machine learning classification algorithms.
{"title":"Explore the Performance of Capsule Neural Network Learning Discrete Features","authors":"Pengfei Shen, Luke Yan, Yanan Xu, Jiaqing Wu, Ting Cai","doi":"10.1109/ICNISC54316.2021.00182","DOIUrl":"https://doi.org/10.1109/ICNISC54316.2021.00182","url":null,"abstract":"The continuous breakthrough of deep learning model in image processing, natural language processing and other fields is mainly due to the strong ability of deep neural network in feature extraction. Based on the idea of capsule neural network, this paper proposes a capsule neural network for general classification problems, and explores the learning ability of capsule network model for classification problems of discrete feature. In order to evaluate the capsule network model, this paper verifies the effect of the model on real datasets, and makes a comparative analysis with common machine learning classification algorithms.","PeriodicalId":396802,"journal":{"name":"2021 7th Annual International Conference on Network and Information Systems for Computers (ICNISC)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114055008","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 : 2021-07-01DOI: 10.1109/ICNISC54316.2021.00162
Yinghui Wu, Ranran Guo
With the development of information technology, the traditional teaching model has emerged problems such as simply imparting knowledge and failing to meet students' personalized learning. Big data technology will provide richer learning resources, teaching methods and learning styles to drive new changes in education. In the era of big data, the blended teaching mode makes full use of the information-based teaching platform to break the drawbacks of the traditional teaching mode, which is of great value and significance to the reform of China's teaching mode. This paper will explore the specific application of blended teaching based on Chaoxing in practical teaching. As a blended teaching platform and analysis tool, Chaoxing can not only grasp students' learning in time and accurately complete learning evaluation, but also optimize teaching design and expand the time and space for teaching and learning. on this basis, this paper also proposes the application strategies of blended teaching mode such as increasing the strength and depth of integration of information technology and curriculum teaching in the era of big data to realize resource integration and make full use of information technology, strengthening teacher training and enhancing information technology application ability, so as to improve the quality of classroom teaching.
{"title":"Research on the Application and Practice of Blended Teaching Mode in Big Data Era","authors":"Yinghui Wu, Ranran Guo","doi":"10.1109/ICNISC54316.2021.00162","DOIUrl":"https://doi.org/10.1109/ICNISC54316.2021.00162","url":null,"abstract":"With the development of information technology, the traditional teaching model has emerged problems such as simply imparting knowledge and failing to meet students' personalized learning. Big data technology will provide richer learning resources, teaching methods and learning styles to drive new changes in education. In the era of big data, the blended teaching mode makes full use of the information-based teaching platform to break the drawbacks of the traditional teaching mode, which is of great value and significance to the reform of China's teaching mode. This paper will explore the specific application of blended teaching based on Chaoxing in practical teaching. As a blended teaching platform and analysis tool, Chaoxing can not only grasp students' learning in time and accurately complete learning evaluation, but also optimize teaching design and expand the time and space for teaching and learning. on this basis, this paper also proposes the application strategies of blended teaching mode such as increasing the strength and depth of integration of information technology and curriculum teaching in the era of big data to realize resource integration and make full use of information technology, strengthening teacher training and enhancing information technology application ability, so as to improve the quality of classroom teaching.","PeriodicalId":396802,"journal":{"name":"2021 7th Annual International Conference on Network and Information Systems for Computers (ICNISC)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127812149","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 : 2021-07-01DOI: 10.1109/ICNISC54316.2021.00126
Qingbing Ji, Xiaoyan Deng, Lulin Ni
Shadowsocks (SS) is a new popular anonymous communication software in recent years. The traffic generated by SS is very difficult to identify. There is also an enhanced version of SS, called ShadowsocksR(SSR), which can disguise SS traffic as traditional protocol traffic, such as HTTP traffic, TLS traffic, etc. This makes the identification of SS traffic more difficult. In reference [16], an identification method of HTTP camouflaging traffic of SS is proposed for the first time. Here, a new identification method is proposed based on dart algorithm. Compared with reference [16], this method has more types and wider range of SSR obfuscated traffic, and has better identification effect for recent SSR obfuscated traffic, with the accuracy, the recall and the precision are all above 98.5%.
{"title":"Research on ShadowsocksR Traffic Identification Based on DART Algorithm","authors":"Qingbing Ji, Xiaoyan Deng, Lulin Ni","doi":"10.1109/ICNISC54316.2021.00126","DOIUrl":"https://doi.org/10.1109/ICNISC54316.2021.00126","url":null,"abstract":"Shadowsocks (SS) is a new popular anonymous communication software in recent years. The traffic generated by SS is very difficult to identify. There is also an enhanced version of SS, called ShadowsocksR(SSR), which can disguise SS traffic as traditional protocol traffic, such as HTTP traffic, TLS traffic, etc. This makes the identification of SS traffic more difficult. In reference [16], an identification method of HTTP camouflaging traffic of SS is proposed for the first time. Here, a new identification method is proposed based on dart algorithm. Compared with reference [16], this method has more types and wider range of SSR obfuscated traffic, and has better identification effect for recent SSR obfuscated traffic, with the accuracy, the recall and the precision are all above 98.5%.","PeriodicalId":396802,"journal":{"name":"2021 7th Annual International Conference on Network and Information Systems for Computers (ICNISC)","volume":"303 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121154933","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 : 2021-07-01DOI: 10.1109/ICNISC54316.2021.00130
Lei Shao, Heyong Yuan, Xinfeng Wang, Wengang Liu, Qiao Zhang
Aiming at the problem of large deformation and soft fracture of soft rock in deep roadway, through theoretical analysis of deformation and failure characteristics of surrounding rock of deep roadway, the space-time evolution law of deformation and failure of surrounding rock of deep soft rock roadway is obtained by FLAC3D software simulation. The results show that: The deformation of surrounding rock in deep soft rock roadway is characterized by roof subsidence, two sides moving inward and floor bulging. Under the action of high stress, the deformation of surrounding rock of soft rock roadway has a certain timeliness. The deformation and failure of surrounding rock of roadway is a changing process with time. The damage degree of roof, floor and two sides of roadway increases with time, and finally tends to a stable state. The deformation presents a distribution law that the de-formation of floor is larger than that of roof and convergence of two sides.
{"title":"Study on Failure Law of Deformation and Instability of Surrounding Rock in Deep Soft Rock Roadway","authors":"Lei Shao, Heyong Yuan, Xinfeng Wang, Wengang Liu, Qiao Zhang","doi":"10.1109/ICNISC54316.2021.00130","DOIUrl":"https://doi.org/10.1109/ICNISC54316.2021.00130","url":null,"abstract":"Aiming at the problem of large deformation and soft fracture of soft rock in deep roadway, through theoretical analysis of deformation and failure characteristics of surrounding rock of deep roadway, the space-time evolution law of deformation and failure of surrounding rock of deep soft rock roadway is obtained by FLAC3D software simulation. The results show that: The deformation of surrounding rock in deep soft rock roadway is characterized by roof subsidence, two sides moving inward and floor bulging. Under the action of high stress, the deformation of surrounding rock of soft rock roadway has a certain timeliness. The deformation and failure of surrounding rock of roadway is a changing process with time. The damage degree of roof, floor and two sides of roadway increases with time, and finally tends to a stable state. The deformation presents a distribution law that the de-formation of floor is larger than that of roof and convergence of two sides.","PeriodicalId":396802,"journal":{"name":"2021 7th Annual International Conference on Network and Information Systems for Computers (ICNISC)","volume":"12 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115965495","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 : 2021-07-01DOI: 10.1109/ICNISC54316.2021.00183
Kaiwen Zhang, Zhiyang Yu, Liqin Qu
Sea state classification plays the important role in maritime safety, management of marine resources, and dynamic monitoring of sea areas. In this study, ResNetl52 model is used for sea states images classification. The data used in this research is the video data provided by the camera installed on the research vessel Dong Fang Hong III of Ocean University of China. The sea states are divided into ten categories according to the driving conditions of the ship and the undulating conditions of the sea. The results show that this method can classify the images of sea states effectively. This method has implications for follow-up studies of sea states, it can provide the basis for classification for the data processing of self-contained optical measuring instruments.
{"title":"Application of Deep Learning in Sea States Images Classification","authors":"Kaiwen Zhang, Zhiyang Yu, Liqin Qu","doi":"10.1109/ICNISC54316.2021.00183","DOIUrl":"https://doi.org/10.1109/ICNISC54316.2021.00183","url":null,"abstract":"Sea state classification plays the important role in maritime safety, management of marine resources, and dynamic monitoring of sea areas. In this study, ResNetl52 model is used for sea states images classification. The data used in this research is the video data provided by the camera installed on the research vessel Dong Fang Hong III of Ocean University of China. The sea states are divided into ten categories according to the driving conditions of the ship and the undulating conditions of the sea. The results show that this method can classify the images of sea states effectively. This method has implications for follow-up studies of sea states, it can provide the basis for classification for the data processing of self-contained optical measuring instruments.","PeriodicalId":396802,"journal":{"name":"2021 7th Annual International Conference on Network and Information Systems for Computers (ICNISC)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114543201","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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