Pub Date : 2017-03-01DOI: 10.1109/IAEAC.2017.8054330
Yang Wei, Y. Yang, Wenxi Fei
Simulation of ultrasound pressure fields is representative computationally demanding algorithm. In order to get results back in finite time, the number of source points and observation points are often limited. The introduction of graphics processing unit (GPU) can significantly speed up this algorithm. We implemented the simulator which based on the same principle as the Ultrasim toolbox, where spherical waves responses from several point sources are accumulated in a set of observation points, hence solving the Rayleigh-Sommerfeld integral. For each calculation at a given observation point is independent of the result at all other observation points, the problem is therefore perfect for GPU computing. Compared with Ultrasim we give a 400 times speedup when simulating on 150K observation points.
{"title":"GPU based simulator of ultrasound pressure fields","authors":"Yang Wei, Y. Yang, Wenxi Fei","doi":"10.1109/IAEAC.2017.8054330","DOIUrl":"https://doi.org/10.1109/IAEAC.2017.8054330","url":null,"abstract":"Simulation of ultrasound pressure fields is representative computationally demanding algorithm. In order to get results back in finite time, the number of source points and observation points are often limited. The introduction of graphics processing unit (GPU) can significantly speed up this algorithm. We implemented the simulator which based on the same principle as the Ultrasim toolbox, where spherical waves responses from several point sources are accumulated in a set of observation points, hence solving the Rayleigh-Sommerfeld integral. For each calculation at a given observation point is independent of the result at all other observation points, the problem is therefore perfect for GPU computing. Compared with Ultrasim we give a 400 times speedup when simulating on 150K observation points.","PeriodicalId":432109,"journal":{"name":"2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115565944","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 : 2017-03-01DOI: 10.1109/IAEAC.2017.8054090
Xiaobo Liu, T. Guo, Zhijian Hu, Hongmei Zhu
This paper first briefly introduces several kinds of typical model of electric arc furnace model and its scope of application, including the harmonic voltage source model, energy balance model and the time-varying resistance model, then build the simulation model and analysis its operating characteristics for each model in MTALAB/Simulink simulation. finally, this paper puts forward a kind of electric arc furnace combination model based on different models, combines the advantages of each model and the feasibility of the model is verified by practical example simulation.
{"title":"The simulation research on electric arc furnace combined model based on MATLAB","authors":"Xiaobo Liu, T. Guo, Zhijian Hu, Hongmei Zhu","doi":"10.1109/IAEAC.2017.8054090","DOIUrl":"https://doi.org/10.1109/IAEAC.2017.8054090","url":null,"abstract":"This paper first briefly introduces several kinds of typical model of electric arc furnace model and its scope of application, including the harmonic voltage source model, energy balance model and the time-varying resistance model, then build the simulation model and analysis its operating characteristics for each model in MTALAB/Simulink simulation. finally, this paper puts forward a kind of electric arc furnace combination model based on different models, combines the advantages of each model and the feasibility of the model is verified by practical example simulation.","PeriodicalId":432109,"journal":{"name":"2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116444140","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 : 2017-03-01DOI: 10.1109/IAEAC.2017.8054131
Xuyang Ding, Zhangjian Wu, Wantao Chen, Y. Liu, Ying Xie, Shimin Cai
In big data era, individuals are surrounded by various kinds of social medium, such as Facebook, Twitter and Microblog. These social media produce vast information every day and support diverse social contagions. However, the dynamics and mechanisms of these social contagions are still obscure and unrevealed because of the big data. In this paper, we propose a novel non-Markovian social contagion model to study behavior spreading under the environment of big data, in which a fraction of global individuals can transmit the behavior information to every susceptible individual, and the remaining local individuals can only transmit the behavior information to neighbors. Through extensive numerical simulations, we find that the global individuals markedly promote the behavior spreading and decrease the critical information transmission probability. In addition, we note that the degree heterogeneity of social network does not change the phenomena qualitatively. Our results may shed some lights in predicting and controlling social contagions. In further, the proposed model may be applied in real simulation platforms for emergency management in big data era.
{"title":"Modeling complex social contagions in big data era","authors":"Xuyang Ding, Zhangjian Wu, Wantao Chen, Y. Liu, Ying Xie, Shimin Cai","doi":"10.1109/IAEAC.2017.8054131","DOIUrl":"https://doi.org/10.1109/IAEAC.2017.8054131","url":null,"abstract":"In big data era, individuals are surrounded by various kinds of social medium, such as Facebook, Twitter and Microblog. These social media produce vast information every day and support diverse social contagions. However, the dynamics and mechanisms of these social contagions are still obscure and unrevealed because of the big data. In this paper, we propose a novel non-Markovian social contagion model to study behavior spreading under the environment of big data, in which a fraction of global individuals can transmit the behavior information to every susceptible individual, and the remaining local individuals can only transmit the behavior information to neighbors. Through extensive numerical simulations, we find that the global individuals markedly promote the behavior spreading and decrease the critical information transmission probability. In addition, we note that the degree heterogeneity of social network does not change the phenomena qualitatively. Our results may shed some lights in predicting and controlling social contagions. In further, the proposed model may be applied in real simulation platforms for emergency management in big data era.","PeriodicalId":432109,"journal":{"name":"2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122651888","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 : 2017-03-01DOI: 10.1109/IAEAC.2017.8054398
Jingshan Li, Caikou Chen, Xielian Hou, Tianchen Dai, Rong Wang
In the calculation of rank minimization, the non-negative sparse low-rank representation classification (NSLRRC) regularizes nuclear norm's each singular value equally, but this limits its flexibility and ability to solve many practical problems, where the singular values with clear physical meanings ought to be treated differently. In this paper, a weighted non-negative sparse low-rank representation classification method (WNSLRRC) is proposed for robust face recognition. Our method adaptively assigns weights, which provides additional discriminating ability to the original non-negative sparse low-rank models for improved performance, on different singular values. Our method is able to assess the test sample and correct classification based on class-specific reconstruction residuals. Experimental results on public face databases testify the robustness and effectiveness of our method in face recognition. Those also show that our method outperforms other state-of-the-art methods.
{"title":"Weighted non-negative sparse low-rank representation classification","authors":"Jingshan Li, Caikou Chen, Xielian Hou, Tianchen Dai, Rong Wang","doi":"10.1109/IAEAC.2017.8054398","DOIUrl":"https://doi.org/10.1109/IAEAC.2017.8054398","url":null,"abstract":"In the calculation of rank minimization, the non-negative sparse low-rank representation classification (NSLRRC) regularizes nuclear norm's each singular value equally, but this limits its flexibility and ability to solve many practical problems, where the singular values with clear physical meanings ought to be treated differently. In this paper, a weighted non-negative sparse low-rank representation classification method (WNSLRRC) is proposed for robust face recognition. Our method adaptively assigns weights, which provides additional discriminating ability to the original non-negative sparse low-rank models for improved performance, on different singular values. Our method is able to assess the test sample and correct classification based on class-specific reconstruction residuals. Experimental results on public face databases testify the robustness and effectiveness of our method in face recognition. Those also show that our method outperforms other state-of-the-art methods.","PeriodicalId":432109,"journal":{"name":"2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122499807","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 : 2017-03-01DOI: 10.1109/IAEAC.2017.8054264
L. Wei, Tao Limin, Ji Zhengnan
In order to compensate 6-DOF relative motion between the ship and large size or weight containers in replenishment operation at sea, a 6-DOF active wave compensation cable-drive parallel robot with anti-swing was designed. It could compensate the 6-DOF motion of cargo and maintain the cargo balance in replenishment operation. The structure and working principle of the cable-drive parallel robot was introduced. The kinematics and dynamics model was established. Swing inhibition principle of the robot was elaborated. The relationship between the robot size and anti-swing workspace was revealed. At last, the simulation study of anti-swing workspace was realized.
{"title":"Research on anti-swing characteristic of redundancy cable-driven parallel robot","authors":"L. Wei, Tao Limin, Ji Zhengnan","doi":"10.1109/IAEAC.2017.8054264","DOIUrl":"https://doi.org/10.1109/IAEAC.2017.8054264","url":null,"abstract":"In order to compensate 6-DOF relative motion between the ship and large size or weight containers in replenishment operation at sea, a 6-DOF active wave compensation cable-drive parallel robot with anti-swing was designed. It could compensate the 6-DOF motion of cargo and maintain the cargo balance in replenishment operation. The structure and working principle of the cable-drive parallel robot was introduced. The kinematics and dynamics model was established. Swing inhibition principle of the robot was elaborated. The relationship between the robot size and anti-swing workspace was revealed. At last, the simulation study of anti-swing workspace was realized.","PeriodicalId":432109,"journal":{"name":"2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)","volume":"138 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114249878","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 : 2017-03-01DOI: 10.1109/IAEAC.2017.8054001
Yunong Zhang, Min Yang, Maotai Zou, Sheng Wu, Binbin Qiu
Static Euclid division has been studied for many decades. In this paper, we focus on time-varying division (or termed, Zhang division). As Zhang dynamics (ZD) and gradient dynamics (GD) have shown powerful abilities to solve a great variety of time-varying problems, we present a ZD model and a GD model for time-varying division by defining a Zhang function and an energy function, respectively. Through illustrative examples, the efficacy of the presented ZD and GD models for online solution of time-varying division is substantiated effectively. In addition, division-by-zero (DBZ) problem is solved readily in this paper, which is well understood.
{"title":"From Euclid division of constant integers to Zhang division of time-varying variables","authors":"Yunong Zhang, Min Yang, Maotai Zou, Sheng Wu, Binbin Qiu","doi":"10.1109/IAEAC.2017.8054001","DOIUrl":"https://doi.org/10.1109/IAEAC.2017.8054001","url":null,"abstract":"Static Euclid division has been studied for many decades. In this paper, we focus on time-varying division (or termed, Zhang division). As Zhang dynamics (ZD) and gradient dynamics (GD) have shown powerful abilities to solve a great variety of time-varying problems, we present a ZD model and a GD model for time-varying division by defining a Zhang function and an energy function, respectively. Through illustrative examples, the efficacy of the presented ZD and GD models for online solution of time-varying division is substantiated effectively. In addition, division-by-zero (DBZ) problem is solved readily in this paper, which is well understood.","PeriodicalId":432109,"journal":{"name":"2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114478925","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 : 2017-03-01DOI: 10.1109/IAEAC.2017.8054062
Kai Zheng, Gangquan Si, Zhou Zhou, Jiaxi Chen, Wenmeng Yue
Focusing on the possibility for observed results with false or erroneous information of sensors which may affect data fusion estimation, a new algorithm is proposed based on self-support degree and hypothesis testing. It is significant to take the consistent test method to identify and remove the observations of failed sensors by checking on the observed results and then the data fusion algorithm could be used for estimation effectively. Based on posteriori probability used in consistent test and the knowledge of hypothesis testing, we regard the problem of consistency test as the hypothesis testing of the difference between two population means. Meantime, the multi-valued problem of multiple sensors is researched. Based on the observed values of different time, each sensor's consistency value of different time about hypothesis testing can be obtained. The results of simulation show the simplicity and effectiveness of the new method based on self-support degree and hypothesis Testing for evaluating the quality of each sensor observations, identifying false and erroneous observed results and providing data fusion estimation with the reliable consistent sensor group.
{"title":"Consistency test based on self-support degree and hypothesis testing for multi-sensor data fusion","authors":"Kai Zheng, Gangquan Si, Zhou Zhou, Jiaxi Chen, Wenmeng Yue","doi":"10.1109/IAEAC.2017.8054062","DOIUrl":"https://doi.org/10.1109/IAEAC.2017.8054062","url":null,"abstract":"Focusing on the possibility for observed results with false or erroneous information of sensors which may affect data fusion estimation, a new algorithm is proposed based on self-support degree and hypothesis testing. It is significant to take the consistent test method to identify and remove the observations of failed sensors by checking on the observed results and then the data fusion algorithm could be used for estimation effectively. Based on posteriori probability used in consistent test and the knowledge of hypothesis testing, we regard the problem of consistency test as the hypothesis testing of the difference between two population means. Meantime, the multi-valued problem of multiple sensors is researched. Based on the observed values of different time, each sensor's consistency value of different time about hypothesis testing can be obtained. The results of simulation show the simplicity and effectiveness of the new method based on self-support degree and hypothesis Testing for evaluating the quality of each sensor observations, identifying false and erroneous observed results and providing data fusion estimation with the reliable consistent sensor group.","PeriodicalId":432109,"journal":{"name":"2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122095520","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 : 2017-03-01DOI: 10.1109/IAEAC.2017.8054469
Yugang Shan, Jiabao Wang, Feng Hao
In order to improve the accuracy and efficiency of multi-model switching Camshift head tracking, an adaptive multi-model switching Camshift head tracking method is proposed. This paper first analyzes the advantages and disadvantages of multi-model switching and multi-model combination, then presents the multi-feature description method of the object. Next, using the Bhattacharyya coefficient as the model switching condition, the update time is determined according to the switching threshold. When exceeding the switching threshold, Bhattacharyya coefficient are calculated by the various models, choosing the maximal similarity model as the object model. Image sequences are tested in the public library, the experimental results show that this algorithm can be implemented for long time head motion image sequence in the case of head translation and rotation with anti-jamming and anti-blocking. By comparing and analyzing the multiple features and RGB multi-model switching algorithm, we can get the conclusion that the proposed algorithm is superior to the latter in stability and accuracy.
{"title":"Camshift head tracking based on adaptive multi-model switching","authors":"Yugang Shan, Jiabao Wang, Feng Hao","doi":"10.1109/IAEAC.2017.8054469","DOIUrl":"https://doi.org/10.1109/IAEAC.2017.8054469","url":null,"abstract":"In order to improve the accuracy and efficiency of multi-model switching Camshift head tracking, an adaptive multi-model switching Camshift head tracking method is proposed. This paper first analyzes the advantages and disadvantages of multi-model switching and multi-model combination, then presents the multi-feature description method of the object. Next, using the Bhattacharyya coefficient as the model switching condition, the update time is determined according to the switching threshold. When exceeding the switching threshold, Bhattacharyya coefficient are calculated by the various models, choosing the maximal similarity model as the object model. Image sequences are tested in the public library, the experimental results show that this algorithm can be implemented for long time head motion image sequence in the case of head translation and rotation with anti-jamming and anti-blocking. By comparing and analyzing the multiple features and RGB multi-model switching algorithm, we can get the conclusion that the proposed algorithm is superior to the latter in stability and accuracy.","PeriodicalId":432109,"journal":{"name":"2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129854003","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}
One of the important advantages of RFID technology is to identify multiple targets at the same time. However, in order to identify multi-object at the same time, it is necessary to solve the problem of improving the performance of tag reading. Among the factors affecting the performance of tag identification, the geometric distribution of multi-tag is the key one. With the advantage of GA-BP neural network in optimization analysis, we do some researches about the impacts of the multi-tag's geometric distribution to the performance of reader. By training a large number of dynamic test data under the gate entrance environment, optimal RFID tag geometric distribution can be predicted by GA-BP neural network under the maximum or minimum reading distance. Furthermore, the dynamic reading performance of multi-tag system could be effectively improved.
{"title":"Optimization analysis of distribution of RFID multi-tag based on GA-BP neural network","authors":"Yujun Zhou, Donghua Wang, Xiao Zhuang, Xiaolei Yu, Zhimin Zhao, Yinshan Yu","doi":"10.1109/IAEAC.2017.8054135","DOIUrl":"https://doi.org/10.1109/IAEAC.2017.8054135","url":null,"abstract":"One of the important advantages of RFID technology is to identify multiple targets at the same time. However, in order to identify multi-object at the same time, it is necessary to solve the problem of improving the performance of tag reading. Among the factors affecting the performance of tag identification, the geometric distribution of multi-tag is the key one. With the advantage of GA-BP neural network in optimization analysis, we do some researches about the impacts of the multi-tag's geometric distribution to the performance of reader. By training a large number of dynamic test data under the gate entrance environment, optimal RFID tag geometric distribution can be predicted by GA-BP neural network under the maximum or minimum reading distance. Furthermore, the dynamic reading performance of multi-tag system could be effectively improved.","PeriodicalId":432109,"journal":{"name":"2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128481760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The actual multi-agent system in a variety of complex network environment, the agent itself may be due to external attack led to the suspension of local exchange of information, The local information exchange between agents is also due to transmit and receive information associated with the communication time delay. In this paper, we study degree distribution entropy and the average two-step degree not only can be used as a measure of the network survivability but also can help to optimize the network survivability. Than degree distribution entropy, the average two-step degree as a complex network heterogeneity measure not only contains the diversity of degree distribution information, and contains the information of network topology. Therefore, the average two-step degree is a better measures of the heterogeneity of complex networks. In addition we can also use the most commonly used method, namely by protecting key nodes increasing network survivability. Also can increase the link redundancy between the key nodes, improve network survivability.
{"title":"Based on the research of complex network invulnerability","authors":"Jiaming Zhu, Yuan Jiang, Ping Zhao, Fujin Jia, Shengwei Miao","doi":"10.1109/IAEAC.2017.8054359","DOIUrl":"https://doi.org/10.1109/IAEAC.2017.8054359","url":null,"abstract":"The actual multi-agent system in a variety of complex network environment, the agent itself may be due to external attack led to the suspension of local exchange of information, The local information exchange between agents is also due to transmit and receive information associated with the communication time delay. In this paper, we study degree distribution entropy and the average two-step degree not only can be used as a measure of the network survivability but also can help to optimize the network survivability. Than degree distribution entropy, the average two-step degree as a complex network heterogeneity measure not only contains the diversity of degree distribution information, and contains the information of network topology. Therefore, the average two-step degree is a better measures of the heterogeneity of complex networks. In addition we can also use the most commonly used method, namely by protecting key nodes increasing network survivability. Also can increase the link redundancy between the key nodes, improve network survivability.","PeriodicalId":432109,"journal":{"name":"2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128561368","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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