Track-Before-Detection (TBD) technology can effectively improve the detection and track performance of weak targets by exchanging energy through time. However, the target with glint effect and angular noise is easy to disappear in more than one of frames. To solve this problem, this paper proposes an improved TBD algorithm based on dynamic neighborhood search. It uses the dynamic neighborhood to keep tracking targets, which needs less computational load than the traditional TBD algorithm. The algorithm is validated by simulation scenes with real target data. The results show that this proposed algorithm can improve the performance of tracking weak targets at a low Signal to Noise Ratio (SNR) about -5dB and it can effectively detect and track those targets with glint effect and angular noise.
{"title":"An Improved Track-before-Detection Algorithm Based on Dynamic Neighborhood Search","authors":"Rui Ni, C. Fan, Xiaotao Huang, Hanhua Zhang","doi":"10.1109/CIIS.2017.31","DOIUrl":"https://doi.org/10.1109/CIIS.2017.31","url":null,"abstract":"Track-Before-Detection (TBD) technology can effectively improve the detection and track performance of weak targets by exchanging energy through time. However, the target with glint effect and angular noise is easy to disappear in more than one of frames. To solve this problem, this paper proposes an improved TBD algorithm based on dynamic neighborhood search. It uses the dynamic neighborhood to keep tracking targets, which needs less computational load than the traditional TBD algorithm. The algorithm is validated by simulation scenes with real target data. The results show that this proposed algorithm can improve the performance of tracking weak targets at a low Signal to Noise Ratio (SNR) about -5dB and it can effectively detect and track those targets with glint effect and angular noise.","PeriodicalId":254342,"journal":{"name":"2017 International Conference on Computing Intelligence and Information System (CIIS)","volume":"170 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133305553","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 derivatives which have the property of the high leverage have become popular tools for investing in the era with a low interest rate. Among these derivatives, the options are considered a simpler way for investing since the Black-Scholes (B-S) pricing model can be used to estimate their reasonable prices. However, the parameter "volatility ± in the B-S model cannot be known in advance and needs be guessed based on the historical trading information regarding the options or the underlying assets. Hence, the problems of forecasting future volatilities had become an interesting and attractive research topic for both researchers and practioners. Among the previous researches, the artificial intelligent techniques had been extensively used and acquired satisfactory results. Therefore, the support vector regression (SVR) technique and krill herd (KH) optimization algorithm are utilized to develop an integrated approach for forecasting the volatilities more accurately in this study. The proposed approach is demonstrated by a case study aiming at forecasting the volatilities of the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) to verify its feasibility and effectiveness. According to the experimental results, the proposed integrated forecasting methodology can produce better forecasting performance, based on the RMSE, R2 or MAPE, than the forecasting models which are built solely based on the SVR. Therefore, it can conclude that the proposed integrated approach can really indeed improve the forecasting, and can be considered an effective and useful assistant tool for an investor to obtain more accurate estimation for the volatility thus helping his/her decision making.
{"title":"Forecasting the Stock Price Volatilities by Integratingthe Support Vector Regression and the Krill Herd Algorithm","authors":"Chih-Chen Hsu","doi":"10.1109/CIIS.2017.16","DOIUrl":"https://doi.org/10.1109/CIIS.2017.16","url":null,"abstract":"The derivatives which have the property of the high leverage have become popular tools for investing in the era with a low interest rate. Among these derivatives, the options are considered a simpler way for investing since the Black-Scholes (B-S) pricing model can be used to estimate their reasonable prices. However, the parameter \"volatility ± in the B-S model cannot be known in advance and needs be guessed based on the historical trading information regarding the options or the underlying assets. Hence, the problems of forecasting future volatilities had become an interesting and attractive research topic for both researchers and practioners. Among the previous researches, the artificial intelligent techniques had been extensively used and acquired satisfactory results. Therefore, the support vector regression (SVR) technique and krill herd (KH) optimization algorithm are utilized to develop an integrated approach for forecasting the volatilities more accurately in this study. The proposed approach is demonstrated by a case study aiming at forecasting the volatilities of the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) to verify its feasibility and effectiveness. According to the experimental results, the proposed integrated forecasting methodology can produce better forecasting performance, based on the RMSE, R2 or MAPE, than the forecasting models which are built solely based on the SVR. Therefore, it can conclude that the proposed integrated approach can really indeed improve the forecasting, and can be considered an effective and useful assistant tool for an investor to obtain more accurate estimation for the volatility thus helping his/her decision making.","PeriodicalId":254342,"journal":{"name":"2017 International Conference on Computing Intelligence and Information System (CIIS)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123210348","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 scheduling of chip sorting operation of LED manufacturing can be treated as a four-stage multiprocessor open shop problem, where each lot (job) with release time have four operations to be processed on a set of processing stages without predetermined necessary route. Each stage has one and more identical sorting machines. For the problem, we considered two objectives of minimizing makespan and total weighted tardiness simultaneously and proposed two simulated annealing (SA) methods to find out near pareto-optimal solutions. A series of computational experiments are conducted to evaluate the performance of the proposed SAs in comparison with exact solutions on various small-size problem instances. For large problems, the proposed SAs are also compared. The results show that performance of the SA1 algorithm was better than the SA2 whatever the test problems are small-size or large-size.
{"title":"A Bi-criterion Simulated Annealing Method to Solve Four-Stage Multiprocessor Open Shops with Dynamic Job Release Time","authors":"Yachih Tsai Huimei Wang, F. Chou","doi":"10.1109/CIIS.2017.61","DOIUrl":"https://doi.org/10.1109/CIIS.2017.61","url":null,"abstract":"The scheduling of chip sorting operation of LED manufacturing can be treated as a four-stage multiprocessor open shop problem, where each lot (job) with release time have four operations to be processed on a set of processing stages without predetermined necessary route. Each stage has one and more identical sorting machines. For the problem, we considered two objectives of minimizing makespan and total weighted tardiness simultaneously and proposed two simulated annealing (SA) methods to find out near pareto-optimal solutions. A series of computational experiments are conducted to evaluate the performance of the proposed SAs in comparison with exact solutions on various small-size problem instances. For large problems, the proposed SAs are also compared. The results show that performance of the SA1 algorithm was better than the SA2 whatever the test problems are small-size or large-size.","PeriodicalId":254342,"journal":{"name":"2017 International Conference on Computing Intelligence and Information System (CIIS)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115919794","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}
Though quality control is implemented at manufacturing side, it is still unavoidable to ship defective products not detected to the customers. When defective products are detected at customer side, the product returning processes will correspondingly activate. However, it leads to the increase of operating costs that manufacturer side should make a certain amount of stock to solve the situation of returning products. Accordingly, the tradeoff is between over-stock and under-stock to balance the cost-cutting and customer satisfaction. The grey models (GMs) are widely applied in short-term time series data prediction; though, the improvement of prediction still exists. Hence, this research reveals a new GM, which employs the mega-trend-diffusion technique, to estimate the background values in the traditional GM. A case is studied from a leading TFT-LCD company in Taiwan. Comparing with two GM models, the proposed model outperformed concerning the case data.
{"title":"Solving the Stock Preparing Problem in Return Materials Authorization Process in TFT-LCD Industry with MTD-based Grey Model","authors":"Chun-Pei Yeh, Tu-hsin Tsai, Che-Wei Chang, I-Hsiang Wen","doi":"10.1109/CIIS.2017.45","DOIUrl":"https://doi.org/10.1109/CIIS.2017.45","url":null,"abstract":"Though quality control is implemented at manufacturing side, it is still unavoidable to ship defective products not detected to the customers. When defective products are detected at customer side, the product returning processes will correspondingly activate. However, it leads to the increase of operating costs that manufacturer side should make a certain amount of stock to solve the situation of returning products. Accordingly, the tradeoff is between over-stock and under-stock to balance the cost-cutting and customer satisfaction. The grey models (GMs) are widely applied in short-term time series data prediction; though, the improvement of prediction still exists. Hence, this research reveals a new GM, which employs the mega-trend-diffusion technique, to estimate the background values in the traditional GM. A case is studied from a leading TFT-LCD company in Taiwan. Comparing with two GM models, the proposed model outperformed concerning the case data.","PeriodicalId":254342,"journal":{"name":"2017 International Conference on Computing Intelligence and Information System (CIIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130820780","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}
A novel path planning method based on particle swarm optimization (PSO) is proposed to achieve cooperative formation cruise for multi-autonomous underwater vehicles (AUV). In particular, inspired by the virtual structure approach, particle in PSO is defined as a set of cooperative routes. These routes are composed by a series of navigation points including the initial points and the target points of corresponding vehicles. Given these navigation points, the optimization can be carried out in the search space described by vectors. By designing a reasonable cost function and a particle updating strategy, the method successfully coordinates the time and space of vehicles before vehicles arrived the formation-constrained positions, and simultaneously, preserves the formation constraint and avoids obstacles during the navigation, which provides a new perspective to address the cooperative path planning problem with a formation constraint. The feasibility and effectiveness of the proposed method are validated by experiments.
{"title":"A Formation-Constrained Cooperative Path Planning Method for Multi-autonomous Underwater Vehicles","authors":"Shuangshuang Du, C. Cai, Houjun Wang, Dongwu Li","doi":"10.1109/CIIS.2017.51","DOIUrl":"https://doi.org/10.1109/CIIS.2017.51","url":null,"abstract":"A novel path planning method based on particle swarm optimization (PSO) is proposed to achieve cooperative formation cruise for multi-autonomous underwater vehicles (AUV). In particular, inspired by the virtual structure approach, particle in PSO is defined as a set of cooperative routes. These routes are composed by a series of navigation points including the initial points and the target points of corresponding vehicles. Given these navigation points, the optimization can be carried out in the search space described by vectors. By designing a reasonable cost function and a particle updating strategy, the method successfully coordinates the time and space of vehicles before vehicles arrived the formation-constrained positions, and simultaneously, preserves the formation constraint and avoids obstacles during the navigation, which provides a new perspective to address the cooperative path planning problem with a formation constraint. The feasibility and effectiveness of the proposed method are validated by experiments.","PeriodicalId":254342,"journal":{"name":"2017 International Conference on Computing Intelligence and Information System (CIIS)","volume":"300 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127073280","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 problem of relay channel (RC) with noncausal channel state information (CSI) available at the relay is considered. With the CSI, the relay can help communication in two ways: 1) by relaying message information; 2) by conveying the CSI to help the destination decode. In previous work, Zaidi et al. established a lower bound by letting the relay send message information by performing Gelfand-Pinsker (GP) coding. While in our schemes, we combine the two ways by letting the relay send message information as well as the compressed CSI to the receivers. We investigate three decodeforward (DF) lower bounds. The first two bounds are obtained by transmitting the message information and the CSI directly. We find this scheme is surprisingly equivalent to Zaidi's. The third bound is established by exploiting a new coding scheme. We show that the third bound is tighter than others. We show our optimal scheme for a class of deterministic SD-RC and give the capacity. We also generalize RC to discrete memoryless multicast network (DM-MN). We characterize the DF lower bound for DM-MN with noncausal CSI only at the relay nodes.
{"title":"Causally and Noncausally Information Bound in Discrete Memoryless Multicast Relay Network","authors":"Ou Wang, Dajin Wang, Jie Shen, Jianguo Yu","doi":"10.1109/CIIS.2017.22","DOIUrl":"https://doi.org/10.1109/CIIS.2017.22","url":null,"abstract":"The problem of relay channel (RC) with noncausal channel state information (CSI) available at the relay is considered. With the CSI, the relay can help communication in two ways: 1) by relaying message information; 2) by conveying the CSI to help the destination decode. In previous work, Zaidi et al. established a lower bound by letting the relay send message information by performing Gelfand-Pinsker (GP) coding. While in our schemes, we combine the two ways by letting the relay send message information as well as the compressed CSI to the receivers. We investigate three decodeforward (DF) lower bounds. The first two bounds are obtained by transmitting the message information and the CSI directly. We find this scheme is surprisingly equivalent to Zaidi's. The third bound is established by exploiting a new coding scheme. We show that the third bound is tighter than others. We show our optimal scheme for a class of deterministic SD-RC and give the capacity. We also generalize RC to discrete memoryless multicast network (DM-MN). We characterize the DF lower bound for DM-MN with noncausal CSI only at the relay nodes.","PeriodicalId":254342,"journal":{"name":"2017 International Conference on Computing Intelligence and Information System (CIIS)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126547642","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}
In the task of entity attribute knowledge base construction, each attribute has many candidate value words. It is impossible to get these candidate value words manually. This paper proposed a template based attribute value words acquisition method, which could acquire the candidate value words of attribute from real corpus automatically. The acquisition of candidate value words were driven by attribute. The candidate value words could be obtained in the context which described the attribute. The expression of a particular text generally had a fixed form. The form could be extracted as a template for the acquisition of candidate value words of attribute. This paper gave the definition of the template and proposed an automatic template generating method. Based on the templates, this paper proposed a procedure of attribute value words acquisition which was constrained by the known attribute words. It extracted attribute value words from annotated corpus by the method of the template instantiation. According to the relationship between attribute word and attribute value word in the template, the relation-net of attribute words and attribute value words could be organized directly. The experimental result showed that the proposed method performance well on the real corpus.
{"title":"Template Based Attribute Value Words Acquisition in Entity Attribute Knowledge Base Construction","authors":"Honglin Wu, Ruo-yi Zhou, Ke Wang","doi":"10.1109/CIIS.2017.66","DOIUrl":"https://doi.org/10.1109/CIIS.2017.66","url":null,"abstract":"In the task of entity attribute knowledge base construction, each attribute has many candidate value words. It is impossible to get these candidate value words manually. This paper proposed a template based attribute value words acquisition method, which could acquire the candidate value words of attribute from real corpus automatically. The acquisition of candidate value words were driven by attribute. The candidate value words could be obtained in the context which described the attribute. The expression of a particular text generally had a fixed form. The form could be extracted as a template for the acquisition of candidate value words of attribute. This paper gave the definition of the template and proposed an automatic template generating method. Based on the templates, this paper proposed a procedure of attribute value words acquisition which was constrained by the known attribute words. It extracted attribute value words from annotated corpus by the method of the template instantiation. According to the relationship between attribute word and attribute value word in the template, the relation-net of attribute words and attribute value words could be organized directly. The experimental result showed that the proposed method performance well on the real corpus.","PeriodicalId":254342,"journal":{"name":"2017 International Conference on Computing Intelligence and Information System (CIIS)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124757852","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}
In the past few decades, there were quite a few learning algorithms developed to extract knowledge from data. However, none of the single algorithms can be applicable to learn all the datasets with favor results because data patterns may represent linear and non-linear. Accordingly, the idea of aggregating the predictions of multiple learning models to improve the forecasting accuracy of a single method was proposed. Nevertheless, how to improve the accuracy of the aggregated predictions when learning small datasets is the objective of this study. Based on the distributions of the predictive errors of learning models, the proposed method learns the weights of the models and then tries to aggregate more precise predictions with the weights. The experiment results show the forecasting errors of the predictions aggregated by the proposed method are significantly lower than the predictions of single models and the averaged predictions.
{"title":"The Prediction Aggregating Procedure for Multi-models In Small Dataset Learning","authors":"Yao-San Lin, Liang-Sian Lin, Der-Chiang Li, Hung-Yu Chen","doi":"10.1109/CIIS.2017.21","DOIUrl":"https://doi.org/10.1109/CIIS.2017.21","url":null,"abstract":"In the past few decades, there were quite a few learning algorithms developed to extract knowledge from data. However, none of the single algorithms can be applicable to learn all the datasets with favor results because data patterns may represent linear and non-linear. Accordingly, the idea of aggregating the predictions of multiple learning models to improve the forecasting accuracy of a single method was proposed. Nevertheless, how to improve the accuracy of the aggregated predictions when learning small datasets is the objective of this study. Based on the distributions of the predictive errors of learning models, the proposed method learns the weights of the models and then tries to aggregate more precise predictions with the weights. The experiment results show the forecasting errors of the predictions aggregated by the proposed method are significantly lower than the predictions of single models and the averaged predictions.","PeriodicalId":254342,"journal":{"name":"2017 International Conference on Computing Intelligence and Information System (CIIS)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117118274","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}
Remote sensing is a technology for remote real-time monitoring surface information, remote sensing images are valuable in civil and military applications. In this paper, aiming at the design of remote sensing image segmentation algorithm, we propose a remote sensing image segmentation method based on multi-agent and MRF. The proposed method extracts and fuses the intensity, texture, edges, and the spatial information in collaboration and parallization, and it provides theoretical and technical support for remote sensing image segmentation task.
{"title":"A MRF-based Multi-agent System for Remote Sensing Image Segmentation","authors":"Huifang Bao, Linli Zhou, Lei Liu, Yue Wu","doi":"10.1109/CIIS.2017.44","DOIUrl":"https://doi.org/10.1109/CIIS.2017.44","url":null,"abstract":"Remote sensing is a technology for remote real-time monitoring surface information, remote sensing images are valuable in civil and military applications. In this paper, aiming at the design of remote sensing image segmentation algorithm, we propose a remote sensing image segmentation method based on multi-agent and MRF. The proposed method extracts and fuses the intensity, texture, edges, and the spatial information in collaboration and parallization, and it provides theoretical and technical support for remote sensing image segmentation task.","PeriodicalId":254342,"journal":{"name":"2017 International Conference on Computing Intelligence and Information System (CIIS)","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132898079","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}
Sen Pan, Junfeng Qiao, Jing Jiang, Jin Huang, Liping Zhang
With the development of wide area distributed computing, how to efficiently and reasonably arrange all kinds of resources in the whole computing environment has become an important research direction. Traditional resource scheduling methods do not consider the impact of task priority on resource scheduling. In order to solve this problem, this paper proposes a distributed resource scheduling algorithm based on hybrid genetic algorithm. The simulation results show that compared with the traditional algorithm, the proposed algorithm has a great advantage in the average number of convergence, resource utilization, average time consuming and the total completion time of tasks.
{"title":"Distributed Resource Scheduling Algorithm Based on Hybrid Genetic Algorithm","authors":"Sen Pan, Junfeng Qiao, Jing Jiang, Jin Huang, Liping Zhang","doi":"10.1109/CIIS.2017.13","DOIUrl":"https://doi.org/10.1109/CIIS.2017.13","url":null,"abstract":"With the development of wide area distributed computing, how to efficiently and reasonably arrange all kinds of resources in the whole computing environment has become an important research direction. Traditional resource scheduling methods do not consider the impact of task priority on resource scheduling. In order to solve this problem, this paper proposes a distributed resource scheduling algorithm based on hybrid genetic algorithm. The simulation results show that compared with the traditional algorithm, the proposed algorithm has a great advantage in the average number of convergence, resource utilization, average time consuming and the total completion time of tasks.","PeriodicalId":254342,"journal":{"name":"2017 International Conference on Computing Intelligence and Information System (CIIS)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132809125","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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