Pub Date : 2012-05-29DOI: 10.1109/ICNC.2012.6234582
Wuxi Shi, Yingxin Ma, Yuchan Chen, Ziguang Guo
An adaptive neural network control scheme is presented for a class of nonlinear discrete-time systems. The unknown nonlinear plants are represented by an equivalent model composed of a simple linear submodel plus a nonlinear submodel around operating points, and a simple linear controller is designed based on the linearization of the nonlinear system, a compensation term, which is implemented with a two-layer recurrent neural network during every sampling period, is introduced to control nonlinear systems, the network weight adaptation law is derived by using Lyapunov theory. The proposed design scheme guarantees that all the signals in closed-loop system are bounded, and the filtering tracking error converges to a small neighborhood of the origin.
{"title":"Adaptive neural network control for a class of nonlinear discrete system","authors":"Wuxi Shi, Yingxin Ma, Yuchan Chen, Ziguang Guo","doi":"10.1109/ICNC.2012.6234582","DOIUrl":"https://doi.org/10.1109/ICNC.2012.6234582","url":null,"abstract":"An adaptive neural network control scheme is presented for a class of nonlinear discrete-time systems. The unknown nonlinear plants are represented by an equivalent model composed of a simple linear submodel plus a nonlinear submodel around operating points, and a simple linear controller is designed based on the linearization of the nonlinear system, a compensation term, which is implemented with a two-layer recurrent neural network during every sampling period, is introduced to control nonlinear systems, the network weight adaptation law is derived by using Lyapunov theory. The proposed design scheme guarantees that all the signals in closed-loop system are bounded, and the filtering tracking error converges to a small neighborhood of the origin.","PeriodicalId":404981,"journal":{"name":"2012 8th International Conference on Natural Computation","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121094026","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 : 2012-05-29DOI: 10.1109/ICNC.2012.6234596
Bo Xiao, Lijun Guo, Yuanyuan Zhang, Rong-Rrong Zhang
In this paper, we propose a method for human segmentation in videos, extending the recent locally competing 1SVM model. There are only local color distributions to be made use of in the model. To generate a consistent segmentation from complex environments, first, we assume we obtain a bounding box around human by using the human detector. Then we incorporate shape prior information inside the bounding box, which biases the segmentation towards typical human shapes. Finally, we show a substantial improvement over C-1SVM method from our experiment.
{"title":"Human instance segmentation from video using locally competing 1SVMs with shape prior","authors":"Bo Xiao, Lijun Guo, Yuanyuan Zhang, Rong-Rrong Zhang","doi":"10.1109/ICNC.2012.6234596","DOIUrl":"https://doi.org/10.1109/ICNC.2012.6234596","url":null,"abstract":"In this paper, we propose a method for human segmentation in videos, extending the recent locally competing 1SVM model. There are only local color distributions to be made use of in the model. To generate a consistent segmentation from complex environments, first, we assume we obtain a bounding box around human by using the human detector. Then we incorporate shape prior information inside the bounding box, which biases the segmentation towards typical human shapes. Finally, we show a substantial improvement over C-1SVM method from our experiment.","PeriodicalId":404981,"journal":{"name":"2012 8th International Conference on Natural Computation","volume":"235 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116327922","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 : 2012-05-29DOI: 10.1109/ICNC.2012.6234608
Zhe-Hao Liang, Wei Lu
It aims at technologically forecasting the serum luteinizing hormone(LH) peak value by means of the artificial neural network combined with the ultrasound in the examination of exciting the gonadotropin releasing hormone(GnRH). In the process, 71 girls of the sexual precocity are selected to take the conventional ultrasonic testing on the uterus and ovary. And then, the uterus size, the ovary size and the inner diameter of the biggest ovarian follicle in the 61 of those selected girls are set to be the input variable while the LH peak value the output variable. And BP neural network is in formation, and another 10 girls are used as testing targets. As a result, the linear regression is used as a method to calculate the real value and the BP network forecasting value, showing that the correlation coefficient of the linear regression is 0.9485 and the slope is 0.9280. In conclusion, the LH peak value in the examination of GnRH can be predicted by using the ultrasound combined with the BP neural network.
{"title":"Prognosis of the sexually-precocious girl's luteinizing hormone peak value with the neural network and ultrasonic","authors":"Zhe-Hao Liang, Wei Lu","doi":"10.1109/ICNC.2012.6234608","DOIUrl":"https://doi.org/10.1109/ICNC.2012.6234608","url":null,"abstract":"It aims at technologically forecasting the serum luteinizing hormone(LH) peak value by means of the artificial neural network combined with the ultrasound in the examination of exciting the gonadotropin releasing hormone(GnRH). In the process, 71 girls of the sexual precocity are selected to take the conventional ultrasonic testing on the uterus and ovary. And then, the uterus size, the ovary size and the inner diameter of the biggest ovarian follicle in the 61 of those selected girls are set to be the input variable while the LH peak value the output variable. And BP neural network is in formation, and another 10 girls are used as testing targets. As a result, the linear regression is used as a method to calculate the real value and the BP network forecasting value, showing that the correlation coefficient of the linear regression is 0.9485 and the slope is 0.9280. In conclusion, the LH peak value in the examination of GnRH can be predicted by using the ultrasound combined with the BP neural network.","PeriodicalId":404981,"journal":{"name":"2012 8th International Conference on Natural Computation","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127096134","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 : 2012-05-29DOI: 10.1109/ICNC.2012.6234765
Zhuhong Zhang, Min Liao, Lei Wang
This work investigates one multi-objective immune genetic algorithm to solve dynamic constrained single-objective multimodal optimization problems in terms of the concept of constraint-dominance and biological immune inspirations. The algorithm assumes searching multiple global optimal solutions along diverse searching directions, by means of the environmental detection and two evolving subpopulations. It exploits various kinds of promising regions through executing the periodical suppression mechanism and periodically adjusting the mutation magnitude. The sufficient diversity of population can be maintained relying upon a dynamic suppression index, and meanwhile the high-quality solutions can be found rapidly during the process of solution search. Comparative experiments show that the proposed approach can not only outperform the compared algorithms, but also rapidly acquire the global optima in each environment for each test problem, and thus it is a competitive optimizer.
{"title":"Multi-objective immune genetic algorithm solving dynamic single-objective multimodal constrained optimization","authors":"Zhuhong Zhang, Min Liao, Lei Wang","doi":"10.1109/ICNC.2012.6234765","DOIUrl":"https://doi.org/10.1109/ICNC.2012.6234765","url":null,"abstract":"This work investigates one multi-objective immune genetic algorithm to solve dynamic constrained single-objective multimodal optimization problems in terms of the concept of constraint-dominance and biological immune inspirations. The algorithm assumes searching multiple global optimal solutions along diverse searching directions, by means of the environmental detection and two evolving subpopulations. It exploits various kinds of promising regions through executing the periodical suppression mechanism and periodically adjusting the mutation magnitude. The sufficient diversity of population can be maintained relying upon a dynamic suppression index, and meanwhile the high-quality solutions can be found rapidly during the process of solution search. Comparative experiments show that the proposed approach can not only outperform the compared algorithms, but also rapidly acquire the global optima in each environment for each test problem, and thus it is a competitive optimizer.","PeriodicalId":404981,"journal":{"name":"2012 8th International Conference on Natural Computation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126085865","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 : 2012-05-29DOI: 10.1109/ICNC.2012.6234677
Guocheng Zou, Liping Jia, Jin Zou
In this paper, we address a class of multiobjective bilevel mixed linear integer programming in which the upper level is a multiobjective linear optimization problem, and the lower level is a single-objective linear programming. For this kind of problem, the leader's decision are represented by zero-one variables, and the follower's decision are represented by continuous variables. Using KKT condition, the lower level is transformed into a series of constraints for the upper level. Based on coding, crossover, mutation, fitness assignment method and select strategy, an improved random-weight genetic algorithm for multiobjective bilevel mixed linear integer programming is proposed. By designing benchmark problems and suitable transformation, the proposed algorithm is compared by an existed branch-bound algorithm.
{"title":"Random-weight based genetic algorithm for multiobjective bilevel mixed linear integer programming","authors":"Guocheng Zou, Liping Jia, Jin Zou","doi":"10.1109/ICNC.2012.6234677","DOIUrl":"https://doi.org/10.1109/ICNC.2012.6234677","url":null,"abstract":"In this paper, we address a class of multiobjective bilevel mixed linear integer programming in which the upper level is a multiobjective linear optimization problem, and the lower level is a single-objective linear programming. For this kind of problem, the leader's decision are represented by zero-one variables, and the follower's decision are represented by continuous variables. Using KKT condition, the lower level is transformed into a series of constraints for the upper level. Based on coding, crossover, mutation, fitness assignment method and select strategy, an improved random-weight genetic algorithm for multiobjective bilevel mixed linear integer programming is proposed. By designing benchmark problems and suitable transformation, the proposed algorithm is compared by an existed branch-bound algorithm.","PeriodicalId":404981,"journal":{"name":"2012 8th International Conference on Natural Computation","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125316508","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 : 2012-05-29DOI: 10.1109/ICNC.2012.6234775
Ruopeng Wang, Hongmin Xu, Hong Shi, Xu You
In this paper, the infinite norm SVM is considered and a novel smoothing approximation function for Support Vector Machine is proposed in attempt to overcome some drawbacks of the former method which are complex, subtle, and sometimes difficult to implement. Firstly, we use Karush-Kuhn-Tucker complementary condition in optimization theory, and the unconstrained non-differentiable optimization model is built. Then the smooth approximation algorithm based on differentiable function is given. Finally, the paper trains the data sets with standard unconstraint optimization method. This algorithm is fast and insensitive to initial point. Theory analysis and numerical results illustrate that the smoothing approximation for the infinite SVM is feasible and effective.
{"title":"A smoothing approximation for L∞ SVM","authors":"Ruopeng Wang, Hongmin Xu, Hong Shi, Xu You","doi":"10.1109/ICNC.2012.6234775","DOIUrl":"https://doi.org/10.1109/ICNC.2012.6234775","url":null,"abstract":"In this paper, the infinite norm SVM is considered and a novel smoothing approximation function for Support Vector Machine is proposed in attempt to overcome some drawbacks of the former method which are complex, subtle, and sometimes difficult to implement. Firstly, we use Karush-Kuhn-Tucker complementary condition in optimization theory, and the unconstrained non-differentiable optimization model is built. Then the smooth approximation algorithm based on differentiable function is given. Finally, the paper trains the data sets with standard unconstraint optimization method. This algorithm is fast and insensitive to initial point. Theory analysis and numerical results illustrate that the smoothing approximation for the infinite SVM is feasible and effective.","PeriodicalId":404981,"journal":{"name":"2012 8th International Conference on Natural Computation","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127886354","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 : 2012-05-29DOI: 10.1109/ICNC.2012.6234734
Lifeng Zhao, Xiaowan Meng
There are lots of steps and complicated calculation in the existing algorithm for solving the maximum flow,and because of improper selection order of augmented path, we cannot obtain the ideal maximum flow. In order to solve these problems in existing algorithm, this paper make some improvement of the existing algorithms, then puts forward a new improved algorithm for solving the maximum flow problem which make use of divide area and the degree of vertex. And it is verified that the improved algorithm is effective and intuitive through the concrete example, and avoid the labeling process, the entire operation process only needs drawing a diagram to be completed.
{"title":"An improved algorithm for solving maximum flow problem","authors":"Lifeng Zhao, Xiaowan Meng","doi":"10.1109/ICNC.2012.6234734","DOIUrl":"https://doi.org/10.1109/ICNC.2012.6234734","url":null,"abstract":"There are lots of steps and complicated calculation in the existing algorithm for solving the maximum flow,and because of improper selection order of augmented path, we cannot obtain the ideal maximum flow. In order to solve these problems in existing algorithm, this paper make some improvement of the existing algorithms, then puts forward a new improved algorithm for solving the maximum flow problem which make use of divide area and the degree of vertex. And it is verified that the improved algorithm is effective and intuitive through the concrete example, and avoid the labeling process, the entire operation process only needs drawing a diagram to be completed.","PeriodicalId":404981,"journal":{"name":"2012 8th International Conference on Natural Computation","volume":"211 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134379534","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 : 2012-05-29DOI: 10.1109/ICNC.2012.6234564
Li-qian Zhou, Zuguo Yu, Guo-Sheng Han, Guang-ming Zhou, De-Sheng Wang
There has been a growing interest in alignment-free methods for phylogenetic analysis using complete genome data. Among them, CVTree method, feature frequency profiles method and dynamical language approach were used to investigate the whole-proteome phylogeny of large dsDNA viruses. Using the data set of large dsDNA viruses from Gao and Qi (BMC Evol. Biol. 2007), the phylogenetic results based on the CVTree method and the dynamical language approach were compared in Yu et al. (BMC Evol. Biol. 2010). In this paper, we first apply dynamical language approach to the data set of large dsDNA viruses from Wu et al. (Proc. Natl. Acad. Sci. USA 2009) and compare our phylogenetic results with those based on the feature frequency profiles method. Then we construct the whole-proteome phylogeny of the larger dataset combining the above two data sets. According to the report of The International Committee on the Taxonomy of Viruses (ICTV), the trees from our analyses are in good agreement to the latest classification of large dsDNA viruses.
人们对使用全基因组数据进行系统发育分析的无比对方法越来越感兴趣。其中，利用CVTree方法、特征频率谱方法和动态语言方法对大型dsDNA病毒的全蛋白质组系统发育进行了研究。利用高和齐(BMC Evol.)的大型dsDNA病毒数据集。Yu et al. (BMC evolution . 2007)对基于CVTree方法和动态语言方法的系统发育结果进行了比较。医学杂志。2010)。在本文中，我们首先将动态语言方法应用于Wu等人(Proc. Natl)的大型dsDNA病毒数据集。学会科学。(美国，2009)，并将我们的系统发育结果与基于特征频率谱方法的结果进行比较。然后结合以上两个数据集构建更大数据集的全蛋白质组系统发育。根据国际病毒分类委员会(International Committee on the Taxonomy of Viruses, ICTV)的报告，我们的分析树与大型dsDNA病毒的最新分类非常一致。
{"title":"Some comparison on whole-proteome phylogeny of large dsDNA viruses based on dynamical language approach and feature frequency profiles method","authors":"Li-qian Zhou, Zuguo Yu, Guo-Sheng Han, Guang-ming Zhou, De-Sheng Wang","doi":"10.1109/ICNC.2012.6234564","DOIUrl":"https://doi.org/10.1109/ICNC.2012.6234564","url":null,"abstract":"There has been a growing interest in alignment-free methods for phylogenetic analysis using complete genome data. Among them, CVTree method, feature frequency profiles method and dynamical language approach were used to investigate the whole-proteome phylogeny of large dsDNA viruses. Using the data set of large dsDNA viruses from Gao and Qi (BMC Evol. Biol. 2007), the phylogenetic results based on the CVTree method and the dynamical language approach were compared in Yu et al. (BMC Evol. Biol. 2010). In this paper, we first apply dynamical language approach to the data set of large dsDNA viruses from Wu et al. (Proc. Natl. Acad. Sci. USA 2009) and compare our phylogenetic results with those based on the feature frequency profiles method. Then we construct the whole-proteome phylogeny of the larger dataset combining the above two data sets. According to the report of The International Committee on the Taxonomy of Viruses (ICTV), the trees from our analyses are in good agreement to the latest classification of large dsDNA viruses.","PeriodicalId":404981,"journal":{"name":"2012 8th International Conference on Natural Computation","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114077153","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 : 2012-05-29DOI: 10.1109/ICNC.2012.6234546
Wudai Liao, Xingfeng Wang, Yuyu Yang, Junyan Wang
In this paper, we introduce a kind of method for solving least mean square problems based on the gradient neural network, including the network model construction, quantitative analysis of the network global convergence and the network convergence rate about the different activation functions. MATLAB simulation results and theoretical analysis results are accordingly consistent, which further confirm the method based on Hopfield neural network has a good effect on solving the least mean square problems.
{"title":"Convergence and robustness analysis of disturbed gradient neural network for solving LMS problem","authors":"Wudai Liao, Xingfeng Wang, Yuyu Yang, Junyan Wang","doi":"10.1109/ICNC.2012.6234546","DOIUrl":"https://doi.org/10.1109/ICNC.2012.6234546","url":null,"abstract":"In this paper, we introduce a kind of method for solving least mean square problems based on the gradient neural network, including the network model construction, quantitative analysis of the network global convergence and the network convergence rate about the different activation functions. MATLAB simulation results and theoretical analysis results are accordingly consistent, which further confirm the method based on Hopfield neural network has a good effect on solving the least mean square problems.","PeriodicalId":404981,"journal":{"name":"2012 8th International Conference on Natural Computation","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127632691","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 : 2012-05-29DOI: 10.1109/ICNC.2012.6234561
Shuai Wang, Xiao Lei, Xiaomin Huang
This paper proposes a method using multi-objective particle swarm optimization (MOPSO) algorithm to solve the multi-objective optimal dispatch problem of reservoir flood control, which take minimum value of the highest water level before dam, minimum value of the releasing peak discharge, and water level after flood season very close to flood control level as the objective functions. By using the archiving technique, crowding distance sorting algorithm and mutation technique to improve the algorithm convergence speed and accuracy and enable the Pareto solution set to converge to optimal front promptly and distribute evenly. The algorithm is applied to optimize the dispatch of the Yuecheng reservoir in upper Zhanghe River of the Haihe basin for typical floods occurred in history and the relative relations between dispatching objectives are analyzed. The result indicates that a lot of noninferior dispatch schemes can be generated in a short time, which can provide scientific basis for the decision-maker to make optimal operation and evaluation decision.
{"title":"Multi-objective optimization of reservoir flood dispatch based on MOPSO algorithm","authors":"Shuai Wang, Xiao Lei, Xiaomin Huang","doi":"10.1109/ICNC.2012.6234561","DOIUrl":"https://doi.org/10.1109/ICNC.2012.6234561","url":null,"abstract":"This paper proposes a method using multi-objective particle swarm optimization (MOPSO) algorithm to solve the multi-objective optimal dispatch problem of reservoir flood control, which take minimum value of the highest water level before dam, minimum value of the releasing peak discharge, and water level after flood season very close to flood control level as the objective functions. By using the archiving technique, crowding distance sorting algorithm and mutation technique to improve the algorithm convergence speed and accuracy and enable the Pareto solution set to converge to optimal front promptly and distribute evenly. The algorithm is applied to optimize the dispatch of the Yuecheng reservoir in upper Zhanghe River of the Haihe basin for typical floods occurred in history and the relative relations between dispatching objectives are analyzed. The result indicates that a lot of noninferior dispatch schemes can be generated in a short time, which can provide scientific basis for the decision-maker to make optimal operation and evaluation decision.","PeriodicalId":404981,"journal":{"name":"2012 8th International Conference on Natural Computation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129250891","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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