Pub Date : 2015-11-01DOI: 10.1109/ICICIP.2015.7388196
Binbin Qiu, Yunong Zhang, Zhi Yang
Lately, Zhang et al have proposed the notion of infinitely many Z-type functions (ZTFs) leading to various Z-type neural nets (ZTNNs), and established a systematic approach (i.e., the general-form ZTNN, GFZTNN) for the real-time solution of a time-varying matrix inverse (also termed, Zhang matrix inverse, ZMI). Being a supplementary and in-depth research, this paper provides the theoretical result on the convergence performance of the GFZTNN model. Besides, such a GFZTNN model is generalized and exploited for computing the time-varying Drazin inverse (TVDI) instead of the usual constant one. Finally, computer simulations with two illustrative examples are performed to show the efficacy and advantage of two specific ZTNN models originating from the GFZTNN model for the realtime solution of ZMI and/or TVDI.
{"title":"Proposal, verification and comparison on infinitely many ZTFs leading to various nets for Zhang matrix inverse solving","authors":"Binbin Qiu, Yunong Zhang, Zhi Yang","doi":"10.1109/ICICIP.2015.7388196","DOIUrl":"https://doi.org/10.1109/ICICIP.2015.7388196","url":null,"abstract":"Lately, Zhang et al have proposed the notion of infinitely many Z-type functions (ZTFs) leading to various Z-type neural nets (ZTNNs), and established a systematic approach (i.e., the general-form ZTNN, GFZTNN) for the real-time solution of a time-varying matrix inverse (also termed, Zhang matrix inverse, ZMI). Being a supplementary and in-depth research, this paper provides the theoretical result on the convergence performance of the GFZTNN model. Besides, such a GFZTNN model is generalized and exploited for computing the time-varying Drazin inverse (TVDI) instead of the usual constant one. Finally, computer simulations with two illustrative examples are performed to show the efficacy and advantage of two specific ZTNN models originating from the GFZTNN model for the realtime solution of ZMI and/or TVDI.","PeriodicalId":265426,"journal":{"name":"2015 Sixth International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125201801","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 : 2015-11-01DOI: 10.1109/ICICIP.2015.7388157
Yunong Zhang, Jinjin Wang, Qingkai Zeng, H. Qiu, Hongzhou Tan
With the world population increasing rapidly, the conflicts between the population and limited resources have become more and more severe. Population growth is a root cause of many environmental and social problems. Therefore, it is of vital importance to make population predictions. However, predictions based on standard cohort-component method fails to consider all relevant impact factors and may neglect some important uncertainty factors. To overcome the inherent limitations, in this article, we present a Chebyshev-activation WASD neuronet approach for the population prediction. This neuronet method is applied to predicting European population, with numerous numerical experiments conducted as a research basis to guarantee the feasibility and validity of our approach. It is predicted with the most possibility that European population will decrease in the near future.
{"title":"Near future prediction of European population through Chebyshev-activation WASD neuronet","authors":"Yunong Zhang, Jinjin Wang, Qingkai Zeng, H. Qiu, Hongzhou Tan","doi":"10.1109/ICICIP.2015.7388157","DOIUrl":"https://doi.org/10.1109/ICICIP.2015.7388157","url":null,"abstract":"With the world population increasing rapidly, the conflicts between the population and limited resources have become more and more severe. Population growth is a root cause of many environmental and social problems. Therefore, it is of vital importance to make population predictions. However, predictions based on standard cohort-component method fails to consider all relevant impact factors and may neglect some important uncertainty factors. To overcome the inherent limitations, in this article, we present a Chebyshev-activation WASD neuronet approach for the population prediction. This neuronet method is applied to predicting European population, with numerous numerical experiments conducted as a research basis to guarantee the feasibility and validity of our approach. It is predicted with the most possibility that European population will decrease in the near future.","PeriodicalId":265426,"journal":{"name":"2015 Sixth International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124171725","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 : 2015-11-01DOI: 10.1109/ICICIP.2015.7388203
Hongquan Shi, Xing-Jun Chen, Wei Qi, Siwei Chong
Aiming at the issue of missile collaborative area air-defense of naval Task Group (TG) under MCE(Multi-platform Collaborative Aerial Defense System) and analyzing the adaptability of the dynamic scheduling policy, this paper proposes the event-driven scheduling framework of the rolling window, studies both the rescheduling stimulated factors of collaborative area air-defense resources and the determination methods of the scheduling window, constructs the dynamic scheduling math model of the TGs collaborative area air-defense which can be solved based on the re-scheduling algorithm of the differential evolution. The simulation result demonstrates that the dynamic scheduling policy is quite rational.
{"title":"Resources dynamic scheduling on the TGs collaborative area air-defense under MCE","authors":"Hongquan Shi, Xing-Jun Chen, Wei Qi, Siwei Chong","doi":"10.1109/ICICIP.2015.7388203","DOIUrl":"https://doi.org/10.1109/ICICIP.2015.7388203","url":null,"abstract":"Aiming at the issue of missile collaborative area air-defense of naval Task Group (TG) under MCE(Multi-platform Collaborative Aerial Defense System) and analyzing the adaptability of the dynamic scheduling policy, this paper proposes the event-driven scheduling framework of the rolling window, studies both the rescheduling stimulated factors of collaborative area air-defense resources and the determination methods of the scheduling window, constructs the dynamic scheduling math model of the TGs collaborative area air-defense which can be solved based on the re-scheduling algorithm of the differential evolution. The simulation result demonstrates that the dynamic scheduling policy is quite rational.","PeriodicalId":265426,"journal":{"name":"2015 Sixth International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"352 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122848824","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 : 2015-11-01DOI: 10.1109/ICICIP.2015.7388156
Yunong Zhang, Ying Fang, Bolin Liao, Tianjian Qiao, Hongzhou Tan
In this paper, a discrete-time Zhang neural network (DTZNN) model, discretized from continuous-time Zhang neural network, is proposed and investigated for performing the online future minimization (OFM). In order to approximate more accurately the 1st-order derivative in computation and discretize more effectively the continuous-time Zhang neural network, a new Taylor-type numerical differentiation formula, together with the optimal sampling-gap rule, is presented and utilized to obtain the Taylor-type DTZNN model. For comparison, Euler-type DTZNN model and Newton iteration, with an interesting link being found, are also presented. Moreover, theoretical results of stability and convergence are presented, which show that the steady-state residual errors of the presented Taylor-type DTZNN model, Euler-type DTZNN model and Newton iteration have a pattern of 0(t3), 0(t2) and 0(t), respectively, with t denoting the sampling gap. Numerical experimental results further substantiate the effectiveness and advantages of the Taylor-type DTZNN model for solving the OFM problem.
{"title":"New DTZNN model for future minimization with cube steady-state error pattern using Taylor finite-difference formula","authors":"Yunong Zhang, Ying Fang, Bolin Liao, Tianjian Qiao, Hongzhou Tan","doi":"10.1109/ICICIP.2015.7388156","DOIUrl":"https://doi.org/10.1109/ICICIP.2015.7388156","url":null,"abstract":"In this paper, a discrete-time Zhang neural network (DTZNN) model, discretized from continuous-time Zhang neural network, is proposed and investigated for performing the online future minimization (OFM). In order to approximate more accurately the 1st-order derivative in computation and discretize more effectively the continuous-time Zhang neural network, a new Taylor-type numerical differentiation formula, together with the optimal sampling-gap rule, is presented and utilized to obtain the Taylor-type DTZNN model. For comparison, Euler-type DTZNN model and Newton iteration, with an interesting link being found, are also presented. Moreover, theoretical results of stability and convergence are presented, which show that the steady-state residual errors of the presented Taylor-type DTZNN model, Euler-type DTZNN model and Newton iteration have a pattern of 0(t3), 0(t2) and 0(t), respectively, with t denoting the sampling gap. Numerical experimental results further substantiate the effectiveness and advantages of the Taylor-type DTZNN model for solving the OFM problem.","PeriodicalId":265426,"journal":{"name":"2015 Sixth International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132538858","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 : 2015-11-01DOI: 10.1109/ICICIP.2015.7388165
Yinfang Song, Wen Sun
In this paper, the anti-synchronization control of memristor-based recurrent neural networks with impulsive perturbations is studied. By using differential inclusions theory, the Lyapnov functional method and the inequality technique, some sufficient conditions are derived to ensure impulsive exponential anti-synchronization of memristor-based recurrent neural networks. The new proposed results involve the impulsive effects and improve the earlier publications. Numerical examples are given to show the effectiveness of our new schemes.
{"title":"Global anti-synchronization of memristor-based recurrent neural networks with time-varying delays and impulsive effects","authors":"Yinfang Song, Wen Sun","doi":"10.1109/ICICIP.2015.7388165","DOIUrl":"https://doi.org/10.1109/ICICIP.2015.7388165","url":null,"abstract":"In this paper, the anti-synchronization control of memristor-based recurrent neural networks with impulsive perturbations is studied. By using differential inclusions theory, the Lyapnov functional method and the inequality technique, some sufficient conditions are derived to ensure impulsive exponential anti-synchronization of memristor-based recurrent neural networks. The new proposed results involve the impulsive effects and improve the earlier publications. Numerical examples are given to show the effectiveness of our new schemes.","PeriodicalId":265426,"journal":{"name":"2015 Sixth International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128754407","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 : 2015-11-01DOI: 10.1109/ICICIP.2015.7388216
Wei Ye, Yibiao Yu
This paper presents a voice conversion technique using deep neural networks (DNNs) to map the spectral envelopes of a source speaker to that of a target speaker. Short-time spectral envelopes are represented by the linear predication cepstrum coefficients (LPCC) parameters, and neighbor frames are gathered to form super-frames. Then the powerful mapping ability of DNN which has a five-layer architecture consisting of three restricted Boltzmann machines (RBMs) was exploited to derive the spectral conversion function. A comparative study of voice conversion using a DNN model and the conventional Gaussian mixture model (GMM) is conducted. Experimental results show the speaker identification rate of conversion speech achieves 97.5% which is 0.8% higher than the performance of GMM method, and the value of average cepstrum distortion is 0.87 which is 5.4% higher than the performance of GMM method. ABX and MOS evaluations indicate that the conversion performance is better than the traditional GMM method under the parallel corpora condition.
{"title":"Voice conversion using deep neural network in super-frame feature space","authors":"Wei Ye, Yibiao Yu","doi":"10.1109/ICICIP.2015.7388216","DOIUrl":"https://doi.org/10.1109/ICICIP.2015.7388216","url":null,"abstract":"This paper presents a voice conversion technique using deep neural networks (DNNs) to map the spectral envelopes of a source speaker to that of a target speaker. Short-time spectral envelopes are represented by the linear predication cepstrum coefficients (LPCC) parameters, and neighbor frames are gathered to form super-frames. Then the powerful mapping ability of DNN which has a five-layer architecture consisting of three restricted Boltzmann machines (RBMs) was exploited to derive the spectral conversion function. A comparative study of voice conversion using a DNN model and the conventional Gaussian mixture model (GMM) is conducted. Experimental results show the speaker identification rate of conversion speech achieves 97.5% which is 0.8% higher than the performance of GMM method, and the value of average cepstrum distortion is 0.87 which is 5.4% higher than the performance of GMM method. ABX and MOS evaluations indicate that the conversion performance is better than the traditional GMM method under the parallel corpora condition.","PeriodicalId":265426,"journal":{"name":"2015 Sixth International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124030554","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 : 2015-11-01DOI: 10.1109/ICICIP.2015.7388212
Yang Xiong, Hua Cheng, Gengguo Cheng
Wireless sensor networks composed of a large number of sensor nodes have emerged recently as a candidate for a wide variety of applications. This paper explores the Rosser state-space model realization problem in regular rectangular wireless sensor networks by using elementary operation. The new elementary operation approach (EOA) proposed recently by the authors for the single-input and single-output (SISO) case will be extended to the multi-input and multi-output (MIMO) case. It is shown that, due to the structural properties of Roesser model, the n-D realization problem can be reduced as an elementary operation problem of a certain n-D polynomial matrix for an n-D transfer matrix represented by a right matrix fraction description (RFD). Then, a general constructive realization procedure will be presented, which can guarantee a regular realization and completely overcome the singularity problem in Galkowski's approach. Finally, an illustrative example will be given to show the details and the effectiveness of the proposed approach.
{"title":"A new realization of MIMO multidimensional system for wireless sensor network","authors":"Yang Xiong, Hua Cheng, Gengguo Cheng","doi":"10.1109/ICICIP.2015.7388212","DOIUrl":"https://doi.org/10.1109/ICICIP.2015.7388212","url":null,"abstract":"Wireless sensor networks composed of a large number of sensor nodes have emerged recently as a candidate for a wide variety of applications. This paper explores the Rosser state-space model realization problem in regular rectangular wireless sensor networks by using elementary operation. The new elementary operation approach (EOA) proposed recently by the authors for the single-input and single-output (SISO) case will be extended to the multi-input and multi-output (MIMO) case. It is shown that, due to the structural properties of Roesser model, the n-D realization problem can be reduced as an elementary operation problem of a certain n-D polynomial matrix for an n-D transfer matrix represented by a right matrix fraction description (RFD). Then, a general constructive realization procedure will be presented, which can guarantee a regular realization and completely overcome the singularity problem in Galkowski's approach. Finally, an illustrative example will be given to show the details and the effectiveness of the proposed approach.","PeriodicalId":265426,"journal":{"name":"2015 Sixth International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128662624","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 : 2015-11-01DOI: 10.1109/ICICIP.2015.7388189
X. Zhao, Bin Zhang, Changsheng Zhang, L. Wang
Accurately estimating the service performance under a given resource configuration is of great importance to the resource provision for services in cloud platforms. To achieve this, it is necessary to build service performance models, the accuracy of which, however, is usually significantly influenced by the scale of training data. In this paper, combining collaborative filtering recommendation (CFR) and artificial neural network (ANN), we present a dynamic service performance modeling approach, called CADM, to improve the accuracy of estimation. In CADM, both performance models based on CFR and ANN are trained at service deployment time and runtime, and the one with lower mean absolute error is chosen to estimate the performance. Moreover, a merit-based threshold is introduced to reduce training costs. The experimental results illustrate that CADM has higher accuracy on different scales of training data, and the merit-based threshold has a significant impact on the estimation accuracy as well as the modeling efficiency.
{"title":"A dynamic approach for estimating service performance in the cloud","authors":"X. Zhao, Bin Zhang, Changsheng Zhang, L. Wang","doi":"10.1109/ICICIP.2015.7388189","DOIUrl":"https://doi.org/10.1109/ICICIP.2015.7388189","url":null,"abstract":"Accurately estimating the service performance under a given resource configuration is of great importance to the resource provision for services in cloud platforms. To achieve this, it is necessary to build service performance models, the accuracy of which, however, is usually significantly influenced by the scale of training data. In this paper, combining collaborative filtering recommendation (CFR) and artificial neural network (ANN), we present a dynamic service performance modeling approach, called CADM, to improve the accuracy of estimation. In CADM, both performance models based on CFR and ANN are trained at service deployment time and runtime, and the one with lower mean absolute error is chosen to estimate the performance. Moreover, a merit-based threshold is introduced to reduce training costs. The experimental results illustrate that CADM has higher accuracy on different scales of training data, and the merit-based threshold has a significant impact on the estimation accuracy as well as the modeling efficiency.","PeriodicalId":265426,"journal":{"name":"2015 Sixth International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127848312","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 : 2015-11-01DOI: 10.1109/ICICIP.2015.7388205
Kai Zhong, Song Zhu, Qiqi Yang
This paper analyzes a class of memristor-based recurrent neural networks with mixed delays involving both discrete and distributed delays by constructing appropriate Lyapunov functionals and using some analytic techniques. Two new adequacy criteria concerning the dissipativity of the addressed neural networks are obtained. Finally, a numerical example is discussed in detail to substantiate our theoretical results.
{"title":"Dissipativity results for memristor-based recurrent neural networks with mixed delays","authors":"Kai Zhong, Song Zhu, Qiqi Yang","doi":"10.1109/ICICIP.2015.7388205","DOIUrl":"https://doi.org/10.1109/ICICIP.2015.7388205","url":null,"abstract":"This paper analyzes a class of memristor-based recurrent neural networks with mixed delays involving both discrete and distributed delays by constructing appropriate Lyapunov functionals and using some analytic techniques. Two new adequacy criteria concerning the dissipativity of the addressed neural networks are obtained. Finally, a numerical example is discussed in detail to substantiate our theoretical results.","PeriodicalId":265426,"journal":{"name":"2015 Sixth International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127022608","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 : 2015-11-01DOI: 10.1109/ICICIP.2015.7388166
Yunfei Zheng, Shiyuan Wang, Yali Feng, Wenjie Zhang, Qingan Yang
In this paper, we propose an new kernel adaptive filter, namely convex combination of quantized kernel least mean square algorithm (CC-QKLMS). By applying the convex combination idea to QKLMS, the CC-QKLMS takes the kernel sizes as the combined variables, which can achieve a fast convergence rate and a low steady-state mean-square error (MSE). In addition, since the quantization method is incorporated in CC-QKLMS, a linear growing network structure is naturally avoided. Simulation results on channel equalization validate the better performance of the CC-QKLMS in terms of the convergence rate and steady-state MSE.
{"title":"Convex combination of quantized kernel least mean square algorithm","authors":"Yunfei Zheng, Shiyuan Wang, Yali Feng, Wenjie Zhang, Qingan Yang","doi":"10.1109/ICICIP.2015.7388166","DOIUrl":"https://doi.org/10.1109/ICICIP.2015.7388166","url":null,"abstract":"In this paper, we propose an new kernel adaptive filter, namely convex combination of quantized kernel least mean square algorithm (CC-QKLMS). By applying the convex combination idea to QKLMS, the CC-QKLMS takes the kernel sizes as the combined variables, which can achieve a fast convergence rate and a low steady-state mean-square error (MSE). In addition, since the quantization method is incorporated in CC-QKLMS, a linear growing network structure is naturally avoided. Simulation results on channel equalization validate the better performance of the CC-QKLMS in terms of the convergence rate and steady-state MSE.","PeriodicalId":265426,"journal":{"name":"2015 Sixth International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125206201","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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