Pub Date : 2021-12-10DOI: 10.1109/ICCSS53909.2021.9722009
Kai Hu, N. Xiao
Human’s responses in communication depend on the context. Specifically, they are the feedback to a sentence or a word in the context. Further, external knowledge needs to be added to provide appropriate information for the human’s answer. DAM (Deep Attention Matching Network), uses the attention mechanism of transformer to expand utterance and response into multi-level granularity representations, and then calculate the granularity similarity at the same level, which has better effects than using traditional RNN (recurrent neural network). Inspired by DAM, we propose to calculate the similarity between granularities at different levels which can explore more useful information for training and learning in this paper. We call this new matching method "shift matching", which is not limited to enhancing DAM, but can be generalized to other models. Our experiments include two parts: the first part compares the improved model with the base, and then compares the classic model to solve multi-round dialogue problem. The second part is to compare the experimental results of the different shift distances. The results are better than that of the state-of-the-art model.
{"title":"Research on Shift Matching to Enhance DAM","authors":"Kai Hu, N. Xiao","doi":"10.1109/ICCSS53909.2021.9722009","DOIUrl":"https://doi.org/10.1109/ICCSS53909.2021.9722009","url":null,"abstract":"Human’s responses in communication depend on the context. Specifically, they are the feedback to a sentence or a word in the context. Further, external knowledge needs to be added to provide appropriate information for the human’s answer. DAM (Deep Attention Matching Network), uses the attention mechanism of transformer to expand utterance and response into multi-level granularity representations, and then calculate the granularity similarity at the same level, which has better effects than using traditional RNN (recurrent neural network). Inspired by DAM, we propose to calculate the similarity between granularities at different levels which can explore more useful information for training and learning in this paper. We call this new matching method \"shift matching\", which is not limited to enhancing DAM, but can be generalized to other models. Our experiments include two parts: the first part compares the improved model with the base, and then compares the classic model to solve multi-round dialogue problem. The second part is to compare the experimental results of the different shift distances. The results are better than that of the state-of-the-art model.","PeriodicalId":435816,"journal":{"name":"2021 8th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123081175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-10DOI: 10.1109/ICCSS53909.2021.9721966
Jing Huang, Zhe Sun, Hui-Juan Zhang, Jia Chen, Shen He
Tens of billions of nodes in the Internet of Things work together, making the boundary between virtual and reality more and more blurred. However, while the Internet age has brought subversive changes to people's lives, it has also brought huge security risks. Therefore, in order to effectively identify malicious nodes and realize the security and credibility of each node in the Internet of Things, this paper proposes an evaluation and management mechanism based on node trust. First, perform direct trust measurement of nodes based on node satisfaction and reliability stored locally; Secondly, the indirect trustworthiness measurement of the node is realized by combining the direct recommendation trust degree and the indirect recommendation trust degree; Finally, according to the comprehensive trust value, it dynamically analyzes the risk and threat of the environment where the node is located, and identifies and eliminates malicious nodes in time. The simulation results show that the evaluation management mechanism proposed in this paper can effectively identify malicious nodes, thereby ensuring the security of the Internet of Things.
{"title":"An evaluation management mechanism based on node trust","authors":"Jing Huang, Zhe Sun, Hui-Juan Zhang, Jia Chen, Shen He","doi":"10.1109/ICCSS53909.2021.9721966","DOIUrl":"https://doi.org/10.1109/ICCSS53909.2021.9721966","url":null,"abstract":"Tens of billions of nodes in the Internet of Things work together, making the boundary between virtual and reality more and more blurred. However, while the Internet age has brought subversive changes to people's lives, it has also brought huge security risks. Therefore, in order to effectively identify malicious nodes and realize the security and credibility of each node in the Internet of Things, this paper proposes an evaluation and management mechanism based on node trust. First, perform direct trust measurement of nodes based on node satisfaction and reliability stored locally; Secondly, the indirect trustworthiness measurement of the node is realized by combining the direct recommendation trust degree and the indirect recommendation trust degree; Finally, according to the comprehensive trust value, it dynamically analyzes the risk and threat of the environment where the node is located, and identifies and eliminates malicious nodes in time. The simulation results show that the evaluation management mechanism proposed in this paper can effectively identify malicious nodes, thereby ensuring the security of the Internet of Things.","PeriodicalId":435816,"journal":{"name":"2021 8th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS)","volume":"2 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123270087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-10DOI: 10.1109/ICCSS53909.2021.9721958
Yan Ji, Jinde Cao
This article studies the parameter estimation to the photovoltaic cell (PV) models. Introducing the gradient search principle, a gradient-based iterative algorithm is derived to determine PV models. This proposed algorithm implements the parameter estimation for the single-diode equivalent circuit of the PV models. Furthermore, to enhance computational efficiency, a model transformation-based iterative method is proposed. Finally, the simulation test results indicate that the gradient-based iterative algorithm is effective.
{"title":"Parameters identification of photovoltaic cell models using the gradient iterative","authors":"Yan Ji, Jinde Cao","doi":"10.1109/ICCSS53909.2021.9721958","DOIUrl":"https://doi.org/10.1109/ICCSS53909.2021.9721958","url":null,"abstract":"This article studies the parameter estimation to the photovoltaic cell (PV) models. Introducing the gradient search principle, a gradient-based iterative algorithm is derived to determine PV models. This proposed algorithm implements the parameter estimation for the single-diode equivalent circuit of the PV models. Furthermore, to enhance computational efficiency, a model transformation-based iterative method is proposed. Finally, the simulation test results indicate that the gradient-based iterative algorithm is effective.","PeriodicalId":435816,"journal":{"name":"2021 8th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS)","volume":"228 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115557989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-10DOI: 10.1109/ICCSS53909.2021.9721946
Jiaqian Wang, Zheng Liu, Hong-gui Han
Fuzzy neural network has been considered as an effective model to apply in many applications. However, due to the training mode based on minimizing the mean squared error, the typical fuzzy neural network suffers from poor robustness for disturbances. To overcome this problem, a robust fuzzy neural network with α-divergence is designed and analyzed in this paper. First, a cost function based on α-divergence is developed to describe the discrepancy between the real output and fuzzy neural network output. Then, a training mode, which minimizes the above function, can reduce the sensibility of disturbances to improve the robustness of fuzzy neural network. Second, an adaptive learning algorithm is employed to adjust the parameter of fuzzy neural network. Then, the proposed fuzzy neural network is able to obtain fast convergence in the learning process. Finally, some benchmarks are used to test the merits of fuzzy neural network. The simulation results illustrate that the proposed fuzzy neural network can achieve good robustness.
{"title":"Design of Robust Fuzzy Neural Network with α-Divergence","authors":"Jiaqian Wang, Zheng Liu, Hong-gui Han","doi":"10.1109/ICCSS53909.2021.9721946","DOIUrl":"https://doi.org/10.1109/ICCSS53909.2021.9721946","url":null,"abstract":"Fuzzy neural network has been considered as an effective model to apply in many applications. However, due to the training mode based on minimizing the mean squared error, the typical fuzzy neural network suffers from poor robustness for disturbances. To overcome this problem, a robust fuzzy neural network with α-divergence is designed and analyzed in this paper. First, a cost function based on α-divergence is developed to describe the discrepancy between the real output and fuzzy neural network output. Then, a training mode, which minimizes the above function, can reduce the sensibility of disturbances to improve the robustness of fuzzy neural network. Second, an adaptive learning algorithm is employed to adjust the parameter of fuzzy neural network. Then, the proposed fuzzy neural network is able to obtain fast convergence in the learning process. Finally, some benchmarks are used to test the merits of fuzzy neural network. The simulation results illustrate that the proposed fuzzy neural network can achieve good robustness.","PeriodicalId":435816,"journal":{"name":"2021 8th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS)","volume":"2013 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128767541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-10DOI: 10.1109/ICCSS53909.2021.9722027
Kuiyuan Zhang, Zhengguang Wu
False data injection attack(FDIA) is a traditional attack for the smart grid. There are many methods for the detection of the FDIA, but few of them can send the attack alarm successfully without an attack model. In this paper, we propose a reinforcement learning-based FDIA detection method for the distributed smart grid. The detection problem is formulated as a partially observable Markov decision process(POMDP) problem, and the observation of the POMDP can be obtained from the estimation of state and attack which come from the Kalman filter. By using the Sarsa algorithm, we can get a Q-table through online training. Finally, we use the IEEE-118 bus power system to evaluate the performance of our detector, and numerical results show the accurate response for the FDIA.
{"title":"A Reinforcement Learning-Based Detection Method for False Data Injection Attack in Distributed Smart Grid","authors":"Kuiyuan Zhang, Zhengguang Wu","doi":"10.1109/ICCSS53909.2021.9722027","DOIUrl":"https://doi.org/10.1109/ICCSS53909.2021.9722027","url":null,"abstract":"False data injection attack(FDIA) is a traditional attack for the smart grid. There are many methods for the detection of the FDIA, but few of them can send the attack alarm successfully without an attack model. In this paper, we propose a reinforcement learning-based FDIA detection method for the distributed smart grid. The detection problem is formulated as a partially observable Markov decision process(POMDP) problem, and the observation of the POMDP can be obtained from the estimation of state and attack which come from the Kalman filter. By using the Sarsa algorithm, we can get a Q-table through online training. Finally, we use the IEEE-118 bus power system to evaluate the performance of our detector, and numerical results show the accurate response for the FDIA.","PeriodicalId":435816,"journal":{"name":"2021 8th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124573663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-10DOI: 10.1109/ICCSS53909.2021.9722030
Tao Zou, H. Wu, Zhijia Zhao, Jianing Zhang
This paper proposes a neural network (NN) control method for a nonlinear 2-DOF helicopter system with time-varying state constraints. By constructing the time-varying barrier Lyapunov technology and the controller designed based on the backstepping method, the system’s states are guaranteed within a predetermined region. The NN is adopted to approximate the unknown function of the system to ensure its tracking performance and stability. Finally, the effectiveness of the derived control is validated by numerical simulation.
{"title":"Time-varying state constraints-based neural network control of a 2-DOF helicopter system","authors":"Tao Zou, H. Wu, Zhijia Zhao, Jianing Zhang","doi":"10.1109/ICCSS53909.2021.9722030","DOIUrl":"https://doi.org/10.1109/ICCSS53909.2021.9722030","url":null,"abstract":"This paper proposes a neural network (NN) control method for a nonlinear 2-DOF helicopter system with time-varying state constraints. By constructing the time-varying barrier Lyapunov technology and the controller designed based on the backstepping method, the system’s states are guaranteed within a predetermined region. The NN is adopted to approximate the unknown function of the system to ensure its tracking performance and stability. Finally, the effectiveness of the derived control is validated by numerical simulation.","PeriodicalId":435816,"journal":{"name":"2021 8th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121193770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-10DOI: 10.1109/ICCSS53909.2021.9721945
Zheng Liu, Hong-gui Han, J. Qiao
Fuzzy broad learning system is regarded as an effective algorithm to utilize the measured data for modeling nonlinear systems. However, due to the possible existence of data inadequate or data loss, it is a challenge to design a suitable fuzzy broad learning system with the data shortage issue for modeling. Therefore, a knowledge transfer-based fuzzy broad learning system is developed in this paper. First, the knowledge extracted from the process is used to construct the initial condition. Then, this model can obtain the precise parameter and structure. Second, a knowledge evaluation mechanism is employed to rebuild the knowledge by judging the correlation and discrepancy. Then, the knowledge can be preferably integrated. Third, a transfer gradient algorithm is employed to adjust the parameters of fuzzy broad learning system. Then, the modeling performance of knowledge transfer-based fuzzy broad learning system can be improved. Finally, a benchmark problem and a practical application are used to test the merits of knowledge transfer-based fuzzy broad learning system. The results demonstrate that this model can achieve superior modeling performance.
{"title":"A Knowledge Transfer-based Fuzzy Broad Learning System for Modeling Nonlinear Systems","authors":"Zheng Liu, Hong-gui Han, J. Qiao","doi":"10.1109/ICCSS53909.2021.9721945","DOIUrl":"https://doi.org/10.1109/ICCSS53909.2021.9721945","url":null,"abstract":"Fuzzy broad learning system is regarded as an effective algorithm to utilize the measured data for modeling nonlinear systems. However, due to the possible existence of data inadequate or data loss, it is a challenge to design a suitable fuzzy broad learning system with the data shortage issue for modeling. Therefore, a knowledge transfer-based fuzzy broad learning system is developed in this paper. First, the knowledge extracted from the process is used to construct the initial condition. Then, this model can obtain the precise parameter and structure. Second, a knowledge evaluation mechanism is employed to rebuild the knowledge by judging the correlation and discrepancy. Then, the knowledge can be preferably integrated. Third, a transfer gradient algorithm is employed to adjust the parameters of fuzzy broad learning system. Then, the modeling performance of knowledge transfer-based fuzzy broad learning system can be improved. Finally, a benchmark problem and a practical application are used to test the merits of knowledge transfer-based fuzzy broad learning system. The results demonstrate that this model can achieve superior modeling performance.","PeriodicalId":435816,"journal":{"name":"2021 8th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124007871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-10DOI: 10.1109/ICCSS53909.2021.9721991
Mingkai Zheng, Kaixin Liu, Nanxin Li, Yuan Yao, Yi Liu
Infrared thermography (IRT) is an efficient non-destructive testing technique, which is widely applied in defect detection of polymer composites. However, the nonlinear nature of the thermographic data and the adverse effects of noise and inhomogeneous background prevent IRT from delivering satisfactory results. A novel deep autoencoder thermography (DAT) method is developed to enhance the contrast between defects and background. The multi-layer structure of the deep autoencoder is used to extract the features. Then, the results of the middle-hidden layer are visualized to show the effects of removing noise and uneven background. As a result, the defect is highlighted in the visualized images. The feasibility of the DAT method is verified using the experiment of carbon fiber reinforced polymer specimen.
{"title":"Deep Autoencoder for Non-destructive Testing of Defects in Polymer Composites","authors":"Mingkai Zheng, Kaixin Liu, Nanxin Li, Yuan Yao, Yi Liu","doi":"10.1109/ICCSS53909.2021.9721991","DOIUrl":"https://doi.org/10.1109/ICCSS53909.2021.9721991","url":null,"abstract":"Infrared thermography (IRT) is an efficient non-destructive testing technique, which is widely applied in defect detection of polymer composites. However, the nonlinear nature of the thermographic data and the adverse effects of noise and inhomogeneous background prevent IRT from delivering satisfactory results. A novel deep autoencoder thermography (DAT) method is developed to enhance the contrast between defects and background. The multi-layer structure of the deep autoencoder is used to extract the features. Then, the results of the middle-hidden layer are visualized to show the effects of removing noise and uneven background. As a result, the defect is highlighted in the visualized images. The feasibility of the DAT method is verified using the experiment of carbon fiber reinforced polymer specimen.","PeriodicalId":435816,"journal":{"name":"2021 8th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121921178","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-10DOI: 10.1109/ICCSS53909.2021.9721985
Meng Gao, Ying Gao, Feng Pei
To further improve the speech separation effect of deep neural networks (DNN), a DNN speech separation algorithm is proposed in this paper based on segmented masking target. The algorithm combines the advantages of IBM and IRM in different signal-to-noise ratio (SNR) regions to construct a segmented masking target that can adapt to changes in SNR as the training target of DNN. In addition, to improve the accuracy of IRM estimation, a two-step prior SNR is used for the effective calculation to further improve the speech separation performance of the DNN model. Finally, the simulation experiments show that the improved target in this paper has a better speech separation effect than IBM and IRM.
{"title":"DNN Speech Separation Algorithm Based on Improved Segmented Masking Target","authors":"Meng Gao, Ying Gao, Feng Pei","doi":"10.1109/ICCSS53909.2021.9721985","DOIUrl":"https://doi.org/10.1109/ICCSS53909.2021.9721985","url":null,"abstract":"To further improve the speech separation effect of deep neural networks (DNN), a DNN speech separation algorithm is proposed in this paper based on segmented masking target. The algorithm combines the advantages of IBM and IRM in different signal-to-noise ratio (SNR) regions to construct a segmented masking target that can adapt to changes in SNR as the training target of DNN. In addition, to improve the accuracy of IRM estimation, a two-step prior SNR is used for the effective calculation to further improve the speech separation performance of the DNN model. Finally, the simulation experiments show that the improved target in this paper has a better speech separation effect than IBM and IRM.","PeriodicalId":435816,"journal":{"name":"2021 8th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132039003","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}
Traffic flow has the characteristics of complex spatial dependence and temporal dependence. Deep learning as a traffic flow prediction method can make full use of the temporal and spatial characteristics of traffic flow. In this paper, the road network is abstracted into a graph structure, the size of the graph structure is dynamically changed, and the graph convolutional neural network (GCN) and the long short term memory network (LSTM) are used to capture the temporal and spatial characteristics of traffic flow to solve the traffic flow prediction problem. Based on the data of vehicle speed in California bay area, the experiment is divided into three prediction scales. The effectiveness of the traffic flow prediction model is verified by experimental comparison.
{"title":"Spatial-temporal Traffic Flow Prediction Model Based on Dynamic Graph Structure","authors":"Q. Zhao, Qi-Wei Sun, Shiyuan Han, Jin Zhou, Yuehui Chen, Xiao-Fang Zhong","doi":"10.1109/ICCSS53909.2021.9722020","DOIUrl":"https://doi.org/10.1109/ICCSS53909.2021.9722020","url":null,"abstract":"Traffic flow has the characteristics of complex spatial dependence and temporal dependence. Deep learning as a traffic flow prediction method can make full use of the temporal and spatial characteristics of traffic flow. In this paper, the road network is abstracted into a graph structure, the size of the graph structure is dynamically changed, and the graph convolutional neural network (GCN) and the long short term memory network (LSTM) are used to capture the temporal and spatial characteristics of traffic flow to solve the traffic flow prediction problem. Based on the data of vehicle speed in California bay area, the experiment is divided into three prediction scales. The effectiveness of the traffic flow prediction model is verified by experimental comparison.","PeriodicalId":435816,"journal":{"name":"2021 8th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130438515","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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