Pub Date : 2021-12-10DOI: 10.1109/ICCSS53909.2021.9721999
Guohuai Lin, Zhijian Cheng, Hongru Ren, Hongyi Li, Renquan Lu
This paper studies the adaptive neural finite-time attitude control problem for six-rotor unmanned aerial vehicles (UAVs) with dead-zone inputs. Under the assumption that control inputs of leader are provided by a human operator, the command-filter-based finite-time attitude control protocol is proposed to achieve leader-follower consensus in finite time. In the control design, the command filter technique and radial basis function neural networks (RBF NNs) are adopted to solve the problems of explosion of complexity and uncertain nonlinear dynamics, respectively. In addition, dead-zone nonlinearities of control inputs are compensated by the boundedness of dead-zone slopes. Based on the presented control scheme, the finite-time stability of UAVs is obtained via the Lyapunov stability theory. Finally, simulation results validate the control property of the proposed strategy.
{"title":"Command-Filter-Based Finite-Time Control for Human-in-the-Loop UAVs With Dead-Zone Inputs","authors":"Guohuai Lin, Zhijian Cheng, Hongru Ren, Hongyi Li, Renquan Lu","doi":"10.1109/ICCSS53909.2021.9721999","DOIUrl":"https://doi.org/10.1109/ICCSS53909.2021.9721999","url":null,"abstract":"This paper studies the adaptive neural finite-time attitude control problem for six-rotor unmanned aerial vehicles (UAVs) with dead-zone inputs. Under the assumption that control inputs of leader are provided by a human operator, the command-filter-based finite-time attitude control protocol is proposed to achieve leader-follower consensus in finite time. In the control design, the command filter technique and radial basis function neural networks (RBF NNs) are adopted to solve the problems of explosion of complexity and uncertain nonlinear dynamics, respectively. In addition, dead-zone nonlinearities of control inputs are compensated by the boundedness of dead-zone slopes. Based on the presented control scheme, the finite-time stability of UAVs is obtained via the Lyapunov stability theory. Finally, simulation results validate the control property of the proposed strategy.","PeriodicalId":435816,"journal":{"name":"2021 8th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS)","volume":"7 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":"114302678","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/ICCSS53909.2021.9721970
Xuena Zhao, Jun-yuan Gu, Zhijie Liu, Wei He
This study focuses on the input quantization control for the linear parabolic PDEs with local piecewise controllers and pointwise measurements. To estimate the unmeasured state for controller design, we construct a PDE observer based on feedback signals. And then a quantization feedback compensator is proposed to exponentially stabilize the linear parabolic PDE systems. The closed-loop system stability is proven by Lyapunov direct method. Further, simulation results are presented to demonstrate the correctness of the theoretical proof.
{"title":"Observer-based feedback control for linear parabolic PDEs with quantized input","authors":"Xuena Zhao, Jun-yuan Gu, Zhijie Liu, Wei He","doi":"10.1109/ICCSS53909.2021.9721970","DOIUrl":"https://doi.org/10.1109/ICCSS53909.2021.9721970","url":null,"abstract":"This study focuses on the input quantization control for the linear parabolic PDEs with local piecewise controllers and pointwise measurements. To estimate the unmeasured state for controller design, we construct a PDE observer based on feedback signals. And then a quantization feedback compensator is proposed to exponentially stabilize the linear parabolic PDE systems. The closed-loop system stability is proven by Lyapunov direct method. Further, simulation results are presented to demonstrate the correctness of the theoretical proof.","PeriodicalId":435816,"journal":{"name":"2021 8th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS)","volume":"31 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":"114419706","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/ICCSS53909.2021.9721963
Haiming Du, Jian Sun, Gang Wang
To handle the strongly coupled, nonlinear and un-modeled disturbance in UAVs, an adaptive terminal sliding mode control strategy based on characteristic modeling is presented, which achieves improved attitude control accuracy and robustness. Specifically, a characteristic model of quadrotor attitude control is first established. Then, using sliding mode control theory, a characteristic model-based adaptive terminal sliding mode control law is designed and utilized to improve control effects. Finally, simulation and real flight experimental results demonstrate that the proposed method enjoys effectiveness and superiority.
{"title":"Attitude Control of Quadrotor UAVs Using Adaptive Terminal Sliding Mode Control","authors":"Haiming Du, Jian Sun, Gang Wang","doi":"10.1109/ICCSS53909.2021.9721963","DOIUrl":"https://doi.org/10.1109/ICCSS53909.2021.9721963","url":null,"abstract":"To handle the strongly coupled, nonlinear and un-modeled disturbance in UAVs, an adaptive terminal sliding mode control strategy based on characteristic modeling is presented, which achieves improved attitude control accuracy and robustness. Specifically, a characteristic model of quadrotor attitude control is first established. Then, using sliding mode control theory, a characteristic model-based adaptive terminal sliding mode control law is designed and utilized to improve control effects. Finally, simulation and real flight experimental results demonstrate that the proposed method enjoys effectiveness and superiority.","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":"122058496","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/ICCSS53909.2021.9721982
Lu Zhang, Qinchao Meng
In this paper, a reference-vector-based strength Pareto evolutionary algorithm 2 (RVSPEA2) is proposed to deal with the multiobjective continuous optimization problems. In the proposed RVSPEA2, an objective normalization technique is firstly applied to guarantee the consistency of disparately scaled objectives. Then an improved solutions selection mechanism, based on the reference vectors generation and niche-selection operation, is designed to improve the diversity and convergence of the optimal solutions. Finally, some benchmark test problems are applied to evaluate the effectiveness of the proposed RVSPEA2 algorithm. The results showed that this algorithm performs well than other compared optimization algorithms on convergence and diversity.
{"title":"A Reference-Vector-Based Strength Pareto Evolutionary Algorithm 2","authors":"Lu Zhang, Qinchao Meng","doi":"10.1109/ICCSS53909.2021.9721982","DOIUrl":"https://doi.org/10.1109/ICCSS53909.2021.9721982","url":null,"abstract":"In this paper, a reference-vector-based strength Pareto evolutionary algorithm 2 (RVSPEA2) is proposed to deal with the multiobjective continuous optimization problems. In the proposed RVSPEA2, an objective normalization technique is firstly applied to guarantee the consistency of disparately scaled objectives. Then an improved solutions selection mechanism, based on the reference vectors generation and niche-selection operation, is designed to improve the diversity and convergence of the optimal solutions. Finally, some benchmark test problems are applied to evaluate the effectiveness of the proposed RVSPEA2 algorithm. The results showed that this algorithm performs well than other compared optimization algorithms on convergence and diversity.","PeriodicalId":435816,"journal":{"name":"2021 8th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS)","volume":"35 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":"122636913","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/ICCSS53909.2021.9722021
Zeyu Ruan, Junhao Hu, Jun Mei
This paper investigates the finite-time synchronization (FETS) issue for a class of chaotic neural networks with time delays via event-triggered intermittent control. The event-triggered intermittent controller, in which intermittent instants are not predesigned, is explored to achieve FETS for delayed chaotic neural networks (DCNNs). By utilizing finite-time theory and constructing Lyapunov functional, several sufficient conditions for FETS are obtained under the designed control scheme. Meanwhile, the Zeno behavior is excluded. Our results about FETS criterion are new and valid, and enrich some of the existing results. In the end, numerical simulation verifies the effectiveness of the theoretical analysis.
{"title":"Finite-time synchronization of delayed chaotic neural networks based on event-triggered intermittent control","authors":"Zeyu Ruan, Junhao Hu, Jun Mei","doi":"10.1109/ICCSS53909.2021.9722021","DOIUrl":"https://doi.org/10.1109/ICCSS53909.2021.9722021","url":null,"abstract":"This paper investigates the finite-time synchronization (FETS) issue for a class of chaotic neural networks with time delays via event-triggered intermittent control. The event-triggered intermittent controller, in which intermittent instants are not predesigned, is explored to achieve FETS for delayed chaotic neural networks (DCNNs). By utilizing finite-time theory and constructing Lyapunov functional, several sufficient conditions for FETS are obtained under the designed control scheme. Meanwhile, the Zeno behavior is excluded. Our results about FETS criterion are new and valid, and enrich some of the existing results. In the end, numerical simulation verifies the effectiveness of the theoretical analysis.","PeriodicalId":435816,"journal":{"name":"2021 8th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS)","volume":"249 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":"122730043","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/ICCSS53909.2021.9721962
Shuang Feng, Bingshu Wang, C. L. Philip Chen
The broad learning system (BLS) has been attracting more and more attention due to its excellent property in the field of machine learning. A great deal of variants and hybrid structures of BLS have also been designed and developed for better performance in some specialized tasks. In this paper, the Chebyshev polynomials are introduced into the BLS to take advantage of their powerful approximation capability, where the feature windows are replaced by a set of Chebyshev polynomials. This new variant, named Chebyshev polynomial BLS (CPBLS), has a light structure with a reduction in computational complexity since the sparse autoencoder is removed. Instead, the dimension of each input sample is expended by n + 1 Chebyshev polynomials, mapping the original feature into a new feature space with higher dimension, which helps to classify the patterns in training. The proposed CPBLS is evaluated by some popular datasets from UCI and KEEL repositories, and it outperforms some representative neural networks and neuro-fuzzy models in terms of classification accuracy. The CPBLS also show some advantages over the recent developed compact fuzzy BLS (CFBLS) which indicates its great potential in future research and real-world applications.
{"title":"Chebyshev Polynomial Broad Learning System","authors":"Shuang Feng, Bingshu Wang, C. L. Philip Chen","doi":"10.1109/ICCSS53909.2021.9721962","DOIUrl":"https://doi.org/10.1109/ICCSS53909.2021.9721962","url":null,"abstract":"The broad learning system (BLS) has been attracting more and more attention due to its excellent property in the field of machine learning. A great deal of variants and hybrid structures of BLS have also been designed and developed for better performance in some specialized tasks. In this paper, the Chebyshev polynomials are introduced into the BLS to take advantage of their powerful approximation capability, where the feature windows are replaced by a set of Chebyshev polynomials. This new variant, named Chebyshev polynomial BLS (CPBLS), has a light structure with a reduction in computational complexity since the sparse autoencoder is removed. Instead, the dimension of each input sample is expended by n + 1 Chebyshev polynomials, mapping the original feature into a new feature space with higher dimension, which helps to classify the patterns in training. The proposed CPBLS is evaluated by some popular datasets from UCI and KEEL repositories, and it outperforms some representative neural networks and neuro-fuzzy models in terms of classification accuracy. The CPBLS also show some advantages over the recent developed compact fuzzy BLS (CFBLS) which indicates its great potential in future research and real-world applications.","PeriodicalId":435816,"journal":{"name":"2021 8th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS)","volume":"10 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":"122757051","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/ICCSS53909.2021.9721975
A. Ramkissoon, W. Goodridge
Mobile Ad Hoc Network Messaging has become an integral part of today’s social communication landscape. They are used in a variety of applications. One major problem that these networks face is the spread of fake news. This problem can have serious deleterious effects on our social data driven society. Detecting fake news has proven to be challenging even for modern day algorithms. This research presents, Veracity, a unique computational social system to accomplish the task of Fake News Detection in MANET Messaging. The Veracity architecture attempts to model social behaviour and human reactions to news spread over a MANET. Veracity introduces five new algorithms namely, VerifyNews, CompareText, PredictCred, CredScore and EyeTruth for the capture, computation and analysis of the credibility and content data features. The Veracity architecture works in a fully distributed and infrastructureless environment. This study validates Veracity using a generated dataset with features relating to the credibility of news publishers and the content of the message to predict fake news. These features are analysed using a machine learning prediction model. The results of these experiments are analysed using four evaluation methodologies. The analysis reveals positive performance with the use of the fake news detection architecture.
{"title":"Veracity: A Fake News Detection Architecture for MANET Messaging","authors":"A. Ramkissoon, W. Goodridge","doi":"10.1109/ICCSS53909.2021.9721975","DOIUrl":"https://doi.org/10.1109/ICCSS53909.2021.9721975","url":null,"abstract":"Mobile Ad Hoc Network Messaging has become an integral part of today’s social communication landscape. They are used in a variety of applications. One major problem that these networks face is the spread of fake news. This problem can have serious deleterious effects on our social data driven society. Detecting fake news has proven to be challenging even for modern day algorithms. This research presents, Veracity, a unique computational social system to accomplish the task of Fake News Detection in MANET Messaging. The Veracity architecture attempts to model social behaviour and human reactions to news spread over a MANET. Veracity introduces five new algorithms namely, VerifyNews, CompareText, PredictCred, CredScore and EyeTruth for the capture, computation and analysis of the credibility and content data features. The Veracity architecture works in a fully distributed and infrastructureless environment. This study validates Veracity using a generated dataset with features relating to the credibility of news publishers and the content of the message to predict fake news. These features are analysed using a machine learning prediction model. The results of these experiments are analysed using four evaluation methodologies. The analysis reveals positive performance with the use of the fake news detection architecture.","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":"129913386","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/iccss53909.2021.9722035
{"title":"[Copyright notice]","authors":"","doi":"10.1109/iccss53909.2021.9722035","DOIUrl":"https://doi.org/10.1109/iccss53909.2021.9722035","url":null,"abstract":"","PeriodicalId":435816,"journal":{"name":"2021 8th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS)","volume":"56 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":"125056741","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/ICCSS53909.2021.9721968
Xiaofeng Xu, Xianglin Bao, Ruiheng Zhang, Xingyu Lu
Zero-shot learning (ZSL) is a challenging but practical task in the computer vision field. ZSL tries to recognize new unknown categories by provided with training data from other known categories. Recently, the ZSL problem can be solved in a supervised learning way by using deep generative models to synthesize data as the training data for unknown categories. In this work, we design an end-to-end supervised ZSL method in which the data generation network and the object classification network are trained jointly. To enhance the generalization performance of the proposed supervised ZSL method, meta-learning strategy is introduced to mitigate the domain shift problem between the synthesized data and the real data of unknown categories. Experimental results on ZSL standard datasets demonstrate the significant superiority of the end-to-end strategy and the meta-learning strategy for the proposed model in ZSL tasks.
{"title":"End-to-End Supervised Zero-Shot Learning with Meta-Learning Strategy","authors":"Xiaofeng Xu, Xianglin Bao, Ruiheng Zhang, Xingyu Lu","doi":"10.1109/ICCSS53909.2021.9721968","DOIUrl":"https://doi.org/10.1109/ICCSS53909.2021.9721968","url":null,"abstract":"Zero-shot learning (ZSL) is a challenging but practical task in the computer vision field. ZSL tries to recognize new unknown categories by provided with training data from other known categories. Recently, the ZSL problem can be solved in a supervised learning way by using deep generative models to synthesize data as the training data for unknown categories. In this work, we design an end-to-end supervised ZSL method in which the data generation network and the object classification network are trained jointly. To enhance the generalization performance of the proposed supervised ZSL method, meta-learning strategy is introduced to mitigate the domain shift problem between the synthesized data and the real data of unknown categories. Experimental results on ZSL standard datasets demonstrate the significant superiority of the end-to-end strategy and the meta-learning strategy for the proposed model in ZSL tasks.","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":"128665158","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/ICCSS53909.2021.9721993
Changwei Lv, Ming Liu, Junwei Duan
With the application of wireless sensor-actuator networks in the Industrial Internet of Things (IIoT), it is crucially important to ensure the real-timing of data transmission. The millimeter wave (mmWave) communicating at the extremely high frequency band is a promising solution for the rapidly expanding data throughput in IIoT, due to the wide usable frequency band. In extremely high frequency band, the channel coherent time will be obviously reduced and becomes shorter than the frame duration. In this case, the channel state information (CSI) acquisition based on channel estimation will provide outdated information for coherent signal detection. Therefore, forecasting the channel variation for real-time data transmission is necessary. In this paper, we investigate the channel prediction methods in both the frequency and time domains for mmWave single-carrier frequency-domain-equalization (SC-FDE) systems. In the frequency domain, the channel prediction is conducted on each subcarrier, while the time domain predictor on each channel tap. As a number of the channel taps in the time domain are mainly composed of estimation noise, we separate these channel taps composed of estimation noise from the significant taps before building the prediction model. In this paper, the autoregressive (AR) model is employed to perform the channel prediction in the both domains. The simulation results show that the time domain predictor increases the prediction accuracy while reducing the computation complexity.
{"title":"Channel Prediction for Real-Time Wireless Communication with MmWave SC-FDE in IIoT Systems","authors":"Changwei Lv, Ming Liu, Junwei Duan","doi":"10.1109/ICCSS53909.2021.9721993","DOIUrl":"https://doi.org/10.1109/ICCSS53909.2021.9721993","url":null,"abstract":"With the application of wireless sensor-actuator networks in the Industrial Internet of Things (IIoT), it is crucially important to ensure the real-timing of data transmission. The millimeter wave (mmWave) communicating at the extremely high frequency band is a promising solution for the rapidly expanding data throughput in IIoT, due to the wide usable frequency band. In extremely high frequency band, the channel coherent time will be obviously reduced and becomes shorter than the frame duration. In this case, the channel state information (CSI) acquisition based on channel estimation will provide outdated information for coherent signal detection. Therefore, forecasting the channel variation for real-time data transmission is necessary. In this paper, we investigate the channel prediction methods in both the frequency and time domains for mmWave single-carrier frequency-domain-equalization (SC-FDE) systems. In the frequency domain, the channel prediction is conducted on each subcarrier, while the time domain predictor on each channel tap. As a number of the channel taps in the time domain are mainly composed of estimation noise, we separate these channel taps composed of estimation noise from the significant taps before building the prediction model. In this paper, the autoregressive (AR) model is employed to perform the channel prediction in the both domains. The simulation results show that the time domain predictor increases the prediction accuracy while reducing the computation complexity.","PeriodicalId":435816,"journal":{"name":"2021 8th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS)","volume":"8 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":"114935523","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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