Pub Date : 2026-03-13DOI: 10.1109/tcyb.2026.3669192
Xin Jin,Xiaojie Chen,Zhengxin Wang
In this article, we study the quasi-synchronization problems in multiplex networks under deception attacks. First, we propose a new model of multiplex networks with interlayer couplings under deception attacks. We assume that the attackers inject false data into the communication channels. Since interlayer couplings are taken into account, we consider not only the case where attacks occur in the intralayer channels, but also the case where attacks occur in the interlayer communication channels. Furthermore, we set two binary variables obeying a random Bernoulli distribution to characterize whether the attacks occur or not. We then design an impulsive controller to enable the nodal states to achieve the desired states. By means of the Lyapunov function method and the average impulsive interval method, we obtain the sufficient conditions under which the nodal states can achieve interlayer quasi-synchronization and intralayer quasi-synchronization, respectively. Naturally, we obtain the sufficient conditions under which the nodal states can achieve complete quasi-synchronization. Furthermore, we introduce a leader and design a different impulsive controller. Using the same theoretical approach, we derive the sufficient conditions under which the nodal states can achieve complete quasi-synchronization. Finally, we provide three numerical examples to confirm the theoretical results.
{"title":"Impulsive Intralayer and Interlayer Quasi-Synchronization Control in Multiplex Networks Under Deception Attacks.","authors":"Xin Jin,Xiaojie Chen,Zhengxin Wang","doi":"10.1109/tcyb.2026.3669192","DOIUrl":"https://doi.org/10.1109/tcyb.2026.3669192","url":null,"abstract":"In this article, we study the quasi-synchronization problems in multiplex networks under deception attacks. First, we propose a new model of multiplex networks with interlayer couplings under deception attacks. We assume that the attackers inject false data into the communication channels. Since interlayer couplings are taken into account, we consider not only the case where attacks occur in the intralayer channels, but also the case where attacks occur in the interlayer communication channels. Furthermore, we set two binary variables obeying a random Bernoulli distribution to characterize whether the attacks occur or not. We then design an impulsive controller to enable the nodal states to achieve the desired states. By means of the Lyapunov function method and the average impulsive interval method, we obtain the sufficient conditions under which the nodal states can achieve interlayer quasi-synchronization and intralayer quasi-synchronization, respectively. Naturally, we obtain the sufficient conditions under which the nodal states can achieve complete quasi-synchronization. Furthermore, we introduce a leader and design a different impulsive controller. Using the same theoretical approach, we derive the sufficient conditions under which the nodal states can achieve complete quasi-synchronization. Finally, we provide three numerical examples to confirm the theoretical results.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"232 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2026-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147447045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-12DOI: 10.1109/tcyb.2026.3667035
Wei Sun,Xueqi Wu,Shun-Feng Su
This article primarily investigates the tracking control problem of high-order uncertain nonlinear systems with odd-rational-power under false data injection (FDI) attacks and malicious attacks, based on the fully actuated system (FAS) theory. Due to the corruption of the state information by an additional attack signal, the true state information cannot be directly used for controller design. To mitigate the impact of unknown FDI attacks, a coordinate transformation is applied using the attacked state. In addition, using a piecewise smooth function approaching a saturation function, a new lemma is proposed to deal with the unknown control gain of the prescribed-time control input saturation and malicious attacks problem. Theoretical analysis demonstrates that the tracking errors converge in the prescribed time and all closed-loop system signals remain bounded. Finally, a numerical example is provided, along with a practical case study based on a single-link robotic manipulator, to validate the effectiveness of the proposed method.
{"title":"Fully Actuated System Approach-Based Tracking Control for High-Order Nonlinear System Under False Data Injection and Malicious Attacks.","authors":"Wei Sun,Xueqi Wu,Shun-Feng Su","doi":"10.1109/tcyb.2026.3667035","DOIUrl":"https://doi.org/10.1109/tcyb.2026.3667035","url":null,"abstract":"This article primarily investigates the tracking control problem of high-order uncertain nonlinear systems with odd-rational-power under false data injection (FDI) attacks and malicious attacks, based on the fully actuated system (FAS) theory. Due to the corruption of the state information by an additional attack signal, the true state information cannot be directly used for controller design. To mitigate the impact of unknown FDI attacks, a coordinate transformation is applied using the attacked state. In addition, using a piecewise smooth function approaching a saturation function, a new lemma is proposed to deal with the unknown control gain of the prescribed-time control input saturation and malicious attacks problem. Theoretical analysis demonstrates that the tracking errors converge in the prescribed time and all closed-loop system signals remain bounded. Finally, a numerical example is provided, along with a practical case study based on a single-link robotic manipulator, to validate the effectiveness of the proposed method.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"73 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2026-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147439320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-12DOI: 10.1109/tcyb.2026.3668987
Zhiliang Lin,Zhuangzhuang Chen,Guanming Zhu,Jiaxian Chen,Jianqiang Li
Offline reinforcement learning (RL) demonstrated remarkable performance in learning valid policies by benefiting from high-quality offline datasets. However, collecting such a dataset is labor-intensive, especially for humanoid locomotion. For this reason, many data augmentation techniques have been proposed to improve the quality of offline datasets through noise injection or data synthesis. However, existing data augmentation methods are noise-sensitive, resulting in limited capability in complex robotic environments. To address these issues, we propose guided amplified learning with Lipschitz constraint (GALC), a novel trajectory augmentation method that employs the reward-amplification-guided conditional diffusion model for noise-insensitive data augmentation. Specifically, we introduce a local Lipschitz continuity constraint to regulate the reverse denoising process from the offline dataset. Consequently, the exploration of the diffusion model can be restricted within the local continuity region of the original dataset, thereby generating high-reward trajectories. Moreover, the generated trajectories are also enforced to be noise-insensitive to perturbations, thus enjoying robustness. Notably, our proposed method can prevent the generation of unsafe actions that do not align with the environment dynamics. Extensive experiments on sparse reward scenarios and high-dimensional robotic tasks show that our proposed GALC achieves significant improvements in both the augmented trajectories and policy performance.
{"title":"GALC: Guided Amplified Learning With Lipschitz Constraint for Robust Trajectory Generation.","authors":"Zhiliang Lin,Zhuangzhuang Chen,Guanming Zhu,Jiaxian Chen,Jianqiang Li","doi":"10.1109/tcyb.2026.3668987","DOIUrl":"https://doi.org/10.1109/tcyb.2026.3668987","url":null,"abstract":"Offline reinforcement learning (RL) demonstrated remarkable performance in learning valid policies by benefiting from high-quality offline datasets. However, collecting such a dataset is labor-intensive, especially for humanoid locomotion. For this reason, many data augmentation techniques have been proposed to improve the quality of offline datasets through noise injection or data synthesis. However, existing data augmentation methods are noise-sensitive, resulting in limited capability in complex robotic environments. To address these issues, we propose guided amplified learning with Lipschitz constraint (GALC), a novel trajectory augmentation method that employs the reward-amplification-guided conditional diffusion model for noise-insensitive data augmentation. Specifically, we introduce a local Lipschitz continuity constraint to regulate the reverse denoising process from the offline dataset. Consequently, the exploration of the diffusion model can be restricted within the local continuity region of the original dataset, thereby generating high-reward trajectories. Moreover, the generated trajectories are also enforced to be noise-insensitive to perturbations, thus enjoying robustness. Notably, our proposed method can prevent the generation of unsafe actions that do not align with the environment dynamics. Extensive experiments on sparse reward scenarios and high-dimensional robotic tasks show that our proposed GALC achieves significant improvements in both the augmented trajectories and policy performance.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"19 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2026-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147439319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-11DOI: 10.1109/tcyb.2026.3662998
Haiwen Wu,Wei Chen,Jinfei Hu
This article investigates the problem of visual tracking of an unknown moving target by a network of robotic manipulators equipped with uncalibrated eye-in-hand cameras. The objective is to ensure that, for each robot, the target's projection is maintained at a specified position on the image plane, despite the uncalibrated camera parameters and uncertain, time-varying feature depths. The target's motion is assumed to be generated by a neutrally stable linear system, whose state and system matrix are not directly accessible to all robots. To address this problem, a distributed control scheme is developed in three steps. First, an adaptive distributed observer is introduced to estimate the motion of the moving target. Second, a novel image-space observer is designed for each robot to estimate the image-space position and to simultaneously provide the estimated image-space velocity, based on which the proposed distributed controller avoids using image-space velocity measurements. Third, by leveraging the linearly parameterized properties of the depth-independent image Jacobian matrix and the depth, adaptive laws are proposed to cope with uncertain parameters in cameras and robots. By using the Lyapunov stability theory, a rigorous analysis is provided to show the stability of the closed-loop system and asymptotic convergence of the image-space tracking errors. The effectiveness of the proposed scheme is illustrated through simulation with a group of three-DOF robotic manipulators.
{"title":"Uncalibrated Visual Tracking Control for Networked Eye-in-Hand Robots by Adaptive Distributed Observer.","authors":"Haiwen Wu,Wei Chen,Jinfei Hu","doi":"10.1109/tcyb.2026.3662998","DOIUrl":"https://doi.org/10.1109/tcyb.2026.3662998","url":null,"abstract":"This article investigates the problem of visual tracking of an unknown moving target by a network of robotic manipulators equipped with uncalibrated eye-in-hand cameras. The objective is to ensure that, for each robot, the target's projection is maintained at a specified position on the image plane, despite the uncalibrated camera parameters and uncertain, time-varying feature depths. The target's motion is assumed to be generated by a neutrally stable linear system, whose state and system matrix are not directly accessible to all robots. To address this problem, a distributed control scheme is developed in three steps. First, an adaptive distributed observer is introduced to estimate the motion of the moving target. Second, a novel image-space observer is designed for each robot to estimate the image-space position and to simultaneously provide the estimated image-space velocity, based on which the proposed distributed controller avoids using image-space velocity measurements. Third, by leveraging the linearly parameterized properties of the depth-independent image Jacobian matrix and the depth, adaptive laws are proposed to cope with uncertain parameters in cameras and robots. By using the Lyapunov stability theory, a rigorous analysis is provided to show the stability of the closed-loop system and asymptotic convergence of the image-space tracking errors. The effectiveness of the proposed scheme is illustrated through simulation with a group of three-DOF robotic manipulators.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"33 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2026-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147393753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article proposes a trajectory tracking strategy for nonuniform quay cranes to suppress flexible cable vibration and attenuate payload swing and rotation, thereby improving tracking accuracy and transport efficiency. To address the challenges posed by time-varying and spatially distributed partial differential equation models, we propose a Bayesian physics-informed neural network (BPINN) framework that integrates tension constraints into the loss function to suppress flexible cable vibrations. In the Bayesian setting, the BPINN acts as a prior model, and Hamiltonian Monte Carlo (HMC) sampling is employed to infer the posterior distribution of the system states. To handle the underactuated nature of the quay crane, differential flatness is exploited to map BPINN-predicted states into a flat output space, where an adaptive backstepping controller is designed to guarantee global uniform ultimate boundedness. Moreover, a multistrategy improved quantum-behaved particle swarm optimization (MIQPSO) scheme is introduced for online tuning of control parameters, achieving a favorable tradeoff between global exploration and fast convergence. Lyapunov analysis establishes closed-loop stability, and simulations and experiments demonstrate fast and accurate tracking as well as robust vibration suppression under external disturbances.
{"title":"Bayesian Physics-Informed Neural Networks With MIQPSO-Backstepping Control for Vibration Suppression in Nonuniform Quay Cranes.","authors":"Huapeng Zhang,Gan Yu,Kairong Duan,Weidong Zhang,Ning Sun,Wei Xie","doi":"10.1109/tcyb.2026.3671062","DOIUrl":"https://doi.org/10.1109/tcyb.2026.3671062","url":null,"abstract":"This article proposes a trajectory tracking strategy for nonuniform quay cranes to suppress flexible cable vibration and attenuate payload swing and rotation, thereby improving tracking accuracy and transport efficiency. To address the challenges posed by time-varying and spatially distributed partial differential equation models, we propose a Bayesian physics-informed neural network (BPINN) framework that integrates tension constraints into the loss function to suppress flexible cable vibrations. In the Bayesian setting, the BPINN acts as a prior model, and Hamiltonian Monte Carlo (HMC) sampling is employed to infer the posterior distribution of the system states. To handle the underactuated nature of the quay crane, differential flatness is exploited to map BPINN-predicted states into a flat output space, where an adaptive backstepping controller is designed to guarantee global uniform ultimate boundedness. Moreover, a multistrategy improved quantum-behaved particle swarm optimization (MIQPSO) scheme is introduced for online tuning of control parameters, achieving a favorable tradeoff between global exploration and fast convergence. Lyapunov analysis establishes closed-loop stability, and simulations and experiments demonstrate fast and accurate tracking as well as robust vibration suppression under external disturbances.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"16 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2026-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147393754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Accurate real-time prediction in dynamic industrial systems is crucial for optimization and efficiency. This article introduces a novel intelligent monitoring framework leveraging proportional-integral-derivative (PID)-optimized deep learning for adaptive time-frequency forecasting to address the challenges posed by nonstationary industrial data. The proposed method uniquely integrates a channel-independent separable dynamic filter (CSDF) that adapts to real-time multivariate process variables, minimizing cross-channel interference. Furthermore, a closed-loop PID optimization strategy enhances the convergence and prediction accuracy of the deep learning model. The effectiveness of this framework is demonstrated through a case study in the prediction of cleaned coal calorific value, a vital parameter for optimizing coal preparation processes and improving energy efficiency. Industrial experiments in this application show that the proposed method achieves a significant 5.36% increase in forecast hit rate (FHR) compared to existing techniques, highlighting its potential for advanced monitoring and control in dynamic systems.
{"title":"PID-Optimized Deep Learning for Adaptive Time-Frequency Forecasting in Dynamic Systems: Coal Calorific Value Prediction.","authors":"Hongwei Liu,Ning Liu,Wen Yu,Xiaoou Li,Yousheng Li,Yao Jia,Tianyou Chai","doi":"10.1109/tcyb.2026.3667581","DOIUrl":"https://doi.org/10.1109/tcyb.2026.3667581","url":null,"abstract":"Accurate real-time prediction in dynamic industrial systems is crucial for optimization and efficiency. This article introduces a novel intelligent monitoring framework leveraging proportional-integral-derivative (PID)-optimized deep learning for adaptive time-frequency forecasting to address the challenges posed by nonstationary industrial data. The proposed method uniquely integrates a channel-independent separable dynamic filter (CSDF) that adapts to real-time multivariate process variables, minimizing cross-channel interference. Furthermore, a closed-loop PID optimization strategy enhances the convergence and prediction accuracy of the deep learning model. The effectiveness of this framework is demonstrated through a case study in the prediction of cleaned coal calorific value, a vital parameter for optimizing coal preparation processes and improving energy efficiency. Industrial experiments in this application show that the proposed method achieves a significant 5.36% increase in forecast hit rate (FHR) compared to existing techniques, highlighting its potential for advanced monitoring and control in dynamic systems.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"1 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2026-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147393755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article aims to investigate the neural network (NN) nonsingular fixed-time adaptive consensus control issue for nonlinear multiagent systems (MASs) with parameter uncertainties. By introducing a generalized intermediate-variable-based disturbance observer (IVBDO), a novel distributed fixed-time NN adaptive controller is constructed based on the quartic Lyapunov function method. Under this protocol, the mismatched external disturbances of each agent are real-time online estimated; meanwhile, the singularity phenomenon during the fixed-time design process can be effectively eliminated. The presented control algorithm not only guarantees that the controlled system is semi-globally uniformly ultimately bounded (SGUUB) but also that the distributed output tracking errors converge to an adjustable compact set of the origin within a fixed-time interval. Simulation results are displayed to check the effectiveness of the suggested approach.
{"title":"Disturbance Observer-Based Neural Network Nonsingular Fixed-Time Adaptive Consensus Control for Uncertain Nonlinear Multiagent Systems.","authors":"Li-Bing Wu,Xiao-Ping Liu,Cun-Gen Liu,Huan-Qing Wang,Sheng-Juan Huang","doi":"10.1109/tcyb.2026.3668933","DOIUrl":"https://doi.org/10.1109/tcyb.2026.3668933","url":null,"abstract":"This article aims to investigate the neural network (NN) nonsingular fixed-time adaptive consensus control issue for nonlinear multiagent systems (MASs) with parameter uncertainties. By introducing a generalized intermediate-variable-based disturbance observer (IVBDO), a novel distributed fixed-time NN adaptive controller is constructed based on the quartic Lyapunov function method. Under this protocol, the mismatched external disturbances of each agent are real-time online estimated; meanwhile, the singularity phenomenon during the fixed-time design process can be effectively eliminated. The presented control algorithm not only guarantees that the controlled system is semi-globally uniformly ultimately bounded (SGUUB) but also that the distributed output tracking errors converge to an adjustable compact set of the origin within a fixed-time interval. Simulation results are displayed to check the effectiveness of the suggested approach.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"54 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2026-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147383565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-10DOI: 10.1109/tcyb.2026.3667151
Zhuwu Shao,Yujuan Wang,Guangdeng Chen,Hongyi Li,Yongduan Song
A novel approach is proposed for flexible performance-based control of strict-feedback systems subject to input saturation. The core design consists of three key components. First, the regulation of the performance function (PF) is achieved by reformulating it as an adaptive modification of its exponential index, exploiting its inherent structural properties. Second, a performance indicator function (PIF) is constructed based on output-side information by analyzing the system behavior in the absence of saturation, thereby avoiding the reliance on input-side compensation signals with limited differentiability. Third, a first-order auxiliary system is designed to adaptively adjust the exponential index in real time, driving the PIF to closely track the upper envelope of the actual tracking error. As a result, the proposed self-adjusting PF (SAPF) is able to maintain a dynamic balance between input and output behaviors by relaxing performance boundaries when constraint violations are imminent while actively accelerating PF contraction to enhance transient performance under saturation constraints. Building on this framework, an SAPF-based control algorithm is developed with rigorous closed-loop stability guarantees. Finally, the effectiveness and superiority of the proposed method are demonstrated through quantitative simulation comparisons with two advanced algorithms in a vehicle lane-keeping task.
{"title":"A New Implementation Pathway: Self-Adjusting Performance Function-Based Control for Strict-Feedback Systems Under Input Saturation.","authors":"Zhuwu Shao,Yujuan Wang,Guangdeng Chen,Hongyi Li,Yongduan Song","doi":"10.1109/tcyb.2026.3667151","DOIUrl":"https://doi.org/10.1109/tcyb.2026.3667151","url":null,"abstract":"A novel approach is proposed for flexible performance-based control of strict-feedback systems subject to input saturation. The core design consists of three key components. First, the regulation of the performance function (PF) is achieved by reformulating it as an adaptive modification of its exponential index, exploiting its inherent structural properties. Second, a performance indicator function (PIF) is constructed based on output-side information by analyzing the system behavior in the absence of saturation, thereby avoiding the reliance on input-side compensation signals with limited differentiability. Third, a first-order auxiliary system is designed to adaptively adjust the exponential index in real time, driving the PIF to closely track the upper envelope of the actual tracking error. As a result, the proposed self-adjusting PF (SAPF) is able to maintain a dynamic balance between input and output behaviors by relaxing performance boundaries when constraint violations are imminent while actively accelerating PF contraction to enhance transient performance under saturation constraints. Building on this framework, an SAPF-based control algorithm is developed with rigorous closed-loop stability guarantees. Finally, the effectiveness and superiority of the proposed method are demonstrated through quantitative simulation comparisons with two advanced algorithms in a vehicle lane-keeping task.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"44 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2026-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147383564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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