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Online Flow Scheduling in Virtualized Time-Sensitive Networks: A Joint Admission control and VNF Embedding Approach

IEEE Transactions on Mobile Computing 2025年第6期24卷 4651-4667页

作者： Zhang, Yajing Chen, Cailian Li, Mingyan You, Chaoqun Guan, Xinping Quek, Tony Q. S. Shanghai Satellite Network Research Institute Co. Ltd. Shanghai200120 China Shanghai Jiao Tong University Department of Automation Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China Chongqing University College of Computer Science Chongqing400044 China Fudan University School of Computer Science Shanghai200433 China Singapore University of Technology and Design 487372 Singapore

Time-sensitive networking (TSN) is proposed to satisfy the increasingly stringent demands of Industrial 4.0 for deterministic transmission. This is achieved by generating a series of centrally configured gate control lists to strictly restrict the forwarding time of arriving flows. However, such a centralized scheme requires prior information of all flows, severely impeding TSN from providing an online response to dynamic industrial applications. To solve this problem, we innovatively propose to use admission control (AC) to realize deterministic transmission in the virtualized TSN network. In this approach, AC is distributively executed on each node and link, whereby flows of applications are served by passing through a series of virtual network functions (VNFs). This distributed AC execution is regarded as a VNF embedding (VNE) process. Specifically, we propose a two-stage online framework, Smart Admission control (SmartAC), to cater to dynamic applications. The first stage, referred to as the static stage, obtains a deterministic VNE solution by synthesizing AC decisions of individual TSN nodes and links. The second stage, referred to as the dynamic stage, fine-tunes VNE solutions obtained from the static stage to adapt to the harsh environment with insufficient resources or limited VNF migration budgets. Simulation results demonstrate the effectiveness of SmartAC in improving response rate and resource utilization ratio. Notably, SmartAC reduces runtime by 90% compared to existing algorithms and exhibits robustness across different network topologies. © 2002-2012 IEEE.

关键词： Budget control

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Blending Parametric Model and Neural Refinement: A Coarse-to-Fine Approach for Predicting Facial Changes in Orthognathic Surgery

Blending Parametric Model and Neural Refinement: A Coarse-to...

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IEEE International Symposium on Biomedical Imaging

作者： Jiawen Yang Dongming He Xuanyu Tian Lei Ma Jingyi Yu Xudong Wang Yuyao Zhang School of Information Science and Technology ShanghaiTech University Shanghai China Shanghai Ninth People's Hospital China Department of Control Science and Engineering School of Electronics and Information Engineering Tongji University Shanghai China

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Accurate prediction of postoperative facial soft tissue changes is crucial for effective preoperative planning in orthognathic deformity surgery. In this paper, we propose a novel coarse-to-fine framework for predicting these changes. The coarse module employs the parametric statistical face model, SCULPTOR, to generate an initial prediction of the postoperative face. SCULPTOR effectively captures the bidirectional linear relationship between the outer facial surface and underlying bone structures, while modeling surgical deformations. In the fine module, the coarse predictions are first converted into UV maps, which are then refined using FocalNet as the facial feature extractor (FFE), incorporating a craniofacial transformation module (CTM) to capture the non-linear relationship between the skeleton and facial surface. Experimental results on patients undergoing orthognathic surgery demonstrate that our method surpasses state-of-the-art techniques in predicting postoperative facial outcomes.

关键词： Surface reconstruction Accuracy Surgery Feature extraction Bones Parametric statistics Planning Reliability Facial features Faces

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Autotuned Non-Linear Extended-State-Observer Based Fixed-Time control for Motor-Drive Servo Systems

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IEEE Transactions on Power Electronics 2025年

作者： Xu, Ruiqi Liu, Jianxing Lin, Xinpo Liu, Zhuang Yan, Fei Gao, Yabin Harbin Institute of Technology Department of Control Science and Engineering Harbin150001 China Harbin Institute of Technology Department of Control Science and Engineering Weihai264209 China Southwest Jiaotong University School of Information Science and Technology Chengdu611756 China

To enhance the position tracking performance of permanent magnet synchronous motors (PMSMs) under unknown disturbances, this paper proposes a fast fixed-time control strategy based on an improved nonlinear extended state observer (NESO). First, load disturbances and nonlinear friction are considered as lumped disturbance, for which a NESO is designed to estimate the lumped disturbance. The control parameters of the NESO are tuned online using a neural network optimization algorithm, eliminating the need for offline training. Then, a robust fixed-time sliding mode control method is proposed, based on an improved nonsingular fast terminal sliding mode manifold, which offers better convergence performance. The Lyapunov method is used to prove the fixed-time stability of the position tracking error system. Finally, the effectiveness of the proposed method is validated on an experimental platform with PMSMs, and it is compared with other advanced fixed-time position control methods. The comparison results confirm that the proposed method exhibits superior control performance. © 1986-2012 IEEE.

关键词： State estimation

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State-Dependent Switching control for Nonlinear Impulsive Switched Systems With Dwell Time

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IEEE Transactions on Automation Science and engineering 2025年 22卷 12093-12101页

作者： Yang, Dan Li, Xiaodi Zhang, Guofeng Lee, Heung Wing Joseph Shandong Normal University School of Mathematics and Statistics Jinan250014 China Yantai University School of Mathematics and Information Science Yantai264005 China Shandong Normal University Shandong Provincial Engineering Research Center of System Control and Intelligent Technology Jinan250358 China The Hong Kong Polytechnic University Department of Applied Mathematics Hong Kong Hong Kong Polytechnic University Shenzhen Research Institute Shenzhen518057 China

This paper investigates the problem of globally exponential stability (GES) for impulsive switched systems via a hybrid switching strategy. First, a switching law combining state-dependent and time-dependent (dwell time) switching strategies is presented in the framework of impulse action. On the basis of the switching law, some GES criteria are proposed by using multiple Lyapunov function technique. A potential relationship among system parameters, impulse action including impulse amplitude and impulse frequency, and the combined switching law is established, which is of vital importance to achieving GES. In addition, when the impulse action involved in switched system is given in terms of stabilizing impulse, the impulsive controller including impulsive gains and impulsive frequency can be designed with the help of the proposed switching law to guarantee the stability of the addressed system. Finally, a simulation example is demonstrated to verify the validity of the proposed switching law and corresponding results. © 2004-2012 IEEE.

关键词： Switches Switched systems control systems Stability criteria Symmetric matrices Automation Lyapunov methods Germanium Electronic mail Trajectory

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Designing 24 novel carbon allotropes in Pmmm space group: First-principles calculation

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Diamond and Related Materials 2025年 155卷

作者： Liu, Heng Xing, Mengjiang Li, Xiaozhen He, Fa Qin, Tianying Zeng, Yurong Fan, Qingyang School of Electronic Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China State Key Laboratory of Electronic Thin Films and Integrated Devices University of Electronic Science and Technology of China Chengdu610054 China Department of Information and Technology Kunming University Kunming650214 China Quanzhou University of Information Engineering Quanzhou362000 China College of Information and Control Engineering Xi'an University of Architecture and Technology Xi'an710055 China

Utilizing advanced high-throughput computational techniques in conjunction with first-principles calculations, a total of 24 carbon allotropes belonging to the Pmmm space group (No. 47) have been successfully identified. Following the optimization of structures, the removal of redundant configurations, and rigorous stability assessments, 24 stable carbon allotropes with sp3 hybridization were finally determined from 107 initial candidates. Notably, 12 of these carbon phases possess superhard properties, and 6 were identified as direct bandgap semiconductors. The analysis revealed that carbon allotropes with greater densities tend to display advantageous properties, including lower enthalpy values, increased hardness, and broader bandgaps. Compared to previously reported carbon allotropes in the Pmmm space group, such as oP16, oP12, oP18, and oP72, the newly developed materials show significantly improved mechanical properties and enhanced stability. The bandgaps of the 24 newly discovered carbon allotropes predominantly range from 2.3 eV to 3.5 eV, with Pmmm C3 exhibiting the largest bandgap at 4.42 eV and Pmmm C16 presenting the smallest bandgap at 1.56 eV. Pmmm C3 not only exhibits outstanding hardness of 73 GPa and an extremely low enthalpy of 0.24 eV/atom but also has a very high Debye temperature of 2017 K. These novel carbon materials are expected to provide a crucial theoretical foundation for future experimental research work. © 2025

关键词： Brinell Hardness

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Identify influential nodes in directed networks: A neighborhood entropy-based method

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Chaos, Solitons and Fractals 2025年 197卷

作者： Dong, Ang Feng, Ru Qiu, Lipeng Wu, Yali Ren, Yuanguang Zhou, Aoran Department of Information and Control Engineering Xi'an University of Technology Xi'an710048 China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an710048 China State Key Laboratory of Metal Extrusion and Forging Equipment Technology China National Heavy Machinery Research Institute Co. Ltd Xi'an710016 China

The identification of influential nodes in complex networks is prominent and actively researched topic, with applications across various fields. Despite the emergence of numerous methods, many of these are inadequate for directed networks due to the asymmetry of information flow. To address this challenge, we propose a Neighborhood Entropy-based Method (NEM) for identifying key nodes in directed networks. Inspired by Shannon entropy, the k-orders in-degree entropy and k-orders out-degree entropy of nodes are defined to capture the neighborhood information of the node. A tunable parameter α is introduced to balance the contribution of in-neighbors and out-neighbors on neighbor influence ability, while a decay factor φk is set to adjust the influence of different order neighbors on the node. The importance of a node can be eventually evaluated by the influence capability of its in-neighbors and itself. To assess the effectiveness of NEM, we benchmark it against six existing methods on six real network datasets. The results demonstrate the high correctness of NEM, with its low time complexity enabling efficient application to large-scale directed networks. © 2025 Elsevier Ltd

关键词： k-nearest neighbors

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Chattering Phenomena in Time-Optimal control for High-Order Chain-of-Integrator Systems With Full State Constraints

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IEEE Transactions on Automatic control 2025年

作者： Wang, Yunan Hu, Chuxiong Li, Zeyang Lin, Yujie Lin, Shize He, Suqin Department of Mechanical Engineering Tsinghua University Beijing China Mechatronic Control Engineering Zhejiang University Hangzhou China Laboratory for Information and Decision Systems Massachusetts Institute of Technology United States Department of Mathematical Sciences Tsinghua University Beijing China

Time-optimal control for high-order chain-of-integrator systems with full state constraints remains an open and challenging problem within the discipline of optimal control. The behavior of optimal control in high-order problems lacks precise characterization. Even the existence of the chattering phenomenon, i.e., the control switches infinitely many times over a finite period, remains unknown. This paper establishes a theoretical framework for chattering in the problem, providing novel findings on the uniqueness of state constraints inducing chattering, the upper bound of switching times in an unconstrained arc during chattering, and the convergence of states and costates to the chattering limit point. For the first time, this paper proves the existence of chattering in the considered problem. The chattering optimal control for 4th-order problems with velocity constraints is precisely solved, providing an approach to plan time-optimal snap-limited trajectories. Other cases of order 4 are proved not to allow chattering. The conclusions rectify a prevalent misconception in the industry concerning the time-optimality of S-shaped trajectories with minimal switching times. © 1963-2012 IEEE.

关键词： Optimal control systems

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Error-Accumulation Improved Newton Algorithm in Model Predictive control for Novel Compliant Actuator-Driven Upper-Limb Exoskeleton

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Tsinghua Science and technology 2025年第5期30卷 1965-1979页

作者： Changxian Xu Jiliang Zhang Keping Liu Jian Wang Zhongbo Sun Department of Mechanical and Electrical Engineering Changchun University of Technology Changchun China College of Information Science and Engineering Northeastern University Shenyang China National Mobile Communications Research Laboratory Southeast University Nanjing China School of Electrical and Information Engineering Jilin Engineering Normal University Changchun China Laboratory of Cognitive and Decision Intelligence for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Department of Control Engineering Changchun University of Technology Changchun China

In this paper, a Novel Compliant Actuator (NCA)-driven Upper-Limb Exoskeleton (ULE) with force controllable, impact resistance, and back drivability is designed to ensure the safety of the subject during Human-Robot Interaction (HRI) processing. Based on the designed NCA-driven ULE, this paper constructs a Model Predictive control Scheme (MPCS) for force trajectory tracking, which minimises future tracking errors by solving an optimal control problem with inequality constraints. In addition, an Error-Accumulation Improved Newton Algorithm (EAINA) is proposed to solve the MPCS for suppressing various noises and external disturbances. The proposed EAINA is theoretically proved to have small steady state for noise conditions and stability of the EAINA using Lyapunov method. Finally, experimental results verify that the proposed MPCS solved by the EAINA in the NCA-driven ULE achieves robustness, fast convergence, strong tolerance and stability for trajectory rehabilitation task.

关键词： Actuators Trajectory tracking Noise Exoskeletons Force Human-robot interaction Prediction algorithms Stability analysis Robustness Predictive control

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Event-triggered memory sliding mode load frequency control of power system with BESSs against frequency-based deception attacks

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Journal of the Franklin Institute 2025年第7期362卷

作者： Qiao, Siwei Liu, Xinghua Wen, Shiping Xiao, Gaoxi Chen, Badong Ge, Shuzhi Sam Department of Power Grid Information and Control Engineering the School of Electrical Engineering Xi'an University of Technology Xi'an710048 China Faculty of Engineering and Information Technology Australian AI Institute University of Technology Sydney UltimoNSW2007 Australia School of Electrical and Electronic Engineering Nanyang Technological University Singapore639798 Singapore Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an710049 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore

An event-triggered memory sliding mode load frequency control (LFC) approach is proposed to address the cyber-security issue of multi-area power systems with battery energy storage systems (BESSs). To reveal the impact of cyber-attacks on the security and stability of multi-area power systems with BESSs, a frequency-based deception attack is considered as a contamination of the communication network. Attackers are assumed to determine the time of cyber-attacks by monitoring the operation status of the power systems with BESSs and designing the attack released condition with historical frequency. An event-triggered mechanism is investigated to reduce the communication pressure. In order to improve the triggering performance, a Lyapunov-based term is introduced in the triggering scheme. To ensure the stability of the power system under deception attacks, a memory sliding mode control (SMC) method is proposed in which the sliding mode surface is designed with a more effective structure. Based on integral inequality and Lyapunov's theorem, the stability conditions of the multi-area power systems with BESSs under historical frequency-based deception attacks are obtained by linear matrix inequality (LMI). The effectiveness of the proposed approach is discussed and confirmed by numerical simulations. © 2025 The Franklin Institute

关键词： Cyber attacks

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Parkinson's disease tremor prediction towards real-time suppression: A self-attention deep temporal convolutional network approach

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Computers in Biology and Medicine 2025年 188卷 109814-109814页

作者： Tan, Guan Yuan Bakibillah, A.S.M. Chan, Ping Yi Tan, Chee Pin Nurzaman, Surya School of Engineering Monash University Bandar Sunway47500 Malaysia School of Information Technology Monash University Bandar Sunway47500 Malaysia Department of Systems and Control Engineering School of Engineering Institute of Science Tokyo Tokyo152-8552 Japan

Accurate prediction of Parkinson's disease tremor (PDT) is crucial for developing assistive technologies;however, this is challenging due to the nonlinear, stochastic, and nonstationary characteristics of PDT, which substantially vary among patients and their activities. Moreover, most models only have one-step prediction capabilities, which causes delays in real-time applications. This paper proposes a self-attention deep temporal convolutional network (SADTCN) model for the real-time prediction of hand-arm PDT signals from different activities and joint angular motions. The SADTCN can capture both short- and long-term dependencies and complex temporal and spatial dynamics of PDT signals and hence, can effectively adapt to varying tremor characteristics. The performance of the proposed model is evaluated using experimental hand-arm PDT data. The results show that the SADTCN outperforms existing deep learning (DL) models by accurately predicting varying tremor amplitudes and frequencies multi-step ahead. Moreover, we performed spectrum analysis on the measured and predicted signal using the short-time Fourier transform (STFT) as a measure of potential active tremor control and found that SADTCN can accurately determine the transience of tremor amplitude in frequency and time. Finally, we run the Wilcoxon signed-rank statistical test and the results show a statistically significant improvement in the proposed model over the other DL models in all conditions. Therefore, the SADTCN can overcome the nonstationary, nonlinear, and stochastic nature of PDT to perform multi-step prediction with high accuracy, robustness, and generalizability in unseen testing data. © 2025 The Authors

关键词： Prediction models

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