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Expand the synchronization range of MEMS linear oscillators based on mode coupling and phase control

Sensors and Actuators A: Physical 2025年 389卷

作者： Dai, Hongsheng Wang, Xuefeng Shi, Zhan Yang, Qiqi Xiao, Zunhao Liu, Zhiqiang Wei, Xueyong Huan, Ronghua Department of Mechanics Key Laboratory of Soft Machines and Smart Devices of Zhejiang Province Zhejiang University Hangzhou China Department of Engineering Mechanics MIIT Key Laboratory of Dynamics and Control of Complex Systems Northwestern Polytechnical University Xi'an China State Key Laboratory for Manufacturing Systems Engineering Xi'an Jiaotong University Xi'an China School of Instrument Science and Technology Xi'an Jiaotong University Xi'an China Huanjiang Laboratory Shaoxing China

Synchronization is a widespread phenomenon observed in both natural and societal systems. The advancement of MEMS technology has facilitated the study and application of synchronization, significantly enhancing the performance of MEMS sensors. However, the limited synchronization range of linear systems has restricted its broader application in sensor technologies. In this article, we propose a method to expand the synchronization range based on mode coupling and phase control. Theoretical derivations and experimental validations were conducted to demonstrate the effectiveness of the proposed approach. Experimental results reveal that the method achieves a synchronization range expansion of more than 50 times for both downward and upward frequency sweeps. Furthermore, the maintenance time of the synchronized state is significantly extended. This achievement is expected to provide a new solution for the application of synchronization-enhanced sensing in various fields, paving the way for improved sensor performance and broader applicability. © 2025 Elsevier B.V.

关键词： Synchronization

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Precise GPS-Denied UAV Self-Positioning via Context-Enhanced Cross-View Geo-Localization

arXiv

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arXiv 2025年

作者： Xu, Yuanze Dai, Ming Cai, Wenxiao Yang, Wankou School of Automation The Key Laboratory of Measurement and Control of Complex Systems of Engineering Southeast University Jiangsu Province Nanjing210096 China Advanced Ocean Institute of Southeast University Jiangsu Province Nantong226019 China Stanford University StanfordCA94305 United States

Image retrieval has been employed as a robust complementary technique to address the challenge of Unmanned Aerial Vehicles (UAVs) self-positioning. However, most existing methods primarily focus on localizing objects captured by UAVs through complex part-based representations, often overlooking the unique challenges associated with UAV self-positioning, such as fine-grained spatial discrimination requirements and dynamic scene variations. To address the above issues, we propose the Context-Enhanced method for precise UAV Self-Positioning (CEUSP), specifically designed for UAV self-positioning tasks. CEUSP integrates a Dynamic Sampling Strategy (DSS) to efficiently select optimal negative samples, while the Rubik’s Cube Attention (RCA) module, combined with the Context-Aware Channel Integration (CACI) module, enhances feature representation and discrimination by exploiting interdimensional interactions, inspired by the rotational mechanics of a Rubik’s Cube. Extensive experimental validate the effectiveness of the proposed method, demonstrating notable improvements in feature representation and UAV self-positioning accuracy within complex urban environments. Our approach achieves state-of-the-art performance on the DenseUAV dataset, which is specifically designed for dense urban contexts, and also delivers competitive results on the widely recognized University-1652 benchmark. Copyright © 2025, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Bearing Fault Diagnosis Under Variable Working Conditions Based on Domain Adaptation and Improved Depthwise Separable Convolution Network

Bearing Fault Diagnosis Under Variable Working Conditions Ba...

引用

IEEE International Conference on Industrial Technology (ICIT)

作者： Zehui Kuang Fan Mao Xingyu Zhao Xiongbo Wan Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education School of Future Technology China University of Geosciences Wuhan China Ministry of Education School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan China

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

Domain adaptation methods and appropriate feature extractors are usually applied to solve the problem that the variable working conditions of bearings affect the effectiveness of the fault diagnostic framework. However, the existing feature extractors are too complex to be deployed in real industries. In this paper, we propose a lightweight framework called improved depthwise separable convolution network (IDSCN) for the diagnosis of bearing faults in variable operating conditions. IDSCN is built on wavelet convolution and depthwise separable residual convolution (DSRC), where the wavelet convolution is used to extract frequency features from the source and target domains, and DSRC guarantees the performance of feature extraction with reduced parameters and computation. To minimise the distribution difference between the source and target domains, the local maximum mean difference is incorporated into the proposed framework. The performance of the proposed framework is validated on the Paderborn University dataset, involving a total of 6 transfer tasks. The experiment shows that the proposed framework achieves satisfactory results and outperforms other frameworks in bearing fault diagnosis.

关键词： Employee welfare Fault diagnosis Vibrations Industries Adaptation models Wavelet domain Convolution Frequency-domain analysis Feature extraction complexity theory

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Double STAR-RIS Enhanced Secure Wireless Communications

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IEEE Transactions on Wireless Communications 2025年

作者： Cai, Yujin Yu, Wenwu Nie, Xiaokai Cheng, Qiang Cui, Tiejun Southeast University School of Mathematics Jiangsu Nanjing211189 China Purple Mountain Laboratories Nanjing211111 China Southeast University School of Automation Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China Southeast University Shenzhen Research Institute Shenzhen518057 China Southeast University State Key Laboratory of Millimeter Waves Nanjing210096 China

Simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) have recently been employed to enhance the secure transmission due to the ability to manipulate the full-space electromagnetic signals. Prior works mainly investigate the single STAR-RIS aided secure communication system, which does not reveal the potential of the cooperative inter-STAR-RIS link. In this paper, a double STAR-RIS enhanced secure communication network involving multiple legitimate users (LUs) and eavesdroppers (Eves) is proposed. For fairness, the minimum secrecy rate maximization problem is formulated by jointly optimizing the active beamforming and passive transmission and reflection (T&R) beamforming. To deal with the highly non-convex nonlinear optimization problem, an active and passive beamforming optimization algorithm is proposed by employing the alternating optimization method. Wherein, a low-complexity sequential constraint relaxation (SCR) based algorithm is designed to solve the passive T&R beamforming optimization problem. Simulation results demonstrate that the secure performance in the double STAR-RIS assisted system outperforms that in the single RIS/double RIS/single STAR-RIS aided network. Furthermore, the proposed algorithm significantly reduces computational time (i.e., resource allocation time) compared with the traditional SCR based beamforming optimization algorithm. © 2002-2012 IEEE.

关键词： Cooperative communication

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Contextual Multi-Armed Bandit-Based Dynamic Cooperative Link Configuration for AUV in UWASNs With Energy Harvesting

引用

IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Dai, Jun Li, Xinbin Han, Song Yu, Junzhi Liu, Zhixin Yanshan University Key Lab of Industrial Computer Control Engineering of Hebei Province Qinhuangdao066004 China Yanshan University Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province Qinhuangdao066004 China Peking University State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and Robotics BIC-ESAT College of Engineering Beijing100871 China

This paper investigates the cooperative link configuration problem for Autonomous Underwater Vehicle (AUV) in Underwater Acoustic (UWA) sensor networks with Energy Harvesting (EH), which aims to maximize long-term cumulative capacity by jointly optimizing cooperation relay, AUV transmission power, and relay transmission power. Subject to unknown time-varying Channel State Information (CSI), unpredictable stochastic EH, and variable communication topology caused by AUV mobility, the proposed problem is an unknown dynamic combination decision optimization problem. To resist the change of the dynamic UWA communication topology with unknown time-varying CSI and EH, a novel collaborative contextual multi-armed bandit learning framework is proposed, which allows relay nodes to learn cooperatively and enriches the learning information of relays. Consequently, the proposed learning framework can resist the change of the dynamic UWA communication topology efficiently, thereby achieving the superior link configuration quickly. Besides, a learning-rate adjustment rule for the dynamic UWA communication topology is proposed to adaptively balance the exploration-exploitation, thereby avoiding missing the real superior joint relay-power configuration. Finally, simulation results show the significantly superiority of the proposed scheme. © 2015 IEEE.

关键词： Cooperative communication

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Target Defense with Multiple Defenders and an Agile Attacker via Residual Policy Learning

arXiv

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arXiv 2025年

作者： Tao, Jiyue Shen, Tongsheng Zhao, Dexin Zhang, Feitian Robotics and Control Laboratory Department of Advanced Manufacturing and Robotics College of Engineering and the State Key Laboratory of Turbulence and Complex Systems Peking University Beijing100871 China National Innovation Institute of Defense Technology Beijing100071 China

The target defense problem involves intercepting an attacker before it reaches a designated target region using one or more defenders. This letter focuses on a particularly challenging scenario in which the attacker is more agile than the defenders, significantly increasing the difficulty of effective interception. To address this challenge, we propose a novel residual policy framework that integrates deep reinforcement learning (DRL) with the force-based Boids model. In this framework, the Boids model serves as a baseline policy, while DRL learns a residual policy to refine and optimize the defenders’ actions. Simulation experiments demonstrate that the proposed method consistently outperforms traditional interception policies, whether learned via vanilla DRL or fine-tuned from force-based methods. Moreover, the learned policy exhibits strong scalability and adaptability, effectively handling scenarios with varying numbers of defenders and attackers with different agility levels. Copyright © 2025, The Authors. All rights reserved.

关键词： Adversarial machine learning

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A port-Hamiltonian framework for displacement-based and rigid formation tracking

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Automatica 2025年 177卷

作者： Li, Ningbo Sun, Zhiyong van der Schaft, Arjan Scherpen, Jacquelien M.A. China Aerodynamics Research and Development Center Mianyang621000 China State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering Science College of Engineering Peking University Beijing100871 China Jan C. Willems Center for Systems and Control University of Groningen Groningen9747 AG Netherlands

This paper proposes a passivity-based port-Hamiltonian (pH) framework for multi-agent displacement-based and rigid formation control and velocity tracking. The control law consists of two parts, where the internal feedback is to track the velocity and the external feedback is to achieve formation stabilization by steering variables of neighboring agents that prescribe the desired geometric shape. Regarding the external feedback, a general framework is proposed for stabilizing different kinds of formations (including displacement-based, distance-based, angle-based, bearing-based and heterogeneous formations) by means of the advantage that the pH model is energy-based and coordinate-free. To solve the issue that the incidence matrix is not of full column rank over cyclic graphs, the graph matrix property is used to prove the convergence to the target sets for the displacement-based formation, while for rigid formations, the algebraic conditions of infinitesimal rigidity are investigated to achieve local asymptotic stability. Furthermore, the rigid formation with heterogeneous constraints is further investigated under this framework and the local asymptotic stability is proved under a mild assumption. Simulations are performed to illustrate the effectiveness of the framework. © 2025 The Author(s)

关键词： Hamiltonians

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Efficient Facial Expression Recognition Network Based on Spot-Adaptive Knowledge Distillation

Efficient Facial Expression Recognition Network Based on Spo...

引用

IEEE International Conference on Industrial Technology (ICIT)

作者： Xin Wen Zhen-Tao Liu Dan Chen School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

Facial expression recognition plays a key role in promoting the development of comprehensive intelligence and building friendly human-computer interaction. Due to the interference of feature noise in expression data, the lightweight facial expression recognition model with fewer parameters is difficult to learn more expression features through simple training, which limits the improvement of its recognition performance. An efficient facial expression recognition network based on Spot-adaptive Knowledge Distillation is proposed in this paper. Inspired by VoVNetV2, the network designed in this paper is lightweightly improved using Depthwise Separable Convolution and the parameter-free SimAM attention mechanism, reducing the number of parameters to 0.21 M. To further improve the recognition accuracy of the model, Spot-adaptive Knowledge Distillation is employed to improve the characterization ability of the model. The recognition accuracies of the student network designed in this paper on the KDEF and RAF-DB datasets are 93.05% and 81.17% respectively after spot-adaptive distillation.

关键词： Knowledge engineering Training Human computer interaction Accuracy Face recognition Computational modeling Noise Neural networks Interference Data models

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Multi-Level Features Fusion for Zero-Shot Object Pose Estimation

Multi-Level Features Fusion for Zero-Shot Object Pose Estima...

引用

IEEE International Conference on Industrial Technology (ICIT)

作者： Nanxin Huang Chi Xu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

Driven by advancements in industrial production and artificial intelligence, the need for pose estimation of new ob-jects in areas like robotic manipulation and virtual reality is increasing. We introduce a zero-shot object pose estimation approach that identifies the poses of objects excluded from the training dataset, removing the requirement for re-modeling. The method is built around a multi-level features fusion framework de-signed to enhance generalization. First, a trainable feature extraction module filters and selects multi-level features extracted by the backbone network. Unlike traditional convolutional ker-nels, we incorporate a dynamic convolution kernel to enhance the feature extraction capability. Second, in the feature fusion module, we adopt a dynamic weight generation strategy to perform weighted fusion of multi-level features. This method enhances template matching by effectively describing similarities between unseen objects (those absent from the training set) and templates, leveraging robust and adaptive feature representations to narrow the gap with seen objects. Experimental results demonstrate that our approach achieves state-of-the-art performance on two popu-lar benchmark datasets, LineMod and LineMod-Occlusion, proves that our method has better generalization than previous models.

关键词： Training Solid modeling Convolution Service robots Pose estimation Zero shot learning Virtual reality Benchmark testing Feature extraction Robustness

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Cooperative Resource Allocation for NOMA-MEC Multi-Cell Network

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IEEE Transactions on Vehicular Technology 2025年

作者： Cai, Yujin Zhang, Zaichen Huang, Yongming Yu, Wenwu Nie, Xiaokai Liu, Hongzhe Southeast University School of Mathematics Jiangsu Nanjing211189 China Southeast University National Mobile Communications Research Laboratory Nanjing211189 China Purple Mountain Laboratories Nanjing211111 China Southeast University School of Automation Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China Southeast University Shenzhen Research Institute Shenzhen518057 China

Integrating non-orthogonal multiple access (NOMA) and mobile edge computing (MEC) enables IoT nodes to offload their complicated tasks to MEC servers. Most of the existing resource allocation schemes in NOMA-MEC networks are based on the centralized optimization framework, which may increase the communicational and computational burden on the central unit. In this paper, the NOMA-MEC multi-cell network without the central unit is considered. To minimize the task completion time and offloading cost, a joint optimization problem including sub-channel allocation, transmit power, offloaded proportions, computing frequencies of IoT nodes and MEC servers, and price of MEC resources is investigated. In order to tackle the formulated mixed integer nonlinear programming problem, a two-stage low-complexity distributed algorithm based on the cooperation of all base stations (BSs) is proposed. Specifically, in Stage 1, a distributed heuristic sub-channel allocation algorithm is designed based on the sub-channel gains. In Stage 2, with the obtained sub-channel allocation result, a cooperative distributed continuous variable optimization algorithm is devised based on the distributed optimization and alternating optimization methods. Numerical results demonstrate the convergence of the proposed two-stage algorithm for three interference cases. Besides, the impacts of system parameters and schemes on the network performance are analyzed. More importantly, the resource allocation time can be significantly shortened by employing the cooperation of BSs compared with the centralized algorithm © 1967-2012 IEEE.

关键词： Nonlinear programming

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