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Multifrequency measurement system with no frequency ambiguity based on two frequency-space mappings

Optical engineering 2025年第3期64卷

作者： Zhang, Jiahong Ma, Di Li, Peiqi Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming China Kunming University of Science and Technology Yunnan Key Laboratory of Computer Technology Applications Kunming China

Multifrequency measurement system with no frequency ambiguity based on two frequency-space mappings has been designed and investigated. By simultaneously mapping the unknown frequencies with a certain frequency shift onto non-flat optical frequency combs (OFCs) and obtaining two beat frequency signals using two balanced photodetectors (BPD), the channel where the measured signal is located can be determined, achieving instantaneous measurement of multifrequencies while effectively addressing five possible types of frequency ambiguity. Simulation results indicate that when the comb line spacing of the OFC is designed as 2 GHz, the measurement range of the system is 0.1 to 30 GHz, with a measurement error within ±1 MHz and a measurement resolution of 2.7 MHz. Furthermore, by adjusting the comb line spacing of the OFC to 4 GHz, the frequency measurement range can be extended to 60 GHz. © 2025 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： Electric frequency measurement

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Plug and Play Detector Design for DC Microgrids With Unknown-Inputs-Based FDI Attack

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IEEE Transactions on Smart Grid 2025年第3期16卷 2052-2064页

作者： Wu, Zhihua Peng, Chen Tian, Engang Zhang, Yajian Shanghai University Shanghai Key Laboratory of Power Station Automation Technology School of Mechatronic Engineering and Automation Shanghai200444 China University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China

DC microgrids, due to their deep integration of control, computing, communication technologies, and physical equipment, are susceptible to cyber-attacks. Consequently, this paper is dedicated to the development of a novel attack-defense framework for generalized DC microgrids. Firstly, an unknown-inputs-based false data injection (FDI) attack strategy is studied from the adversary’s perspective, unlike traditional stealthy attacks requiring non-minimum phase zeros or unstable poles, which conceals the attack signal as false unknown inputs (FUI) to maliciously disrupt current sharing and voltage balancing. Secondly, a comprehensive analysis of the stealthiness and destructiveness of FUI attack is provided, and a dual-observer-based detector is well constructed to detect the FUI attack and isolate the compromised distributed generation units. Then, structured Lyapunov matrix and semidefinite programming are ingeniously employed to solve the distributed observer gains simultaneously. Moreover, plug and play (PnP) performance is also analyzed to ensure the scalability of proposed FUI attack detector. Finally, the destructiveness and stealthiness of proposed FUI attack, as well as the effectiveness of designed detection scheme are demonstrated through simulations using MATLAB/SimPowerSystems Toolbox. © 2010-2012 IEEE.

关键词： MATLAB

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Interference Suppression and Jitter Elimination Ability-Based Adaption Tracking Guidance for Robotic Fishes

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 126-137页

作者： Dongfang Li Jie Huang Rob Law Xin Xu Limin Zhu Edmond Q.Wu IEEE the School of Electrical Engineering and Automation Fuzhou University the Key Laboratory of System Control and Information Processing Ministry of Education the School of Electrical Engineering and Automation Fuzhou University 5G+Industrial Internet Institute Fuzhou University the University of Macau the College of Mechatronics and Automation National University of Defense Technology the School of Mechanical Engineering Shanghai Jiao Tong University the Department of Computer Science and Engineering Shanghai Jiao Tong University

This work presents an adaptive tracking guidance method for robotic fishes. The scheme enables robots to suppress external interference and eliminate motion jitter. An adaptive integral surge line-of-sight guidance rule is designed to eliminate dynamics interference and sideslip issues. Limited-time yaw and surge speed observers are reported to fit disturbance variables in the model. The approximation values can compensate for the system's control input and improve the robots' tracking ***, this work develops a terminal sliding mode controller and third-order differential processor to determine the rotational torque and reduce the robots' run jitter. Then, Lyapunov's theory proves the uniform ultimate boundedness of the proposed method. Simulation and physical experiments confirm that the technology improves the tracking error convergence speed and stability of robotic fishes.

关键词： Adaptive integral surge line-of-sight approximation robotic fish speed observer

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A trajectory-level robot skill learning method based on fuzzy systems

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science China(Information sciences) 2025年第5期68卷 38-52页

作者： Chao ZENG Zhen DENG Boyang TI Yongsheng GAO Department of Computer Science University of Liverpool School of Mechanical Engineering and Automation Fuzhou University State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences State Key Laboratory of Robotics and System Harbin Institute of Technology

Learning from demonstration(LfD) allows for the effective transfer of human manipulation skills to a robot by building a model that represents these skills based on a limited number of demonstrated ***,a skilllearning model that can comprehensively satisfy multiple requirements,such as computational complexity,modeling accuracy,trajectory smoothness,and robustness,is still ***,this work aims to provide such a model by employing fuzzy ***,we introduce an LfD model named Takagi-Sugeno-Kang fuzzy system-based movement primitives(TSKFMPs),which exploits the advantages of the fuzzy theory for effective robotic imitation learning of human *** work formulates the TSK fuzzy system and gradient descent(GD) as imitation learning models,leveraging recent advancements in GD-based optimization for fuzzy *** study takes a two-step strategy.(ⅰ) The input-output relationships of the model are established using TSK fuzzy systems based on demonstration *** this way,the skill is encoded by the model parameter in the latent space.(ⅱ) GD is used to optimize the model parameter to increase the modeling accuracy and trajectory *** further explain how learned trajectories are adapted to new task scenarios through local *** conduct multiple tests using an open dataset to validate our method,and the results demonstrate performance comparable with those of other ***,we implement it in a real-world case study.

关键词： robot skill learning movement primitives TSK fuzzy systems gradient descent robotics

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A Relative-Error-Dependent TOD Protocol for Cyber-Physical Systems under an Event-Triggered Communication

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2025年 3卷 318-328页

作者： Yang, Hongchenyu Peng, Chen Tian, Engang Shanghai University School of Mechatronic Engineering and Automation Shanghai Key Laboratory of Power Station Automation Technology Shanghai200444 China University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China

To deal with different data magnitudes or measurement units of different system nodes, a novel relative-error-dependent try-once-discard (RED-TOD) communication protocol with event-triggered detectors is proposed to improve the accuracy and reliability of node selection for band-limited cyber-physical systems. With the combined effect of the absolute error and the relative error, a novel node activation condition is proposed to comprehensively reflect and fairly compare node transmission requirements. Under such a stricter scheduling rule, the node selection is more accurate as required and the available communication bandwidth is utilized with higher efficiency. Moreover, periodic sampling module and event-triggered detectors are placed before RED-TOD scheduling scheme to deal with redundancy and inaccurate transmissions of the existing variable sampling scheme. Taking the proposed scheduling scheme and non-small network-induced delay into account, an impulsive closed-loop system model is well established. Sufficient criteria for input-to-state stabilization are derived by employing the Lyapunov-Krasovskii functional approach. A searching algorithm is also presented to co-design optimized scheduler parameters and controller gains. Furthermore, to avoid continuous calculations, the next transmission instant and the dormant period of the event-triggered detector are predicted according to the present information carried by each node. Finally, an illustrative example is employed to demonstrate the effectiveness and superiority of the proposed scheduling method. © 2023 IEEE.

关键词： Feedback control

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UDI-3DVT: A Novel Network for 3-D Reconstruction via Uncertainty-Driven Sampling and Incremental Visual-Tactile Fusion

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IEEE Sensors Journal 2025年第6期25卷 10358-10369页

作者： Zhang, Chenyang Zhang, Shaohua Li, Yao Chen, Wenbai Gao, Guowei Wu, Peiliang Yanshan University Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province School of Information Science and Engineering Qinhuangdao066004 China Beijing Information Science and Technology University School of Automation Beijing100192 China

The fusion of visual and tactile senses allows robots to reconstruct and understand objects, aiding in downstream tasks such as object recognition and object grasping. However, most existing visual-tactile reconstruction methods typically overlook the role of visual point cloud processing in reconstruction, sampling points using generative-based methods rather than uncertainty-driven approaches. In addition, these methods only concatenate visual and tactile features, failing to fully leverage the complementary advantages of these two modalities, resulting in unsatisfactory reconstruction results. To address this issue, leveraging the open-source GelSight tactile sensor, this article proposes a novel method called UDI-3DVT, which reconstructs 3-D shapes by combining visual point clouds and multiple tactile images. Specifically, we explore the uncertainty of point clouds, performing additional sampling on high-uncertainty edge points, and propose an attention-based point cloud sampling method. Then, the framework leverages the efficiency of convolutional neural network (CNN) in image processing and the transformer's superiority in context modeling by using CNN for tactile features and transformer for visual features. A feature fusion module based on 1 × 1 convolution is designed to aggregate features from two branches. By gradually adding tactile features, we achieve incremental visual-tactile integration. Furthermore, since the contact-based perception of tactile sensors may cause slight deformations in objects, UDI-3DVT supports the reconstruction of both rigid and deformable objects. Extensive experiments demonstrate that our proposed framework outperforms current state-of-the-art (SOTA) methods, especially the Chamfer distance (CD) metric increased by 14.8%. © 2001-2012 IEEE.

关键词： Image reconstruction

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Brain-Conditional Multimodal Synthesis: A Survey and Taxonomy

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1080-1099页

作者： Mai, Weijian Zhang, Jian Fang, Pengfei Zhang, Zhijun South China University of Technology School of Automation Science and Engineering Guangzhou510640 China Southeast University School of Computer Science and Engineering Nanjing210096 China South China University of Technology Key Library of Autonomous Systems and Network Control Ministry of Education The School of Automation Science and Engineering Guangzhou510640 China Institute for Super Robotics Huangpu Guangzhou510555 China Nanchang University Jiangxi Thousand Talents Plan Nanchang330031 China Jishou University College of Computer Science and Engineering Jishou416000 China Guangdong Artificial Intelligence and Digital Economy Laboratory Pazhou Lab Guangzhou510335 China Shaanxi University of Technology Shaanxi Provincial Key Laboratory of Industrial Automation School of Mechanical Engineering Hanzhong723001 China Changsha Normal University School of Information Science and Engineering Changsha410100 China Guangdong University of Petrochemical Technology School of Automation Science and Engineering Institute of Artificial Intelligence and Automation Maoming525000 China

In the era of artificial intelligence generated content (AIGC), conditional multimodal synthesis technologies (e.g., text-to-image) are dynamically reshaping the natural content. Brain signals, serving as potential reflections of how the brain interprets external information, exhibit a distinctive one-to-many correspondence with various external modalities. This correspondence makes brain signals emerge as a promising guiding condition for multimodal synthesis (e.g., image, text, and audio), which is crucial for developing practical brain–computer interface systems and unraveling complex mechanisms underlying human perception. This survey comprehensively examines the emerging field of brain-conditional multimodal synthesis, termed AIGC-brain, to delineate the current landscape and future directions. To begin, related neuroimaging datasets and generative models are introduced as the foundation of AIGC-brain decoding and analysis. Next, we present a comprehensive taxonomy according to AIGC-brain methodologies, followed by task-specific representative work and implementation details to facilitate in-depth comparison and analysis. Quality assessments are then introduced for both qualitative and quantitative evaluation. Finally, this survey explores insights gained, outlining current challenges and prospects of AIGC-brain. As a pioneering survey, this article paves the way for future advances in AIGC-brain research. © 2020 IEEE.

关键词： Taxonomies

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Machine Learning-Based Methods for Materials Inverse Design: A Review

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computers, Materials & Continua 2025年第2期82卷 1463-1492页

作者： Yingli Liu Yuting Cui Haihe Zhou Sheng Lei Haibin Yuan Tao Shen Jiancheng Yin Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming650093China Yunnan Key Laboratory of Computer Technologies Application Kunming University of Science and TechnologyKunming650500China Yunnan Tin Company Limited Tin Industry Branch Gejiu661000China Faculty of Materials Science and Engineering Kunming University of Science and TechnologyKunming650093China

Finding materials with specific properties is a hot topic in materials *** materials design relies on empirical and trial-and-error methods,requiring extensive experiments and time,resulting in high *** the development of physics,statistics,computer science,and other fields,machine learning offers opportunities for systematically discovering new *** through machine learning-based inverse design,machine learning algorithms analyze the mapping relationships between materials and their properties to find materials with desired *** paper first outlines the basic concepts of materials inverse design and the challenges faced by machine learning-based approaches to materials inverse ***,three main inverse design methods—exploration-based,model-based,and optimization-based—are analyzed in the context of different application ***,the applications of inverse design methods in alloys,optical materials,and acoustic materials are elaborated on,and the prospects for materials inverse design are *** authors hope to accelerate the discovery of new materials and provide new possibilities for advancing materials science and innovative design methods.

关键词： Materials inverse design machine learning target properties deep learning new materials discovery

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Optimal Production Capacity Matching for Blockchain-Enabled Manufacturing Collaboration With the Iterative Double Auction Method

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 550-562页

作者： Ying Chen Feilong Lin Zhongyu Chen Changbing Tang Cailian Chen the Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province Zhejiang Normal University the School of Computer Science and Technology Zhejiang Normal University IEEE the College of Physics and Electronic Information Engineering Zhejiang Normal University the Department of Automation Shanghai Jiao Tong University

The increased demand for personalized customization calls for new production modes to enhance collaborations among a wide range of manufacturing practitioners who unnecessarily trust each other. In this article, a blockchain-enabled manufacturing collaboration framework is proposed, with a focus on the production capacity matching problem for blockchainbased peer-to-peer(P2P) collaboration. First, a digital model of production capacity description is built for trustworthy and transparent sharing over the blockchain. Second, an optimization problem is formulated for P2P production capacity matching with objectives to maximize both social welfare and individual benefits of all participants. Third, a feasible solution based on an iterative double auction mechanism is designed to determine the optimal price and quantity for production capacity matching with a lack of personal information. It facilitates automation of the matching process while protecting users' privacy via blockchainbased smart contracts. Finally, simulation results from the Hyperledger Fabric-based prototype show that the proposed approach increases social welfare by 1.4% compared to the Bayesian game-based approach, makes all participants profitable,and achieves 90% fairness of enterprises.

关键词： Blockchain iterative double auction manufacturing collaboration production capacity matching

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Learning Video Salient Object Detection Progressively From Unlabeled Videos

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IEEE Transactions on Multimedia 2025年 27卷 2423-2435页

作者： Xu, Binwei Jiang, Qiuping Liang, Haoran Zhang, Dingwen Liang, Ronghua Chen, Peng Ningbo University Faculty of Information Science and Engineering Ningbo315211 China Zhejiang University of Technology College of Computer Science and Technology Hangzhou310023 China Northwestern Polytechnical University Brain and Artificial Intelligence Laboratory School of Automation Xi'an710072 China

Recently, deep learning-based video salient object detection (VSOD) has achieved some breakthroughs, but these methods rely on expensive annotated videos with pixel-wise annotations or weak annotations. In this paper, based on the similarities and differences between VSOD and image salient object detection (SOD), we propose a novel VSOD method via a progressive framework that locates and segments salient objects in sequence without utilizing any video annotation. To efficiently use the knowledge learned in the SOD dataset for VSOD efficiently, we introduce dynamic saliency to compensate for the lack of motion information of SOD during the locating process while maintaining the same fine segmenting process. Specifically, we utilize the coarse locating model trained on the image dataset, to identify frames with both static and dynamic saliency. Locating results of these frames are selected as spatiotemporal location labels. Moreover, by tracking salient objects in adjacent frames, the number of spatiotemporal location labels is increased. On the basis of these location labels, a two-stream locating network with an optical flow branch is proposed to capture salient objects in videos. The results with respect to five public benchmarks demonstrate that our method outperforms the state-of-the-art weakly and unsupervised methods. © 1999-2012 IEEE.

关键词： Self-supervised learning

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