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MSC-APFNN: Fault Diagnosis Deep Transfer Network for Rotating Machine based on Multi-scale Convolutional Extraction and Adaptive Pruning Fuzzy Inference system

MSC-APFNN: Fault Diagnosis Deep Transfer Network for Rotatin...

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International Conference on Artificial Intelligence and Autonomous Transportation, AIAT 2024

作者： Zhou, Yanrun Lei, Zihao Wen, Guangrui Su, Yu Zhang, Zhifen National Key Lab of Aerospace Power System and Plasma Technology Xi’an Jiaotong University Xi’an710049 China State Key Laboratory for Manufacturing Systems Engineering Xi’an Jiaotong University Xi’an710049 China Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System Xi’an710049 China

ISBN: (纸本)9789819639687

Intelligent fault diagnosis on mechanical transmission system of the train is of great importance to ensure the efficient operation of high-speed trains. However, due to the complex and changeable operation conditions (OCs), it is difficult to ensure the uniformity of the data distribution obtained by real-time acquisition, which sets a higher standard for the design of intelligent algorithms. Empowered by deep transfer learning (DTL) methods, the fuzzy inference system provides an effective solution to the abovementioned problem. In this paper, an Adaptive Pruning Fuzzy Inference system (APFIS) is proposed predicated on the fuzzy theory and the basic structure of fuzzy neural network, along with the hybrid model MSC-APFNN constructed by combining the multi-scale convolutional network with the proposed APFIS. This model can be employed as a backbone for the application to transfer diagnostic detection of rotating systems based on adversarial networks. Experimental results demonstrate that the proposed model achieves excellent diagnostic results under variable operating conditions, which provide a novel insight into the optimization of the traditional convolutional and fully-connected deep network structure and offer a promising avenue for the research of fuzzy decision-making in the context of fault diagnostics for high-speed trains. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Fuzzy neural networks

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MemoFlow: Modifying Explicit Motion of Inconsistency in Optical Flow 27th

MemoFlow: Modifying Explicit Motion of Inconsistency in Op...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Wang, Mengfei Shi, Wenjun Zhu, Dongchen Wang, Lei Li, Jiamao Bionic Vision System Laboratory State Key Laboratory of Transducer Technology Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai200050 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9783031781124

Due to the limitation of training datasets and global motion understanding to optical flow estimation, current methods only focus on local clues and ignore the motion continuity of consecutive frames, resulting in an inconsistent motion problem. After theoretical analysis, we find the multi-frame methods need to pay more attention to the continuity between cost volumes than two-frame methods. Thus, our method is based on a multi-frame framework and introduces extra object coordinates in each frame by the segmentation model to revise the matching pairs in cost volume. Specifically, we introduce a Cost Volume Adaptation Module, including a Bbox Spatial Queries to store coordinates information and a Correlation Query Queue to query the object position of different frames. On the Sintel and KITTI test benchmark, our proposed MemoFlow achieves 1.00 and 1.69 average endpoint error (AEPE) on the clean and final passes and an F1-all error of 4.43%, ranking 1st among all three-frame methods and two-frame methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Optical correlation

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E-3DGS: 3D Gaussian splatting with exposure and motion events

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Applied Optics 2025年第14期64卷 3897-3908页

作者： Yin, Xiaoting Shi, Hao Bao, Yuhan Bing, Zhenshan Liao, Yiyi Yang, Kailun Wang, Kaiwei State Key Laboratory of Modern Optical Instrumentation Zhejiang University Hangzhou China The Chair of Robotics AI and Real-Time Systems Technical University of Munich Munich Germany College of Information Science and Electronic Engineering Zhejiang University Hangzhou China School of Robotics and National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha China

Achieving 3D reconstruction from images captured under optimal conditions has been extensively studied in the vision and imaging fields. However, in real-world scenarios, challenges such as motion blur and insufficient illumination often limit the performance of standard frame-based cameras in delivering high-quality images. To address these limitations, we incorporate a transmittance adjustment device at the hardware level, enabling event cameras to capture both motion and exposure events for diverse 3D reconstruction scenarios. Motion events (triggered by camera or object movement) are collected in fast-motion scenarios when the device is inactive, while exposure events (generated through controlled camera exposure) are captured during slower motion to reconstruct grayscale images for high-quality training and optimization of event-based 3D Gaussian splatting (3DGS). Our framework supports three modes: high-quality reconstruction using exposure events, fast reconstruction relying on motion events, and balanced hybrid optimizing with initial exposure events followed by high-speed motion events. On the EventNeRF dataset, we demonstrate that exposure events significantly improve fine detail reconstruction compared to motion events and outperform frame-based cameras under challenging conditions such as low illumination and overexposure. Furthermore, we introduce EME-3D, a real-world 3D dataset with exposure events, motion events, camera calibration parameters, and sparse point clouds. Our method achieves faster and higher-quality reconstruction than event-based NeRF and is more cost-effective than methods combining event and RGB data. By combining motion and exposure events, E-3DGS sets a new benchmark for event-based 3D reconstruction with robust performance in challenging conditions and lower hardware demands. The source code and dataset are available on GitHub. © 2025 Optica Publishing Group.

关键词： Exposure controls

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Characterization Method of Thermal Stability Boundary for High Proportion New Energy Power system Based on Sensitivity Clustering 9th

Characterization Method of Thermal Stability Boundary for Hi...

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9th Purple Mountain Form on Smart Grid Protection and Control, PMF 2024

作者： Wang, Weitao Xu, Taishan Zhang, Jinlong Bao, Yanhong Song, Jiaqi Yu, Mengjie Zhao, Xijie State Key Laboratory of Technology and Equipment for Defense Against Power System Operational Risks Nanjing211106 China NARI Technology Co. Ltd. Nanjing211106 China

ISBN: (纸本)9789819616701

Characterizing thermal stability boundary is fundamental to the operation and control of power system, directly impacting its safety and economic efficiency. With the integration of large-scale renewable energy sources, operation modes of power system have become increasingly complex and variable. The influence characteristics of multi-dimensional variables on system boundary tend toward strong nonlinearity and coupling, posing challenges for traditional characterization methods. This paper first analytically derives the thermal stability boundary manifold and its gradient vector trajectory based on a single-machine equivalent system, exploring the nonlinear nature of system boundary manifold. Secondly, considering the active-reactive power coupling characteristics of renewable energy devices, it analyzes the nonlinear and non-convex features and patterns of system boundary manifold under typical scenarios such as weak sending and weak receiving conditions. Finally, a segmented approximation method suitable for high-dimensional nonlinear boundary manifold is proposed. This method utilizes sensitivity analysis and the K-Medoids algorithm to perform clustering analysis of high-dimensional gradient vectors and segmentation of boundary manifold, followed by fitting linear approximation expressions for each segment using the least squares method. The practicality and effectiveness of the proposed method are validated through a case study of the IEEE 3-machine 9-bus system. © state Grid Electric Power 2025.

关键词： Sensitivity analysis

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Research on an automatic light field refocusing method for a camera array

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Optics Express 2025年第10期33卷 20964-20980页

作者： Pan, Yihua Zhao, Yushan Li, Yuyang Liu, Bin Chen, Ping Shanxi Key Laboratory of Intelligent Detection Technology & Equipment School of Information and Communication Engineering North University of China Shanxi Taiyuan 030051 China Key Laboratory of Photoelectronic Imaging Technology System of Ministry of Education of China School of Optics and Photonics Beijing Institute of Technology Beijing 100081 China State Key Laboratory of Extreme Environment Optoelectronic Dynamic Testing Technology and Instruments North University of China Shanxi Taiyuan 030051 China

Light field information includes the position and angle information of light in space. A camera array is one of the methods for obtaining light field information, which combines and arranges multiple cameras in space to achieve multi-angle imaging of the same target. Based on the light field information, a computational imaging method can be used to achieve refocusing technology for a camera array (also known as optical synthetic aperture imaging technology). In this technology, by setting the focusing coefficient, the light field information can be focused on different depths. This mode is called taking photos first and focusing later. However, in order to focus on a specific object surface, traditional refocusing methods require users to manually adjust the focusing coefficient multiple times, which is time-consuming and user-unfriendly. Therefore, in this paper, we propose an automatic focusing method based on the optimal focusing coefficient map (OFCM) to address the manual refocusing problem. This method transforms the refocusing problem into obtaining the optimal focus coefficient map. In the process of solving OFCM, the threshold filtering method and the neighborhood filling method are used to optimize the results and improve the robustness of the proposed method. The experimental results show that our proposed method has higher imaging quality and faster running efficiency compared to automatic refocusing methods based on disparity maps and gradient functions. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Biomedical imaging Computational imaging Imaging techniques Plenoptic imaging Point spread function Real time imaging

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A Novel Transfer Learning Method for Sub/Super-Synchronous Oscillation Mode Identification in Power systems

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IEEE Internet of Things Journal 2025年第11期12卷 18385-18396页

作者： Fang, Jiashu Kong, Lingran Li, Aobing Hu, Weihao University of Electronic Science and Technology of China School of Mechanical and Electrical Engineering Chengdu610054 China North China Electric Power University State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources Beijing102206 China

With the increasing integration of renewable energy in power systems, accurate and rapid mode identification of the sub/super-synchronous oscillation (sub/super-SO) is essential for ensuring grid security. This paper presents a deep learning (DL)-based method for early prediction of the dominant frequency in sub/super-SO events. To address the challenge of limited sub/super-SO samples for DL training, we propose a domain-adversarial neural network-based transfer learning framework, which leverages easily obtainable forced oscillation data to capture sub/super-SO features. Our method introduces several enhancements over previous approaches in both model structure and optimization. First, an encoder-decoder module with a mask component is designed to reconstruct missing data from field sub/super-SO events, utilizing a specialized reconstruction loss function. Second, the proposed dual-predictor configuration applies supervised learning in both source and target domains, imposing stronger optimization constraints. Furthermore, the proposed method exhibits generality and robustness when the topologies of target and source wind farms differ, highlighting its potential for practical applications in power systems. © 2014 IEEE.

关键词： Structural optimization

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Assessment of Multilevel Intransitive Noninterference by Nonblocking Analysis

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IEEE Transactions on Automatic Control 2025年第5期70卷 2902-2917页

作者： Zhong, Wenjing Zhao, Jinjing Hu, Hesuan Xidian University School of Electro-Mechanical Engineering Xi'an710071 China National Key Laboratory of Science and Technology on Information System Security Beijing100101 China Nanyang Technological University School of Computer Science and Engineering College of Engineering 639798 Singapore Xi'an Jiaotong University State Key Laboratory for Manufacturing Systems Engineering Xi'an710054 China

Security of system behavior is a kind of information flow security, which is achieved by confusing the intruders via the indistinguishability of system behaviors. Noninterference is a typical notion to describe information flow security, for which multilevel intransitive noninterference (MINI) is an advanced variant. Since there is a lack of rigorous approach to assessing MINI, this article achieves so via observability theory. For systems modeled by labeled Petri nets (LPNs), two MINI properties, i.e., positive MINI (PMINI) and bipolar MINI (BMINI), are considered. First, a necessary and sufficient condition for their assessment is established via language equivalence. Language equivalence analyses for PMINI and BMINI are based on the existing trace equivalence and the proposed INI bisimulation, respectively. INI bisimulation is more comprehensive to describe negative noninterference than bisimulation. Second, another necessary and sufficient condition is established after the transformation of MINI assessment problem to nonblocking analysis problem. The core of such a problem transformation is the stepwise construction of nonblocking analyzer. This stepwise construction allows MINI assessment to proceed online before terminating at an appropriate time. In addition, this stepwise construction fully employs the concurrency of LPNs so that MINI can be assessed in a multithreaded way. Both online and multithreaded MINI assessments can improve assessment efficiency. © 1963-2012 IEEE.

关键词： Petri nets

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SeaFree-GS: Reconstructing Underwater 3D Scenes With True Appearances

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IEEE Signal Processing Letters 2025年 32卷 2114-2118页

作者： Liu, Shaohua Gao, Ning Fu, Shaowen Zhong, Xiaoqing Li, Hongjue Beihang University School of Astronautics Shenyuan Honors College Beijing100191 China Beihang University School of Astronautics Beijing100191 China Beijing Aerospace Automatic Control Institute Beijing100854 China National Key Laboratory of Science and Technology on Aerospace Intelligent Control Beijing100854 China China Academy of Space Technology Beijing100094 China Beijing Key Laboratory of System Design for Reusable Launch Vehicle State Key Laboratory of High- Efficiency Reusable Aerospace Transportation Technology China

Reconstructing underwater 3D scenes with accurate appearances is crucial for numerous tasks. However, existing underwater 3D reconstruction methods often fail to restore the true scene appearance due to degradation in underwater images caused by water effects. In this letter, we propose SeaFree-GS, a novel approach leveraging 3D Gaussian Splatting (3DGS) to reconstruct underwater scenes with their true appearances. Specifically, we introduce a Degradation-Aware Dual-Color Modeling strategy, where each Gaussian is assigned an intrinsic color representing the true scene appearance and a viewpoint-dependent degraded color to incorporate water effects. For a given viewpoint, this strategy physically derives the corresponding degraded colors from the intrinsic colors to render the underwater image. To improve reconstruction accuracy, we introduce a Content-Based Loss for selective enhancement of supervision over foreground and background regions, and a Coarse-Grained Depth Loss to enforce additional geometric constraints. Experiments on three datasets demonstrate that SeaFree-GS achieves state-of-the-art performance in Underwater True Appearance Reconstruction, and also performs competitively in Underwater Novel View Synthesis. © 1994-2012 IEEE.

关键词： 3D reconstruction

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DP4AuSu: Autonomous Surgical Framework for Suturing Manipulation Using Diffusion Policy With Dynamic Time Wrapping-Based Locally Weighted Regression

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International Journal of Medical robotics and Computer Assisted Surgery 2025年第3期21卷 e70072页

作者： Xu, Wenda Tan, Zhihang Cao, Zexin Ma, Haofei Wang, Gongcheng Wang, Han Wang, Weidong Du, Zhijiang State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin China Department of Industrial and Systems Engineering The Hong Kong Polytechnic University Hong Kong

Background: Emerging imitation learning (IL) approaches have provided innovative solutions for completing surgical robotic suturing autonomously, significantly aiding surgeons in their manipulations. Methods: We introduce Diffusion Policy for Autonomous Suturing (DP4AuSu), a novel framework that leverages diffusion policy (DP) and dynamic time wrapping-based locally weighted regression to achieve autonomous robotic suturing. Results: In simulation, DP4AuSu achieved a 94% success rate for insertion subtasks over 50 trials. In a real-world setting, it achieves 85% success rate over 20 trials for suturing manipulations in 390.55–41.59s faster than conventional diffusion policy. Conclusions: Our novel framework can capture the multimodality in demonstrations and successfully learn the suturing policy and reduce the suturing time. To the best of our knowledge, this work represents the first application of diffusion policy for robotic suturing. We hope this research paves the way for the automation of more complex surgical tasks. © 2025 John Wiley & Sons Ltd.

关键词： Robot learning

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Enhancing Robot Learning Through Cognitive Reasoning Trajectory Optimization Under Unknown Dynamics

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IEEE robotics and Automation Letters 2025年第6期10卷 5401-5408页

作者： Dong, Qingwei Wu, Tingting Zeng, Peng Zang, Chuanzhi Wan, Guangxi Cui, Shijie Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang Institute of Automation Shenyang110016 China Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China China Mobile Research Institute Beijing100053 China Shenyang University of Technology School of Artifcial Intelligence Shenyang110870 China

In the domain of robot learning, equipping robots with the capability to swiftly acquire operational skills poses a significant challenge. Currently, reinforcement learning techniques are adept at addressing dynamic, unstructured problems involving rich contact scenarios. However, the convergence rate of these algorithms is often slow due to the high dimensionality of the robot state-action mapping space and the extensive initial policy search space. Meanwhile, advancements in large language models (LLMs) have endowed these models with a degree of logical reasoning ability, enabling them to take goal-oriented actions proactively during the initial phase of a robotic task. These models can implicitly generate features of states and uncover underlying patterns in trajectory generation. Yet, in complex manipulative tasks involving rich contact scenarios, LLMs still fall short. Thus, integrating the robust interactive capabilities of reinforcement learning with the strong logical reasoning of LLMs, and enhancing policy search with LLMs, could potentially accelerate the speed of policy searches significantly. In this letter, we introduce a Cognitive Reasoning Trajectory Optimization method. This approach utilizes Low-level Cognitive Control Tuning to enable LLMs with robust logical reasoning to make effective single-step decisions in Markov Decision Process (MDP) tasks. By fitting dynamic models with high-quality cognitive reasoning data and optimizing control strategies, this method constrains the policy search space and enhances the efficiency of trajectory optimization. Experimental results on various manipulative tasks using the Sawyer robot in the Mujoco simulator validate the effectiveness of the proposed algorithm. © 2016 IEEE.

关键词： Markov processes

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