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SEFormer:A Lightweight CNN-Transformer Based on Separable Multiscale Depthwise Convolution and Efficient Self-Attention for Rotating Machinery Fault Diagnosis

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computers, Materials & Continua 2025年第1期82卷 1417-1437页

作者： Hongxing Wang Xilai Ju Hua Zhu Huafeng Li State Key Laboratory of Mechanics and Control for Aerospace Structures Nanjing University of Aeronautics and AstronauticsNanjing210016China School of Computer Science and Engineering Nanyang Technological UniversitySingapore639798Singapore

Traditional data-driven fault diagnosis methods depend on expert experience to manually extract effective fault features of signals,which has certain ***,deep learning techniques have gained prominence as a central focus of research in the field of fault diagnosis by strong fault feature extraction ability and end-to-end fault diagnosis ***,utilizing the respective advantages of convolution neural network(CNN)and Transformer in local and global feature extraction,research on cooperating the two have demonstrated promise in the field of fault ***,the cross-channel convolution mechanism in CNN and the self-attention calculations in Transformer contribute to excessive complexity in the cooperative *** complexity results in high computational costs and limited industrial *** tackle the above challenges,this paper proposes a lightweight CNN-Transformer named as SEFormer for rotating machinery fault ***,a separable multiscale depthwise convolution block is designed to extract and integrate multiscale feature information from different channel dimensions of vibration ***,an efficient self-attention block is developed to capture critical fine-grained features of the signal from a global ***,experimental results on the planetary gearbox dataset and themotor roller bearing dataset prove that the proposed framework can balance the advantages of robustness,generalization and lightweight compared to recent state-of-the-art fault diagnosis models based on CNN and *** study presents a feasible strategy for developing a lightweight rotating machinery fault diagnosis framework aimed at economical deployment.

关键词： CNN-Transformer separable multiscale depthwise convolution efficient self-attention fault diagnosis

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Nonsingular fast predefined time convergence sliding mode control for construction robot

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ISA Transactions 2025年 161卷 109-121页

作者： Yin, Chun-Wu Ding, Yun-Peng Sun, Hou-Jun College of Information and Control Engineering Xi'an University of Architecture and Technology Shaanxi Xi'an710055 China School of Computer Science Northwestern Polytechnical University Xi'an710072 China

To solve the practical engineering problem that the angle of a construction robot with an arbitrary initial value completely and accurately track the desired trajectory, this paper presents a control strategy for extending the desired trajectory, and a sliding mode controller with predefined time convergence is designed for an uncertain construction robot. Firstly, the special characteristics of the construction robot's trajectory tracking control are analyzed, an extended desired trajectory control strategy including the arrival phase and the precise tracking phase is proposed, and the design method of the extended desired trajectory in the arrival stage is given. Then, based on the improved non-singular fast sliding mode surface and a novel stability criterion of predefined time convergence, a predefined time convergence sliding mode controller based on a state observer was designed for the construction robot, which enables the angle tracking error of the construction robot to converge to zero within the specified time, ensures the angle completely and accurately tracking the original desired trajectory. Comparative simulation results show that the angle tracking error based on the proposed algorithm reaches 0.0004 rad, with an accuracy improvement of 33 %, and solves the problem of control torque chattering. © 2025 ISA

关键词： Sliding mode control

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SKINC-NET: an efficient Lightweight Deep Learning Model for Multiclass skin lesion classification in dermoscopic images

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Multimedia Tools and Applications 2025年第13期84卷 12531-12557页

作者： Asif, Sohaib Qurrat-ul-Ain Khan, Saif Ur Rehman Amjad, Kamran Awais, Muhammad School of Computer Science and Engineering Central South University Hunan Changsha China School of Public Health Central South University Hunan Changsha China School of Control Science and Engineering Central South University Hunan Changsha China School of Minerals Processing and Bioengineering Central South University Hunan Changsha China

Diagnosing skin cancer through visual image examinations is time-consuming and error-prone, as the similar appearance and variations within each type of skin cancer challenge the efficiency and accuracy. computer-aided techniques, notably convolutional neural networks (CNNs), demand substantial computational resources and exhibit a high level of time complexity, rendering them impractical for real-world applications necessitating rapid and precise skin cancer detection. To address this issue, this research introduces SKINC-NET, a streamlined deep learning (DL) model specifically designed to achieve precise diagnoses of various skin cancer types while minimizing computational resource requirements. SKINC-NET can classify seven types of skin lesions using dermoscopic images, designed with dense layers, batch normalization (BN), and LeakyReLU activation functions. Numerous experiments were conducted on the HAM10000 dataset, using a data augmentation approach to tackle the class imbalance problem. The study compared the SKINC-NET model’s performance with four transfer learning models, namely ResNet50, VGG16, MobileNetV2, and EfficientNetB0. Several ablation studies were conducted to identify the optimal hyperparameters that lead to precise skin cancer diagnosis. The SKINC-NET model demonstrates its superiority by achieving the highest overall classification accuracy of 98.54% with minimal computation. The experimental findings affirm that the SKINC-NET model showcases remarkable robustness and presents several significant benefits, including lower computational complexity, a smaller number of trainable parameters, and superior performance compared to existing state-of-the-art models. SKINC-NET can assist dermatologists in making appropriate decisions regarding the early detection of skin cancer. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Convolutional neural networks

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Adaptive Particle Swarm Optimization of U-net Parameters for Marine Aquaculture Image Segmentation 13

Adaptive Particle Swarm Optimization of U-net Parameters for...

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13th International Conference on Intelligent control and Information Processing, ICICIP 2025

作者： Zhao, Jin Wang, Jun Han, Min Fan, Jianchao School of Computer Science and Engineering Chongqing Three Gorges University Chongqing China Department of Computer Science Department of Data Science City University of HongKong Hong Kong School of Control Science and Engineering Dalian University of Technology Dalian China

ISBN: (纸本)9798331516147

Marine aquaculture image segmentation plays a crucial role in managing aquatic resources and environmental protection. Traditional deep learning models rely on manual parameter tuning for image segmentation, which limits their efficiency and accuracy. This paper proposes an adaptive Particle Swarm Optimization (PSO) algorithm to optimize the parameters of the U-net model automatically. The algorithm dynamically adjusts the PSO parameters based on the population’s entropy and clustering metrics and employs a hill-climbing algorithm to address the issue of the PSO easily falling into local optima, enhancing the algorithm’s adaptability. The proposed algorithm updates and iterates particles to automatically find suitable model parameters. The remote sensing data used in this experiment were from the Yellow Sea aquaculture area near Dalian, China, with a segmentation accuracy of 91.8%. This method improves segmentation accuracy, reduces the burden of manual parameter tuning, and provides an effective solution for optimizing deep learning models. © 2025 IEEE.

关键词： Aquaculture

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A Survey of Distributed Algorithms for Aggregative Games

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IEEE/CAA Journal of Automatica Sinica 2025年第5期12卷 859-871页

作者： Li, Huaqing Li, Jun Ran, Liang Zheng, Lifeng Huang, Tingwen College of Electronic and Information Engineering Southwest University Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Chongqing400715 China College of Computer Science and Engineering Chongqing University of Technology Chongqing400054 China School of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen518107 China

Game theory-based models and design tools have gained substantial prominence for controlling and optimizing behavior within distributed engineering systems due to the inherent distribution of decisions among individuals. In non-cooperative settings, aggregative games serve as a mathematical framework model for the interdependent optimal decision-making problem among a group of non-cooperative players. In such scenarios, each player's decision is influenced by an aggregation of all players' decisions. Nash equilibrium (NE) seeking in aggregative games has emerged as a vibrant topic driven by applications that harness the aggregation property. This paper presents a comprehensive overview of the current research on aggregative games with a focus on communication topology. A systematic classification is conducted on distributed algorithm research based on communication topologies such as undirected networks, directed networks, and time-varying networks. Furthermore, it sorts out the challenges and compares the algorithms' convergence performance. It also delves into real-world applications of distributed optimization techniques grounded in aggregative games. Finally, it proposes several challenges that can guide future research directions. © 2014 Chinese Association of Automation.

关键词： Nash equilibrium

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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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Multi-scale network for single image deblurring based on ensemble learning module

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Multimedia Tools and Applications 2025年第11期84卷 9045-9064页

作者： Wu, WenBo Pan, Yun Su, Na Wang, JingTao Wu, ShaoChuan Xu, ZeKun Yu, YouJian Liu, YaPeng State Key Laboratory of Media Convergence and Communication Communication University of China Beijing100024 China School of Computer and Cyberspace Security Communication University of China Beijing100024 China School of Electronics and Information Engineering Harbin Institute of Technology Harbin150080 China College of Computer and Control Engineering Northeast Forestry University Harbin150040 China School of Computer and Information Engineering Tianjin Chengjian University Tianjin300384 China School of Computer Science and Technology Anhui University Hefei230601 China

In this paper, we have identified two primary issues with current multi-scale image deblurring methods. On the one hand, the blurring scale is ignored. On the other hand, the context information of images is not fully utilized, and the spatial detail information of images is lost. To better solve the above problems, on top of that, this paper proposes a multi-scale network for single image deblurring. The designed network contains two parts: 1) Ensemble Learning Model: The Weak Learning Model in this module connects multiple weak learners in series to increase network depth. Moreover, through multi-scale feature ensemble module, the output features and intermediate features of different weak learner are integrated, and finally strong learner is used to enhance the features with semantic information at different scales;2) Spatial Feature Enhancement Module: This module can preserve spatial detail information of images and compensate for the disadvantages of losing image spatial details due to continuous downsampling in the Ensemble Learning Model structure. Our proposed deblurring model has been extensively evaluated on several benchmark datasets, and the model achieves superior performance compared to the most advanced deblurring methods. In particular, the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) values are 33.57 dB and 0.965 on the GoPro dataset, respectively, with PSNR and SSIM improving by 0.49 dB and 0.002 over NAFNet, and 0.65 dB and 0.004 over Restormer. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Learning systems

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State Estimation of a Spatial 2-D Linear Diffusion Process With Mobile Sensors

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IEEE Transactions on Automation science and engineering 2025年 22卷 12024-12032页

作者： Zhang, Xiao-Wei Chen, Kui Wu, Huai-Ning Wang, Jin-Liang Wang, Zi-Peng Beijing University of Technology School of Information Science and Technology Beijing100124 China Tiangong University School of Control Science and Engineering Tianjin300387 China Beihang University Science and Technology on Aircraft Control Laboratory School of Automation Science and Electrical Engineering Beijing100191 China Beihang University Hangzhou International Innovation Institute Hangzhou311115 China Tiangong University Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems School of Computer Science and Technology Tianjin300387 China Beijing University of Technology School of Information Science and Technology Beijing Institute of Artificial Intelligence Beijing100124 China

This paper studies the state observer design of a spatial two-dimensional (2-D) linear diffusion process described by a linear parabolic partial differential equation (PDE) under mobile sensors. Firstly, we analyze the well-posedness of the PDE system and give the structure form of the state observer with mobile sensors. Subsequently, according to the number of mobile sensors, the 2-D space domain is divided into multiple sub-domains, and the mobile sensors are guided by the projection operator method, which can guarantee that the mobile sensors can only move in their respective 2-D sub-domains. Then, in the light of Lyapunov theory, Poincaré-Wirtinger inequality and Barbalat lemma, we propose an observation-plus-guidance design method to ensure the asymptotic stability of the state estimation error system. In the designed mobile strategy, the actual guidance of mobile sensors is essentially a physical synthesis of two direction guidance laws, where two dimensional guidance laws are designed separately. Moreover, the existence condition of the observer is given by linear matrix inequalities. At last, a numerical example is provided to demonstrate the efficacy of the proposed design scheme. © 2004-2012 IEEE.

关键词： Asymptotic stability

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Compression Format and Systolic Array Structure Co-design for Accelerating Sparse Matrix Multiplication in DNNs 24th

Compression Format and Systolic Array Structure Co-design f...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Cao, Yongxiang Jiang, Jixiang Zhao, Guocheng Wang, Wei Jiang, Hongxu Song, Yanfei School of Computer Science and Engineering Beihang University Beijing China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (纸本)9789819615445

The systolic array (SA) architecture is widely used in accelerator/AI chip design due to its excellent matrix multiplication acceleration effect. However, the mismatch between the traditional sparse matrix compression format and the SA architecture results in poor SA performance in computing sparse matrices. To solve the problem, we propose a method for co-designing the sparse compression format and SA architecture. This work proposes the Vector Group Compressed Coordinate (VGCC) data stream compression format and designs a sparse SA architecture based on hardware encoding and decoding. VGCC can handle data streams of any size without increasing the index bit width. The VGCC compression size is significantly smaller than general sparse compression formats such as COO/CSR. In a sparse matrix with a sparsity of 90%, it saves 67.13% of storage space compared to the state-of-the-art ECOO data stream compression format and achieves an average acceleration of 1.54 times. We also propose a VGCC-friendly SA architecture. Our SA architecture engine reduces logic consumption by 51.6% compared to Sparse TPU while achieving a 3.23x speedup. To mitigate the issue of high no-load rates in systolic arrays, we introduce a Data Transmission Priority (DTP) data matching algorithm. With sparsity levels ranging from 50% to 90%, the algorithm’s calculation speed is enhanced by 1.124x to 1.991x. We implemented a sparse DNN accelerator on an FPGA Zynq UltraScale+MPSoC ZCU102, utilizing the VGCC compression format proposed in this study for compressing input data. When compared to the S2 Engine, the accelerator achieved a 3.37x speed improvement in matrix multiplication with 75% sparsity, significantly enhancing the speed of sparse matrix multiplication. The accelerator achieved an average throughput of 1079.33GOP/s when inferencing DNNs. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Systolic arrays

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End-to-End Audio Pattern Recognition Network for Overcoming Feature Limitations in Human-Machine Interaction

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computers, Materials & Continua 2025年第5期83卷 3187-3210页

作者： Zijian Sun Yaqian Li Haoran Liu Haibin Li Wenming Zhang School of Information Science and Engineering Yanshan UniversityQinhuangdao066004China The Key Laboratory for Special Fiber and Fiber Sensor of Hebei Province Yanshan UniversityQinhuangdao066004China School of Electrical Engineering Yanshan UniversityQinhuangdao066004China Key Laboratory of Industrial Computer Control Engineering of Hebei Province Yanshan UniversityQinhuangdao066004China

In recent years,audio pattern recognition has emerged as a key area of research,driven by its applications in human-computer interaction,robotics,and *** methods,which rely heavily on handcrafted features such asMel filters,often suffer frominformation loss and limited feature representation *** address these limitations,this study proposes an innovative end-to-end audio pattern recognition framework that directly processes raw audio signals,preserving original information and extracting effective classification *** proposed framework utilizes a dual-branch architecture:a global refinement module that retains channel and temporal details and a multi-scale embedding module that captures high-level semantic ***,a guided fusion module integrates complementary features from both branches,ensuring a comprehensive representation of audio ***,the multi-scale audio context embedding module is designed to effectively extract spatiotemporal dependencies,while the global refinement module aggregates multi-scale channel and temporal cues for enhanced *** guided fusion module leverages these features to achieve efficient integration of complementary information,resulting in improved classification *** results demonstrate the model’s superior performance on multiple datasets,including ESC-50,UrbanSound8K,RAVDESS,and CREMA-D,with classification accuracies of 93.25%,90.91%,92.36%,and 70.50%,*** results highlight the robustness and effectiveness of the proposed framework,which significantly outperforms existing *** addressing critical challenges such as information loss and limited feature representation,thiswork provides newinsights and methodologies for advancing audio classification and multimodal interaction systems.

关键词： Audio pattern recognition raw audio end-to-end network feature fusion

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