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Evolving temporal convolutional neural networks for modeling turntable servo systems

Applied Soft Computing 2025年 172卷

作者： Xie, Cheng Xue, Bing Zhang, Mengjie Chen, Songlin Liu, Yang Control and Simulation Center School of Astronautics Harbin Institute of Technology Heilongjiang Harbin150001 China Centre for Data Science and Artificial Intelligence & School of Engineering and Computer Science Victoria University of Wellington Wellington6140 New Zealand Shenyang Institute of Automation Chinese Academy of Sciences Liaoning Shenyang110169 China

Turntable servo systems are important experimental equipment used for semi-physical simulation and testing of aircraft, which have very strict requirements for tracking performance. To achieve high-precision servo performance, some advanced model-based control methods have been used recently, but the control performance is greatly influenced by the accuracy of the model. Therefore, this paper proposes evolving temporal convolutional networks (TCNs) for accurately modeling turntable servo systems. Considering the advantages of TCNs in capturing local dependencies in sequence data and parallel computing, a TCN is adopted to compensate the unmodeled dynamics to significantly improve the accuracy of the existing dynamics model. Then, an evolutionary algorithm (EA) based architecture optimization algorithm for the above TCN model is proposed, and the network performance is continuously improved by simulating the population evolution in nature. Finally, the weight inheritance method is utilized to evaluate the performance of individuals during the evolution process to effectively improve the execution efficiency of the whole algorithm. In addition, the subnets inherited from the supernet meet the real-time requirements of the actual tasks by setting real-time constraints on the supernet. The experimental results demonstrate the superiority of the proposed model against the peer competitors in terms of the prediction performance. In addition, the proposed optimization algorithm also shows higher execution efficiency as verified by ablation experiments. © 2025 Elsevier B.V.

关键词： Evolutionary algorithms (EAs) System identification and modeling Temporal convolutional networks (TCNs) Turntable servo systems

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A Comprehensive Methodology of Field-Oriented control Design With Parameter Variation Analysis for Interior Permanent Magnet Synchronous Machine Drives

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IEEE Access 2025年 13卷 89524-89541页

作者： Tiago Davi Curi Busarello Abdullah Bubshait Oruganti V. S. R. Varaprasad Abdulhakeem Alsaleem Marcelo Godoy Simões Department of Control Automation and Computation Engineering Federal University of Santa Catarina Florianopolis Brazil Department of Electrical Engineering College of Engineering King Faisal University Al Ahsa Saudi Arabia Smart Transportation Electrification and Energy Research (STEER) Group Department of Electrical Computer and Software Engineering Faculty of Engineering and Applied Science Ontario Tech University Oshawa ON Canada Department of Electrical Engineering College of Engineering Qassim University Buraydah Saudi Arabia School of Technology and Innovations University of Vaasa Vaasa Finland

This paper proposes a comprehensive methodology for Field-Oriented control (FOC) with parameter variation analysis for Interior Permanent Magnet Synchronous Machines (IPMSM). The modeling approach for an IPMSM is first presented, followed by a step-by-step procedure for designing a vector-controlled strategy. The formulation is based on a dq-rotating reference frame aligned with the rotor shaft position. A key distinction of this methodology from traditional approaches is that the current controller is designed in the time domain based on desired time constants, while the speed control is formulated within a frequency response domain framework. The proposed hybrid approach enables accurate tuning of the Proportional-Integrator (PI) controllers for both current and speed control loops. Additionally, a parameter variation analysis is conducted to enhance the proposed methodology. The validity region for the design procedure is presented, ensuring that for any wide speed variation of the machine, loop gains are properly tuned. One of the main advantages of the proposed methodology is that it provides a fast, reliable, and accurate technique for implementing IPMSM drive systems. Results from a controller Hardware-in-the-Loop (C-HIL) setup with an external microcontroller are presented. The comprehensive design approach is validated under two different IPMSM parameter sets, demonstrating its effectiveness.

关键词： PI control Stators Tuning Mathematical models Velocity control Rotors Vectors Stator windings Robustness Microcontrollers

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Aerodynamic reconstruction of wind turbines using terrestrial laser scanning: Methodology, validation, and error analysis

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Energy Conversion and Management 2025年 334卷

作者： Xie, Zhen Lin, Zhongwei Cai, Qinlin Chen, Zhenyu State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Control and Computer Engineering North China Electric Power University Beijing102206 China China Huaneng Group Co. Ltd. Beijing100031 China

Aerodynamic performance analysis is essential for improving wind turbine efficiency and reliability, yet it is often constrained by the lack of aerodynamic models, either due to commercial confidentiality or the legacy turbine types. This study proposes a novel method for aerodynamic reconstruction of wind turbines using Terrestrial Laser Scanning (TLS). The method allows for efficiently capturing turbine geometry and aerodynamic properties without requiring blade disassembly. The specific procedures, including TLS-based data acquisition, data preprocessing, and blade parameterization, are outlined systematically to demonstrate the complete framework. A field implementation on a 2 MW commercial wind turbine proved its effectiveness, with validation against historical data and manufacturer-supplied performance curves showing strong consistency. Challenges such as blade twist variations and wind speed measurement inaccuracies are analyzed as potential error sources, and strategies for enhancing precision and reliability are provided. The proposed method offers a competitive, engineering-oriented solution for wind turbine aerodynamic reconstruction, laying a foundation for enhanced turbine design, operational efficiency, and long-term performance optimization. © 2025 Elsevier Ltd

关键词： Laser applications

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Decoupling While Coupling: Towards More Accurate Stereo Image Sand Removal Beyond Certainty

Decoupling While Coupling: Towards More Accurate Stereo Imag...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Bingcai Wei Hui Liu Chuang Qian Yi Jia Wangyu Wu Zhishan Li School of Computer Science Wuhan University Wuhan China GNSS Research Center Wuhan Unicersity Wuhan China Intelligent Transport Systems Research Center Wuhan University of Technology Wuhan China College of Information and Control Engineering Jilin Institute of Chemical Technology Jilin China University of Liverpool Liverpool UK School of Computer Science and Engineering South China University of Technology Guangzhou China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Stereo image sand removal is crucial to improve the perceptual quality for autonomous driving perception. Existing methods often fall short in accurately estimating the uncertainty inherent in degraded images, leading to suboptimal outcomes. To address this, we introduce a novel framework named Decoupling While Coupling(DWC). DWC pioneers the integration of inter-view uncertainty estimation, cross-view uncertainty-aware interaction and block-wise uncertainty representation for superior stereo image sand removal. For cross-view information interaction, we propose an Uncertainty-aware Cross-view Attentive Interaction module(UCAI) to cope with the lack of uncertainty estimation ability in the existing cross-view information interaction mechanism. For the uncertainty perception and information interaction within the inter-view, we propose a Distribution Modeling Coupling Block(DMCB), which transmits the representation of uncertainty between each backbone module. For block-wise uncertainty estimation, we use our proposed Uncertainty-aware Distribution Feature Modulator(UDFM) as the backbone of DWC to modulate the uncertainty inside the neural network itself. Extensive experimental validations on our proposed stereo image sand removal dataset SandST confirm the efficacy of DWC. Our method not only achieves higher PSNR and SSIM, but also exhibits enhanced robustness against various sand degrees and patterns.

关键词： Couplings Uncertainty Neural networks Estimation Signal processing Robustness Stereo vision Image restoration Speech processing Autonomous vehicles

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Data security and access control method of blockchain with revocable attribute encryption in steel supply chain

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Peer-to-Peer Networking and Applications 2025年第4期18卷 1-28页

作者： Gao, Jie Zhang, Xiaohong Zhong, Shaojiang School of Information Engineering Jiangxi Province Key Laboratory of Multidimensional Intelligent Perception and Control Jiangxi University of Science and Technology Ganzhou China College of Modern Economics and Management Jiangxi University of Finance and Economics Gongqingcheng China Department of Computer Science Purdue University West Lafayette USA

With the rapid development of the steel supply chain, the volume of data generated has increased dramatically, revealing potential risks associated with traditional coarse-grained data access control mechanisms and centralized architectures. To address these challenges, a blockchain-based data access control method utilizing attribute revocation ciphertext policies is proposed. This method enhances existing schemes by introducing a pre-decryption process and integrating an attribute revocation list, thereby enabling real-time attribute revocation. In terms of security, this approach is validated based on the Decisional Bilinear Diffie-Hellman (DBDH) hypothesis, which is a difficult problem under asymmetric groups, ensuring data security. By leveraging the decentralized nature of blockchain technology, this method mitigates the risks associated with single points of failure in central certification institutions. The token request mechanism introduced enables users to request multiple resources simultaneously, thereby reducing the frequency of operations. Compliant requests are authenticated, granted permissions, and subsequently returned to the user via a token. Additionally, this method integrates the Interplanetary File System (IPFS) and employs both chain-up and chain-down storage modes, effectively addressing issues related to insufficient blockchain capacity and system efficiency. The research validates the efficacy of this approach in enhancing efficiency and resolving challenges within the steel supply chain, providing substantial support for the industry’s digital transformation. Experimental results indicate that when handling large-scale attribute sets, both pre-decryption and decryption times remain stable at approximately 100 milliseconds, thereby achieving efficient and fine-grained access control to blockchain data. Overall, the scheme demonstrates excellent performance in terms of feature richness, practicality, and high efficiency, effectively mitigat

关键词： Authentication

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LSU-NET: LIGHTWEIGHT AUTOMATIC ORGANS SEGMENTATION NETWORK FOR MEDICAL IMAGES

arXiv

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arXiv 2025年

作者： Ding, Yujie Teng, Shenghua Li, Zuoyong Chen, Xiao College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao266590 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control School of Computer and Big Data Minjiang University Fuzhou350121 China

UNet and its variants have widespread applications in medical image segmentation. However, the substantial number of parameters and computational complexity of these models make them less suitable for use in clinical settings with limited computational resources. To address this limitation, we propose a novel Lightweight Shift U-Net (LSU-Net). We integrate the Light Conv Block and the Tokenized Shift Block in a lightweight manner, combining them with a dynamic weight multi-loss design for efficient dynamic weight allocation. The Light Conv Block effectively captures features with a low parameter count by combining standard convolutions with depthwise separable convolutions. The Tokenized Shift Block optimizes feature representation by shifting and capturing deep features through a combination of the Spatial Shift Block and depthwise separable convolutions. Dynamic adjustment of the loss weights at each layer approaches the optimal solution and enhances training stability. We validated LSU-Net on the UWMGI and MSD Colon datasets, and experimental results demonstrate that LSU-Net outperforms most state-of-the-art segmentation architectures. Copyright © 2025, The Authors. All rights reserved.

关键词： Convolution

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SimGate: A deep learning surrogate model for predicting microstructure evolution using the phase-field method

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Computational Materials Science 2025年 256卷

作者： Wu, Pin Huang, Haiwang Yang, Qingcheng Qian, Bo Gao, Yongxin Yang, Yiguo Zhang, Huiran Zhen, Qiang School of Computer Engineering and Science Shanghai University Shanghai China Shanghai Key Laboratory of Mechanics in Energy Engineering School of Mechanics and Engineering Science Shanghai Institute of Applied Mathematics and Mechanics Shanghai University Shanghai China Shanghai Institute of Aircraft Mechanics and Control Shanghai China Research Center of Nano Science and Technology College of Sciences Shanghai University China State Key Laboratory of Advanced Refractories Shanghai University Shanghai China

This study introduces SimGate, a novel deep learning surrogate model for predicting microstructure evolution using the phase-field method. Combining the temporal modeling capabilities of "Simpler yet better video prediction (SimVP)" with the multi-order aggregation features of "Multi-order gated aggregation network (MogaNet)", SimGate leverages robust temporal dynamics alongside spatial and channel aggregation modules to ensure precise detail capture and spatial consistency. To demonstrate SimGate's ability to tackle challenging scenarios, high-temperature sintering simulations of polycrystalline cerium dioxide (CeO2) particles were selected as a test case. These simulations, chosen for their complexity, involve both Cahn–Hilliard-type and Allen–Cahn-type phase-field equations along with intricate interfacial dynamics, and they were validated through experimental data. SimGate accurately predicts the sintering process from limited initial time steps and exhibits strong extrapolation capabilities in modeling unseen microstructures over extended time scales. Compared to traditional phase-field simulations, which require hours per case, SimGate reduces computational time to seconds while maintaining a prediction accuracy of around 90%. Additionally, point-wise error analysis shows that the average accuracy is improved by 7.80% and 12.41% compared with the original SimVP and well-known Long Short-Term Memory Networks (LSTM), respectively. An ablation analysis was performed to reveal the contributions of key components in the proposed SimGate framework. By significantly enhancing computational efficiency and accuracy, SimGate demonstrates broad potential as a generalizable microstructure prediction model applicable to diverse material and mechanical processing scenarios beyond sintering. © 2025

关键词： Sintering

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DEEPCON: Improving Distributed Deep Learning Model Consistency in Edge-Cloud Environments via Distillation

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Qian, Bin Qian, Jiaxu Wen, Zhenyu Wu, Di He, Shibo Chen, Jiming Ranjan, Rajiv Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou China Zhejiang University of Technology Institute of Cyberspace Security College of Information Engineering Hangzhou China University of St Andrews School of Computer Science United Kingdom Newcastle University United Kingdom

In a typical distributed Deep Learning (DL) based application, models are configured differently to meet the requirements of resource constraints. For instance, a large ResNet56 model is deployed on the cloud server while a small lightweight MobileNet model is more suitable for the end-user device with fewer computation resources. However, the heterogeneity of the model architectures and configurations may bring a systemic problem -models may produce different outputs when given the same input. This inconsistency problem may cause severe system failure of prediction agreement inside the application. Current research has not studied the systemic design for efficiently detecting and reducing the inconsistency among models in distributed DL applications. With the increasing scale of distributed DL applications, the challenges of inconsistency mitigation should consider both algorithm and system design. To this end, we design and implement DEEPCON, an adaptive deployment system across the edge-cloud layer with over-the-air model updates. We implement ASRS sampling for efficiently sampling data to reveal the real data distribution as well as model prediction inconsistency. Then, we implement DMML-Par, an asynchronous parallel training algorithm for quickly updating the models and reducing inconsistency. implements over-the-air updates with a set of APIS to enable seamless inconsistency detection and reduction in such deep learning applications. Our experiment results on both vision and language tasks demonstrate that DMML could improve the model consistency up to 4%, 7%, and 13% at CIFAR10/100 and IMDB datasets without sacrificing the accuracy of individual models. We also show that the ASRS sampling can save 90% network bandwidth of data transmission and that DMML-Par is up to 60% faster compared to simple synchronous parallel training. 2015 IEEE.

关键词： Distributed cloud

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Interpretable deep temporal neural networks for in-situ monitoring under varying conditions in micro-electrical discharge machining

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Journal of Manufacturing Systems 2025年 81卷 222-237页

作者： Long Ye Cheng Guo Ming Wu Jun Qian Nan Yu Dominiek Reynaerts Institute of Materials and Processes School of Engineering University of Edinburgh Edinburgh EH9 3FB UK Guangdong Provincial Key Laboratory of Micro/Nano Optomechatronics Engineering College of Mechatronics and Control Engineering Shenzhen University Shenzhen 518060 PR China Micro & Precision Engineering Group Department of Mechanical Engineering KU Leuven Leuven 3001 Belgium Members Flanders Make Leuven Belgium Department of Computer Science KU Leuven Leuven 3001 Belgium

Monitoring is critical enabler of digitalization in modern manufacturing, supporting enhanced process control, quality assurance, and real-time decision-making. By integrating data-driven techniques with the powerful capabilities of deep learning, monitoring systems can efficiently extract valuable insights from complex, high-dimensional time-series data. However, traditional data-driven approaches often lack interpretability, limiting their adoption in industrial applications that demand high reliability and accountability. To address this challenge, this paper proposes an interpretable monitoring framework based on a deep temporal neural network (DTNN). Designed with a modular architecture, the DTNN integrates key components for embedding, temporal feature learning and classification, enabling it to effectively capture complex underlying patterns in temporal process data and overcome the limitations of conventional methods. The DTNN’s capabilities are demonstrated in the context of micro-electrical discharge machining (micro-EDM), a prominent non-traditional machining process known for producing intricate and high-precision components. Through a pulse discrimination task utilizing a large dataset of reliable labels, the DTNN achieves superior classification accuracy under varying processing parameters while providing interpretable insights into discharge phenomena. Furthermore, the DTNN monitoring approach is applied to a deep-hole drilling process in micro-EDM, enabling closed-loop control of discharge status and ensuring long-term process stability. The DTNN’s modular design, interpretability and real-time adaptability underscore its potential for advancing data-driven monitoring systems in digital manufacturing.

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Dual-beam photothermal material trace detection method based on self-mixing interference

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Applied Optics 2025年第16期64卷 4549-4558页

作者： Yan Zhao Yunlong Zhi Hui Zhao Daming Dong Jigang Tong Chunling Tu School of Electrical Engineering and Automation Tianjin University of Technology Tianjin 300384 China Tianjin Key Laboratory for Control Theory and Applications in Complicated Systems Tianjin 300384 China National Engineering Research Center for Intelligent Agricultural Equipment Beijing Academy of Agriculture and Forestry Sciences Beijing 100097 China Department of Computer Systems Engineering Faculty of ICT Tshwane University of Technology Pretoria 0188 South Africa

In this paper, a dual-beam photothermal self-mixing substance trace detection system is proposed. The crystal violet (CV) solution of the sample undergoes a thermal lens effect under the action of pump photoperiod excitation, and the refractive index changes cause the deviation of the probe beam, and the self-mixing interference (SMI) effect occurs when the modulated light returns to the cavity. By combining theoretical and experimental results, the power fluctuation of the SMI signal caused by the thermo-optic effect is linearly related to CV concentration and other parameters. At an excitation power of 24 mW, the detection limit can reach $8.0 \times {10^{- 8}}\;{\rm mol/L}$ . This method does not require labeling or complex CV pretreatment and has high sensitivity and flexibility, providing a guide for CV characterization in biological, environmental, and pharmaceutical research.

关键词： Gaussian beams Laser beams Optical fields Phase shift Refractive index Spatial resolution

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