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14th International Conference on Educational and Information technology, ICEIT 2025

作者： Jin-Gang, Jiang Jiang-Long, Xiong Ya-Ping, Wang Kai-Rui, Wang Tao, Shen Yudong, Bao National Experimental Teaching Demonstration Center for Mechanical Engineering Experiment and Practice Harbin University of Science and Technology Harbin China School of Mechanical and Power Engineering Harbin University of Science and Technology Harbin China School of Measurement and Communication Engineering Harbin University of Science and Technology Harbin China

ISBN: (纸本)9798331540883

Aiming to address the current problems in robot training teaching, this paper focuses on the construction of industry-teaching-innovation cases for robot training under the integration of virtuality and reality, such as the disconnection between training content and industry practice, delays in digital transformation, and the lack of training resources. Relying on the intelligent manufacturing industry academy (IMIA), the teaching model integrates virtual simulation training with practical operation to enhance students' practical skills and innovative abilities. The study introduces complex technical challenges faced by enterprises in production, combining a dual-mentor system and a collaborative resource-sharing mechanism between school and enterprises. This ensures that students can master key skills required by enterprises during training, while innovation and entrepreneurship education is integrated throughout the whole training process to stimulate students' creative thinking and entrepreneurial mindset. © 2025 IEEE.

关键词： Students

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Channel pruning method driven by similarity of feature extraction capability

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Soft Computing 2025年第2期29卷 1207-1226页

作者： Sun, Chuanmeng Chen, Jiaxin Li, Yong Wang, Yu Ma, Tiehua State Key Laboratory of Dynamic Measurement Technology North University of China Shanxi Taiyuan030051 China School of Electrical and Control Engineering North University of China Shanxi Taiyuan030051 China State Key Laboratory of Coal Mine Disaster Dynamics and Control Chongqing University Chongqing400044 China

Channel pruning is a method to compress convolutional neural networks, which can significantly reduce the number of model parameters and the computational amount. Current methods that focus on the internal parameters of a model and feature mapping information rely on artificially set a priori criteria or reflect filter attributes by partial feature mapping, which lack the ability to analyze and discriminate the channel feature extraction and ignore the basic reasons for the similarity of the channels. This study developed a pruning method based on similar structural features of channels, called SSF. This method focuses on analysing the ability to extract similar features between channels and exploring the characteristics of channels producing similar feature mapping. First, adaptive threshold coding was introduced to numerically transform the channel characteristics into structural features, and channels with similar coding results could generate highly similar feature mapping. Secondly, the spatial distance was calculated for the structural features matrix to obtain the similarity between channels. Moreover, in order to keep rich channel classes in the pruned network, different class cuts were made on the basis of similarity to randomly remove some of the channels. Thirdly, considering the differences in the overall similarity of different layers, this study determined the appropriate pruning ratio for different layers on the basis of the channel dispersion degree reflected by the similarity. Finally, extensive experiments were conducted on image classification tasks, and the experimental results demonstrated the superiority of the SSF method over many existing techniques. On ILSVRC-2012, the SSF method reduced the floating-point operations (FLOPs) of the ResNet-50 model by 57.70% while reducing the Top-1 accuracy only by 1.01%. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Convolutional neural networks

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DSCCNet for high-quality 4K computer-generated holograms

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Optics Express 2025年第6期33卷 13733-13747页

作者： Xu, Zhenqi Leng, Junmin Dai, Ping Wang, Chao Key Laboratory of Information and Communication Systems Ministry of Information Industry Beijing Information Science and Technology University Beijing100101 China Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument Beijing Information Science and Technology University Beijing100101 China School of Information and Communications Engineering Beijing Information Science and Technology University Beijing100101 China

With the increasing demand for high-quality 3D holographic reconstruction, visual clarity and accuracy remain significant challenges in various imaging applications. Current methods struggle for higher image resolution and to resolve such issues as detail loss and checkerboard artifacts. To address these challenges, we propose the model Depthwise Separable Complex-valued Convolutional Network (DSCCNet) for phase-only computer-generated holography (CGH). This deep learning framework integrates complex-valued convolutions with depthwise separable convolutions to enhance reconstruction precision and improve model training efficiency. Additionally, the diffuser is employed to reduce checkerboard artifacts in defocused parts of 3D CGH. Experimental results demonstrate that DSCCNet can obtain 4K images reconstructed with more intricate details. The reconstruction quality of both 2D and 3D layered objects is enhanced. Validation on 100 images from the DIV2K dataset shows an average PSNR above 37 dB and an average SSIM above 0.95. The proposed model provides an effective solution for high-quality CGH applications. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Computer generated holography

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Robust Federated Learning Algorithm Based on Chaotic Encryption and Dual-Server Detection

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IEEE Internet of Things Journal 2025年

作者： Kang, Shouqiang Wu, Yuxuan Wang, Yujing Wang, Qingyan Liang, Xintao Harbin University of Science and Technology School of Measurement and Control Technology and Communication Engineering Harbin150080 China

Federated learning allows users to collaboratively train models, but studies have shown that local model parameters may leak users’ privacy. Furthermore, the global model in federated learning could be compromised by Byzantine attacks. To address these issues, a robust federated learning algorithm based on chaotic encryption and dual-server detection (CDRFL) is proposed. First, CDRFL introduces a lightweight chaotic encryption method, where ciphertexts have a unique property of mutual cancellation, allowing the plaintext global model parameters to be derived without decryption. Moreover, CDRFL allows users to encrypt with different keys, further reducing the risk of privacy leakage. Next, a dual-server detection scheme is designed to defend against Byzantine attacks while preserving user privacy. Lastly, a reputation score mechanism with a forgetting function is proposed to fairly distinguish Byzantine participants. Experimental results demonstrate that, compared with popular robust aggregation schemes, CDRFL improves testing accuracy by 3.26% to 9.06%. When compared to existing chaotic encryption federated learning schemes, CDRFL enhances the ability to defend against Byzantine attacks and reduces time and communication overhead by 73.63% and 95.40%, respectively. © 2014 IEEE.

关键词： Federated learning

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Data-Driven Prediction of Coefficient of Friction in Wet Friction Components: A Model Development and Interpretability Analysis

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Journal of Tribology 2025年第7期147卷 074601页

作者： Wu, Jianpeng Zhao, Peng Cui, Jiahao Wang, Liyong Yang, Chengbing Ouyang, Jianping Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing Information Science and Technology University 12 Xiaoying East Road Haidian District Beijing100192 China School of Mechanical and Engineering University of Science and Technology Beijing 30 Xueyuan Road Haidian District Beijing100083 China Mechanical Electrical Engineering School Beijing Information Science and Technology University 12 Xiaoying East Road Haidian District Beijing100192 China

Predicting the coefficient of friction (COF) is essential for enhancing the efficiency and reliability of mechanical systems. Nevertheless, traditional mechanistic models relying on fixed values or fitted curves fail to accurately capture this complexity. To address this issue, this paper proposes a model for predicting the COF of wet friction components using an extreme gradient boosting (XGBoost) algorithm optimized by the sparrow search algorithm (SSA). This model effectively captures the nonlinear relationships among relative speed, pressure, temperature, and COF. As a result, the proposed SSA-XGBoost model exhibits excellent predictive performance with a root mean square error (RMSE) of only 0.063, and 88.3% of the COF predictions have a relative error of less than 1%, significantly outperforming other deep-learning algorithms. Additionally, to enhance the understanding of the COF prediction results for wet friction components, the SHapley Additive exPlanations (SHAP) model is used to explore the influence of relative speed, pressure, and temperature on the predicted COF values. Copyright © 2024 by ASME.

关键词： Deep learning

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DESIGN AND ANALYSIS OF A SLOTTED PLANAR ANTENNA WITH PARASITIC RING PATCH FOR EFFICIENT 5G MICROWAVE APPLICATIONS

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Telecommunications and Radio engineering (English translation of Elektrosvyaz and Radiotekhnika) 2025年第1期84卷 71-82页

作者： Yadav, Swati Varun Yadav, Manish Varun Gupta, Ridhi Yadav, Dinesh Bhati, Kirti Panwar Ali, Tanweer Singh, Vikas Department of Instrumentation and Control Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal Karnataka India Department of Aeronautical and Automobile Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal Karnataka India Department of Electronics and Communication Engineering Manipal University Jaipur Rajasthan Jaipur India School of Electronics Devi Ahilya University M.P Indore India Department of Electronics and Communication Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal Karnataka India

Design and analysis of a slotted planar antenna with a parasitic ring patch for efficient 5G microwave applications is presented. The antenna design aims to strike a balance between complexity and size efficiency. Its front side features a rectangular patch at the center, comprising L-shaped, T-shaped, and inverse T-shaped elements, alongside four parasitic rings on top. The central feed utilizes a 50-ohm strip line connected to a microstrip line. On the back side, the antenna incorporates three parasitic rings on top, four horizontal strips arranged in an increasing order, and a ground plane with a small rectangular slot. To evaluate its performance, the antenna is simulated using CST Microwave Studio on an FR-4 substrate with overall dimensions measuring 13 × 15 × 1.5 mm3. The results demonstrate an impressive impedance bandwidth of 60.8% and a return loss of –28 dB within the frequency range of 3.2–6 GHz, with a central frequency of 4.6 GHz. Throughout its operational range, the radiation patterns remain constant, exhibiting stable polar patterns and efficient performance with a wide gain of 4.81 dBi and an efficiency of 82%;the proposed antenna proves to be suitable for 5G microwave applications. © 2025 by Begell House, Inc.

关键词： Microwave antennas

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Thermal constraint control of reversible solid oxide cell for optimal system efficiency

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International Journal of Hydrogen Energy 2025年 134卷 198-211页

作者： Xu, Yuanwu Yin, Jie Wang, Jingjing Wu, Xiao-long Peng, Jingxuan Tang, Ruifeng Zou, Suhang Fu, Xiaowei Li, Xi School of Artificial Intelligence and Automation Wuhan University of Science and Technology Hubei Wuhan430081 China School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry Huazhong University of Science and Technology Hubei Wuhan430074 China School of Information Engineering Nanchang University Jiangxi Nanchang330031 China School of Electrical and Electronic Engineering Nanyang Technological University 639798 Singapore China-Poland Belt and Road Joint Laboratory on Measurement and Control Technology Huazhong University of Science and Technology Hubei Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518055 China

Reversible solid oxide cell (RSOC) demonstrates considerable promise in multimodal energy conversion architectures, particularly as a technology for hydrogen-based energy storage systems. Nevertheless, under dynamic operating regimes with time-varying current inputs, realizing time-efficient and reliable transitions to steady-state operation remains difficult. This difficulty arises from the intricate coupling of thermal, electrical, and gaseous processes within the RSOC system. This study aims to address these challenges by developing a 5 kW mathematical model of the RSOC system. The proposed model enables systematic investigation of thermal safety issues arising during current switching operations while introducing a thermal constraint control strategy to enhance both system efficiency and thermal safety performance. This efficiency-driven study employs the Grid Search algorithm to identify optimal steady-state operating points for the RSOC system, ensuring strict adherence to safety constraints across varying current inputs. Furthermore, by integrating the pre-identified optimal operating points as feedforward control with Model Predictive control (MPC)-based feedback correction, this hybrid strategy effectively mitigates stack temperature gradient overshoot while synchronously ensuring optimal system efficiency throughout current transition processes. Under simulated variable current inputs, the proposed RSOC system model and control strategy demonstrate the capability to constrain stack temperature gradient overshoot within 2.3K while enhancing transient efficiency by 5 %–8 %, achieving a peak system efficiency of 85 %. It provides a novel methodology for safe and efficient working with intermittent renewable energy. © 2025 Hydrogen Energy Publications LLC

关键词： Thermal variables control

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Synchronous Estimation Scheme of PMSM Mechanical Parameters: A Decoupled Extended Sliding Mode Observer

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IEEE Transactions on Power Electronics 2025年第8期40卷 10877-10888页

作者： Chang, He Wang, Zuo Yang, Jun Li, Shihua Lu, Shaowu Southeast University School of Automatin Nanjing210096 China Ministry of Education Key Laboratory of Measurement and Control of CSE Nanjing210096 China Loughborough University Department of Aeronautical and Automotive Engineering LoughboroughLE11 3TU United Kingdom Wuhan University of Science and Technology School of Information Science and Engineering Wuhan430081 China

In the permanent magnet synchronous motor drives (PMSM), high-quality controller optimization and condition monitoring rely heavily on accurate knowledge of mechanical parameters. To address this need, this study presents a novel decoupled extended sliding mode observer (DESMO) capable of achieving high-quality synchronous estimation of the moment of inertia and the load disturbance. An extended state-space equation is, firstly, constructed by extending the mechanical equations. Subsequently, the novel DESMO is designed by introducing two new recursive equations. On this basis, the excitation weight on the moment of inertia is analyzed, resulting in the derivation of Lyapunov-mechanism-based decoupling updating conditions (DOCs) to further exploit the DESMO's estimation potential. Furthermore, when the DOCs are not all satisfied, the second observation equation is derived with the reaching law of the moment of inertia set to zero to achieve state decoupling during estimation process. With the switching excitation of DOCs, notably, the DESMO is capable of serving as a more competitive candidate for observing multi-mechanical parameters, as demonstrated through simulation and experimental results. © 1986-2012 IEEE.

关键词： Permanent magnets

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Simulation on Electromagnetic Shielding Effectiveness of Multi-layer Cylindrical Metal Enclosures 16th

Simulation on Electromagnetic Shielding Effectiveness of Mul...

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16th International Conference on Modelling, Identification and control, ICMIC 2024

作者： Yu, Feng Pan, Wang Haiyang, Wang Bing, Zhou Yinxia, Gao Yinjie, Wang Wei, Jia Changxin, Chen State Key Laboratory of Dynamic Measurement Technology North University of China Taiyuan030051 China School of Electrical and Control Engineering North University of China Taiyuan030051 China

ISBN: (纸本)9789819617760

For the problem of optimal shielding design of metal shielding enclosures in complex environments, multi-layer cylindrical metal enclosures is modeled and simulated by using simulation software. In the simulation, the shielding effectiveness of the multi-layer enclosures with different diameter-to-length ratios and alternating combinations of different materials is compared and analyzed. The results show that the multi-layer shielding enclosure with optimal shielding effectiveness is composed of stainless steel on the outer layer, silver on the middle layer and permalloy on the inner layer. And the larger the length-diameter ratio of multi-layer cylindrical metal enclosure, the better the shielding effect. The simulation study of electromagnetic shielding effectiveness of multi-layer cylindrical metal enclosures provides support for the study of multi-layer metal shielding enclosures in complex electromagnetic environments. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electromagnetic shielding

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Optimization of BLE Broadcast Mode in Offline Finding Network

arXiv

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arXiv 2025年

作者： Zhang, Li Feng, Cheng Xia, Tian School of Computer and Control Engineering Northeast Forestry University China School of Measurement-control Technology and Communications Engineering Harbin University of Science and Technology China

In the Offline Finding Network(OFN), offline Bluetooth tags broadcast to the surrounding area, the finder devices receiving the broadcast signal and upload location information to the IoT(Internet of Things) cloud servers, thereby achieving offline finding of lost items. This process is essentially a Bluetooth low energy (BLE) neighbor discovery process(NDP). In the process, the variety of Bluetooth scan modes caused by the scan interval and scan window settings affects the discovery latency of finder devices finding the tag broadcast packets. To optimize the experience of searching for lost devices, we propose the CPBIS-mechanism, a certain proportion broadcast-intervals screening mechanism that calculates the most suitable two broadcast intervals and their proportion for offline tags. This reduces discovery latency in the BLE NDP, improves the discovery success rate, further enhances the user experience. To our knowledge, we are the first to propose a comprehensive solution for configuring the broadcast interval parameters of advertisers in BLE NDP, particularly for configurations involving two or more broadcast intervals. We evaluated the results obtained by CPBIS on the nRF52832 chip. The data shows that the CPBIS-mechanism achieves relatively low discovery latencies for multiple scan modes. © 2025, CC BY.

关键词： Bluetooth

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