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26th International Conference on Industrial technology, ICIT 2025

作者： Wen, Xin Liu, Zhen-Tao Chen, Dan School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China

ISBN: (纸本)9798331521950

Facial expression recognition plays a key role in promoting the development of comprehensive intelligence and building friendly human-computer interaction. Due to the interference of feature noise in expression data, the lightweight facial expression recognition model with fewer parameters is difficult to learn more expression features through simple training, which limits the improvement of its recognition performance. An efficient facial expression recognition network based on Spot-adaptive Knowledge Distillation is proposed in this paper. Inspired by VoVNetV2, the network designed in this paper is lightweightly improved using Depthwise Separable Convolution and the parameter-free SimAM attention mechanism, reducing the number of parameters to 0.21 M. To further improve the recognition accuracy of the model, Spot-adaptive Knowledge Distillation is employed to improve the characterization ability of the model. The recognition accuracies of the student network designed in this paper on the KDEF and RAF-DB datasets are 93.05% and 81.17% respectively after spot-adaptive distillation. © 2025 IEEE.

关键词： Facial Expression Recognition Knowledge Distillation Lightweight Neural Network

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An automated FBG localization algorithm for distributed strain sensing system using weak FBG array 4

An automated FBG localization algorithm for distributed stra...

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4th International Computational Imaging Conference, CITA 2024

作者： Liang, Changshuo Zhou, Xiaoxu Wang, Yu Bai, Qing Gao, Yan Zhang, Hongjuan Jin, Baoquan Key Laboratory of Advanced Transducers and Intelligent Control Systems Ministry of Education and Shanxi Province Taiyuan University of Technology Taiyuan China Shanxi Transportation Technology Research & Development Co. Ltd. Shanxi Taiyuan China College of Electrical and Power Engineering Taiyuan University of Technology Taiyuan China

ISBN: (纸本)9781510688834

We proposed a Fiber Bragg grating (FBG) localization algorithm for Optical Frequency Domain Reflectometry (OFDR) sensing system with high density weak FBG array. The wavelength selection characteristics of FBG, which are revealed in the distance-wavelength domain spectrum of OFDR signal, are used as the standard for FBG localization. The OFDR system for FBG strain sensing using the proposed method achieved a strain sensing on 45.36 m long FBG array, which is capable of sensing strain up to 3000 μΕ, with spatial resolution of 1 cm, and root mean square error (RMSE) of 0.00192 nm. © 2025 SPIE.

关键词： Fiber Bragg gratings

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Preface to Special Issue on Optoelectronic Neuromorphic Devices

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Journal of Semiconductors 2025年第2期46卷 2-4页

作者： Zhenyi Ni Zhongqiang Wang Jia Huang Xiaodong Pi State key Laboratory of Silicon and Advanced Semiconductor Materials&School of Materials Science and Engineering Zhejiang UniversityHangzhou 310027China Shangyu Institute of Semiconductor Materials Shaoxing 312300China Key Laboratory of UV-Emitting Materials and Technology of Ministry of Education Northeast Normal UniversityChangchun 130024China School of Materials Science and Engineering&National Key Laboratory of Autonomous Intelligent Unmanned Systems Tongji UniversityShanghai 201804China Institute of Advanced Semiconductors&Zhejiang Provincial Key Laboratory of Power Semiconductor Materials and Devices ZJ-UHangzhou Global Scientific and Technological Innovation CenterZhejiang UniversityHangzhou 311200China

The rapid advancement of artificial intelligence(AI),recognized with Nobel Prizes in both Physics and Chemistry in 2024,has been revolutionizing countless aspects of modern life,driving innovations across diverse fields and reshaping how people interact with *** its great advances,the computing of AI systems is now approaching the limit of traditional computing based on the von Neumann architecture,which is characterized by separated memory and process units-a challenge known as the"von Neumann bottleneck".

关键词： approaching separated Neumann

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Applying Explanatory Artificial Intelligence Using LIME to Analyze Arabic Online Handwriting Recognition Models

Applying Explanatory Artificial Intelligence Using LIME to A...

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International Conference on Information Networking

作者： Hasanien Ali Talib Alothman Wafa Lejmi Safa Ameur Mohamed Ali Mahjoub ISITCom - Higher Institute of Computer Science and Communication Sousse Tunisia LATIS - Laboratory of Advanced Technology and Intelligent Systems National Engineering School of Sousse (ENISo) University of Sousse Sousse Tunisia Mechatronics Engineering Department University of Mosul Mosul Iraq

ISBN: (数字)9798331506940

ISBN: (纸本)9798331506957

This study applies interpretable artificial intelligence (XAI) techniques in an unprecedented way to improve transparency in online Arabic handwriting recognition models. By using the LIME (Local Model-Independent Explanations) technique to analyze and interpret a convolutional neural network (CNN) model trained on the ADAB dataset. LIME highlights the key features that the model relies on in handwriting recognition. This research demonstrates the effectiveness of the LIME technique in uncovering the factors that the model relies on in an unconventional way and provides detailed explanations of how the decisions are made, which enhances transparency and increases users’ confidence in these models. The results provide useful information for improving the performance of handwriting recognition systems and a beginning for applying XAI techniques on a larger scale, including using more complex datasets and additional interpretation techniques to gain a deeper understanding of how models work with different handwriting styles. This study paves the way for the use of interpretable techniques in the field of Arabic handwriting recognition.

关键词： Deep learning Handwriting recognition Analytical models Explainable AI Shape Decision making Predictive models Prediction algorithms Data models Convolutional neural networks

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Evolution Design Methodology of the Metamorphic Mechanisms Inspired by Gene Deduction

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Journal of Mechanical Design 2025年第8期147卷 083302页

作者： Zhang, Yang Xu, Peng Li, Bing Guangdong Provincial Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robots Key University Laboratory of Mechanism & Machine Theory and Intelligent Unmanned Systems of Guangdong School of Robotics and Advanced Manufacture Harbin Institute of Technology Shenzhen 518052 China

This article proposes an evolutionary design method for metamorphic mechanisms inspired by gene deduction. First, the correlations between biological evolution and mechanical evolution, biological systems and mechanical systems, and genes and mechanisms are revealed. There are many similarities between biology and mechanism. Biological principles can be applied to the study and design of the mechanisms to adapt well to tasks and environments. The correlation between genetic operation and mechanical design is developed. The selection operator, recombination operator, and mutation operator of the mechanisms are deduced by inspiring genes. To verify the rationality of these operators, they are used to construct 5R (revolute joints) overconstrained closed-loop linkage, 6R metamorphic closed-loop linkage, 7R deployable closed-loop linkage, and 2RP (prismatic joint) RR metamorphic parallel mechanism (MPM). The mobilities of these mechanisms are analyzed via the atlas method. Finally, the evolutionary design method of MPMs is introduced and used to design the mechanisms with 2R1T (two rotation one translation) and 1R2T modes, which verify the effectiveness of the method. The proposed evolutionary design method provides a new theoretical tool for the design of metamorphic single-loop and parallel mechanisms.

关键词： bio-inspired design Chain Design methodology evolution design Design Parallel mechanisms Kinematics metamorphic mechanisms single-loop/parallel mechanisms Kinematic chains gene deduction atlas method Biology Bifurcation

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Techno-economic analysis of fuel cell trucks with different powertrain hybridization and hydrogen resources by 2040: case study of China

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International Journal of Hydrogen Energy 2025年 132卷 174-182页

作者： Nan, Long Xu, Feiyang Sui, Pang-Chieh Feng, Yanbiao Hao, Han Li, Shunxi Huang, Feng Hubei Key Laboratory of Advanced Technology for Automotive Components Hubei Collaborative Innovation Center for Automotive Components Technology and Hubei Research Center for New Energy & Intelligent Connected Vehicle School of Automotive Engineering Wuhan University of Technology Hubei 430070 China Institute for Integrated Energy Systems University of Victoria VictoriaBCV8W 2Y2 Canada School of Mechanical Engineering University of Science & Technology Beijing Beijing100083 China State Key Laboratory of Automotive Safety and Energy Tsinghua University Beijing100084 China

This study conducts a techno-economic analysis of fuel cell electric trucks with different hybridization factors (i.e., the power distribution ratio between batteries and fuel cells) by evaluating their life-cycle carbon dioxide (CO2) emissions and total cost of ownership (TCO) from 2020 to 2040. It integrates the energy consumption of trucks with varying hybridization factors under different driving cycles with China's hydrogen production, transportation models, and electric grid structure. Through this analysis, the TCO and emissions of individual trucks are examined across different scenarios. The study reveals that highly hybridized trucks offer advantages in both emissions reduction and cost savings across all hydrogen production and transport pathways. A 67% hybridization factor results in a 5%-9% reduction in emissions and a 34%-46% decrease in TCO compared to the lowest hybridization factor. The energy-saving and emission-reducing effects of highly hybridized fuel cell electric vehicles (FCEVs) also depend on the driving cycle, with optimal performance observed in the Urban Dynamometer Driving Schedule for Heavy-Duty Vehicles (UDDSHDV) cycle, while minimal advantages appear in the China Heavy-Duty Commercial Vehicle Test Cycle for Heavy Trucks (CHTC-HT) cycle. Furthermore, life-cycle emissions of trucks decrease gradually over time for all hydrogen production pathways, with the electrolysis of grid electricity showing the fastest decline. By 2040, trucks powered by hydrogen from grid electricity will have slightly lower emissions than those using coal, while green hydrogen remains the cleanest option. In conclusion, adopting fuel cell trucks with high hybridization factors, powered by green hydrogen, presents significant economic and environmental benefits for the future of transportation. © 2025 Hydrogen Energy Publications LLC

关键词： Commercial vehicles

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Artificial Intelligence Techniques With Digital Twin for Fault Diagnosis in Interconnected systems: A Review

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IEEE Access 2025年 13卷 91860-91874页

作者： Imen Nakti Majdi Mansouri Rami Al-Hmouz Atef Khedher Laboratory of Advanced Technology and Intelligent Systems ENISO University of Sousse Sousse Tunisia Department of Electrical and Computer Engineering College of Engineering Sultan Qaboos University Muscat Oman

As industrial systems become more complex and interconnected, diagnosing faults accurately and in real time has become increasingly challenging. This paper explores how combining artificial intelligence with digital twin technology can address these challenges. We focus on developing hybrid Artificial intelligence models that leverage diverse data sources to enhance fault detection and diagnosis, enabling secure and distributed diagnostics. Digital twins, virtual models of physical systems, are shown to enhance predictive maintenance and decision-making by providing real-time system insights. What sets our work apart is the way we integrate these technologies to create scalable, adaptive, and context-aware diagnostic solutions. We demonstrate the potential of this approach across applications such as smart grids, manufacturing, and autonomous systems. Our goal is to provide researchers and practitioners with a practical and forward-looking framework for developing intelligent, reliable fault diagnosis systems in today’s data-rich industrial environments.

关键词： Artificial intelligence Real-time systems Sensors Fault detection Fault diagnosis Interconnected systems intelligent sensors Smart grids Digital twins Adaptation models

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Medical Ultrasound Image Improvement Based on Denoising Convolutional Autoencoder

Medical Ultrasound Image Improvement Based on Denoising Conv...

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IEEE SSD International Multi-Conference on systems, Signals and Devices

作者： Taher Slimi Ahlem Djeha Anouar Ben Khalifa Université de Jendouba Institut National des Technologies et des Sciences du Kef Le Kef Tunisie LATIS-Laboratory of Advanced Technology and Intelligent Systems Université de Sousse Ecole Nationale d'Ingénieurs de Sousse Sousse Tunisie

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

The challenge of enhancing ultrasound images while retaining essential features is vital in the medical field. Speckle noise significantly degrades image quality and can obscure important details. In this paper, we propose a new enhancement approach that integrates a Non-Subsampled Shearlet Transform (NSST) with a denoising autoencoder. By leveraging the capabilities of NSST for multi-scale and directional decomposition, our method effectively separates fine anatomical features from speckle noise. Our approach results in substantial speckle reduction while preserving critical details, thus enhancing the visual quality of ultrasound images. Experimental evaluations reveal the effectiveness of the proposed method in noise suppression and feature preservation, measured through various performance metrics. This innovative strategy represents a significant advancement in ultrasound image processing, with potential benefits for clinical diagnostics.

关键词： Image quality Visualization Ultrasonic imaging Ultrasonic variables measurement Noise Noise reduction Autoencoders Speckle Feature extraction Medical diagnostic imaging

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Ultrasound Imaging Enhancement Using Denoising AutoEncoders

Ultrasound Imaging Enhancement Using Denoising AutoEncoders

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IEEE SSD International Multi-Conference on systems, Signals and Devices

作者： Taher Slimi Radhia Ferjaoui Anouar Ben Khalifa Université de Jendouba Institut National des Technologies et des Sciences du Kef Le Kef Tunisie LATIS- Laboratory of Advanced Technology and Intelligent Systems Université de Sousse Ecole Nationale d'Ingeniéurs de Sousse Sousse Tunisie

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

In ultrasound imaging, speckle noise is acknowledged as a major challenge, affecting visual clarity and compromising diagnostic accuracy. This degradation can lead to erroneous clinical evaluations, highlighting the urgent need to enhance denoising techniques. Various solutions have been proposed to tackle this issue. Convolutional Neural Network-based Denoising AutoEncoders (CNN-DAE) aim to mitigate this issue. However, this strategy suffers from potential loss of intricate details, excessive smoothing of images, and limited ability to adapt to different noise characteristics. In this research, we propose a CNN based Edge Detection Denoising AutoEncoders (CNN-EDDA). The proposed approach focuses on speckle denoising and enhancing the contours of anatomical structures, providing sharper and more precise definitions. This clarity enables the algorithm to effectively denoise the image by identifying regions with abrupt variations, allowing for the distinction of important details from speckle noise. By clarifying the edges, we preserve essential image details while reducing noise. This synergy between filtering and edge detection is crucial for ensuring reliable, high-quality results, facilitating more effective clinical interpretation. Our proposed CNN-EDDA method was evaluated according to subjective visual analyses as well as objective measurements, demonstrating better results than current state-of-the-art methods, with optimal performance metrics.

关键词： Deep learning Visualization Ultrasonic imaging Image edge detection Noise reduction Noise Autoencoders Anatomical structure Speckle Convolutional neural networks

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Enhancing Breast Ultrasound Diagnostics Using GANs and Guided Filtering

Enhancing Breast Ultrasound Diagnostics Using GANs and Guide...

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IEEE SSD International Multi-Conference on systems, Signals and Devices

作者： Taher Slimi Anouar BEN Khalifa Université de Jendouba Institut National des Technologies et des Sciences du Kef Le Kef Tunisie LATIS- Laboratory of Advanced Technology and Intelligent Systems Université de Sousse Ecole Nationale d'Ingénieurs de Sousse Sousse Tunisie

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

In ultrasound imaging, speckle noise presents a significant challenge, undermining image clarity and the accuracy of diagnostic interpretations. To address this critical issue, We develop a new method for improving the quality of ultrasound images using a Denoising Generative Adversarial Network complemented by a Guided Filter (DNGAN-GF). This method uses a neural network architecture to learn how to generate denoised images from noisy inputs while preserving important anatomical contours and details. Our innovative approach effectively transforms noisy images into remarkably clear visual representations. The DNGAN learns to capture the characteristics of clean image data, while the GF refines the results, ensuring the fidelity of anatomical structures. Qualitative assessments conducted by experts highlight the exceptional sharpness of contours and anatomical features, making anomaly interpretation more intuitive and accessible. This improvement not only assists radiologists in their evaluations but also has the potential to reduce diagnostic errors. Quantitative analyses reveal substantial increases in PSNR and SSIM metrics, confirming the superiority of our proposed DNGAN-GF method compared to conventional techniques. By significantly enhancing the visibility of anatomical details, our proposed solution can improve diagnostic accuracy and redefine standards in medical imaging, ultimately elevating the quality of patient care.

关键词： Visualization Ultrasonic imaging Accuracy Noise Noise reduction Transforms Speckle Breast Generative adversarial networks Noise measurement

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