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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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Application of Particle Swarm Optimization Algorithm Based on Adaptive Inertia Adjustment Strategy in Scheduling of the Coking Process 26

Application of Particle Swarm Optimization Algorithm Based o...

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26th International Conference on Industrial technology, ICIT 2025

作者： Chen, Yiheng Yin, Fan Chen, Xiaochong Ren, Yi Hu, Jie Wu, Min School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

ISBN: (纸本)9798331521950

The sccheduling for pushing plan during the coking process critically affects the efficiency and stability of production. However, the complexity with mutiple-stage during production makes it difficult to design an efficient coke pushing plan. To address this issue, this paper proposes a scheduling method based on the particle swarm optimization algorithm. Firstly, the fuzzy c-means clustering is utilized to categorize actual operating conditions as either normal or abnormal, thereby facilitating the scheduling of pushing plan under disparate conditions. Subsequently, the scheduling problem for the pushing plan is transformed into a traveling salesman problem, and scheduling models under various conditions are established. Finally, to accelerate the convergence and enhance the algorithm's global search capability, an adaptive inertia adjustment strategy is employed to dynamically regulate the velocity and position of particles. The proposed method has been implemented in the coking process. Through the analysis of application results, the completion coefficient of pushing plan has been increased by 4.25%, demonstrating that the proposed has advantages in scheduling the pushing plan during the actual coking process. © 2025 IEEE.

关键词： Particle swarm optimization (PSO)

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Bearing Fault Diagnosis Under Variable Working Conditions Based on Domain Adaptation and Improved Depthwise Separable Convolution Network

Bearing Fault Diagnosis Under Variable Working Conditions Ba...

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IEEE International Conference on Industrial technology (ICIT)

作者： Zehui Kuang Fan Mao Xingyu Zhao Xiongbo Wan Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education School of Automation China University of Geosciences Wuhan 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 School of Future Technology China University of Geosciences Wuhan China Ministry of Education School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan China

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

Domain adaptation methods and appropriate feature extractors are usually applied to solve the problem that the variable working conditions of bearings affect the effectiveness of the fault diagnostic framework. However, the existing feature extractors are too complex to be deployed in real industries. In this paper, we propose a lightweight framework called improved depthwise separable convolution network (IDSCN) for the diagnosis of bearing faults in variable operating conditions. IDSCN is built on wavelet convolution and depthwise separable residual convolution (DSRC), where the wavelet convolution is used to extract frequency features from the source and target domains, and DSRC guarantees the performance of feature extraction with reduced parameters and computation. To minimise the distribution difference between the source and target domains, the local maximum mean difference is incorporated into the proposed framework. The performance of the proposed framework is validated on the Paderborn University dataset, involving a total of 6 transfer tasks. The experiment shows that the proposed framework achieves satisfactory results and outperforms other frameworks in bearing fault diagnosis.

关键词： Employee welfare Fault diagnosis Vibrations Industries Adaptation models Wavelet domain Convolution Frequency-domain analysis Feature extraction Complexity theory

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A Robust Convolutional Neural Network for Electrical Impedance Tomography Image Reconstruction

A Robust Convolutional Neural Network for Electrical Impedan...

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

作者： Mariem Hafsa Kailun Lin Najoua Essoukri Ben Amara Olfa Kanoun Ecole Nationale d'Ingénieurs de Sousse LATIS-Laboratory of Advanced Technology and Intelligent Systems Université de Sousse Sousse Tunisie Professorship of Measurement and Sensor Technology Technische Universität Chemnitz Chemnitz Germany Department of Mechanical Engineering Technische Universität Chemnitz Chemnitz Germany

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

Electrical Impedance Tomography (EIT) is a widely used non-invasive imaging method in a variety of application fields. Most EIT applications require an accurate and high-quality reconstructed inner conductivity distribution image. Several solutions have been developed for reliable EIT image reconstruction, but they still suffer from limitations such as low spatial resolution and inconsistency in real measurement scenarios. In this paper, we propose using physically based, extensive simulation data to train a Convolutional Neural Network (CNN) model with 5-fold cross-validation. We generate a set of 10.000 data samples, which builds a comprehensive basis for its training and ensures reliability and generalization capability. The quantitative evaluation demonstrates good reconstruction performance, achieving a Mean Squared Error (MSE) of 0.0151 and an Image Correlation Coefficient (ICC) of 0.97 in the best-performing fold for the simulated data. The method's effectiveness is particularly validated through experimental measurements, where it significantly outperforms traditional Gauss-Newton reconstruction in terms of image sharpness and boundary definition. The algorithm is robust and easily applicable for EIT imaging.

关键词： Training Image quality Electrical impedance tomography Voltage measurement Impedance measurement Imaging Robustness Convolutional neural networks Spatial resolution Image reconstruction

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