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14th International Workshop of Advanced Manufacturing and Automation, IWAMA 2024

作者： Zhou, Lin Wang, Chen Yang, Shuai Huang, Peng College of Mechanical Engineering Hubei University of Automotive Technology Shiyan442002 China China Academy of Engineering Science and Technology Shiyan Industrial Technology Research Institute Shiyan442000 China Shanghai Key Laboratory of Intelligent Manufacturing and Robotics Shanghai University Shanghai200072 China

ISBN: (纸本)9789819626243

Deep learning-based methods for detecting targets in images have advanced significantly in recent years and shown to have tremendous application potential across various domains. But in real-world applications, these methods are frequently constrained by the lack of labelled samples, particularly in situations with small sample sizes. As a result, it has become a crucial area of research to determine how to carry out target detection efficiently. This paper conducts a thorough analysis of image target detection technology under small sample conditions. It does this by methodically elaborating on the definition of the problem of small-sample image target detection and by thoroughly discussing a number of small-sample learning approaches, the most prominent of which are data enhancement-based, migration learning-based, and meta-learning-based. By discussing various algorithms and the fundamental idea behind the process, the benefits and drawbacks of the current approaches to small-sample detection are made evident. Sample identification issues. Lastly, the technological challenges of the current small sample target identification and the direction of future improvement are explored. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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Review of research on Battery Defect Detection and Recovery 14th

Review of Research on Battery Defect Detection and Recovery

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14th International Workshop of Advanced Manufacturing and Automation, IWAMA 2024

作者： Huang, Peng Wang, Chen Yang, Shuai Zhou, Lin College of Mechanical Engineering Hubei University of Automotive Technology Shiyan442002 China China Academy of Engineering Science and Technology Shiyan Industrial Technology Research Institute Shiyan442000 China Shanghai Key Laboratory of Intelligent Manufacturing and Robotics Shanghai University Shanghai200072 China

ISBN: (纸本)9789819626243

Deep learning technology has injected new vitality into the defect detection and disassembly recycling of batteries. This paper describes the defect detection and disassembly recycling of batteries. Firstly, this paper outlines a protocol for detecting imperfections that relies on traditional machine vision procedures, encompassing both image domain analysis and transform domain analysis. Secondly, this paper describes the applications of deep learning in defect detection, including supervised training, unsupervised data mining, limitedly supervised learning, etc. Then, deep neural network applications in the disassembly and recycling of waste batteries is described. Finally, defect inspection relying on traditional machine vision methodologies, the approach for anomaly detection utilizing hierarchical learning, and the implementation of deep neural networks technology in the disassembly and recycling of waste batteries are all summarized herein. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Machine vision

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Deep Learning Model for Metal Gear Defect Detection 14th

Deep Learning Model for Metal Gear Defect Detection

引用

14th International Workshop of Advanced Manufacturing and Automation, IWAMA 2024

作者： Yang, Shuai Wang, Chen Zhou, Lin College of Mechanical Engineering Hubei University of Automotive Technology Shiyan442002 China China Academy of Engineering Science and Technology Shiyan Industrial Technology Research Institute Shiyan442000 China Shanghai Key Laboratory of Intelligent Manufacturing and Robotics Shanghai University Shanghai200072 China

ISBN: (纸本)9789819626243

The quality of the entire mechanical system is directly impacted by the quality of metal gears, an essential transmission component. As such, detecting defects on the end face of metal gears accurately holds significant practical importance with the advancement of industrial automation. Traditional methods for defect detection often involve manual visual inspection or basic image processing techniques, leading to inefficiencies and inaccuracies. In this study, we introduce a novel model for detecting defects on metal gear endfaces utilizing deep learning technology. Through comparison with current mainstream algorithms, our model demonstrated superior performance in terms of accuracy and efficiency, highlighting the vast potential of deep learning in enhancing metal gear endface defect detection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Gear transmissions

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Quantum-Chiplet: A Novel Python-Based Efficient and Scalable Design Methodology for Quantum Circuit Verification and Implementation

arXiv

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arXiv 2025年

作者： Kao, Yu-Ting Lu, Hao-Yu Chang, Yeong-Jar Lu, Darsen Industrial Technology Research Institute Taiwan Intelligent Computing Industrial Doctorate Program Miin Wu School of Computing National Cheng Kung University Taiwan Institute of Microelectronics Department of Electrical Engineering National Cheng Kung University Taiwan

Analysis and verification of quantum circuits are highly challenging, given the exponential dependence of the number of states on the number of qubits. For analytical derivation, we propose a new quantum polynomial representation (QPR) to facilitate the analysis of massively parallel quantum computation and detect subtle errors. For the verification of quantum circuits, we introduce Quantum-Chiplet, a hierarchical quantum behavior modeling methodology that facilitates rapid integration and simulation. Each chiplet is systematically transformed into quantum gates. For circuits involving n qubits and k quantum gates, the design complexity is reduced from "greater than O(2n)" to O(k). This approach provides an open-source solution, enabling a highly customized solution for quantum circuit simulation within the native Python environment, thereby reducing reliance on traditional simulation packages. A quantum amplitude estimation example demonstrates that this method significantly improves the design process, with more than 10x speed-up compared to IBM Qiskit at 14 qubits. Copyright © 2025, The Authors. All rights reserved.

关键词： Qubits

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Evaluation of Cuffless Blood Pressure Estimation Accuracy at Each Heartbeat During Exercise 6th

Evaluation of Cuffless Blood Pressure Estimation Accuracy a...

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6th International Conference on Biomedical and Health Informatics, ICBHI 2024

作者： Ono, Kouki Maeda, Yuka Yoshimura, Takumi Huang, Ming Tamura, Toshiyo Master’s Program in Intelligent and Mechanical Interaction Systems University of Tsukuba Tsukuba305-8577 Japan Institute of Systems and Information Engineering University of Tsukuba Tsukuba305-8577 Japan Medical and Welfare Engineering Tokyo Metropolitan College of Industrial Technology Tokyo116-8523 Japan School of Data Science Nagoya City University Nagoya467-8501 Japan Future Robotics Organization Waseda University Tokyo169-8050 Japan

ISBN: (纸本)9783031863226

Continuous blood pressure monitoring is crucial because of its dynamic nature, influenced by factors such as physical activity and mental stress. Although cuffless blood pressure estimation is a promising technique for continuous monitoring, its accuracy during drastic fluctuations, especially during exercise, remains insufficiently validated. In this study, we evaluated a simple Pulse Arrival Time based blood pressure estimation method and several improved methods for sharp fluctuations induced by exercise. Sixteen subjects in the experiment, with data from 13 valid for analysis. The proposed method achieved an average correlation of 0.527 in systolic blood pressure, suggesting that cuffless blood pressure estimation could be applied to estimate continuous blood pressure variability during exercise. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Blood pressure

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Polytope Volume Monitoring Problem: Formulation and Solution via Parametric Linear program Based Control Barrier Function

arXiv

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arXiv 2025年

作者： Wu, Shizhen Dong, Jinyang Fang, Xu Sun, Ning Fang, Yongchun Institute of Robotics and Automatic Information System College of Artificial Intelligence Nankai University Tianjin300353 China The Institute of Intelligence Technology and Robotic Systems Shenzhen Research Institute of Nankai University Shenzhen518083 China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Dalian116024 China

Motivated by the latest research on feasible space monitoring of multiple control barrier functions (CBFs) as well as polytopic collision avoidance, this paper studies the Polytope Volume Monitoring (PVM) problem, whose goal is to design a control law for inputs of nonlinear systems to prevent the volume of some state-dependent polytope from decreasing to zero. Recent studies have explored the idea of applying Chebyshev ball method in optimization theory to solve the case study of PVM;however, the underlying difficulties caused by nonsmoothness have not been addressed. This paper continues the study on this topic, where our main contribution is to establish the relationship between nonsmooth CBF and parametric optimization theory through directional derivatives for the first time, so as to solve PVM problems more conveniently. In detail, inspired by Chebyshev ball approach, a parametric linear program (PLP) based nonsmooth barrier function candidate is established for PVM, and then, sufficient conditions for it to be a nonsmooth CBF are proposed, based on which a quadratic program (QP) based safety filter with guaranteed feasibility is proposed to address PVM problems. Finally, a numerical simulation example is given to show the efficiency of the proposed safety filter. © 2025, CC BY.

关键词： Chebyshev filters

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DefoGAN: an end-to-end single defocus image deblurring method

DefoGAN: an end-to-end single defocus image deblurring metho...

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2024 International Conference on Optical and Photonic Engineering, icOPEN 2024

作者： Zhao, Xixuan Ma, Ningchen Kan, Jiangming School of Technology Beijing Forestry University No. 35 Qinghua East Road Haidian District Beijing100083 China Key Laboratory of State Forestry Administration on Forestry Equipment and Automation No. 35 Qinghua East Road Haidian District Beijing100083 China Foshan-Zhongke Innovation Research Institute of Intelligent Agriculture and Robotics Jingu Zhichuang Industrial Community No. 2 Yongan North Road Dawei Community Guicheng Street Nanhai District Foshan528251 China

ISBN: (数字)9781510688117

ISBN: (纸本)9781510688100

Defocus blur is one of the most commonly arising types of artifacts when taking photos. Blind defocus image deblurring is a classical ill-posed problem, as we have to estimate two variables with one observation. The blur in the defocus image is usually spatially varying which makes the estimation of clear image more challenging. In this paper, we propose an end-to-end blind defocus deblurring network (DefoGAN) by repeatedly incorporating an De-RES Block. DefoGAN network is based on the generate adversarial network, while utilizing feature pyramid network (FPN) for multi-scale feature extractor. The proposed DefoGAN is shown to improve the generated images' quality and authenticity and achieve a balance of quality and speed than other compared methods. Ablation studies and evaluation metrics proved the effectiveness of the proposed method. © 2025 SPIE.

关键词： Deblurring Feature extraction Image restoration Network architectures Ablation Image processing Image quality

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A Novel Composite Film with Superhydrophobic Graphene for Anti-Icing/Deicing Via Chemical-Assisted Magnetically Controllable Picosecond Laser Writing

SSRN

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SSRN 2025年

作者： Chang, Haozhe Zhang, Zhen Wang, Peng Zhang, Guojun School of Aerospace Engineering Huazhong University of Science and Technology Hubei Wuhan430074 China Guangdong Intelligent Robotics Institute Guangdong Dongguan523808 China Guangdong HUST Industrial Technology Research Institute Guangdong Provincial Key Laboratory of Digital Manufacturing Equipment Guangdong Dongguan523808 China Key Laboratory of Icing and Anti/De-icing China Aerodynamics Research and Development Center Sichuan Mianyang621000 China State Key Laboratory of Intelligent Manufacturing Equipment and Technology School of Mechanical Science and Engineering Huazhong University of Science and Technology Hubei Wuhan430074 China

Icing affects the safe operation of the aircraft and can also damage other outdoor equipment, causing safety hazards. Passive anti-icing of superhydrophobic surface helps to reduce the energy consumption of active thermal deicing. The magnetically controllable direct laser writing (MDLW) was used to fabricate the micro-scale tapered structure and nano-scale layered structure. The superhydrophobic was enhanced by the chemical modification assisted magnetically controllable direct laser writing (C-MDLW), and the graphene capillaries were wrapped to form nanoscale particles. The different material removal mechanisms during the two laser scans, including photochemical mechanism, photothermal mechanism, and ionizing plasma, were described. The differences in surface morphology and chemical elements were analyzed to illustrate their effects on superhydrophobicity. The 'fog' shaped nano graphene capillary structure and high content of polar molecules result in strong hydrophilicity, while the nano particles and non-polar molecules formed after chemical modification are the key reasons for the transformation into superhydrophobicity. The contact angle of C-MDLW was as high as 163.0 °, and the roll-off angle was 1.8 °. Droplets at different heights can bounce on surfaces with different inclination angles. Compared with the raw surface, the icing time was delayed by about 29.5 times. Combined with the electric heating film with a good Joule heating effect, the surface temperature increased. Because of the long icing time, the electric energy was saved by about 86% in the icing and deicing period. © 2025, The Authors. All rights reserved.

关键词： Picosecond lasers

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Diffusion-Based mmWave Radar Point Cloud Enhancement Driven by Range Images

arXiv

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arXiv 2025年

作者： Wu, Ruixin Li, Zihan Wang, Jin Xu, Xiangyu Yu, Huan Zheng, Zhi Huang, Kaixiang Lu, Guodong The State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou310058 China Zhejiang Key Laboratory of Industrial Big Data and Robot Intelligent Systems Zhejiang University Hangzhou310058 China Robotics Research Center of Yuyao City Ningbo315400 China State Key Laboratory of Robotics and Systems Department of Mechatronics Engineering Harbin Institute of Technology Harbin150001 China

Millimeter-wave (mmWave) radar has attracted significant attention in robotics and autonomous driving. However, despite the perception stability in harsh environments, the point cloud generated by mmWave radar is relatively sparse while containing significant noise, which limits its further development. Traditional mmWave radar enhancement approaches often struggle to leverage the effectiveness of diffusion models in super-resolution, largely due to the unnatural range-azimuth heatmap (RAH) or bird’s eye view (BEV) representation. To overcome this limitation, we propose a novel method that pioneers the application of fusing range images with image diffusion models, achieving accurate and dense mmWave radar point clouds that are similar to LiDAR. Benefitting from the projection that aligns with human observation, the range image representation of mmWave radar is close to natural images, allowing the knowledge from pre-trained image diffusion models to be effectively transferred, significantly improving the overall performance. Extensive evaluations on both public datasets and self-constructed datasets demonstrate that our approach provides substantial improvements, establishing a new state-of-the-art performance in generating truly three-dimensional LiDAR-like point clouds via mmWave radar. Copyright © 2025, The Authors. All rights reserved.

关键词： Radar imaging

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Hybrid deep learning model to predict the ultimate tensile strength of friction stir welded joints

引用

Engineering Applications of Artificial Intelligence 2025年 154卷

作者： Mishra, Akshansh Miloudi, Abdelkader Sefene, Eyob Messele Arroussi, Chaaben Chekalil, Ismail Muthanna, Bassam Gamal Nasser School of Industrial and Information Engineering Politecnico Di Milano Milan Italy Sidi Bel Abbes Algeria Department of Mechanical Engineering National Taiwan University of Science and Technology Taipei Taiwan Faculty of Mechanical and Industrial Engineering Bahir Dar Institute of Technology Bahir Dar University P.O. Box 26 Bahir Dar Ethiopia LPTPM Departement of Mechanical Engineering Faculty of Technology Hassiba BenBouali University of Chlef P.O. Box. 151 Hay Salem Chlef02000 Algeria Interdisciplinary Research Center for Intelligent Manufacturing & Robotics King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia Department of Mechatronics Engineering Faculty of Engineering and Computing University of Science and Technology Aden Yemen

Accurate prediction of tensile strength in friction stir welded joints is essential to improve the reliability of welded structures. Traditional Artificial Neural Networks (ANNs) often struggle to capture the complex relationships between welding parameters, leading to limited predictive accuracy. To address this challenge, a hybrid model was developed, combining Long Short-Term Memory (LSTM) and dense layers to leverage the ability of LSTM to learn sequential patterns and dependencies between inputs such as rotational speed, feed rate, and tool tilt angle. This hybrid model was compared to a traditional ANN model using a dataset containing welding parameters and tensile strength results. Performance indicators, including correlation coefficient and Mean Square Error (MSE), showed that the LSTM + Dense hybrid model achieved a higher correlation coefficient (0.9803) and lower MSE on the validation sets, indicating improved predictive ability. Analyses of error distribution and convergence of training and validation losses also showed that the hybrid model had higher consistency and reliability with reduced overfitting, even when dealing with outliers. The LSTM + Dense model was shown to significantly outperform the traditional ANN, making it a powerful tool for predicting tensile strength in welding applications with complex and nonlinear data. Although the accuracy and reliability of the proposed hybrid model have improved, limitations remain, including the need for larger datasets to enhance generalization and potential challenges in real-time implementation. © 2025 Elsevier Ltd

关键词： Deep neural networks

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