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UPB Scientific Bulletin, Series C: Electrical Engineering and Computer Science 2025年第1期87卷 121-138页

作者： Zheng, Qingchun Wu, Moudong Zhu, Peihao Yang, Bin Li, Shubo Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin300384 China China

This paper introduces D-Fusion SLAM, an advanced VSLAM system based on ORB-SLAM2. D-Fusion SLAM integrates depth information to enhance feature selection, aiming to improve accuracy and speed in visual SLAM applications. The system mainly comprises two modules: an image grid filtering module based on grayscale information and a feature selection module integrating depth information. These modules effectively eliminate redundant and useless features, ensuring high-quality features for pose estimation. We tested D-Fusion SLAM on the TUM dataset and compared it with ORB-SLAM2. Experimental results demonstrate that D-Fusion SLAM outperforms ORB-SLAM2, significantly enhancing the system's accuracy and robustness. © 2025, Politechnica University of Bucharest. All rights reserved.

关键词： Feature Selection

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An Improved Compressive Sensing-Based Model for Efficient Analysis of Monostatic Electromagnetic Scattering Problems

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IEEE Microwave and Wireless Technology Letters 2025年第4期35卷 388-391页

作者： Qi, Qi Fan, Yunuo Cao, Xinyuan Liu, Yi Kong, Meng Huang, Zhixiang Wu, Xianliang Hefei Normal University Anhui Province Key Laboratory of Simulation Calculation and Design for Electronic Information System Hefei230601 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Hefei230601 China

To further accelerate the analysis of monostatic electromagnetic (EM) scattering problems of objects, an improved compressive sensing (CS)-based model is proposed. First, an orthogonal subspace spanned by the wide-angle induced currents is identified by means of the wide-angle incident sources. Then, the sparse transform of the induced currents is replaced by the subspace projection. In addition, an optimal number of measurements is determined, only slightly larger than the dimension of the subspace. Finally, the recovery algorithm is simplified to the least-squares method to solve the resulting overdetermined system. Compared with the traditional CS-based model, the proposed model significantly improves the efficiency of the recovery step and optimizes both the requirement for and the way of determining the number of measurements. © 2023 IEEE.

关键词： Least squares approximations

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Blockchain-Enabled Secure, Fair and Scalable Data Sharing in Zero-Trust Edge-End Environment

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IEEE Journal on Selected Areas in Communications 2025年第6期43卷 2056-2069页

作者： Xu, Xiaolong Meng, Ke Xiang, Haolong Cui, Guangming Xia, Xiaoyu Dou, Wanchun Nanjing210044 China Nanjing University of Information Science and Technology School of Software and the Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services Nanjing210044 China RMIT University School of Computing Technologies MelbourneVIC3000 Australia Nanjing University State Key Laboratory for Novel Software Technology Nanjing210023 China

In edge computing, the Zero-Trust Security Model (ZTSM), as a key enabling technology for next-generation networks, plays a crucial role in providing authentication for addressing data sharing concerns, such as frequent data breaches, data misuse, and cyberattacks. However, due to the complexity and diversity of edge environments, ZTSM struggles to meet the security requirements of data sharing frameworks solely through enhanced authentication. Consequently, such frameworks with ZTSM still face challenges in ensuring data integrity, evaluating various node behaviors, and coping with the increasing complexity of node attributes. To address these issues, we propose a blockchain-enabled secure, fair and scalable data sharing framework in a zero-trust edge-end environment in this paper. Specifically, we first propose a Merkle forest-based data storage model for the classified storage of loosely coupled data, consequently enhancing the scalability of the model. Then, we design a node behavior-based reputation assessment mechanism to ensure fairness during data sharing. Moreover, a data sharing protocol supervised by smart contract is proposed, working with the aforementioned storage and assessment schemes, to ensure the security of data sharing. Finally, comprehensive security analysis validates the security, fairness and scalability of the proposed framework. Extensive experimental results show that, as transaction volume grows, the time cost of data traversal in the storage model becomes progressively more efficient. Additionally, when the size of the smart contract is increased tenfold, the maximum time cost of the data sharing protocol rises by only 4.98 times. © 1983-2012 IEEE.

关键词： Data integrity

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Novel rivet in flat electromagnetic self-piercing riveting with no concave die of CFRP/aluminum structures for enhancing performance

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Journal of Manufacturing Processes 2025年 148卷 160-172页

作者： Jin, Jiageng Qin, Jiachang Xu, Yuanna Li, Guangyao Cui, Junjia Jiang, Hao State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle Hunan University Changsha410082 China Shenzhen Automotive Research Institute Beijing Institute of Technology Shenzhen518057 China Shenzhen Huashun Intelligent Equipment Co. Ltd Shenzhen518057 China

The self-piercing riveting (SPR) is an ideal process for joining dissimilar materials such as carbon fiber-reinforced plastics (CFRP) and aluminum alloys. However, piercing CFRP with a rivet leg causes significant damage, thereby reducing joint performance. To address this issue, this study proposes a novel process called flat electromagnetic self-pierce riveting with no concave die (FE-SPR) to join 2.0-mm-thick CFRP and 5052 aluminum alloy sheets. This method is an improvement based on traditional semi-hollow rivets to enhance structural strength and reduce CFRP damage. A comparative analysis of cross-sectional quality, quasi-static mechanical properties, forming mechanisms, and failure behavior were conducted between joints fabricated with traditional semi-hollow rivet (T-rivet) and novel semi-solid rivet (N-rivet). The results showed that the N-rivet joint, unlike the T-rive joint, exhibited upsetting deformation of the rivet shank and secondary expansion of the rivet leg. These features help suppress CFRP damage and increase undercuts. The average area of CFRP damage for T-rivet joint was 33.99 mm2, 135.1 % higher than the N-rivet with 5°. The peak load and energy absorption of the N-rivet with 5° were 4154 N and 11.81 J, 18.3 % and 13.5 % higher than T-rivet joints, respectively. All joints exhibited one failure mode, characterized by rivet pull-out from the aluminum plate while maintaining adhesion to the CFRP plate. This study offers a reliable solution for lightweight multi-material structures in automotive and aerospace applications. © 2025 The Society of Manufacturing Engineers

关键词： Riveting

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Effect of Free-End Torsion on the Corrosion and Mechanical Properties for Mg-3Zn-0.2Ca Alloy

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Acta Metallurgica Sinica(English Letters) 2025年第1期38卷 59-70页

作者： Li-Lan Gao Jiang Ma Yan-Song Tan Xiao-Hao Sun Qi-Jun Gao De-Bao Liu Chun-Qiu Zhang Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical EngineeringTianjin University of TechnologyTianjin300384China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of TechnologyTianjin300384China School of Materials Science and Engineering Tianjin University of TechnologyTianjin300384China

Magnesium alloys with excellent degradability and biocompatibility are promising materials for biomedical implants,saving patients the burden of second ***,their mechanical properties and corrosion resistance are significantly below the requirements for implant *** study aims to improve the mechanical and corrosion resistance properties of Mg-3Zn-0.2Ca alloy by pre-torsion treatment,and find out the optimal shear *** rod-shaped Mg-3Zn-0.2Ca alloy specimens were pre-torsion treated at different torsion *** effect of free-end torsion on Mg-3Zn-0.2Ca alloy was characterized through microstructure analysis,mechanical testing,and corrosion ***-torsion treatment can refine grain and induce twins and dislocations in Mg-3Zn-0.2Ca *** surface hardness increases with the increase in torsion *** yield strength and ultimate strength initially increase and then decrease with increasing torsion angle,while ductility decreases with increasing torsion *** with a shear strain of 30%exhibit the highest tensile strength,reaching 284.78±10.62 MPa,with an elongation of 19.37±1.66%.Furthermore,they show a significant improvement in fatigue lives both before and after *** the stress amplitude is 120 MPa,the fatigue lives for specimens without pre-torsion treatment are 54,275 cycles and 4324 cycles before and after pre-corrosion,while they increased significantly to 92,015 cycles and 5050 cycles with the 30%shear strain,***,the 30%shear strain specimens show a significant reduction in corrosion *** conclusion,pre-torsion treatment can effectively modify the microstructure of Mg-3Zn-0.2Ca alloy,enhancing both mechanical properties and corrosion *** optimal shear strain for this improvement is 30%.This study provides a practical method to enhance the mechanical properties and corrosion resistance of Mg-3Zn-0.2Ca alloy,making it more suitable for biomedical implan

关键词： Magnesium alloy Pre-torsion Microstructure Mechanical property Corrosion resistance

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Multi-Scale Time Series Segmentation Network Based on Eddy Current Testing for Detecting Surface Metal Defects

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 528-538页

作者： Xiaorui Li Xiaojuan Ban Haoran Qiao Zhaolin Yuan Hong-Ning Dai Chao Yao Yu Guo Mohammad S.Obaidat George Q.Huang the School of Intelligence Science and Technology University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering the Key Laboratory of Intelligent Bionic Unmanned Systems and the Institute of Materials Intelligent Technology Liaoning Academy of Materials IEEE the Department of Computer Science Hong Kong Baptist University the School of Computer and Communication Engineering Key Laboratory of Advanced Materials and Devices for Post-Moore Chips Ministry of Education University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing the School of Computer and Communication Engineering University of Science and Technology Beijing the King Abdullah Ⅱ School of Information Technology The University of Jordan the Department of Computational Intelligence the School of Computing SRM University the School of Engineering The Amity University The Hong Kong Polytechnic University

In high-risk industrial environments like nuclear power plants, precise defect identification and localization are essential for maintaining production stability and safety. However, the complexity of such a harsh environment leads to significant variations in the shape and size of the defects. To address this challenge, we propose the multivariate time series segmentation network(MSSN), which adopts a multiscale convolutional network with multi-stage and depth-separable convolutions for efficient feature extraction through variable-length templates. To tackle the classification difficulty caused by structural signal variance, MSSN employs logarithmic normalization to adjust instance distributions. Furthermore, it integrates classification with smoothing loss functions to accurately identify defect segments amid similar structural and defect signal subsequences. Our algorithm evaluated on both the Mackey-Glass dataset and industrial dataset achieves over 95% localization and demonstrates the capture capability on the synthetic dataset. In a nuclear plant's heat transfer tube dataset, it captures 90% of defect instances with75% middle localization F1 score.

关键词： Eddy current testing nondestructive testing semantic segmentation time series analysis

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Truthful Mechanism for Resource Allocation and Pricing in Vehicle-Assisted Mobile Edge computing

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8171-8186页

作者： Liu, Xi Liu, Jun Li, Weidong Qujing Normal University Key Laboratory of Intelligent Sensor and System Design School of Information Engineering Qujing655001 China Qujing Normal University Engineering Research Center of Intelligent System and Advanced Materials Yunnan Province Universities Qujing655001 China Yunnan College of Business Management School of Education Kunming650000 China Yunnan University School of Mathematics and Statistics Kunming650504 China

Mobile edge computing (MEC) provides stable and powerful computing services, and vehicle computing provides rich sensing services. Therefore, we develop vehicle-assisted mobile edge computing (VAMEC), integrating MEC and vehicle computing to maximize their advantages and broaden their applications. We address the resource allocation and pricing problem in VAMEC, in which the vehicle, as a resource provider, collaborates with the MEC servers to provide services to users. To enable MEC servers and vehicles to work in cooperation, we propose a mixed auction model that consists of two-step auctions: the first one-way auction and the second double auction. We consider three types of users based on their characteristics, where users participate in different auctions depending on the types. Users and vehicles are self-interested and may submit untrue values to harm the system's profits. Therefore, we propose the overall truthfulness for the mixed auction model, that is users or vehicles that cannot obtain higher utility by declaring untrue values regardless of which auction they participate in. To the best of our knowledge, this is the first study proposing the overall truthfulness and showing how to design the mechanism that achieves it. We then propose an overall truthfulness mechanism and show it achieves individual rationality, budget balance, and consumer sovereignty. Simulation results indicate that the proposed mechanism can increase social welfare by at least 24%, compared with other mechanisms. © 1967-2012 IEEE.

关键词： Mobile edge computing

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Target-background feature blocks and aberrance repressed correlation filters for real-time UAV tracking

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Signal, Image and Video Processing 2025年第7期19卷 1-11页

作者： Jia, Yunwei Liu, Bo Liu, Zhenzhong Wang, Tianyang Yv, Kaiying Wang, Shuo Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin300384 China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology Tianjin300384 China

In recent years, Unmanned Aerial Vehicle (UAV) tracking technology has been widely applied in fields such as agriculture and aviation. However, due to limitations in UAV computational resources and battery life, deep learning-based tracking algorithms are difficult to deploy directly. Therefore, efficient and robust tracking methods based on Discriminative Correlation Filters (DCF) have become the mainstream for UAV tracking. The traditional DCF method updates the model with a fixed step size, making it prone to contamination, while the boundary effect further limits its tracking capability. To address the issue of model contamination, an appearance model update and masking mechanism was designed, introducing feature confidence and overall confidence thresholds to evaluate the quality of features and images. Features and images with poor quality are masked, and the learning rate is reduced to prevent model contamination. This paper proposes the Target-Background Feature Block and Aberrance Repressed Correlation Filter (TBFBARCF), which introduces target and background regularization terms into the spatial filter to mitigate boundary effects and incorporates an aberrance repression regularization term into the temporal aberrance repressed filter to reduce response map aberrance. The two filters mutually constrain each other and are jointly trained. Experiments conducted on three challenging datasets, OTB100, UAV123, and UAVDT, demonstrate that the TBFBARCF tracker outperforms 11 other DCF-based trackers. Its average precision and average success rate reach 0.756 and 0.62, respectively, and it operates at a speed of 36.6 frames per second (FPS) on a CPU platform. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Drones

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Advancements in graphene-based composites: A review of the emerging applications in healthcare

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Smart Materials in Medicine 2025年第1期6卷 120-138页

作者： Batool, Farwa Muhammad, Saz Muazzam, Rabia Waqas, Muhammad Ullah, Zia Roy, Shubham Zhang, Yinghe Wang, Ke Guo, Bing Department of Chemistry Lahore Garrison University DHA Phase VI Lahore Pakistan School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen518055 China School of Science Shenzhen Key Laboratory of Flexible Printed Electronics Technology Shenzhen Key Laboratory of Advanced Functional Carbon Materials Research and Comprehensive Application Harbin Institute of Technology Shenzhen518055 China Government Queen Mary Graduate College Lahore Pakistan

Graphene and related materials are emerging revolutionary materials due to fascinating stimuli-responsive physiochemical properties having widespread applications in developing advanced nano-systems for detection, diagnostics, and therapeutics in biomedical fields. Various materials like metal nanoparticles, polymers, dyes, drugs, and proteins could be combined yielding graphene-based composite materials (GBCMs) to synergistically fulfill different biomedical requirements. The 2D conjugated structure, easy surface functionalization via covalent or van der Waal interactions, and combination with different components for versatile functionalities altogether contribute to the development of GBCM for healthcare applications. This review specifically focuses on GBCMs and discusses specific material properties relevant to their material applications in healthcare. Furthermore, the synthesis, toxicity, and biocompatibility of these GBCMs, and their interactions with biological materials are thoroughly discussed. Importantly, the recent cutting-edge applications in the field of healthcare, including its role in the development of antibacterial, anti-viral, and photothermal therapies, wound healing, drug delivery, neurodegenerative disease therapy, and biosensing based on GBCMs are well-updated. Additionally, the applications of GBCMs in point-of-care devices, wearable electronics, and tissue engineering are well summarized. We hope this review opens up more possibilities to design more innovative healthcare materials derived from graphene. © 2025 The Authors

关键词： Biocompatibility

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MBF-DTI: A Fused Multi-dimensional Biochemical Feature-Based Drug Target Prediction Method Based on Heterogeneous Graph Attention Networks 10th

MBF-DTI: A Fused Multi-dimensional Biochemical Feature-Based...

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10th China Health Information Processing Conference, CHIP 2024

作者： Zhao, Haixue Yao, Kui Liu, Yunjiong Che, Chao Tang, Lin Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian University Dalian116622 China School of Software Engineering Dalian University Dalian116622 China

ISBN: (纸本)9789819637515

Drug-target interaction prediction help reduce the cost and time of drug development. However, existing research often overlooks the complexity of biological interactions. To address this issue, this paper proposes a DTI prediction model that integrates multi-dimensional biochemical features. Specifically, the model utilizes a heterogeneous graph attention network to capture the topological relationships among biological entities from diverse data, providing a deep understanding of the interactions among drugs, proteins, diseases, and side effects. A molecular attention Transformer network and a CBiNet module are used to extract the key structural features of drugs and targets. By automatically optimizing the weight distribution between drugs and targets, the model enhances the information transfer during network training, significantly improving model performance. Experimental results on real-world datasets demonstrate that the proposed model outperforms the current state-of-the-art methods in the field. Among the top 50 novel COVID-19 therapeutic drugs predicted by our model, 30 have been supported by clinical trials or scientific literature, further demonstrating the effectiveness of our proposed method in drug repurposing. Experimental data and supplementary materials are available online at: https://***/Eadog/MBF-DTI. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： COVID-19

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