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Study on the wear resistance of 2024-T351 aluminum alloy strengthened by ultrasonic-assisted laser shock peening

Journal of Materials Science 2025年第13期60卷 5954-5976页

作者： Meng, Xiankai Cheng, Zilong Zhou, Jianzhong Song, Fuyang Zhao, Xianhua Wu, Wei Gao, Fei Cai, Jie Xue, Wei Liu, Yang Institute of Advanced Manufacturing and Modern Equipment Technology Jiangsu University Zhenjiang212013 China School of Mechanical Engineering Jiangsu University Zhenjiang212013 China Department of Research and Development Shandong Huawin Electrica and Mechanical Technology Co. Ltd Jinan250098 China International Science & Technology Cooperation Base for Laser Processing Robot Wenzhou University Wenzhou325035 China

The ultrasonic-assisted laser shock peening (ULP) technique, through the combined strengthening effects of laser shock peening and ultrasonic peening, can enhance the wear resistance and fatigue resistance of metal materials. This paper uses the 2024-T351 aluminum alloy as the subject for ULP enhancement experiments. Comprehensive tests including microstructure, phase structure, surface morphology, microhardness, residual stress, and wear resistance were conducted. Furthermore, coefficient of friction (COF), wear volume loss, and wear morphology were analyzed to reveal the improvements in wear resistance and the transformations in wear mechanisms induced by ULP treatment. Significant grain refinement and high-density dislocations are induced by ULP treatment, which results in pronounced work hardening effects, achieving a surface microhardness of 191.9 HV and a hardened layer depth of 850 µm. Compared to the untreated samples, the high-hardened surface induced by ULP reduces the possibility of material peeling from the surface, leading to a decrease in average COF and wear volume loss by up to 37.9% and 40.8%, respectively. Additionally, the ULP treatment effectively suppress adhesive wear, significantly improving the wear morphology and enhancing the wear resistance of the aluminum alloy. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Grain refinement

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A Latent Diffusion Based Image Generation Method for Anomaly Detection 8th

A Latent Diffusion Based Image Generation Method for Anomaly...

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8th International Conference on Computing, Control and Industrial Engineering, CCIE 2024

作者： Wang, Liangliang Song, Lian Yang, Zhuo Li, Bingyao Liang, Peidong School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan China Wuhan University of Technology Chongqing Research Institute Chongqing China Institute of Advanced Manufacturing Technology Quanzhou China Fujian Key Laboratory of Intelligent Operation and Maintenance Robot Technology Quanzhou China

ISBN: (纸本)9789819769360

The primary issue in visual anomaly detection is lack of sufficient samples, which makes it difficult for real-world applications unless an effective anomalous image generation algorithm is proposed. To this end, this paper formulates a latent diffusion model to generate a variety of anomalous images by adapting the initial latent tensors to the anomaly. Therefore, the diffusion process is speeded up via transferring the image synthesis in a low-dimensional space, with anomalous labels provided. The pseudo-anomaly images and pseudo-labels are then used for data augmentation for anomaly detection based on YOLO-v8, and quantitative and qualitative experiments on NEU-DET demonstrate the applicability and superiority of our approach on precision and diversity. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Anomaly detection Deep learning Diffusion model Image generation

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FLASH: Low-Latency Serverless Model Inference with Multi-Core Parallelism in Edge 29

FLASH: Low-Latency Serverless Model Inference with Multi-Cor...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Li, Yanbo Lin, Yanying Peng, Shijie Tang, Yingfei Xiang, Tian Song, Wei Ye, Kejiang Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China Southern University of Science and Technology China University of Chinese Academy of Sciences China Intelligent Robot Research Center Zhejiang Laboratory China

ISBN: (纸本)9798350330717

Low response latency holds a pivotal role in the landscape of edge deep learning model inference, yet the constrained resources within edge computing environments often limit its full potential. Within the scope of this paper, we substantiate that even amid the resource limitations inherent to edge computing environments, it remains feasible to curtail model response latency by elevating multi-core parallel efficiency. Our research encompasses a comprehensive analysis of the parallel acceleration effects observed across models featuring diverse parameter magnitudes during the inference process. This analysis culminates in the development of FLASH, an online deep model inference system tailored for Serverless edge inference, strategically optimized through the utilization of multi-core parallelism. FLASH exhibits dynamic adaptability by modulating the number of CPU cores within computational instances in accordance with traffic request loads. It also employs a dynamic scaling mechanism to finely adjust model placement, ultimately facilitating inference acceleration and mitigating the concomitant cold start overhead. Empirical experimentation conducted across a spectrum of burst-level workloads serves to underscore FLASH's capacity, resulting in an average reduction in response latency by 33% and a maximum reduction of 75%, while concurrently realizing a throughput enhancement of 2.94x. © 2023 IEEE.

关键词： Edge-Cloud Collaborative Network-Efficiency Serverless Inference

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Enhanced hydrogen storage properties of NaBH4–Mg（BH4）2 composites by NdF3 addition

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Progress in Natural Science:Materials International 2021年第4期31卷 521-526页

作者： Jianguang Yuan Jinting Chen Haixiang Huang Yujie Lv Bogu Liu Zhongyu Li Bao Zhang Wei Lv Ying Wu Central Iron & Steel Research Institute Advanced Technology & Materials Co. Ltd Jiangsu JITRI Advanced Energy & Materials Research Institute Co. Ltd Advanced Materials Research Institute North China Electric Power University Jiangsu Provincial Key Laboratory of Special Robot Technology Hohai University

As two important members of complex hydrides,Mg（BH4）2and Na BH4have a high gravimetric capacity （14.9 and10.8 wt%,respectively）.In this study,the Mg（BH4）2was synthesized by the ion exchange ***,the Mg（BH4）2and Na BH4composites with different amounts （30,40 and 50 wt%） of NdF3were prepared by mechanical *** of the Nd F3on the microstructural evolution and hydrogen storage properties were *** results show that Nd F3catalyst can significantly improve the dehydrogenation kinetics of the eutectic composites of Na BH4–Mg（BH4）*** onset hydrogen desorption temperature of the composites is about88.6C,which is about 110C lower than that of Mg（BH4）2and Na ***（BH4）2–Na BH4-0.5NdF3composites can released 5.2 wt%H2at 250C within 30 min,and the dehydrogenation capacity is significantly higher than that of Mg（BH4）2–Na *** analysis of the dehydrogenation mechanism reveals that NdF3takes participate in the reaction to generate Na MgF3to promote the dehydrogenation reaction process of the composites.

关键词： Hydrogen storage materials Mg(BH4)2 NaBH4 NdF3 Ball-milling Hydrogen desorption performance

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Multimodal Fusion Induced Attention Network for Industrial VOCs Detection

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6385-6398页

作者： Kang, Yu Shi, Kehao Tan, Jifang Cao, Yang Zhao, Lijun Xu, Zhenyi Anhui University Anhui Provincial Key Laboratory of Multimodal Cognitive Computation Hefei230601 China Yangtze River Delta Hart Robot Industry Technology Research Institute Wuhu241000 China University of Science and Technology of China Anhui Province Key Laboratory of Intelligent Low-Carbon Information Technology and Equipment Hefei230088 China University of Science and Technology of China Department of Automation Hefei230026 China Anhui University AHU-IAI AI Joint Laboratory Hefei230601 China University of Science and Technology of China Institute of Advanced Technology Hefei230088 China

Industrial volatile organic compounds (VOCs) emissions and leakage have caused serious problems to the environment and public safety. Traditional VOCs monitoring systems require professionals to carry gas sensors into the emission area to collect VOCs, which might cause secondary hazards. VOCs infrared (IR) imaging visual inspection technology is a convenient and low-cost method. However, current visual detection methods with VOCs IR imaging are limited due to blurred imaging and indeterminate gas shapes. Moreover, major works pay attention to only IR modality for VOCs emissions detection, which would neglect semantic expressions of VOCs. To this end, we propose a dual-stream fusion detection framework to deal with visible and IR features of VOCs. Additionally, a multimodal fusion induced attention (MFIA) module is designed to realize feature fusion across modalities. Specifically, MFIA uses the spatial attention fusion module (SAFM) to mine association among modalities in terms of spatial location and generates fused features by spatial location weighting. Then, the modality adapter (MA) and induced attention module (IAM) are proposed to weight latent VOCs regions in IR features, which alleviates the problem of noise interference and degradation of VOCs characterization caused by fusion. Finally, comprehensive experiments are carried out on the challenging VOCs dataset, and the mAP@0.5 and F1-score of the proposed model are 0.527 and 0.601, which outperforms the state-of-the-art methods by 3.3% and 3.4%, respectively. © 2024 IEEE.

关键词： Volatile organic compounds

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The research of distributed CO2 monitoring technology based on NB-IoT 4

The research of distributed CO2 monitoring technology based ...

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4th International Computational Imaging Conference, CITA 2024

作者： Hu, Chun Li, Yanlin Zheng, Dezhi Cheng, Yufei Peng, Peng Nie, Sichong Advanced Research Institute of Multidisciplinary Sciences Beijing Institute of Technology Beijing100081 China MIIT Key Laboratory of Complex-field Intelligent Exploration Beijing Institute of Technology Beijing100081 China State Key Laboratory of CNS/ATM Beijing Institute of Technology Beijing100081 China Chongqing Caeri Robot Technology Co. Ltd. Certification Department Chongqing400722 China Innovation&Research Institute of HIWING Technology Academy Beijing100074 China

ISBN: (纸本)9781510688834

CO2 is one of the primary contributors to the greenhouse effect, and its monitoring and control are critical for achieving carbon peaking and neutrality goals. Therefore, large-scale distributed monitoring of CO2 concentrations in the environment is of significant importance. To investigate the diffusion patterns of CO2 in windless environments, this paper designs and implements a distributed CO2 monitoring system. The system adopts the three-layer architecture of the Internet of Things (IoT) as its overall framework, utilizing a self-developed CO2 sensor as the sensing layer node. The system employs the Narrowband Internet of Things (NB-IoT) protocol to achieve multi-node, wide-coverage, and low-power data transmission and reception. A custom database is developed as the application layer to store, classify, and manage data collected by the nodes. Experimental results provide CO2 monitoring data, which are analyzed to summarize the variation patterns of CO2 concentration over time and distance. By comparing these results with existing gas diffusion models, it is found that the trends are consistent, thereby demonstrating the reliability and stability of the proposed system. © 2025 SPIE.

关键词： Carbon capture and utilization

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PM2.5 Concentration Prediction Based on Coupling Multivariable Parameters LSTM 6

PM2.5 Concentration Prediction Based on Coupling Multivariab...

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6th International Conference on Information Science, Electrical, and Automation Engineering, ISEAE 2024

作者： Zheng, Dezhi Zheng, Jinxiong Lu, Shijia Li, Zhongxiang Peng, Peng Liu, Bei Advanced Research Institute of Multidisciplinary Science Beijing Institute of Technology Beijing100081 China Yangtze Delta Region Academy Beijing Institute of Technology Jiaxing314019 China MIIT Key Laboratory of Complex-Field Intelligent Exploration Beijing Institute of Technology Beijing100081 China Chongqing Caeri Robot Technology Co. Ltd. Chongqing400722 China Beijing Research Institute of Telemetry Beijing100076 China

ISBN: (纸本)9781510682924

As one of the major air pollutants, PM2.5 poses serious threats to human health and daily life. Improving the accuracy of PM2.5 predictions is of significant importance for pollution control, as it can effectively reduce its harmful impact. Long Short-Term Memory (LSTM) networks are capable of handling long-term dependencies and are commonly used for modeling and remembering long sequence data, making them a key tool for PM2.5 prediction. Existing LSTM models often use a single parameter for PM2.5 prediction, without considering the coupling effects of multiple meteorological factors with different pollutants and their concentrations. Additionally, using a single optimization algorithm may lead to optimization parameters getting stuck in local optimal solutions, disrupting the optimization results. In this study, multiple meteorological factors highly correlated with PM2.5 data using Pearson correlation coefficients (PCCs) and pollutants with concentrations were selected to construct a coupled correlation model. Furthermore, to improve the reliability of optimization results, the Sparrow Search Algorithm (SSA) with strong local search capability and the Particle Swarm Optimization algorithm (PSO) with strong global search capability were employed to simultaneously optimize LSTM hyperparameters and obtain the optimal solution. Using hourly meteorological data from multiple stations in Beijing from March 1, 2013, to February 28, 2017, as the data source, the SSA-PSO algorithm was used to obtain the optimal solution for network hyperparameters. The coupled correlation model of multiple meteorological parameters with multiple pollutants and concentrations was established. The research results indicate that considering the coupled correlation model of meteorological parameters with pollutants and concentrations can effectively improve the prediction accuracy of PM2.5, and the more types of input parameters, the higher the model prediction accuracy. The R2 of the coupled

关键词： Health risks

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RGB-Based Category-Level Object Pose Estimation via Depth Recovery and Adaptive Refinement

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IEEE robotics and Automation Letters 2025年第6期10卷 5377-5384页

作者： Yang, Hui Sun, Wei Liu, Jian Zheng, Jin Zeng, Zhiwen Mian, Ajmal Hunan University National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering Changsha410082 China Hunan University State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Changsha410082 China Central South University School of Architecture and Art Changsha410082 China University of Western Australia Department of Computer Science and Software Engineering CrawleyWA6009 Australia

Category-level pose estimation methods have received widespread attention as they can be generalized to intra-class unseen objects. Although RGB-D-based category-level methods have made significant progress, reliance on depth image limits practical application. RGB-based methods offer a more practical and cost-effective solution. However, current RGB-based methods struggle with object geometry perception, leading to inaccurate pose estimation. We propose depth recovery and adaptive refinement for category-level object pose estimation from a single RGB image. We leverage DINOv2 to reconstruct the coarse scene-level depth from the input RGB image and propose an adaptive refinement network based on an encoder-decoder architecture to dynamically improve the predicted coarse depth and reduce its gap from the ground truth. We introduce a 2D–3D consistency loss to ensure correspondence between the point cloud obtained from depth projection and the objects in the 2D image. This consistency supervision enables the model to maintain alignment between the depth image and the point cloud. Finally, we extract features from the refined point cloud and feed them into two confidence-aware rotation regression branches and a translation and size prediction residual branch for end-to-end training. Decoupling the rotation matrix provides a more direct representation, which facilitates parameter optimization and gradient propagation. Extensive experiments on the REAL275 and CAMERA25 datasets demonstrate the superior performance of our method. Real-world estimation and robotic grasping experiments demonstrate our model robustness to occlusion, clutter environments, and low-textured objects. Our code and robotic grasping video are available at DA-Pose. © 2016 IEEE.

关键词： Depth perception

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Parameter-Sharing-Based Average-Consensus Time Synchronization in IoT Networks

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IEEE Internet of Things Journal 2023年第9期10卷 8215-8227页

作者： Shi, Fanrong Yang, Simon X. Mukherjee, Mithun Jiang, Hong Da Costa, Daniel Benevides Wong, Wing-Kwong Southwest University of Science and Technology Robot Technology Used for Special Environment Key Laboratory of Sichuan Province School of Information Engineering Mianyang621010 China School of Engineering University of Guelph Advanced Robotics and Intelligent Systems Laboratory GuelphONN1G 2W1 Canada Nanjing University of Information Science and Technology School of Artificial Intelligence School of Future Technology Nanjing210044 China Technology Innovation Institute Abu Dhabi United Arab Emirates National Yunlin University of Science and Technology Future Technology Research Center Douliu64002 Taiwan

Average-consensus protocol is one of the ways to develop distributed time-synchronization algorithms in Internet of Things (IoT) networks. However, the large number of iteration leads to a common time notion issue in nodes. This poses a critical challenge in the convergence of the time-synchronization algorithm and resulting asymptotic convergence in the average consensus protocol. In this article, a parameter-sharing-based average-consensus time-synchronization (PACTS) algorithm is proposed. For fast convergence, the proposed PACTS quickly forwards the time information to multihop nodes and employs multihop average-consensus instead of single-hop average consensus. Specifically, a node asynchronously and periodically broadcasts the relative clock offset estimation of neighbors with its local time information. Meanwhile, the relative clock offset estimation of the multihop node is calculated and used to estimate the average value. Consequently, an average consensus among local multihop nodes is obtained. As a result, the iteration number and convergence time are significantly reduced over the network. Finally, the experimental results indicate that the proposed PACTS algorithm has low complexity, high accuracy, and quick convergence. © 2023 IEEE.

关键词： Topology

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RA Loss: Relation-Aware Loss for Robust Person Re-identification 16th

RA Loss: Relation-Aware Loss for Robust Person Re-identific...

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16th Asian Conference on Computer Vision, ACCV 2022

作者： Wang, Kan Hu, Shuping Cheng, Jun Cheng, Jun Pang, Jianxin Tan, Huan Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Guangdong Provincial Key Laboratory of Robot Localization and Navigation Technology UBTech Robotics Corp Ltd. Shenzhen China Institute for Infocomm Research A*STAR Singapore Singapore

ISBN: (纸本)9783031262838

Previous relation-based losses in person re-identification (ReID) typically comprise two sequential steps: they firstly sample both positive pair and negative pair and then deploy constraints to simultaneously improve intra-identity compactness and inter-identity separability. However, existing relation-based losses usually place emphasis on exploring the relation between images and therefore consider only several pairs during each optimization. This inevitably leads to different convergence status for pairs of the same kind and brings about the intra-pair variance problem. Accordingly, we propose a novel Relation-Aware (RA) loss to address the intra-pair variance via exploring the informative relation across pairs. In brief, we introduce a macro-constraint and a micro-constraint. The macro-constraint encourages the separation of positive pair and negative pair via pushing far apart the two "centers" of the positive pair and the negative pair. The "center" of each kind of pair are obtained via averaging all the pairs of the same kind. The micro-constraint further enhances the compactness by minimizing the discrepancies among pairs of the same kind. The two constraints work cooperatively to relieve the intra-pair variance and improve the quality of pedestriansąŕ representation. Results of extensive experiments on three widely used ReID benchmarks, i.e., Market-1501, DukeMTMC-ReID and CUHK03, demonstrate that the RA loss brings improvements over existing relation-based losses. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep learning

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