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2024 International Conference on Measurement, Communication, and Virtual reality, MCVr 2024

作者： Jilin, Jiang Shimiao, Wang Qi, Gao Shuping, Wang Xinyu, Chai Heng, Li School of Biomedical Engineering Shanghai Jiao Tong University Shanghai200240 China School of Materials Science and Engineering Shanghai Jiao Tong University Shanghai200240 China Technical Centre for Animal Plant & Food Inspection and Quarantine Shanghai Customs Shanghai200135 China

ISBN: (纸本)9781510691773

Multi-object detection, as one of the core tasks in computer vision, aims to identify all foreground objects in an image, determining their categories and locations. It has significant application prospects in fields such as automated detection, medical imaging, autonomous driving, security, and surveying. Currently, most pre-trained large models have been open-sourced, which means we can treat them as general feature extractors for specific downstream tasks. Inspired by GLIP and dINO models, the Grounding dINO model was proposed, which can detect arbitrary objects based on text prompts with high detection accuracy. This project focuses on the problem of quarantine pests, such as invasive weeds and insects, in imported grain, which is crucial for quarantine inspection at ports. Grounding dINO was optimized and trained on a manually annotated dataset of grain weeds and insects, allowing the model to adapt and focus on the target domain, thereby enhancing its performance and potential application value in grain, weed, and insect recognition tasks. Experimental results show that the model exhibits excellent performance in object detection, achieving high average precision (AP[ IoU=0.50:0.95]=0.867) and average recall (Ar[ IoU=0.50:0.95]=0.947), especially at an IoU of 0.50. Moreover, the comparison of pre- and post-training models proves the effectiveness of pre-training, leading to more prominent performance of the model in datasets of weeds, insects, and wheat images, thus improving detection accuracy. © 2025 SPIE.

关键词： Grain (agricultural product)

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Few-Shot Meta-Learning for dynamic Indoor Fingerprinting Localization Based on CSI Images

Few-Shot Meta-Learning for Dynamic Indoor Fingerprinting Loc...

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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Jiao, Jiyu Wang, Xiaojun Han, Chengpei Huang, Yuhua Zhang, Yizhuo Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Purple Mountain Laboratories Nanjing211111 China

ISBN: (纸本)9798350368369

While fingerprinting localization is favored for its effectiveness, it is hindered by high data acquisition costs and the inaccuracy of static database-based estimates. Addressing these issues, this work presents an innovative dynamic indoor localization method using a few-shot meta-learning algorithm to optimize performance with limited CSI data. By leveraging the 'Learning to Learn' paradigm, this method utilizes past localization tasks and requires only a few updates, enhancing adaptability and learning efficiency in new environments. Furthermore, we introduce a task-weighted loss to improve knowledge transfer within this framework. Experiments confirm the method's robustness and superiority, achieving a notable 25.08% average gain in Mean Euclidean distance (MEd), proving especially effective in scenarios with limited CSI data. © 2025 IEEE.

关键词： Convolutional neural networks

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A Novel dual-Layer PM Variable Leakage Flux Machine for Electrified Vehicle Applications

A Novel Dual-Layer PM Variable Leakage Flux Machine for Elec...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Liu, dabin Yang, Hui Liu, Xing Tu, rui Li, Ya Liu, Xiping Fang, Shuhua Shen, Yiming Lin, Heyun Southeast University School of Electrical Engineering Nanjing China Anhui University School of Electrical Engineering and Automation Nanjing China Jiangxi University of Science and Technolodgy School of Electrical Engineering and Automation China Nanyang Technological University School of Electrical and Electronic Engineering Singapore

ISBN: (纸本)9798350376067

This paper proposes a novel dual-layer permanent magnet variable leakage flux machine (dLPM-VLFM) for electrified vehicle (EV) applications, which can solve the drawbacks in conventional variable-leakage-flux permanent magnet machines (VLF-PMMs) with single layer PM, such as compromised torque density due to the significant leakage flux. The proposed design features a dLPM arrangement and unique leakage flux paths. The leakage flux paths are purposely placed at both ends of the second-layer PMs to introduce variable-leakage-flux (VLF) property. Therefore, the proposed machine can well balance the performance conflicts between low-speed torque capability and high-speed efficiency, enabling broader design space to achieve VLF property. Firstly, the design approach and specification of the proposed machine are introduced and investigated, respectively. The operating principle of the proposed dLPM-VLFM is studied, and a multi-objective optimization is performed to achieve the optimal design. Afterwards, the electromagnetic performance of the proposed machine is evaluated and compared with a benchmark conventional PMM using finite-element (FE) method. Finally, a mechanical stress analysis is conducted, verifying the mechanical reliability of the proposed machine. © 2024 IEEE.

关键词： Magnetic levitation vehicles

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Joint Spatial-Temporal Modeling and Contrastive Learning for Self-supervised Heart rate Measurement 3

Joint Spatial-Temporal Modeling and Contrastive Learning for...

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3rd Vision-Based remote Physiological Signal Sensing Challenge and Workshop, rePSS 2024

作者： Qian, Wei Li, Qi Li, Kun Wang, Xinke Sun, Xiao Wang, Meng Guo, dan China Ministry of Education China Institute of Artificial Intelligence Hefei Comprehensive National Science Center China Anhui University China Zhejiang University China Anhui Zhonghuitong Technology Co. Ltd. China

This paper briefly introduces the solutions developed by our team, HFUT-VUT, for Track 1 of selfsupervised heart rate measurement in the 3rd Vision-based remote Physiological Signal Sensing (rePSS) Challenge hosted at IJCAI 2024. The goal is to develop a self-supervised learning algorithm for heart rate (Hr) estimation using unlabeled facial videos. To tackle this task, we present two self-supervised Hr estimation solutions that integrate spatial-temporal modeling and contrastive learning, respectively. Specifically, we first propose a non-end-to-end self-supervised Hr measurement framework based on spatial-temporal modeling, which can effectively capture subtle rPPG clues and leverage the inherent bandwidth and periodicity characteristics of rPPG to constrain the model. Meanwhile, we employ an excellent end-to-end solution based on contrastive learning, aiming to generalize across different scenarios from complementary perspectives. Finally, we combine the strengths of the above solutions through an ensemble strategy to generate the final predictions, leading to a more accurate Hr estimation. As a result, our solutions achieved a remarkable rMSE score of 8.85277 on the test dataset, securing 2nd place in Track 1 of the challenge. © 2024 Copyright for this paper by its authors.

关键词： contrastive learning heart rate rPPG Self-supervised spatial-temporal modeling

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Evaluation of the mechanical properties of stiffened composite panels under consideration of water load 11

Evaluation of the mechanical properties of stiffened composi...

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2024 11th International Conference on Advanced Manufacturing Technology and Materials Engineering, AMTME 2024

作者： Yang, Junchao Chen, Xiangming Wang, Zhe Zou, Peng Zhu, Qiang National Key Laboratory of Strength and Structural Integrity Aircraft Strength Research Institute of China Shaanxi Xi'an710065 China China Special Vehicle Research Institute Hubei Jingmen448035 China

To meet the strength evaluation requirements of a typical stiffened composite panel of the lower fuselage of a new generation amphibious aircraft underwater load, the experimental testing technique and finite element analysis method were investigated. Firstly, according to the loading and support requirements of the typical stiffened composite lower fuselage panel, a testing device for combined axial and pneumatic loading was designed and developed. Then, a finite element model of the stiffened composite plate and the combined load testing device were created. Based on the experiments and finite element analysis, the static strength of the stiffened composite plate was verified to meet the load-bearing capacity requirements, and the strain behavior during loading was determined. Compared with the experimental results, the maximum strain error at the skin is 6.16%, and the maximum strain error at the stiffener is -11.6%. This confirms the effectiveness of the finite element model and also confirms that the test technique and equipment can accurately simulate in-plane axial loading and out-of-plane water loading. Finally, based on the validated finite element model, the load-bearing capacity of the stiffened composite panel under combined loads was predicted. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： Amphibious aircraft

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research and Application of Wear Prediction Method of NC Milling Cutter Based on data-driven 11

Research and Application of Wear Prediction Method of NC Mil...

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11th International Conference of Information and Communication Technology, ICTech 2022

作者： Liu, Zhenduo Yang, dongsheng University of Chinese Academy of Sciences Beijing100049 China Liaoning Key Lab. of Domestic Industrial Control Platform Technology on Basic Hardware Software Shenyang110168 China

ISBN: (数字)9781665496940

ISBN: (纸本)9781665496940

As the basic equipment of the industry, the tool wear will affect the quality of the processed products and production efficiency, so how to use the tool condition monitoring information to accurately predict the residual life of the tool has a high application value. In the application background of industrial big data and industrial equipment fault prediction and health management, the predictive evaluation of milling cutter wear degree of CNC machine tool is carried out by using machine learning method, and then the tool life is accurately predicted. In the process of research and analysis, the milling cutter original data were preprocessed and feature extraction was carried out. A feature set screening method was adopted to screen out feature sets related to the degree of tool wear degradation. Finally, the remaining life of milling cutter was accurately predicted by machine learning model. © 2022 IEEE.

关键词： Feature extraction

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Effect of Nitrogen and Sulfur Co‑doped Graphene on the Electrochemical Hydrogen Storage Performance of Co_(0.9)Cu_(0.1)Si Alloy

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Acta Metallurgica Sinica(English Letters) 2023年第6期36卷 1023-1037页

作者： Wenhao Fan Jianxun Zhao dayong Liu Qingcheng Liang Wanqiang Liu Qingshuang Wang Heng Liu Peng Chen Shang Gao Xinlong Bao Yong Cheng Xinwei Wang Xin Guo School of Materials Science and Engineering Changchun University of Science and TechnologyChangchun 130022China Engineering Research Center of Optoelectronic Functional Materials Ministry of EducationChangchun 130022China College of Electronic Science and Engineering Jilin UniversityChangchun 130022China School of Life Science and Technology Changchun University of Science and TechnologyChangchun 130022China Research Center for Nanotechnology Changchun University of Science and TechnologyChangchun 130022China Research Institute of Changchun University of Science and Technology in Chongqing Chongqing 401120China State Key Laboratory of Rare Earth Resource Utilization Changchun Institute of Applied ChemistryCASChangchun 130022China

Co_(0.9)Cu_(0.1)Si alloy was prepared by mechanical alloying ***-doped graphene(NG)and nitrogen–sulfur codoped graphene(NSG)were prepared by hydrothermal method.5 wt%graphene oxide,NG and NSG were doped into Co_(0.9)Cu_(0.1)Si alloy,respectively,by ball milling to improve the electrochemical hydrogen storage performance of the composite material.X-ray diffraction and scanning electron microscopy were used to characterize the structure and morphology of the composite material,and the LANd battery test system and three-electrode battery system were used to test the electrochemical performance of the composite *** composite material showed better discharge capacity and better cycle stability than the pristine *** addition,in order to study the optimal ratio of NSG,3%,5%,7%and 10%of NSG were doped into Co_(0.9)Cu_(0.1)Si alloy,***_(0.9)Cu_(0.1)Si alloy doped with 5%NSG had the best performance among all the *** best discharge capacity was 580.1 mAh/g,and its highest capacity retention rate was 64.1%.The improvement in electrochemical hydrogen storage performance can be attributed to two *** the one hand,the electrocatalytic performance of graphene is improved by co-doping nitrogen and sulfur,on the other hand,graphene has excellent electrical conductivity.

关键词： Co_(0.9)Cu_(0.1)Si alloy Nitrogen-sulfur co-doped graphene(NSG) Composite material Electrochemical hydrogen storage

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Combinational benefit of antihistamines and remdesivir for reducing SArS-CoV-2 replication and alleviating inflammation-induced lung injury in mice

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Zoological research 2022年第3期43卷 457-468页

作者： Meng-Li Wu Feng-Liang Liu Jing Sun Xin Li Jian-ru Qin Qi-Hong Yan Xia Jin Xin-Wen Chen Yong-Tang Zheng Jin-Cun Zhao Jian-Hua Wang College of Life Science Henan Normal UniversityXinxiangHenan 453007China Guangzhou Institutes of Biomedicine and Health Chinese Academy of SciencesGuangzhouGuangdong 510530China Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences Kunming Institute of ZoologyChinese Academy of SciencesKunmingYunnan 650223China State Key Laboratory of Respiratory Disease National Clinical Research Center for Respiratory DiseaseGuangzhou Institute of Respiratory HealthFirst Affiliated Hospital of Guangzhou Medical UniversityGuangzhouGuangdong 510182China University of Chinese Academy of Sciences Beijing 100049China

COVId-19 is an immune-mediated inflammatory disease caused by SArS-CoV-2 infection,the combination of anti-inflammatory and antiviral therapy is predicted to provide clinical *** recently demonstrated that mast cells(MCs)are an essential mediator of SArS-CoV-2-initiated *** also showed that spike protein-induced MC degranulation initiates alveolar epithelial inflammation for barrier disruption and suggested an off-label use of antihistamines as MC stabilizers to block degranulation and consequently suppress inflammation and prevent lung *** this study,we emphasized the essential role of MCs in SArS-CoV-2-induced lung lesions in vivo,and demonstrated the benefits of co-administration of antihistamines and antiviral drug remdesivir in SArS-CoV-2-infected ***,SArSCoV-2 spike protein-induced MC degranulation resulted in alveolar-capillary injury,while pretreatment of pulmonary microvascular endothelial cells with antihistamines prevented adhesion junction disruption;predictably,the combination of antiviral drug remdesivir with the antihistamine loratadine,a histamine receptor 1(Hr1)antagonist,dampened viral replication and inflammation,thereby greatly reducing lung *** findings emphasize the crucial role of MCs in SArS-CoV-2-induced inflammation and lung injury and provide a feasible combination antiviral and anti-inflammatory therapy for COVId-19 treatment.

关键词： SArS-CoV-2 Mast cell remdesivir Antihistamine Combinational therapy

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Application of Improved Particle Swarm Optimization Algorithm in reservoir Optimal Scheduling 2

Application of Improved Particle Swarm Optimization Algorith...

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2022 2nd International Conference on Artificial Intelligence, Big data and Algorithms, CAIBdA 2022

作者： Zhang, Zhongbo Geng, Simin He, Xiaoyan Zhang, Shuanghu Tian, Yu Li, Hui China Water Resources and Hydropower Research Institute Beijing100038 China Development Research Center of the Ministry of Water Resources Beijing100038 China

ISBN: (纸本)9783800760268

The reservoir optimization and dispatch are hounded by the problems of large scale and complex structure, the involvement of a large number of decision variables, and complex constraints, as well as the high-dimensional, nonlinear, and strong constraints. In view of this, the traditional optimization method is difficult to solve directly or has low calculation efficiency, and there are problems such as premature maturity. In this study, the particle swarm optimization (PSO) algorithm based on the inertia weight of the decaying cosine curve overcomes the shortcomings of the existing PSO algorithm, which is easy to fall into the local optimum, enhances the global and local search capabilities, and offers an improved PSO algorithm. By applying the above optimization algorithm to the reservoir optimization dispatch model, it is found that the method is easy to implement, and the efficiency is high, which provides a new way for the solution of the reservoir optimization dispatch model. © VdE VErLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Efficiency

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Small-Object detection with Super resolution Embedding 1

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3rd International Conference on Artificial Intelligence, 2022

作者： Tang, Wenyi Xu, Qiucheng ding, Hui Wu, Lianghao Shi, Yanyang State Key Laboratory of Air Traffic Management System and Technology Nanjing Research Institute of Electronic Engineering Nanjing210007 China

ISBN: (数字)9789811694233

ISBN: (纸本)9789811694226

The detection performance of ground vehicles on the airport surface in video-sensor based air traffic control surveillance has not been satisfactory compared to larger planes, especially in panoramic images of remote tower applications. Inspired by the success of GAN-based super resolution and oversampling augmentation methods, we apply a new super-resolution network to resize cropped small samples and augment each of those images by resize-and-pasting small objects many times. It allows us to trade off the quantity of the detector on large objects with that on small objects. We propose an architecture with two components: ESrGAN and detection network. We use residual dense blocks for ESrGAN, and for the detector network, we use one state-of-the-art detector (YOLOv3). Extensive experiments on a public (car overhead with context) dataset and another self-assembled airport surface dataset show superior performance of our method compared to the standalone state-of-the-art object detectors. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Object detection

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