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Study of Impact Resistance Based on Porcupine Quills Bionic Thin-walled Structure

Journal of Bionic engineering 2023年第5期20卷 1942-1955页

作者： Tianshu Huang Zhengyu Mao Lijun Chang Xingyuan Huang Zhihua Cai Hunan Provincial Key Laboratory of Health Maintenance for Mechanical Equipment Hunan University of Science and TechnologyXiangtanChina School of Mechanical Engineering Hunan University of Science and TechnologyXiangtanChina

Using an electron microscope to observe the microstructure of a porcupine quills cross-section and a bionic method,a new bionic structure was *** performance of the structure in terms of energy absorption,maximum impact force withstood,and impact force efficiency was evaluated using Ansys finite element simulation software to simulate the structure's *** examine the impact of ribs on the structural performance of the bionic porcupine quills,a control structure was *** to the results of the finite element simulation,the presence of ribs in the Bionic porcupine quills structure can transfer stress uniformly to the overall structure and share stress for some of the rupture-prone *** reduce stress concentration in specific areas and increase the impact force efficiency of the *** SEA and IFE values of bionic porcupine quills were 30.01 kJ/kg and 84.22%,*** structure is then optimized for parameter design in order to find the optimal structure by response surface in order to improve the structure's SEA and decrease its *** order to evaluate the precision of the response surface,the optimal structure predicted is validated using finite element simulation.

关键词： Bionic structure design Thin-walled structure Impact resistance Specific energy absorption Multi-objective optimization

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A Lowcost UAV Task Offloading Scheme based on Trustable and Trackable Data Routing

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2023年第9期9卷 1-16页

作者： Bai, Jing Gui, Jinsong Huang, Guosheng Dong, Mianxiong Wang, Tian Zhang, Shaobo Liu, Anfeng School of Computer Science and Engineering Central South University Changsha China School of Information Science and Engineering Hunan First Normal University Changsha China School of Computer Science and Engineering Muroran Institute of Technology Japan Artificial Intelligence and Future Networks Beijing Normal University & UIC Zhuhai China School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China

As a promising edge computing paradigm, task offloading involves transferring data from resource-limited devices to high-performance servers to expedite processing. However, devices in isolated networks without direct Internet connections face challenges in task offloading. To address this issue, we propose a novel Low-cost Unmanned Aerial Vehicle (UAV) Task Offloading Scheme based on Trustable and Trackable Data Routing (LTOTT) for deadline-aware tasks in non-connected networks. The main contributions of LTOTT are as follows: (1) A novel dissemination method that devices route different numbers of Copied Tasks (CTs) and Task Computing Notices (TCNs) in different directions based on task deadlines is proposed to enable the UAV to get tasks earlier and complete them in time. (2) In order to reduce the risk of malicious attacks during the spreading of CTs and TCNs, a trust evaluation based on a trackable data routing method is proposed to ensure secure transmission. (3) In addition, based on the evaluated trust and the received information, a dynamic UAV flight trajectory optimization is proposed to enable tasks completed before their deadlines. A large number of experimental results show that LTOTT increases the task completion rate by 41.41% - 134.15%;reduces average delay and UAV's flight distance respectively by 26.88% - 51.52%, 16.37% -73.40% compared with the existing schemes. IEEE

关键词： Antennas

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Transformer-Based Person Re-Identification: A Comprehensive Review

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年第7期9卷 1-19页

作者： Sarker, Prodip Kumar Zhao, Qingjie Uddin, Md. Kamal School of Computer Science and Technology Beijing Institute of Technology China Department of Computer Science and Telecommunication Engineering Noakhali Science and Technology University Bangladesh

In the evolving landscape of surveillance and security applications, the task of person re-identification(re-ID) has significant importance, but also presents notable difficulties. This task entails the process of accurately matching and identifying persons across several camera views that do not overlap with one another. This is of utmost importance to video surveillance, public safety, and person-tracking applications. However, vision-related difficulties, such as variations in appearance, occlusions, viewpoint changes, cloth changes, scalability, limited robustness to environmental factors, and lack of generalizations, still hinder the development of reliable person re-ID methods. There are few approaches have been developed based on these difficulties relied on traditional deep-learning techniques. Nevertheless, recent advancements of transformer-based methods, have gained widespread adoption in various domains owing to their unique architectural properties. Recently, few transformer-based person re-ID methods have developed based on these difficulties and achieved good results. To develop reliable solutions for person re-ID, a comprehensive analysis of transformer-based methods is necessary. However, there are few studies that consider transformer-based techniques for further investigation. This review proposes recent literature on transformer-based approaches, examining their effectiveness, advantages, and potential challenges. This review is the first of its kind to provide insights into the revolutionary transformer-based methodologies used to tackle many obstacles in person re-ID, providing a forward-thinking outlook on current research and potentially guiding the creation of viable applications in real-world scenarios. The main objective is to provide a useful resource for academics and practitioners engaged in person re-ID. IEEE

关键词： Cameras

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HACS: An Enhancement Framework for Deep Code Search Benefiting from Hard Negative Samples 36

HACS: An Enhancement Framework for Deep Code Search Benefiti...

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36th International Conference on Software engineering and Knowledge engineering, SEKE 2024

作者： Song, Qihong Liu, Jianxun Hu, Haize School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China

ISBN: (纸本)1891706594

Code search aims to retrieve relevant code snippets from large code repositories based on query, promoting code reuse and enhancing software development efficiency. Deep Learning is a powerful approach for code search, in which the hard negative samples within training batches critically impact model performance. However, most existing deep code search models only randomly sample negative samples, resulting in a paucity or complete lack of hard negative samples. To address this limitation, we introduce a novel enhancement framework named HACS to optimize the composition of negative samples within batches, thus enhancing the training effectiveness of deep code search models. The core idea is to increase the count of hard negative samples within the negative samples corresponding to each query in the training batch. Specifically, HACS utilizes deep reinforcement learning techniques for sampling hard negative samples and implements vector-level mixed data augmentation strategy to generate hard negative samples. We evaluated our framework on a public dataset covering six programming languages. Experimental results reveal that HACS significantly improves the code search performance of existing models. © 2024 Knowledge Systems Institute Graduate school. All rights reserved.

关键词： Deep reinforcement learning

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Additives strategies to improving cycling stability of Li metal batteries by modifying electrode electrolyte interphases

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Progress in Natural science:Materials International 2024年第2期34卷 338-344页

作者： Yuan Li Pengfei Huang Chunyu Cui Xiaoyuan Ma Li Chen Zekun Li Linhai Li Yuhong Tian School of Biological and Chemical Engineering Guangxi University of Science and Technology School of College of Chemistry and Chemical Engineering Hunan University Isky Future Energy Institute

Li metal batteries（LMBs） have attracted much attention due to the ultra-high theoretical capacity of the Li metal anode（LMA）.However,the uncontrollable dendrites growth and low coulombic efficiency hinder their practical ***,we explore 3,5-difluoropyridine（2F-BD） as a novel electrolyte additive to enable highperformance *** 2F-BD additive participates in and modifies the solvation structure of Li ions by reducing the coordinated number of PF6-at the electrode surface,leading to the formation of a LiF and Li3N-rich solid electrolyte interphase（SEI）.The LiF component increases the robustness of SEI and suppresses the formation of Li dendrites,while the Li3N component facilitates the transportation and reaction kinetics of Li *** a result,the Li‖Li symmetric cell presents a stable cycling performance of up to 500 h at a current density of 1 mA *** coupling with LiFePO4cathode,the obtained full cell achieves high specific capacities of 106.06 mA h g-1and 84.98 mA h g-1at 2.55 mA cm-2and 5.10 mA cm-2,respectively,and maintains a high capacity retention of84.0% after 1000 cycles at 0.13 mA cm-2,with an average cycling CE of 99.58%.

关键词： Li metal batteries Li dendrites Electrolyte additive LiF and Li3N-Rich electrode-electrolyte interphase LiFePO4 cathode

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Disentangling Dynamics: Advanced, Scalable and Explainable Imputation for Multivariate Time Series

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IEEE Transactions on Knowledge and Data engineering 2025年第7期37卷 4010-4022页

作者： Liu, Shuai Li, Xiucheng Chen, Yile Jiang, Yue Cong, Gao Nanyang Technological University School of Computer Science and Engineering 639798 Singapore School of Computer Science and Technology Guangdong 518055 China

Missing values pose a formidable obstacle in multivariate time series analysis. Existing imputation methods rely on entangled representations that struggle to simultaneously capture multiple orthogonal time-series patterns, leading to suboptimal performance and limited interpretability. Meanwhile, requiring the entire data span as input renders these models impractical for long time series. To address these issues, we propose TIDER and its enhanced version, AdaTIDER. TIDER employs low-rank matrix factorization and disentangled temporal representations to model intricate dynamics like trend, seasonality, and local bias. However, TIDER is limited to single-period modeling and does not explicitly capture dependencies between channels. To overcome these limitations, AdaTIDER incorporates adaptive cross-channel dependency modeling and multi-period seasonality representations. These advancements enable it to dynamically capture variable relationships and complex multi-period patterns, significantly enhancing imputation accuracy and interpretability, while maintaining TIDER's scalability. Extensive experiments on real-world datasets validate the superiority of our models in imputation accuracy, scalability, interpretability, and robustness. © 1989-2012 IEEE.

关键词： Matrix factorization

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Genome-wide analyses of mi RNAs in mycorrhizal plants in response to late blight and elucidation of the role of miR319c in tomato resistance

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Horticultural Plant Journal 2024年第6期10卷 1371-1382页

作者： Xiaoxu Zhou Zhengjie Wang Chenglin Su Jun Cui Jun Meng Yushi Luan School of Bioengineering Dalian University of TechnologyDalianLiaoning 116024China School of Computer Science and Technology Dalian University of TechnologyDalianLiaoning 116024China College of Life Science Hunan Normal UniversityChangshaHunan 410081China

Tomato(Solanum lycopersicum), an economically important vegetable crop cultivated worldwide, often suffers massive financial losses due to Phytophthora infestans(P. infestans) spread and breakouts. Arbuscular mycorrhiza(AM) fungi mediated biocontrol has demonstrated great potential in plant resistance. However, little information is available on the regulation of mycorrhizal tomato resistance against P. ***, microRNAs(miRNAs) sequencing technology was used to analyse miRNA and their targets in the mycorrhizal tomato after *** infection. Our study showed a lower severity of necrotic lesions in mycorrhizal tomato than in nonmycorrhizal controls. We investigated 35 miRNAs that showed the opposite expression tendency in mycorrhizal and nonmycorrhizal tomato after P. infestans infection when compared with uninfected P. infestans. Among them, miR319c was upregulated in mycorrhizal tomato leaves after pathogen infection. Overexpression of miR319c or silencing of its target gene(TCP1) increased tomato resistance to P. infestans, implying that miR319c acts as a positive regulator in tomato after pathogen infection. Additionally, we examined the induced expression patterns of miR319c and TCP1 in tomato plants exposed to salicylic acid(SA) treatment, and SA content and the expression levels of SA-related genes were also measured in overexpressing transgenic plants. The result revealed that miR319c can not only participates in tomato resistance to P. infestans by regulating SA content, but also indirectly regulates the expression levels of key genes in the SA pathway by regulating TCP1. In this study, we propose a novel mechanism in which the miR319c in mycorrhizal tomato increases resistance to P. infestans.

关键词： Tomato Arbuscular mycorrhiza fungi Phytophthora infestans miRNA

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Cooperative output regulation of heterogeneous directed multi-agent systems:a fully distributed model-free reinforcement learning framework

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science China(Information sciences) 2025年第2期68卷 170-185页

作者： Xiongtao SHI Yanjie LI Chenglong DU Huiping LI Chaoyang CHEN Weihua GUI Guangdong Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics School of Mechanical Engineering and AutomationHarbin Institute of Technology (Shenzhen) School of Automation Central South University School of Marine Science and Technology Northwestern Polytechnical University School of Information and Electrical Engineering Hunan University of Science and Technology

In this paper, the cooperative output regulation(COR) problem of a class of unknown heterogeneous multi-agent systems(MASs) with directed graphs is studied via a model-free reinforcement learning(RL) based fully distributed eventtriggered control(ETC) strategy. First, we consider the scenario that the exosystem is accessible globally to all agents, an internal model-based augmented algebraic Riccati equation(AARE) is constructed, and its solution is learned by the proposed model-free RL algorithm via online input-output data. Further, for the scenario that the exosystem is accessible only to its adjacent followers, the distributed observers are designed for each agent to get the state of the exosystem, and an internal modelbased fully distributed adaptive ETC protocol is then synthesized to construct the corresponding AARE, and the feedback gain matrix is learned in a model-free fashion. The model-free RL-based control protocol proposed in this paper can not only remove the prior knowledge of agents' dynamics, but also release the dependence on global information by the adaptive event-triggered mechanism(ETM) and the new graph-based Lyapunov function. Finally, simulation results are illustrated to show the feasibility and effectiveness of the proposed control scheme.

关键词： model-free reinforcement learning unknown heterogeneous multi-agent systems fully distributed event-triggered control directed graph

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STLL: Spatio-Temporal Graph and Lane Sequence Based Lightweight Vehicle Trajectory Prediction 10

STLL: Spatio-Temporal Graph and Lane Sequence Based Lightwei...

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10th IEEE Smart World Congress, SWC 2024

作者： Liu, Yuzhen Huang, Lei Wang, Xiaoliang Ai, Yuanyuan Zhou, Xiaolan Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan China

ISBN: (纸本)9798331520861

Accurate and swift prediction of surrounding vehicle trajectories is essential for autonomous driving. Currently, numerous methods have achieved excellent accuracy in trajectory prediction but they often overlook the prediction speed. Autonomous vehicles must quickly respond to various situations for safe driving. Therefore, we propose a lightweight model named STLL. We model the interactions among vehicles using a graph that fully considers the spatio-temporal information. Additionally, we represent all lanes by a sequence and employ an efficient method to encode lanes. These approaches effectively enable the model to extract and fuse features of vehicles and lanes, leading to accurate and swift trajectory predictions. We conduct our experiments on the widely recognized real-world trajectory prediction datasets: nuScenes and Argoverse. The results demonstrate that our method not only significantly reduces prediction time and the number of model parameters but also maintains high prediction accuracy. © 2024 IEEE.

关键词： Prediction models

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A Fast Spectral Clustering Algorithm Based on Granular-Ball and Similarity Graph Approximation 3

A Fast Spectral Clustering Algorithm Based on Granular-Ball ...

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3rd International Conference on Cloud Computing, Big Data Application and Software engineering, CBASE 2024

作者： Ye, Xueqi Chen, Linshu Hu, Anxing Liang, Wei School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China

ISBN: (纸本)9798331506582

Spectral clustering is widely used due to its effectiveness in handling nonlinear data, but its performance is limited by high computational complexity when processing large-scale datasets. To address this issue, this paper proposes a fast spectral clustering algorithm based on granular-balls and similarity graph approximation (GBASC). Through granular-ball computation, fine-grained data is transformed into coarse-grained granular-balls, reducing the scale of the similarity graph. The paper proposes a fast approximation algorithm for granular-ball similarity graphs based on kernel density estimation, achieving a sparse approximation of the fully connected granular-ball similarity graph. This method significantly reduces the time and space complexity of constructing the granular-ball similarity graph, and by generating sparse matrices, it improves the efficiency of Laplacian matrix eigenvalue decomposition. Experimental results demonstrate that GBASC achieves high clustering performance and computational efficiency on large-scale datasets. © 2024 IEEE.

关键词： Eigenvalues and eigenfunctions

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