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Dynamic crushing behaviors and enhanced energy absorption of bio-inspired hierarchical honeycombs with different topologies

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Defence Technology（防务技术） 2023年第4期22卷 99-111页

作者： Xin-chun Zhang Nan-nan Liu Chao-chao An He-xiang Wu Na Li Ke-ming Hao Department of Mechanical Engineering North China Electric Power UniversityBaoding071003China Hebei Key Laboratory of Electric Machinery Health Maintenance&Failure Prevention North China Electric Power UniversityBaoding071003China School of Civil Engineering Northeast Forestry UniversityHarbin150040China Department of Intelligent Engineering Hebei Software InstituteBaoding071000China

In order to pursue good crushing load uniformity and enchance energy absorption efficiency of conventional honeycombs, a kind of bio-inspired hierarchical honeycomb model is proposed by mimicking the arched crab shell structures. Three bio-inspired hierarchical honeycombs(BHHs) with different topologies are designed by replacing each vertex of square honeycombs with smaller arc-shaped structures. The effects of hierarchical topologies and multi-material layout on in-plane dynamic crushings and absorbed-energy capacities of the BHHs are explored based on the explicit finite element(FE) *** deformation modes can be observed from the BHHs, which mainly depend upon hierarchical topologies and impact velocities. According to energy efficiency method and one-dimensional(1D) shock theory, calculation formulas of densification strains and plateau stresses for the BHHs are derived to characterize the dynamic bearing capacity, which is consistent well with FE results. Compared with conventional honeycombs, the crushing load efficiency and energy absorption capacity of the BHHs can be improved by changing the proper hierarchical topology and multi-material layout. These researches will provide theoretical guidance for innovative design and dynamic response performance controllability of honeycombs.

关键词： Bio-inspired honeycombs Crushing load uniformity Energy absorption Hierarchical topologies Finite element analysis

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User story clustering in agile development:a framework and an empirical study

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Frontiers of Computer Science 2023年第6期17卷 43-59页

作者： Bo YANG Xiuyin MA Chunhui WANG Haoran GUO Huai LIU Zhi JIN School of Information Science and Technology Beijing Forestry UniversityBeijing 100083China Engineering Research Center for Forestry Oriented Intelligent Information Processing National Forestry and Grassland AdministrationBeijing 100083China School of Information Science and Technology North China University of TechnologyBeijing 100144China College of Computer Science and Technology Inner Mongolia Normal UniversityHohhot 010020China Department of Computer Science and Software Engineering Swinburne University of TechnologyHawthorn VIC 3122Australia Key Laboratory of High Confidence Software Technologies(Peking University) Ministry of EducationBeijing 100871China Institute of Software School of Computer SciencePeking UniversityBeijing 100871China

Agile development aims at rapidly developing software while embracing the continuous evolution of user requirements along the whole development *** stories are the primary means of requirements collection and elicitation in the agile development.A project can involve a large amount of user stories,which should be clustered into different groups based on their functionality’s similarity for systematic requirements analysis,effective mapping to developed features,and efficient ***,the current user story clustering is mainly conducted in a manual manner,which is time-consuming and subjective to human *** this paper,we propose a novel approach for clustering the user stories automatically on the basis of natural language ***,the sentence patterns of each component in a user story are first analysed and determined such that the critical structure in the representative tasks can be automatically extracted based on the user story *** similarity of user stories is calculated,which can be used to generate the connected graph as the basis of automatic user story *** evaluate the approach based on thirteen datasets,compared against ten baseline *** results show that our clustering approach has higher accuracy,recall rate and F1-score than these *** is demonstrated that the proposed approach can significantly improve the efficacy of user story clustering and thus enhance the overall performance of agile *** study also highlights promising research directions for more accurate requirements elicitation.

关键词： user story agile development user story mapping clustering

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How to Design the Emergency of Takeover Warning and Takeover Scene to Make Drivers have Better Takeover Performance? 8

How to Design the Emergency of Takeover Warning and Takeover...

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8th CAA International Conference on Vehicular Control and Intelligence, CVCI 2024

作者： Wang, Zhihao Chen, Haolin Tang, Qiuyang Zhao, Xiaohua Li, Zhenlong Fu, Qiang China Automotive Engineering Research Institute Co. Ltd. State Key Laboratory of Intelligent Vehicle Safety Technology Electronic Communication and Software Evaluation Research Center Chongqing China Beijing University of Technology Beijing Key Laboratory of Traffic Engineering Beijing China China Automotive Engineering Research Institute Co. Ltd. Electronic Communication and Software Evaluation Research Center Chongqing China

ISBN: (纸本)9798331504892

This study aims to explore the driver's takeover performance in different emergency takeover scenes and takeover warnings, and to respond to how to design the emergency degree of takeover warning. First, we designed 4 takeover events based on takeover scenes (accident scene - emergency, ramp scene - non-emergency) and takeover request time variables (5s- emergency, l0s- non-emergency). 42 drivers participated in the driving simulation experiment, and 8 evaluation indicators were obtained from four dimensions: control behavior, eye tracking, heart rate and vehicle performance. The results show that the driver's takeover performance is globally optimal in non-emergency scenes and non-emergency warnings. In non-emergency situations, drivers can reduce stress behavior and maintain good driving status. After receiving the non-emergency takeover warning, the driver's control and psychological performance are better in the emergency scene. This study can provide a reference for a comprehensive evaluation of takeover performance in different takeover scenes and warnings, and can also be applied to the human-machine interaction takeover warning design of automated vehicles. © 2024 IEEE.

关键词： Automobile driver simulators

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SeeMe: An intelligent edge server selection method for location-aware business task computing over IIoT

SeeMe: An intelligent edge server selection method for locat...

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作者： Dou, Wanchun Liu, Bowen Duan, Jirun Dai, Fei Qi, Lianyong Xu, Xiaolong State Key Laboratory for Novel Software Technology Nanjing University Nanjing China College of Big Data and Intelligent Engineering Southwest Forestry University Kunming China School of Computer Science Qufu Normal University Qufu China School of Computer and Software Nanjing University of Information Science and Technology Nanjing China

In the past few years, latency-sensitive task computing over the industrial internet of things (IIoT) has played a key role in an increasing number of intelligent applications, such as intelligent self-driving vehicles and unmanned aircraft systems. The edge computing paradigm provides a basic functional infrastructure for across-domain business task computing on distributed edge servers. With this observation, a trade-off between the mobile devices and the fixed edge servers is needed to run moving task computing in a low-latency way. Given this challenge, an intelligent server selection method, named SeeMe, is proposed in this paper. Technically speaking, this method aims at minimizing the communication capacity and the transferring capacity in a multiobjective optimization way to find a low-latency edge server. The experiments and comparison analysis verify the availability of our method. © 2022 John Wiley & Sons Ltd.

关键词： Economic and social effects

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An End-to-End Training Method to Ensemble Bi-Linear Models for Knowledge Graph Completion

An End-to-End Training Method to Ensemble Bi-Linear Models f...

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2023 International Conference on Machine Learning and Cybernetics, ICMLC 2023

作者： Cen, Si Liu, Han Liu, Chao Zou, Xiaoying Dai, Guoquan Wang, Xizhao College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China

ISBN: (纸本)9798350303780

The study of ensemble learning in knowledge graph embedding (KGE) shows that combining multiple individual KGE models can perform better on knowledge graph completion. However, existing KGE ensemble methods ignore the creation of model diversity because these methods independently train individual models, which are short of training interaction. To create rich model diversity, we propose a novel training method for ensemble bilinear models (EBM) for the problem of knowledge graph completion. EBM uses a weighted loss to allow individual KGE models to interact during training. In this way, the relations in the knowledge graph can be automatically modeled by the most appropriate model from the KGE individual ones. The experiments on knowledge graph completion show that EBM has richer diversity and performs better than the single bilinear model and the ensemble methods without interaction. © 2023 IEEE.

关键词： Knowledge graph

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The stiffness of elastomeric diaphragm in pneumatic springs 7

The stiffness of elastomeric diaphragm in pneumatic springs

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7th International Conference on Aeronautical, Aerospace and Mechanical engineering, AAME 2024

作者： Bai, Yumei Che, Jixing Wu, Mingkai Wu, Jiulin Jiang, Wei State Key Laboratory of Intelligent Manufacturing Equipment and Technology Huazhong University of Science and Technology Hubei Wuhan430074 China School of Automation and Software Engineering Shanxi University Shanxi Tai Yuan030031 China

The design of pneumatic springs applied in precision vibration isolation platforms requires an accurate mathematical model. Many validation experiments have shown that neglecting the effect of the diaphragm on the response of a isolator can lead to a significant error between the observed and predicted behavior. This paper modifies the standard pneumatic spring model by considering the effects of the diaphragm on stiffness. A theoretical model for diaphragm stiffness is presented. The model equates the inner and outer rings of the diaphragm into two segments of composite material subjected to unidirectional stretching. The elastic modulus and stiffness of each part of anisotropic materials were obtained using the theory of composite mechanics, combined with the working conditions of the diaphragm. Finally, the dynamic stiffness experimental platform was built to verify theoretical model. The result indicates that considering the stiffness of elastomeric diaphragm, the error is reduced from 27.33% to 7.5%. © Published under licence by IOP Publishing Ltd.

关键词： Diaphragms

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Unsigned Road Incidents Detection Using Improved RESNET From Driver-View Images

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1203-1216页

作者： Li, Changping Wang, Bingshu Zheng, Jiangbin Zhang, Yongjun Chen, C.L. Philip Northwestern Polytechnical University School of Software Xi’an710129 China Shenzhen University Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen518060 China Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China South China University of Technology School of Computer Science and Engineering Guangzhou510641 China

Frequent road incidents cause significant physical harm and economic losses globally. The key to ensuring road safety lies in accurately perceiving surrounding road incidents. However, the highly dynamic nature of traffic introduces significant challenges, particularly in detecting sudden and temporary incidents. In this article, we propose a novel detection framework, multihead attention ResNet with dynamic bottleneck (DB_RESNET_MHA), to identify physical unsigned road incidents. Our approach introduces three key innovations. First, we develop a tailored data augmentation strategy to create images that closely mimic the complex variations found in real-world road environments. Second, we enhance model expressiveness by employing attention mechanisms to nonlinearly integrate convolutional kernels within the residual network. Furthermore, we refine the prediction head by applying spatially distinct attention weights, enabling the model to capture intricate correlations between different features more effectively. To demonstrate the effectiveness of our method, we create a dataset for unsigned road incidents (UnsignRI), comprising a total of 16 323 images that capture 12 distinct types of incidents. It stands out as the most comprehensive dataset in the field, encompassing a wide range of geographical features and incident categories. Experimental results show that DB_RESNET_MHA achieves an average accuracy of 96.2% and a f1-score of 0.955 across various categories of unsigned incidents, surpassing other models. © 2020 IEEE.

关键词： Highway accidents

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Cross-modal Search Method of Technology Video based on Adversarial Learning and Feature Fusion 8

Cross-modal Search Method of Technology Video based on Adver...

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8th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2022

作者： Liu, Xiangbin Du, Junping Liang, Meiyu Li, Ang Beijing University of Posts and Telecommunications Beijing Key Laboratory of Intelligent Communication Software and Multimedia School of Computer Science National Pilot Software Engineering School Beijing100876 China

ISBN: (纸本)9781665477352

Technology videos contain rich multi-modal information. In cross-modal information search, the data features of different modalities cannot be compared directly, so the semantic gap between different modalities is a key problem that needs to be solved. To address the above problems, this paper proposes a novel Feature Fusion based Adversarial Cross-modal Retrieval method (FFACR) to achieve text-to-video matching, ranking and searching. The proposed method uses the framework of adversarial learning to construct a video multimodal feature fusion network and a feature mapping network as generator, a modality discrimination network as discriminator. Generator and discriminator are trained alternately based on adversarial learning, so that the data obtained by the feature mapping network is semantically consistent with the original data and the modal features are eliminated. Experimental results demonstrate that the proposed method performs better in text-to-video search on the self-built datasets of technology videos. © 2022 IEEE.

关键词： Semantics

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Adversarial Guided Gradient Estimation Hashing for Cross-modal Retrieval 8

Adversarial Guided Gradient Estimation Hashing for Cross-mod...

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8th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2022

作者： Lu, Kangkang Liang, Meiyu Xue, Zhe Cao, Xiaowen Yin, Mengran Zhao, Zehua Beijing University of Posts and Telecommunications Beijing Key Laboratory of Intelligent Communication Software and Multimedia School of Computer Science National Pilot Software Engineering School Beijing100876 China

ISBN: (纸本)9781665477352

Due to low storage cost and fast query speed, deep hashing methods are widely used in cross-modal retrieval. However, the 'heterogeneous gap' between multi-modal data is still a challenge. Moreover, a major difficulty in deep hashing lies in the discrete constraints imposed on the network output. Existing solutions usually use relaxation techniques, but this inevitably produces quantization error, leading to sub-optimal hash code. In this paper, we propose Adversarial Guided Gradient Estimation Hashing (AGEH). Firstly, in order to bridge the heterogeneous gap between different modal data, a cross-modal adversarial feature learning network is constructed to learn cross-modal semantic associations. Secondly, to solve the discrete optimization problem of hash code, we propose a hashing optimization strategy based on gradient estimation for sign function, which strictly uses sign function to maintain discrete constraints in forward propagation, while in back propagation, the gradient is directly transmitted to the previous layer and thus avoid quantization error. Extensive experiments conducted on two cross-modal benchmark datasets show that our proposed AGEH outperforms several state-of-the-art methods. © 2022 IEEE.

关键词： Backpropagation

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Boosting Adversarial Training with Learnable Distribution

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Computers, Materials & Continua 2024年第3期78卷 3247-3265页

作者： Kai Chen Jinwei Wang James Msughter Adeke Guangjie Liu Yuewei Dai School of Electronics and Information Engineering Nanjing University of Information Science and TechnologyNanjing210044China Key Laboratory of Intelligent Support Technology for Complex Environments Ministry of EducationNanjing210044China School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China Nanjing Center for Applied Mathematics Nanjing211135China

In recent years,various adversarial defense methods have been proposed to improve the robustness of deep neural *** training is one of the most potent methods to defend against adversarial ***,the difference in the feature space between natural and adversarial examples hinders the accuracy and robustness of the model in adversarial *** paper proposes a learnable distribution adversarial training method,aiming to construct the same distribution for training data utilizing the Gaussian mixture *** distribution centroid is built to classify samples and constrain the distribution of the sample *** natural and adversarial examples are pushed to the same distribution centroid to improve the accuracy and robustness of the *** proposed method generates adversarial examples to close the distribution gap between the natural and adversarial examples through an attack algorithm explicitly designed for adversarial *** algorithm gradually increases the accuracy and robustness of the model by scaling ***,the proposed method outputs the predicted labels and the distance between the sample and the distribution *** distribution characteristics of the samples can be utilized to detect adversarial cases that can potentially evade the model *** effectiveness of the proposed method is demonstrated through comprehensive experiments.

关键词： Adversarial training feature space learnable distribution distribution centroid

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