检索结果-南通市图书馆

维普期刊数据库

Joint UAV trajectory and communication design with heterogeneous multi-agent reinforcement learning

在线全文

学校读者我要写书评

暂无评论

Science China(Information Sciences) 2024年第3期67卷 225-245页

作者： Xuanhan ZHOU Jun XIONG Haitao ZHAO Xiaoran LIU Baoquan REN Xiaochen ZHANG Jibo WEI Hao YIN College of Electronic Science and Technology National University of Defense Technology Systems engineering institute Academy of Military Sciences PLA

Unmanned aerial vehicles(UAVs) are recognized as effective means for delivering emergency communication services when terrestrial infrastructures are unavailable. This paper investigates a multiUAV-assisted communication system, where we jointly optimize UAVs’ trajectories, user association, and ground users(GUs)’ transmit power to maximize a defined fairness-weighted throughput metric. Owing to the dynamic nature of UAVs, this problem has to be solved in real time. However, the problem’s non-convex and combinatorial attributes pose challenges for conventional optimization-based algorithms, particularly in scenarios without central controllers. To address this issue, we propose a multi-agent deep reinforcement learning(MADRL) approach to provide distributed and online solutions. In contrast to previous MADRLbased methods considering only UAV agents, we model UAVs and GUs as heterogeneous agents sharing a common objective. Specifically, UAVs are tasked with optimizing their trajectories, while GUs are responsible for selecting a UAV for association and determining a transmit power level. To learn policies for these heterogeneous agents, we design a heterogeneous coordinated QMIX(HC-QMIX) algorithm to train local Q-networks in a centralized manner. With these well-trained local Q-networks, UAVs and GUs can make individual decisions based on their local observations. Extensive simulation results demonstrate that the proposed algorithm outperforms state-of-the-art benchmarks in terms of total throughput and system fairness.

关键词： unmanned aerial vehicle (UAV) trajectory design resource allocation multi-agent deep reinforcement learning (MADRL) heterogeneous agents

An Intelligent Framework for Resilience Recovery of FANETs with Spatio-Temporal Aggregation and Multi-Head Attention Mechanism

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2024年第5期79卷 2375-2398页

作者： Zhijun Guo Yun Sun YingWang Chaoqi Fu Jilong Zhong Air Traffic Control and Navigation College Air Force Engineering UniversityXi’an710000China institute of Systems engineering Academy of Military ScienceBeijing100101China Equipment Management and UAV engineering College Air Force Engineering UniversityXi’an710038China Defense Innovation institute Academy of Military ScienceBeijing100071China

Due to the time-varying topology and possible disturbances in a conflict environment,it is still challenging to maintain the mission performance of flying Ad hoc networks(FANET),which limits the application of Unmanned Aerial Vehicle(UAV)swarms in harsh environments.This paper proposes an intelligent framework to quickly recover the cooperative coveragemission by aggregating the historical spatio-temporal network with the attention mechanism.The mission resilience metric is introduced in conjunction with connectivity and coverage status information to simplify the optimization model.A spatio-temporal node pooling method is proposed to ensure all node location features can be updated after destruction by capturing the temporal network structure.Combined with the corresponding Laplacian matrix as the hyperparameter,a recovery algorithm based on the multi-head attention graph network is designed to achieve rapid recovery.Simulation results showed that the proposed framework can facilitate rapid recovery of the connectivity and coverage more effectively compared to the existing studies.The results demonstrate that the average connectivity and coverage results is improved by 17.92%and 16.96%,respectively compared with the state-of-the-art model.Furthermore,by the ablation study,the contributions of each different improvement are compared.The proposed model can be used to support resilient network design for real-time mission execution.

关键词： Resilience cooperative mission FANET spatio-temporal node pooling multi-head attention graph network

Track correlation algorithm based on CNN-LSTM for swarm targets

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of Systems engineering and Electronics 2024年第2期35卷 417-429页

作者： CHEN Jinyang WANG Xuhua CHEN Xian Research institute for National Defense engineering Academy of Military SciencesLuoyang 471023China Information engineering University Zhengzhou 450001China School of Computer Science and Technology Xidian UniversityXi’an 710071China

The rapid development of unmanned aerial vehicle(UAV) swarm, a new type of aerial threat target, has brought great pressure to the air defense early warning system. At present, most of the track correlation algorithms only use part of the target location, speed, and other information for correlation.In this paper, the artificial neural network method is used to establish the corresponding intelligent track correlation model and method according to the characteristics of swarm targets.Precisely, a route correlation method based on convolutional neural networks (CNN) and long short-term memory (LSTM)Neural network is designed. In this model, the CNN is used to extract the formation characteristics of UAV swarm and the spatial position characteristics of single UAV track in the formation,while the LSTM is used to extract the time characteristics of UAV swarm. Experimental results show that compared with the traditional algorithms, the algorithm based on CNN-LSTM neural network can make full use of multiple feature information of the target, and has better robustness and accuracy for swarm targets.

关键词： track correlation correlation accuracy rate swarm target convolutional neural network(CNN) long short-term memory(LSTM)neural network

维普期刊数据库

An Integral Method for Solving Dynamic Equations with Fluid–Solid Coupling

在线全文

学校读者我要写书评

暂无评论

Acta Mechanica Solida Sinica 2024年第1期37卷 99-108页

作者： Xin Zhang Jie Liu Pu Xue Shuowen Yan Yahao Xu M.S.Zahran School of Aeronautics Northwestern Polytechnical UniversityXi'an 710072China School of Mechatronical engineering Beijing Institute of TechnologyBeijing 100081China Department of Civil engineering Military Technical CollegeCairo 11435Egypt

In this work,a new methodology is presented to mainly solve the fluid–solid interaction(FSI)equation.This methodology combines the advantages of the Newmark precise integral method(NPIM)and the dual neural network(DNN)method.The NPIM is employed to modify the exponential matrix and loading vector based on the DNN integral method.This involves incorporating the basic assumption of the Newmark-βmethod into the dynamic equation and eliminating the acceleration term from the dynamic equilibrium equation.As a result,the equation is reduced to a first-order linear equation system.Subsequently,the PIM is applied to integrate the system step by step within the NPIM.The DNN method is adopted to solve the inhomogeneous term through fitting the integrand and the original function with a pair of neural networks,and the integral term is solved using the Newton–Leibniz formula.Numerical examples demonstrate that the proposed methodology significantly improves computing efficiency and provides sufficient precision compared to the DNN method.This is particularly evident when analyzing large-scale structures under blast loading conditions.

关键词： Newmark precise integral method(NPIM) Dual neural networks(DNN) Fluid-solid interaction(FSI) Dynamic time-history analysis Blast loading

How to implement a knowledge graph completeness assessment with the guidance of user requirements

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of Systems engineering and Electronics 2024年第3期35卷 679-688页

作者： ZHANG Ying XIAO Gang institute of Systems engineering Academy of Military SciencesBeijing 100107China

In the context of big data, many large-scale knowledge graphs have emerged to effectively organize the explosive growth of web data on the Internet. To select suitable knowledge graphs for use from many knowledge graphs, quality assessment is particularly important. As an important thing of quality assessment, completeness assessment generally refers to the ratio of the current data volume to the total data volume.When evaluating the completeness of a knowledge graph, it is often necessary to refine the completeness dimension by setting different completeness metrics to produce more complete and understandable evaluation results for the knowledge graph.However, lack of awareness of requirements is the most problematic quality issue. In the actual evaluation process, the existing completeness metrics need to consider the actual application. Therefore, to accurately recommend suitable knowledge graphs to many users, it is particularly important to develop relevant measurement metrics and formulate measurement schemes for completeness. In this paper, we will first clarify the concept of completeness, establish each metric of completeness, and finally design a measurement proposal for the completeness of knowledge graphs.

关键词： knowledge graph completeness assessment relative completeness user requirement quality management

维普期刊数据库

Multi-Material Topology Optimization of 2D Structures Using Convolutional Neural Networks

在线全文

学校读者我要写书评

暂无评论

Computer Modeling in engineering & Sciences 2024年第8期140卷 1919-1947页

作者： Jiaxiang Luo Weien Zhou Bingxiao Du Daokui Li Wen Yao College of Aerospace Science and engineering National University of Defense TechnologyChangsha410073China Defense Innovation institute Chinese Academy of Military ScienceBeijing100071China Intelligent Game and Decision Laboratory Chinese Academy of Military ScienceBeijing100071China

In recent years,there has been significant research on the application of deep learning(DL)in topology optimization(TO)to accelerate structural design.However,these methods have primarily focused on solving binary TO problems,and effective solutions for multi-material topology optimization(MMTO)which requires a lot of computing resources are still lacking.Therefore,this paper proposes the framework of multiphase topology optimization using deep learning to accelerate MMTO design.The framework employs convolutional neural network(CNN)to construct a surrogate model for solving MMTO,and the obtained surrogate model can rapidly generate multi-material structure topologies in negligible time without any iterations.The performance evaluation results show that the proposed method not only outputs multi-material topologies with clear material boundary but also reduces the calculation cost with high prediction accuracy.Additionally,in order to find a more reasonable modeling method for MMTO,this paper studies the characteristics of surrogate modeling as regression task and classification task.Through the training of 297 models,our findings show that the regression task yields slightly better results than the classification task in most cases.Furthermore,The results indicate that the prediction accuracy is primarily influenced by factors such as the TO problem,material category,and data scale.Conversely,factors such as the domain size and the material property have minimal impact on the accuracy.

关键词： Multi-material topology optimization convolutional neural networks deep learning finite element analysis

Memory-enhanced text style transfer with dynamic style learning and calibration

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Science China(Information Sciences) 2024年第4期67卷 181-196页

作者： Fuqiang LIN Yiping SONG Zhiliang TIAN Wangqun CHEN Diwen DONG Bo LIU College of Computer Science and Technology National University of Defense Technology Department of Computer Science and engineering The Hong Kong University of Science and Technology Strategic Assessments and Consultation institute Academy of Military Sciences

Text style transfer aims to rephrase a sentence to match the desired style while retaining the original content. As a controllable text generation task, mainstream approaches use content-independent style embedding as control variables to guide stylistic generation. Nonetheless, stylistic properties are contextsensitive even under the same style. For example, “delicious” and “helpful” convey positive sentiments,although they are more likely to describe food and people, respectively. Therefore, desired style signals must vary with the content. To this end, we propose a memory-enhanced transfer method, which learns fine-grained style representation concerning content to assist transfer. Rather than employing static style embedding or latent variables, our method abstracts linguistic characteristics from training corpora and memorizes subdivided content with the corresponding style representations. The style signal is dynamically retrieved from memory using the content as a query, providing a more expressive and flexible latent style space. To address the imbalance between quantity and quality in different content, we further introduce a calibration method to augment memory construction by modeling the relationship between candidate styles.Experimental results obtained using three benchmark datasets confirm the superior performance of our model compared to competitive approaches. The evaluation metrics and case study also indicate that our model can generate diverse stylistic phrases matching context.

关键词： style transfer memory-enhanced method text generation deep learning text representation

Discrete Element Modelling of Damage Evolution of Concrete Considering Meso-Structure of ITZ

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Computer Modeling in engineering & Sciences 2024年第6期139卷 3495-3511页

作者： Weiliang Gao Shixu Jia Tingting Zhao Zhiyong Wang institute of Defense engineering Academy of Military SciencesChinese People’s Liberation ArmyBeijing100850China College of Mechanical and Vehicle engineering Taiyuan University of TechnologyTaiyuan030024China State Key Laboratory of Explosion Science and Technology Beijing Institute of TechnologyBeijing100081China

The mechanical properties of interfacial transition zones(ITZs)have traditionally been simplified by reducing the stiffness of cement in previous simulation methods.A novel approach based on the discrete element method(DEM)has been developed for modeling concrete.This new approach efficiently simulates the meso-structure of ITZs,accurately capturing their heterogeneous properties.Validation against established uniaxial compression experiments confirms the precision of thismodel.The proposedmodel canmodel the process of damage evolution containing cracks initiation,propagation and penetration.Under increasing loads,cracks within ITZs progressively accumulate,culminating in macroscopic fractures that traverse themortarmatrix,forming the complex,serpentine path of cracks.This study reveals four distinct displacement patterns:tensile compliant,tensile opposite,mixed tensile-shear,and shear opposite patterns,each indicative of different stages in concrete’s damage evolution.The widening angle of these patterns delineates the progression of cracks,with the tensile compliant pattern signaling the initial crack appearance and the shear opposite pattern indicating the concrete model’s ultimate failure.

关键词： Discrete element method damage evolution interfacial transition zone meso-structure model

Machine-learned wearable sensors for real-time hand-motion recognition: toward practical applications

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

National Science Review 2024年第2期11卷 231-263页

作者： Kyung Rok Pyun Kangkyu Kwon Myung Jin Yoo Kyun Kyu Kim Dohyeon Gong Woon-Hong Yeo Seungyong Han Seung Hwan Ko Department of Mechanical engineering Seoul National University IEN Center for Human-Centric Interfaces and engineering Institute for Electronics and NanotechnologyGeorgia Institute of Technology School of Electrical and Computer engineering Georgia Institute of Technology Department of Chemical engineering Stanford University Department of Mechanical engineering Ajou University George W.Woodruff School of Mechanical engineering Georgia Institute of Technology institute of Advanced Machinery and Design(SNU-IAMD) Seoul National University

Soft electromechanical sensors have led to a new paradigm of electronic devices for novel motion-based wearable applications in our daily lives. However, the vast amount of random and unidentified signals generated by complex body motions has hindered the precise recognition and practical application of this technology. Recent advancements in artificial-intelligence technology have enabled significant strides in extracting features from massive and intricate data sets, thereby presenting a breakthrough in utilizing wearable sensors for practical applications. Beyond traditional machine-learning techniques for classifying simple gestures, advanced machine-learning algorithms have been developed to handle more complex and nuanced motion-based tasks with restricted training data sets. Machine-learning techniques have improved the ability to perceive, and thus machine-learned wearable soft sensors have enabled accurate and rapid human-gesture recognition, providing real-time feedback to users. This forms a crucial component of future wearable electronics, contributing to a robust human–machine interface. In this review, we provide a comprehensive summary covering materials, structures and machine-learning algorithms for hand-gesture recognition and possible practical applications through machine-learned wearable electromechanical sensors.

关键词： wearable sensor soft electronics artificial intelligence machine learning human–machine interfaces gesture recognition