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Target Detection-Oriented RGCN Inference Enhancement Method

computers, Materials & Continua 2025年第4期83卷 1219-1237页

作者： Lijuan Zhang Xiaoyu Wang Songtao Zhang Yutong Jiang Dongming Li Weichen Sun School of Internet of Things Engineering Wuxi UniversityWuxi214105China College of Computer Science and Engineering Changchun University of TechnologyChangchun130012China Computer Science and Technology at Guohao Academy Tongji UniversityShanghai200092China China North Vehicle Research Institute Beijing100072China

In this paper,a reasoning enhancement method based on RGCN(Relational Graph Convolutional Network)is proposed to improve the detection capability of UAV(Unmanned Aerial Vehicle)on fast-moving military targets in urban battlefield *** combining military images with the publicly available VisDrone2019 dataset,a new dataset called VisMilitary was built and multiple YOLO(You Only Look Once)models were tested on *** to the low confidence problem caused by fuzzy targets,the performance of traditional YOLO models on real battlefield images decreases ***,we propose an improved RGCN inference model,which improves the performance of the model in complex environments by optimizing the data processing and graph network *** results show that the proposed method achieves an improvement of 0.4%to 1.7%on mAP@0.50,which proves the effectiveness of the model in military target *** research of this paper provides a new technical path for UAV target detection in urban battlefield,and provides important enlightenment for the application of deep learning in military field.

关键词： RGCN target detection urban battlefield YOLO visual reasoning

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A Residual Graph Convolutional Network Algorithm Based on Unfolded WMMSE for Power Allocation 10

A Residual Graph Convolutional Network Algorithm Based on Un...

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10th IEEE Smart World Congress, SWC 2024

作者： Yang, Jinhong Miao, Chunwei Zhang, Jian Central South University School of Computer Science and Engineering Hunan Changsha China

ISBN: (纸本)9798331520861

Optimal power allocation is pivotal for enhancing network capacity and performance in wireless networks. To tackle the challenges posed by the high computational complexity of traditional algorithms and the limited interpretability and generalization of deep learning-based methods, inspired by the principles of deep unfolding and graph neural networks (GNNs), we propose a novel weighted minimum mean square error (WMMSE) unfolding-based residual graph convolutional network architecture (UWMMSE-RGCN). Specifically, our method unfolds the WMMSE algorithm, maintaining the iterative structure of WMMSE, and utilize GNNs to learn the key iterative variables of WMMSE to approximate the complex computational steps of the iterative variables. The methodology aims to capitalize on domain knowledge while achieve exceptional performance and computational efficiency. Additionally, we have developed a residual graph convolutional network (RGCN) by integrating residual connection into the GNNs. This design promotes the integration of features across diverse neighborhood scopes of nodes, thereby bolstering the architecture's generalization performance. Theoretical analysis confirms the permutation equivariance of our method, which ensures its generalization to different network topologies. Experiments validate that the proposed unfolded architecture surpasses other deep unfolding algorithms in terms of performance and generalization. It demonstrates potential for scalability to larger network sizes and adaptability to diverse channel conditions. © 2024 IEEE.

关键词： Convolutional neural networks

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A Spectral Convolutional Neural Network Model Based on Adaptive Fick’s Law for Hyperspectral Image Classification

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computers, Materials & Continua 2024年第4期79卷 19-46页

作者： Tsu-Yang Wu Haonan Li Saru Kumari Chien-Ming Chen School of Artificial Intelligence(School of Future Technology) Nanjing University of Information Science&TechnologyNanjing210044China College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China Department of Mathematics Chaudhary Charan Singh UniversityMeerutUttar Pradesh250004India

Hyperspectral image classification stands as a pivotal task within the field of remote sensing,yet achieving highprecision classification remains a significant *** response to this challenge,a Spectral Convolutional Neural Network model based on Adaptive Fick’s Law Algorithm(AFLA-SCNN)is *** Adaptive Fick’s Law Algorithm(AFLA)constitutes a novel metaheuristic algorithm introduced herein,encompassing three new strategies:Adaptive weight factor,Gaussian mutation,and probability update *** adaptive weight factor,the algorithmcan adjust theweights according to the change in the number of iterations to improve the performance of the *** helps the algorithm avoid falling into local optimal solutions and improves the searchability of the *** probability update strategy helps to improve the exploitability and adaptability of the *** the AFLA-SCNN model,AFLA is employed to optimize two hyperparameters in the SCNN model,namely,“numEpochs”and“miniBatchSize”,to attain their optimal ***’s performance is initially validated across 28 functions in 10D,30D,and 50D for CEC2013 and 29 functions in 10D,30D,and 50D for *** results indicate AFLA’s marked performance superiority over nine other prominent optimization ***,the AFLA-SCNN model was compared with the Spectral Convolutional Neural Network model based on Fick’s Law Algorithm(FLA-SCNN),Spectral Convolutional Neural Network model based on Harris Hawks Optimization(HHO-SCNN),Spectral Convolutional Neural Network model based onDifferential Evolution(DE-SCNN),SpectralConvolutionalNeuralNetwork(SCNN)model,and SupportVector Machines(SVM)model using the Indian Pines dataset and Paviauniversity *** experimental results show that the AFLA-SCNN model outperforms other models in terms of Accuracy,Precision,Recall,and F1-score on Indian Pines and Pavia *** them,the Accuracy of the AFLA-SCNN model on India

关键词： Adaptive Fick’s law algorithm spectral convolutional neural network metaheuristic algorithm intelligent optimization algorithm hyperspectral image classification

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Knowledge Learning With Crowdsourcing:A Brief Review and Systematic Perspective

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IEEE/CAA Journal of Automatica Sinica 2022年第5期9卷 749-762页

作者： Jing Zhang IEEE School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210094China

Big data have the characteristics of enormous volume,high velocity,diversity,value-sparsity,and uncertainty,which lead the knowledge learning from them full of *** the emergence of crowdsourcing,versatile information can be obtained on-demand so that the wisdom of crowds is easily involved to facilitate the knowledge learning *** the past thirteen years,researchers in the AI community made great efforts to remove the obstacles in the field of learning from *** concentrated survey paper comprehensively reviews the technical progress in crowdsourcing learning from a systematic perspective that includes three dimensions of data,models,and learning *** addition to reviewing existing important work,the paper places a particular emphasis on providing some promising blueprints on each dimension as well as discussing the lessons learned from our past research work,which will light up the way for new researchers and encourage them to pursue new contributions.

关键词： Crowdsourcing data fusion learning from crowds learning paradigms learning with uncertainty

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Structure-Sensitive Graph Dictionary Embedding for Graph Classification

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2962-2972页

作者： Liu, Guangbu Zhang, Tong Wang, Xudong Zhao, Wenting Zhou, Chuanwei Cui, Zhen Nanjing University of Science and Technology Key Laboratory of Intelligent Perception and Systems for High-Dimensional School of Computer Science and Engineering Nanjing210094 China

Graph structure expression plays a vital role in distinguishing various graphs. In this work, we propose a structure-sensitive graph dictionary embedding (SS-GDE) framework to transform input graphs into the embedding space of a graph dictionary for the graph classification task. Instead of a plain use of a base graph dictionary, we propose the variational graph dictionary adaptation (VGDA) to generate a personalized dictionary (named adapted graph dictionary) for catering to each input graph. In particular, for the adaptation, the Bernoulli sampling is introduced to adjust substructures of base graph keys according to each input, which increases the expression capacity of the base dictionary tremendously. To make cross-graph measurement sensitive as well as stable, multisensitivity Wasserstein encoding is proposed to produce the embeddings by designing multiscale attention on optimal transport. To optimize the framework, we introduce mutual information as the objective, which further deduces variational inference of the adapted graph dictionary. We perform our SS-GDE on multiple datasets of graph classification, and the experimental results demonstrate the effectiveness and superiority over the state-of-the-art methods. © 2020 IEEE.

关键词： Embeddings

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A two-dimensional MoS_(2) array based on artificial neural network learning for high-quality imaging

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Nano Research 2023年第7期16卷 10139-10147页

作者： Long Chen Siyuan Chen Jinchao Wu Luhua Chen Shuai Yang Jian Chu Chengming Jiang Sheng Bi Jinhui Song School of Mechanical Engineering Dalian University of TechnologyDalian 116024China School of Computer Science Southwest Petroleum UniversityChengdu 610500China

As the basis of machine vision,the biomimetic image sensing devices are the eyes of artificial *** recent years,with the development of two-dimensional(2D)materials,many new optoelectronic devices are developed for their outstanding ***,there are still little sensing arrays based on 2D materials with high imaging quality,due to the poor uniformity of pixels caused by material defects and fabrication ***,we propose a 2D MoS_(2)sensing array based on artificial neural network(ANN)*** equipping the MoS_(2)sensing array with a“brain”(ANN),the imaging quality can be effectively *** the test,the relative standard deviation(RSD)between pixels decreased from about 34.3%to 6.2%and 5.49%after adjustment by the back propagation(BP)and Elman neural networks,*** peak signal to noise ratio(PSNR)and structural similarity(SSIM)of the image are improved by about 2.5 times,which realizes the re-recognition of the distorted *** provides a feasible approach for the application of 2D sensing array by integrating ANN to achieve high quality imaging.

关键词： two-dimensional MoS_(2) sensing array artificial neural network individual difference imaging quality

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Unleashing the power of pseudo-code for binary code similarity analysis

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Cybersecurity 2023年第2期6卷 44-61页

作者： Weiwei Zhang Zhengzi Xu Yang Xiao Yinxing Xue School of Computer Science and Engineering University of Science and Technology of ChinaHefeiChina School of Computer Science and Engineering Nanyang Technological UniversitySingaporeSingapore Institute of Information Engineering Chinese Academy of SciencesBeijingChina School of Cyber Security University of Chinese Academy of SciencesBeijingChina

Code similarity analysis has become more popular due to its significant applicantions,including vulnerability detection,malware detection,and patch *** the source code of the software is difficult to obtain under most circumstances,binary-level code similarity analysis(BCSA)has been paid much attention *** recent years,many BCSA studies incorporating Al techniques focus on deriving semantic information from binary functions with code representations such as assembly code,intermediate representations,and control flow graphs to measure the ***,due to the impacts of different compilers,architectures,and obfuscations,binaries compiled from the same source code may vary considerably,which becomes the major obstacle for these works to obtain robust *** this paper,we propose a solution,named UPPC(Unleashing the Power of Pseudo-code),which leverages the pseudo-code of binary function as input,to address the binary code similarity analysis challenge,since pseudocode has higher abstraction and is platform-independent compared to binary *** selectively inlines the functions to capture the full function semantics across different compiler optimization levels and uses a deep pyramidal convolutional neural network to obtain the semantic embedding of the *** evaluated UPPC on a data set containing vulnerabilities and a data set including different architectures(X86,ARM),different optimization options(O0-O3),different compilers(GCC,Clang),and four obfuscation *** experimental results show that the accuracy of UPPC in function search is 33.2%higher than that of existing methods.

关键词： Binary code similarity Machine learning Software security Pseudo-code

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Proactive Caching at the Wireless Edge:A Novel Predictive User Popularity-Aware Approach

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computer Modeling in engineering & sciences 2024年第8期140卷 1997-2017页

作者： Yunye Wan Peng Chen Yunni Xia Yong Ma Dongge Zhu Xu Wang Hui Liu Weiling Li Xianhua Niu Lei Xu Yumin Dong College of Computer Science Chongqing UniversityChongqing400044China School of Computer and Software Engineering Xihua UniversityChengdu610039China School of Computer and Information Engineering Jiangxi Normal UniversityNanchang330022China Electric Power Research Institute of State Grid Ningxia Electric Power Co. Ltd.Yinchuan750002China College of Mechanical and Vehicle Engineering Chongqing UniversityChongqing400030China School of Computer Science and Technology Beijing Institute of TechnologyBeijing100083China School of Computer Science and Technology Dongguan University of TechnologyDongguan523808China School of Emergency Management Xihua UniversityChengdu610039China College of Computer and Information Science Chongqing Normal UniversityChongqing401331China

Mobile Edge Computing(MEC)is a promising technology that provides on-demand computing and efficient storage services as close to end users as *** an MEC environment,servers are deployed closer to mobile terminals to exploit storage infrastructure,improve content delivery efficiency,and enhance user ***,due to the limited capacity of edge servers,it remains a significant challenge to meet the changing,time-varying,and customized needs for highly diversified content of ***,techniques for caching content at the edge are becoming popular for addressing the above *** is capable of filling the communication gap between the users and content providers while relieving pressure on remote cloud ***,existing static caching strategies are still inefficient in handling the dynamics of the time-varying popularity of content and meeting users’demands for highly diversified entity *** address this challenge,we introduce a novel method for content caching over MEC,i.e.,*** synthesizes a content popularity prediction model,which takes users’stay time and their request traces as inputs,and a deep reinforcement learning model for yielding dynamic caching *** results demonstrate that PRIME,when tested upon the MovieLens 1M dataset for user request patterns and the Shanghai Telecom dataset for user mobility,outperforms its peers in terms of cache hit rates,transmission latency,and system cost.

关键词： Mobile edge computing content caching system average cost deep reinforcement learning collaborative mechanism

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A Decoupling-Type Strategy for the Allen-Cahn Equation on Curved Surfaces

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Journal of Mathematical Research with Applications 2025年第1期45卷 73-83页

作者： Xiaoman XIE Qing PAN School of Computer and Communication Engineering Changsha University of Science & Technology

In this paper, we construct an efficient decoupling-type strategy for solving the Allen-Cahn equation on curved surfaces. It is based on an FEM-EIEQ(Finite Element Method and explicit-Invariant Energy Quadratization) fully discrete scheme with unconditional energy stability. Spatially the FEM is adopted, using a triangular mesh discretization strategy that can be adapted to complex regions. Temporally, the EIEQ approach is considered, which not only linearizes the nonlinear potential but also gives a new variable that we combine with the nonlocal splitting method to achieve the fully decoupled computation. The strategy can successfully transform the Allen-Cahn system into some completely independent algebraic equations and linear elliptic equations with constant coefficients, we only need to solve these simple equations at each time step. Moreover, we conducted some numerical experiments to demonstrate the effectiveness of the strategy.

关键词： FEM-EIEQ Allen-Cahn equation surface

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Q-learning based strategy analysis of cyber-physical systems considering unequal cost

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Intelligent and Converged Networks 2023年第2期4卷 116-126页

作者： Xin Chen Jixiang Cheng Luanjuan Jiang Qianmu Li Ting Wang Dafang Li School of Management Science and Engineering Nanjing University of Finance and EconomicsNanjing 210023China School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210014China

This paper proposes a cyber security strategy for cyber-physical systems(CPS)based on Q-learning under unequal cost to obtain a more efficient and low-cost cyber security defense strategy with misclassification *** system loss caused by strategy selection errors in the cyber security of CPS is often considered ***,sometimes the cost associated with different errors in strategy selection may not always be the same due to the severity of the consequences of ***,unequal costs referring to the fact that different strategy selection errors may result in different levels of system losses can significantly affect the overall performance of the strategy selection *** introducing a weight parameter that adjusts the unequal cost associated with different types of misclassification errors,a modified Q-learning algorithm is proposed to develop a defense strategy that minimizes system loss in CPS with misclassification interference,and the objective of the algorithm is shifted towards minimizing the overall ***,simulations are conducted to compare the proposed approach with the standard Q-learning based cyber security strategy method,which assumes equal costs for all types of misclassification *** results demonstrate the effectiveness and feasibility of the proposed research.

关键词： cyber security Q-learning policy selection unequal cost misclassification interference

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