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An Energy Distribution Correlation Judgment Method for Interrupted Sampling Repeater Jamming Suppression

Progress In Electromagnetics Research B 2024年 105卷 59-78页

作者： Li, Ji Su, Fan Wang, Wei Yan, Rui Li, Jialiang College of Computer and Communication Engineering Changsha University of Science & Technology Changsha410000 China

Interrupted Sampling Repeater Jamming (ISRJ) can produce several false targets through intermittent sampling and forwarding of the intercepted signals. The paper proposes an interference identification and suppression method based on Short-Time Fourier Transform-Energy Distribution Correlation Judgment (STFT-EDCJ) to lessen the impact of the false targets mixed in echo pulses. Firstly, the method obtains the energy distribution of echoes in the time-frequency domain employing the short-time Fourier transform, extracts the time slice of higher energy targets through energy peak detection, and then calculates the Pearson correlation coefficient (PCC) of the energy distribution in the frequency domain of each target time slice to construct the Target PCC Datasets (TPCCD). Secondly, it distinguishes between the real target and false targets after echo pulse pressure by the range and specificity of TPCCD. Finally, it uses mapping the time domain position of the false targets to suppress interference. The abundant simulation results verify the proposed method’s effectiveness, and the Monte Carlo simulation demonstrates the method’s effectiveness under ISRJ models. © (2023), (Electromagnetics Academy). All Rights Reserved.

关键词： Monte Carlo methods

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A Privacy Preservation Method for Attributed Social Network Based on Negative Representation of Information

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computer Modeling in Engineering & sciences 2024年第7期140卷 1045-1075页

作者： Hao Jiang Yuerong Liao Dongdong Zhao Wenjian Luo Xingyi Zhang Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and TechnologyAnhui UniversityHefei230601China Chongqing Research Institute School of Computer Science and Artificial IntelligenceWuhan University of TechnologyWuhan430070China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies School of Computer Science and TechnologyHarbin Institute of TechnologyShenzhen518055China

Due to the presence of a large amount of personal sensitive information in social networks,privacy preservation issues in social networks have attracted the attention of many *** by the self-nonself discrimination paradigmin the biological immune system,the negative representation of information indicates features such as simplicity and efficiency,which is very suitable for preserving social network ***,we suggest a method to preserve the topology privacy and node attribute privacy of attribute social networks,called ***,a negative survey-based method is developed to disturb the relationship between nodes in the social network so that the topology structure can be kept ***,a negative database-based method is proposed to hide node attributes,so that the privacy of node attributes can be preserved while supporting the similarity estimation between different node attributes,which is crucial to the analysis of social *** evaluate the performance of the AttNetNRI,empirical studies have been conducted on various attribute social networks and compared with several state-of-the-art methods tailored to preserve the privacy of social *** experimental results show the superiority of the developed method in preserving the privacy of attribute social networks and demonstrate the effectiveness of the topology disturbing and attribute hiding *** experimental results show the superiority of the developed methods in preserving the privacy of attribute social networks and demonstrate the effectiveness of the topological interference and attribute-hiding components.

关键词： Attributed social network topology privacy node attribute privacy negative representation of information negative survey negative database

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Automatic Positioning of Intelligent Mobile Robot Based on Improved Front-End Scanning Matching

Journal of Network Intelligence

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Journal of Network Intelligence 2025年第1期10卷 559-572页

作者： Wang, Hong Wang, Yi-Gui Zhang, Jie Department of Computer and Software Engineering Shandong College of Electronic Technology Jinan250200 China School of Computer Science and Technology Shandong Jianzhu University Jinan250101 China College of Agricultural Science and Technology Yana Royal Polytechnic University Chiang Mai50000 Thailand

Traditional autonomous navigation methods for mobile robots mainly rely on geometric feature-based LiDAR scan-matching algorithms, but in complex environments, this method is often affected due to the presence of moving objects, occlusions, and other interfering factors, resulting in a decrease in positioning accuracy. With the growing demand for robotics applications in logistics, security, exploration and other fields, the need for robust autonomous navigation and high-precision mapping in highly dynamic and complex environments is becoming more and more urgent. To solve this problem, an improved front-end scan matching algorithm based on Frequency Modulated Continuous Wave (FMCW) LiDAR is proposed in this paper. Firstly, by using the target Doppler velocity information provided by the FMCW LiDAR, we design a novel point cloud segmentation algorithm based on velocity clustering, which is able to effectively distinguish between stationary and moving objects, and avoid the dynamic interference affecting the position estimation. Secondly, we introduce the Gaussian Mixture Model Sampling Consistency (GMMSC) algorithm, which is more robust to reject the mis-matched pairs in the scanning matching process and improve the alignment accuracy. Finally, based on the residual high-quality matched pairs, we combine the classical ICP algorithm with the robust kernel function to further enhance the stability of the position estimation in the case of occlusion and local mismatch. The experimental results show that the proposed improved algorithm significantly improves the mapping capability of mobile robots in complex environments compared with the existing techniques. The average relative error of the improved front-end scanning matching algorithm is reduced by 40.6 % compared with the pre-improved one. © 2025, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Mobile robots

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nDirect2: A High-Performance Library for Direct Convolutions on Multicore CPUs

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IEEE Transactions on computers 2025年第6期74卷 1829-1843页

作者： Yang, Weiling Wang, Pengyu Fang, Jianbin Dong, Dezun Pang, Zhengbin He, Runxi Zhang, Peng Tang, Tao Huang, Chun Che, Yonggang Ren, Jie National University of Defense Technology College of Computer Science and Technology Changsha410073 China Shaanxi Normal University Xi’an710069 China

Convolution kernels are widely seen in high-performance computing (HPC) and deep learning (DL) workloads and are often responsible for performance bottlenecks. Prior works have demonstrated that the direct convolution approach can outperform the conventional convolution implementation. Although well-studied, the existing approaches for direct convolution are either incompatible with the mainstream DL data layouts or lead to suboptimal performance. We design nDirect2, a novel direct convolution approach that targets multi-core CPUs commonly found in smartphones and HPC systems. nDirect2 is compatible with the data layout formats used by mainstream DL frameworks and offers new optimizations for the computational kernel, data packing, advanced operator fusion, and parallelization. We evaluate nDirect2 by applying it to representative convolution kernels and demonstrating how well it performs on four distinct ARM-based CPUs and an X86-based CPU. Experimental results show that nDirect2 outperforms four state-of-the-art convolution approaches across most evaluation cases and hardware architectures. © 1968-2012 IEEE.

关键词： Convolution

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The Novel Instance Segmentation Method Based on Multi-Level Features and Joint Attention

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Chinese Journal of Electronics 2023年第5期32卷 1160-1168页

作者： XU Bowen LU Yinan WU Tieru GUO Xiaoxin College of Computer Science and Technology Jilin University College of Mathematics Jilin University

Instance segmentation is an important task in computer vision. In order to enhance the multilevel features expression ability of the segmentation networks, a novel module is proposed in this paper. Firstly,we design a weighted bi-directional feature fusion way by computing the weight distribution function of bi-directional feature pyramid network. Secondly, we propose a joint attention mechanism to effectively filter different levels of feature information by adopting serial and parallel ways to combine the channel attention and spatial attention modules. At the same time, the module uses dynamic convolution to stabilize the calculation speed while improve the 6.7% mean average precision of *** experiments on the COCO dataset demonstrate that the module can effectively improve the performance of the existing instance segmentation networks.

关键词： computer vision Convolution Neural networks Bidirectional control Information filters Real-time systems Task analysis

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SCARE: A Novel Framework to Enhance Chinese Harmful Memes Detection

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IEEE Transactions on Affective Computing 2025年第2期16卷 933-945页

作者： Gu, Tianlong Feng, Mingfeng Feng, Xuan Wang, Xuemin Jinan University College of Information Science and Technology College of Cyber Security Guangzhou510632 China Guilin University of Electronic Science and Technology College of Computer and Information Security Guilin541004 China Guilin University of Electronic Science and Technology Guangxi Key Laboratory of Trusted Software Guilin541004 China

Harmful meme detection presents a significant multimodal challenge that necessitates contextual background knowledge and comprehensive inference. Although some research studies have been related to harmful meme detection in English, detecting harmful memes in Chinese is also an unresolved issue. In this paper, to bridge this gap, we constructed a Chinese harmful meme detection dataset, named CHMEMES. Furthermore, existing multimodal alignment methods have shown poor performance in tasks involving harmful meme detection, where there is a mismatch between the image and text components. To improve the task, we propose a multimodal framework Semantic Contrastive Alignment fRamEwork (SCARE), which enables fully representing both cross-modal and intra-modal information. For cross-modal information, we introduce a cross-modal contrast alignment objective to maximize the mutual information between image and text. For intra-modal information, we design a new intra-modal contrast objective to achieve more robust visual and textual representation learning. Moreover, we present a simple yet efficient vision prompt tuning paradigm for parameter-efficient harmful meme detection. We conduct extensive experiments on the constructed Chinese dataset and the existing English dataset. Experimental results show that our method outperforms state-of-the-art baselines in harmful meme detection. © 2010-2012 IEEE.

关键词： Semantics

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Multi-scale persistent spatiotemporal transformer for long-term urban traffic flow prediction

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Journal of Electronic science and technology 2024年第1期22卷 53-69页

作者： Jia-Jun Zhong Yong Ma Xin-Zheng Niu Philippe Fournier-Viger Bing Wang Zu-kuan Wei School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu611731China College of Computer Science&Software Engineering Shenzhen UniversityShenzhen518060China School of Computer Science Southwest Petroleum UniversityChengdu610500China

Long-term urban traffic flow prediction is an important task in the field of intelligent transportation,as it can help optimize traffic management and improve travel *** improve prediction accuracy,a crucial issue is how to model spatiotemporal dependency in urban traffic *** recent years,many studies have adopted spatiotemporal neural networks to extract key information from traffic ***,most models ignore the semantic spatial similarity between long-distance areas when mining spatial *** also ignore the impact of predicted time steps on the next unpredicted time step for making long-term ***,these models lack a comprehensive data embedding process to represent complex spatiotemporal *** paper proposes a multi-scale persistent spatiotemporal transformer(MSPSTT)model to perform accurate long-term traffic flow prediction in *** adopts an encoder-decoder structure and incorporates temporal,periodic,and spatial features to fully embed urban traffic data to address these *** model consists of a spatiotemporal encoder and a spatiotemporal decoder,which rely on temporal,geospatial,and semantic space multi-head attention modules to dynamically extract temporal,geospatial,and semantic *** spatiotemporal decoder combines the context information provided by the encoder,integrates the predicted time step information,and is iteratively updated to learn the correlation between different time steps in the broader time range to improve the model’s accuracy for long-term *** on four public transportation datasets demonstrate that MSPSTT outperforms the existing models by up to 9.5%on three common metrics.

关键词： Graph neural network Multi-head attention mechanism Spatio-temporal dependency Traffic flow prediction

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A Dynamic Clustering Method for Maximizing Energy Efficiency in C-V2X Metaverse 10

A Dynamic Clustering Method for Maximizing Energy Efficiency...

引用

10th IEEE Smart World Congress, SWC 2024

作者： Ma, Mingyang Feng, Xin Zhang, Jing Liu, Haohua Wang, Jiacheng Sun, Geng Changchun University of Science and Technology College of Computer Science and Technology Changchun China Nanyang Technological University College of Computer Science and Engineering Singapore Jilin University College of Computer Science and Technology Changchun China

ISBN: (纸本)9798331520861

The Metaverse can leverage intelligent traffic management technology to simulate the Cellular Vehicle-to-Everything (C-V2X) environment, integrating closely with the Internet of Vehicles due to its advanced connectivity and real-time data processing capabilities. These features make it possible to create a smart transportation system that closely mirrors the real world. Dynamic clustering is an effective network management method for handling the large number of mobile vehicles within the Metaverse, particularly in emergency scenarios where base stations (BSs) may fail. However, frequent changes in network topology due to high-speed vehicles result in unstable clusters and low communication efficiency. In this study, a dynamic clustering algorithm based on motion vector similarity (DA-MVS) is proposed for constructing stable clusters, and an adaptive adjustment clusters strategy is presented based on the change rate of vehicle numbers to improve energy efficiency (EE). Furthermore, we propose an elite chaotic perturbed sparrow search algorithm (ECPSSA) to optimize the EE of the C-V2X. Compared to the multiple baseline algorithm, the successful transmission rate can be improved by up to 76.9%, and the EE is enhanced by 20.97%. © 2024 IEEE.

关键词： Information management

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AAV-Assisted Joint Mobile Edge Computing and Data Collection via Matching-Enabled Deep Reinforcement Learning

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IEEE Internet of Things Journal 2025年第12期12卷 19782-19800页

作者： Wang, Boxiong Kang, Hui Li, Jiahui Sun, Geng Sun, Zemin Wang, Jiacheng Niyato, Dusit Jilin University College of Computer Science and Technology Changchun130012 China Jilin University College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun130012 China Nanyang Technological University College of Computing and Data Science Jurong West 639798 Singapore

Autonomous aerial vehicle (AAV)-assisted mobile edge computing (MEC) and data collection (DC) have been popular research issues. Different from existing works that consider MEC and DC scenarios separately, this article investigates a multi-AAV-assisted joint MEC-DC system. Specifically, we formulate a joint optimization problem to minimize the MEC latency and maximize the collected data volume. This problem can be classified as a nonconvex mixed integer programming problem that exhibits long-term optimization and dynamics. Thus, we propose a deep reinforcement learning-based approach that jointly optimizes the AAV movement, user transmit power, and user association in real time to solve the problem efficiently. Specifically, we reformulate the optimization problem into an action space-reduced Markov decision process (MDP) and optimize the user association by using a two-phase matching-based association (TMA) strategy. Subsequently, we propose a soft actor-critic (SAC)-based approach that integrates the proposed TMA strategy (SAC-TMA) to solve the formulated joint optimization problem collaboratively. Simulation results demonstrate that the proposed SAC-TMA is able to coordinate the two subsystems and can effectively reduce the system latency and improve the DC volume compared with other benchmark algorithms. © 2014 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Kinetic simulation of early-stage precipitation behavior in Mg-RE binary alloys during aging process

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Journal of Magnesium and Alloys 2025年第3期13卷 1078-1087页

作者： Hao Jiang Shao-Xiang Li Long-Long Hao Wei-Jian Li Zhi-Qiang Han Liang-Xing Lv Liang Zhen School of Materials Science and Engineering Harbin Institute of TechnologyHarbin 150001China School of Materials Science and Engineering Tsinghua UniversityBeijing 100084China School of Materials Science and Engineering Chongqing UniversityChongqing 400044China College of Nuclear Equipment and Nuclear Engineering Yantai UniversityYantai 264005China

The precipitation behavior of magnesium-rare earth(Mg-RE)alloys plays a crucial role for their ***,the precipitation happens at small-length and long-time scales,making it challenging to be analyzed by state-of-the-art experimental ***,we combine the advantages of both molecular dynamic forceﬁelds on describing atom interactions and Monte Carlo method on describing diffusive events to develop an embedded atom method(EAM)potential based kinetic Monte Carlo(KMC)*** the proposed model,we simulated the formation and evolution of Y clusters in Mg-Y alloy formed by the vacancy mechanism,and rationalize the simulation results using aberration-corrected scanning transmission electron microscopy *** conducted a systematic analysis of the atomic structure,the evolution kinetics and path of the Y cluster by tracing Y atoms and comparing with density function theory(DFT)*** work reveals that,all solute columns in a same cluster trend to grow along the[0001]Mg direction *** method presented is not only used for the Mg-Y alloy but also other Mg-RE alloys such as Mg-Gd as illustrated in the last part of the paper.

关键词： Mg-RE alloys Kinetic Monte Carlo method DFT calculations Solute clusters

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