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IET International Radar Conference 2023, IRC 2023

作者： Lin, Huiping Yin, Junjun Wang, Hongmiao Yang, Jian Fudan University Shanghai China School of Computer and Communication Engineering University of Science and Technology Beijing China Department of Electronic Engineering Tsinghua University Beijing China

ISBN: (纸本)9781839539954

In this paper, we propose a novel edge detection method for polarimetric synthetic aperture radar (PolSAR) images based on polarimetric nonsubsampled contoured transform (PolNSCT). Nonsubsampled contourlet transform (NSCT) can provide flexible multiscale and directional decomposition, but it is susceptible to noise interference. In order to overcome this problem, we propose PolNSCT by combining multiscale polarimetric information in a new fashion. More specifically, in the multiscale decomposition, the coefficients of the nonsubsampled pyramid in the PolNSCT are calculated by measuring the polarimetric differences in polarimetric domain between the adjacent sub-band levels, instead of using the polarimetric contrast only or the differences in primary domain of the adjacent sub-bands. In this way, the proposed method detects edges more effectively in complex texture regions and produces less false alarms. Experimental results on an AirSAR image demonstrate the effectiveness and great potential of the proposed method. © The Institution of engineering & technology 2023.

关键词： Contourlet transform

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Improving graph collaborative filtering with network motifs

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Neural Computing and Applications 2025年第16期37卷 9413-9432页

作者： Zhang, Yuqi Yu, Jian Liu, Zhizhong Wang, Guiling Nguyen, Minh Sheng, Quan Z. Wang, Nancy Department of Computer and Information Sciences Auckland University of Technology Auckland Central1010 New Zealand School of Computer and Control Engineering Yantai University Shandong Yantai264005 China School of Information Science and Technology North China University of Technology Beijing100144 China School of Computing Macquarie University SydneyNSW2109 Australia

Deep learning on graphs, specifically graph convolutional networks (GCNs), has exhibited exceptional efficacy in the domain of recommender systems. Most GCNs have a message-passing architecture that enables nodes to aggregate information from neighbours iteratively through multiple layers. This enables GCNs to learn from higher-order information, but the model does not allow for direct captions of the local structural patterns. Our rationale is to investigate the effectiveness of capturing such local patterns for graph-based collaborative filtering to enhance model’s learning ability per layer. This technique combines lower-order and higher-order interactions during layer-wise propagation. In this paper, we propose MotifGCN to aggregate both lower-order and higher-order information in each graph convolution layer. Specifically, we develop dedicated algorithms of generating motif adjacency matrices. The matrices are then used for motif-enhanced neighbourhood aggregation in each layer. As this paper focuses on recommender systems, MotifGCN is built on the basis of bipartite graphs. Our experiments on four real-world datasets show that MotifGCN has a superior performance compared to various state-of-the-art methods. © The Author(s) 2025.

关键词： Collaborative filtering

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Harmonic Source Identification Based on VGG-MIX-KAN Model 4

Harmonic Source Identification Based on VGG-MIX-KAN Model

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4th International Conference on Intelligent Power and Systems, ICIPS 2024

作者： Jiang, Wendong Dong, Qiutong State Grid Zhejiang Electric Power Co. Ltd Zhejiang Province China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing China

ISBN: (纸本)9798331532765

Harmonic source identification is an essential part of harmonic research in power systems, aiming to accurately determine the sources of harmonic currents or voltages within the power system. With the widespread application of power electronic technology and the increasing complexity of power grid structures, harmonic issues have become increasingly prominent, significantly impacting the safety, stable operation, and power quality of power systems. Therefore, harmonic source identification technology has garnered extensive attention and research. This study proposes a model based on Kolmogorov-Arnold Networks (KAN) for identifying harmonic sources. The model includes three core components: a feature extraction module, a cross-domain feature fusion module, and a classification module. The feature extraction module uses a convolutional neural network to extract key features from the time series waveforms and frequency domain spectra of harmonic sources, forming their respective feature vectors. Subsequently, we have constructed a cross-domain feature fusion mechanism that merges feature vectors from both the time and frequency domains to enhance the expressive power of the features. Ultimately, these enhanced feature vectors are fed into the KAN for final classification prediction. Experimental results demonstrate that the model proposed in this paper outperforms current mainstream algorithms in the task of identifying harmonic sources in terms of performance. © 2024 IEEE.

关键词： Frequency domain analysis

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DP-GenFL: a local differentially private federated learning system through generative data

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science China(Information sciences) 2023年第8期66卷 275-276页

作者： Jun LI Kang WEI Chuan MA Feng SHU School of Electronic and Optical Engineering Nanjing University of Science and Technology Zhejiang Lab Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education School of Information and Communication Engineering Hainan University

With the rapid development of the Internet of Things(Io T),the amount of data from intelligent devices is propagating at unprecedented scales. Meanwhile, machine learning(ML),which relies heavily on such data, is revolutionizing many aspects of our lives [1]. However, conventional centralized ML offers little scalability for efficiently processing this huge amount of data.

关键词： Learning systems

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DSAFF-Net:A Backbone Network Based on Mask R-CNN for Small Object Detection

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computers, Materials & Continua 2023年第2期74卷 3405-3419页

作者： Jian Peng Yifang Zhao Dengyong Zhang Feng Li Arun Kumar Sangaiah Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and TechnologyChangsha410114China School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China School of Computing Science and Engineering Vellore Institute of Technology(VIT)Vellore632014India

Recently,object detection based on convolutional neural networks(CNNs)has developed *** backbone networks for basic feature extraction are an important component of the whole detection ***,we present a new feature extraction strategy in this paper,which name is *** this strategy,we design:1)a sandwich attention feature fusion module(SAFF module).Its purpose is to enhance the semantic information of shallow features and resolution of deep features,which is beneficial to small object detection after feature fusion.2)to add a new stage called D-block to alleviate the disadvantages of decreasing spatial resolution when the pooling layer increases the receptive *** method proposed in the new stage replaces the original method of obtaining the P6 feature map and uses the result as the input of the regional proposal network(RPN).In the experimental phase,we use the new strategy to extract *** experiment takes the public dataset of Microsoft Common Objects in Context(MS COCO)object detection and the dataset of Corona Virus Disease 2019(COVID-19)image classification as the experimental object *** results show that the average recognition accuracy of COVID-19 in the classification dataset is improved to 98.163%,and small object detection in object detection tasks is improved by 4.0%.

关键词： Small object detection classification RPN MS COCO COVID-19

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Beam Training and Tracking in mmWave communication:A Survey

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China communications 2024年第6期21卷 1-22页

作者： Wang Yi Wei Zhiqing Feng Zhiyong Yangtze Delta Region Institute(Quzhou) University of Electronic Science and Technology of ChinaQuzhou 324003China School of Information and Communication Engineering University of Electronic Science and Technology of ChinaChengdu 611731China Key Laboratory of Universal Wireless Communications Ministry of EducationBeijing University of Posts and TelecommunicationsBeijing 100876China

Communicating on millimeter wave(mmWave)bands is ushering in a new epoch of mobile communication which provides the availability of 10 Gbps high data rate ***,mmWave links are easily prone to short transmission range communication because of the serious free space path loss and the blockage by *** overcome these challenges,highly directional beams are exploited to achieve robust links by hybrid *** aligning the transmitter and receiver beams,*** training,is vitally important to high data rate ***,it may cause huge overhead which has negative effects on initial access,handover,and ***,the mobility patterns of users are complicated and dynamic,which may cause tracking error and large tracking *** efficient beam tracking method has a positive effect on sustaining robust *** article provides an overview of the beam training and tracking technologies on mmWave bands and reveals the insights for future research in the 6th Generation(6G)mobile ***,some open research problems are proposed to realize fast,accurate,and robust beam training and *** hope that this survey provides guidelines for the researchers in the area of mmWave communications.

关键词： 6G beam tracking beam training beamforming mmWave

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Stealthy False Data Injection Attack Using Improved Singular Value Decomposition in Smart Grid 7

Stealthy False Data Injection Attack Using Improved Singular...

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7th International Conference on Information and computer Technologies, ICICT 2024

作者： Yang, Liqun Li, Chunan Wei, Chaoren Yang, Jian Qu, Gang Zhang, Liang Zhang, Yipeng Li, Tongliang Beihang University School Of Cyber Science And Technology Beijing China Beihang University School Of Computer Science And Engineering Beijing China State Grid Corporation Of China East China Branch Shanghai China North China University Of Technology School Of Information Science And Technology Beijing China Beijing Information Science And Technology University Computer School Beijing China

ISBN: (纸本)9798350385625

Smart grid is a crucial Cyber-Physical system and is prone to cyber-attacks. In this paper, we propose a novel false data injection attack (FDIA) construction mechanism. Firstly, the measurements are preprocessed to eliminate the outliers caused by the system failure, communication failure and data missing. Then, the preprocessed measurements are decomposed into a multiplication of the unitary matrixes by the proposed improved Singular Value Decomposition (SVD) algorithm. To achieve more accurate decomposed matrix, we introduce the regularization constraints into the objective of SVD. At last, the FDIA is constructed based on the decomposed matrix in a sparse way. We assess the performance of the proposed FDIA with comprehensive simulations on IEEE 14- and 118-bus systems. The results indicate that the proposed FDIA can be performed in a stealthy and sparse way. © 2024 IEEE.

关键词： State estimation

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Medical data publishing based on average distribution and clustering

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CAAI Transactions on Intelligence technology 2022年第3期7卷 381-394页

作者： Tong Yi Minyong Shi Haibin Zhu School of Computer Science and Engineering Guangxi Normal UniversityGuilinChina School of Computer Science Communication University of ChinaBeijingChina Department of Computer Science and Mathematics Nipissing UniversityNorth BayOntarioCanada

Most of the data publishing methods have not considered sensitivity protection,and hence the adversary can disclose privacy by sensitivity *** with this problem,this paper presents a medical data publishing method based on sensitivity *** protect the sensitivity,the sensitivity of disease information is determined by *** seek the trade-off between information utility and privacy security,the new method focusses on the protection of sensitive values with high sensitivity and assigns the highly sensitive disease information to groups as evenly as *** experiments are conducted on two real-world datasets,of which the records include various attributes of *** measure sensitivity protection,the authors define a metric,which can evaluate the degree of sensitivity ***,additional information loss and discernability metrics are used to measure the availability of released *** experimental results indicate that the new method can provide better privacy than the traditional one while the information utility is *** value protection,the proposed method can provide sensitivity protection and available releasing for medical data.

关键词： data publishing information utility security semantics sensitive values sensitivity

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DeBERTa-GRU: Sentiment Analysis for Large Language Model

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computers, Materials & Continua 2024年第6期79卷 4219-4236页

作者： Adel Assiri Abdu Gumaei Faisal Mehmood Touqeer Abbas Sami Ullah Department of Informatics for Business College of BusinessKing Khalid UniversityAbha61421Saudi Arabia Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia School of Electrical and Information Engineering Zhengzhou UniversityZhengzhou450001China Department of Computer Science and Technology Beijing University of Chemical TechnologyBeijing100029China Department of Computer Science Government College University FaisalabadFaisalabadPunjab38000Pakistan

Modern technological advancements have made social media an essential component of daily *** media allow individuals to share thoughts,emotions,and *** analysis plays the function of evaluating whether the sentiment of the text is positive,negative,neutral,or any other personal emotion to understand the sentiment context of the *** analysis is essential in business and society because it impacts strategic *** analysis involves challenges due to lexical variation,an unlabeled dataset,and text distance *** execution time increases due to the sequential processing of the sequence ***,the calculation times for the Transformer models are reduced because of the parallel *** study uses a hybrid deep learning strategy to combine the strengths of the Transformer and Sequence models while ignoring their *** particular,the proposed model integrates the Decoding-enhanced with Bidirectional Encoder Representations from Transformers(BERT)attention(DeBERTa)and the Gated Recurrent Unit(GRU)for sentiment *** the Decoding-enhanced BERT technique,the words are mapped into a compact,semantic word embedding space,and the Gated Recurrent Unit model can capture the distance contextual semantics *** proposed hybrid model achieves F1-scores of 97%on the Twitter Large Language Model(LLM)dataset,which is much higher than the performance of new techniques.

关键词： DeBERTa GRU Naive Bayes LSTM sentiment analysis large language model

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High-quality Trajectory Generation for Autonomous Driving: A Lightweight Federated Learning-based Diffusion Model

High-quality Trajectory Generation for Autonomous Driving: A...

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2024 IEEE Global communications Conference, GLOBECOM 2024

作者： Gao, Runquan Kang, Jiawen Lai, Bingkun Xu, Minrui Sun, Geng Zhang, Tao Zhang, Weiting Yang, Dong Guangdong University of Technology School of Automation Guangzhou China Nanyang Technological University School of Computer Science and Engineering Singapore Jilin University College of Computer Science and Technology Changchun China Beijing Jiaotong University School of Software Engineering Beijing China Beijing Jiaotong University School of Electronic and Information Engineering Beijing China

ISBN: (纸本)9798350351255

Vehicle trajectory data plays a pivotal role in simulation testing for autonomous driving. Hence, there exist well-established trajectory generation methods employing deep generative models to generate trajectories mapping the distribution of the original dataset, thereby augmenting existing trajectory datasets. However, these methods typically rely on large datasets gathered by governmental or organizational entities for central training, which may pose data privacy, security, and accessibility issues. Therefore, it is challenging to generate high-quality traffic trajectory data while preserving privacy which involves a delicate balance between these two objectives. To deal with this challenge, we introduce Federated Learning into the diffusion model and propose a Federated Learning-based diffusion model (FedDifftraj) to generate traffic trajectory data. Unlike existing central training methods, FedDifftraj aggregates model parameters uploaded by different vehicles and then updates a global model. Additionally, there is a substantial communication overhead incurred during the training of the federated diffusion model. Therefore, we quantize the local diffusion model before uploading it to the parameter server. Through extensive simulations on real-world datasets, FedDifftraj can generate high-quality traffic trajectory data that is consistent with the results of the central training while preserving privacy and reducing communication overhead by 93.74% when utilizing 8-bit quantization. © 2024 IEEE.

关键词： Differential privacy

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