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A Feature-Aided Multiple Model Algorithm for Maneuvering Target Tracking

IEEE/CAA Journal of Automatica Sinica 2024年第2期11卷 566-568页

作者： Yiwei Tian Meiqin Liu Senlin Zhang Ronghao Zheng Shanling Dong College of Electrical Engineering Zhejiang University National Key Laboratory of Industrial Control Technology Zhejiang University IEEE National Key Laboratory of Human-Machine Hybrid Augmented Intelligence Xi’an Jiaotong University

Dear Editor,This letter deals with the tracking problem for non-cooperative maneuvering targets based on the underwater sensor networks. Considering the acoustic intensity feature of underwater targets, a feature-aide... 详细信息

关键词： underwater Aided letter

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Colour Variation Minimization Retinex Decomposition and Enhancement with a Multi-Branch Decomposition Network

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Chinese Journal of Electronics 2023年第4期32卷 908-919页

作者： DENG Jiawei YU Zhenming PANG Guangyao Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University School of Information and Communication Guilin University of Electronic Technology

This paper proposes a colour variation minimization retinex decomposition and enhancement with a multi-branch decomposition network(CvmD-net)to remove single image darkness. The network overcomes the problem that retinex deep learning model relies on matching bright images to process dark images. Specifically, our method takes two stages to light up the darkness in initial images: image decomposition and brightness optimization. We propose an input constant feature prior mechanism(ICFP) based on reflection constant features. The mechanism extracts structure and colour from the input images and constrains the reflected images output from the decomposition model to reduce color distortion and artifacts. The noise amplification during decomposition is addressed by a multi-branch decomposition network. Sub-networks with different structures are employed to focus on different prediction tasks. This paper proposes a reference mechanism for input brightness. This mechanism optimizes the output brightness distribution by calculating the reference brightness of the dark *** results on two benchmark datasets, namely,LOL and ZeroDCE, demonstrate that the proposed method can better balance dense noise interference and colour restoration. For the evaluation on real images, we collect Skynet images at night to verify the performance of the proposed approach. Compared with the state-of-the-art non-reference retinex decomposition-enhancement models,this paper has the best brightness optimization.

关键词： Reflectivity Image color analysis Brightness Interference Feature extraction Minimization Reflection

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Parameter-Free Shifted Laplacian Reconstruction for Multiple Kernel Clustering

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 1072-1074页

作者： Xi Wu Zhenwen Ren F.Richard Yu the School of National Defence Science and Technology Southwest University of Science and TechnologyMianyang 621010China the School of National Defence Science and Technology Southwest University of Science and TechnologyMianyang 621010 the Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou UniversityWuzhou 543002 the Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ) Shenzhen 518060China

Dear Editor,This letter proposes a parameter-free multiple kernel clustering(MKC)method by using shifted Laplacian *** MKC can effectively cluster nonlinear data,but it faces two main challenges:1)As an unsupervised method,it is up against parameter problems which makes the parameters intractable to tune and is unfeasible in real-life applications;2)Only considers the clustering information,but ignores the interference of noise within Laplacian.

关键词： parameter letter Parameter

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Phishing detection on Ethereum via transaction subgraphs embedding

IET Blockchain

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IET Blockchain 2023年第4期3卷 194-203页

作者： Lv, Haifeng Ding, Yong Guangxi Key Laboratory of Machine Vision and Intelligent Control WuZhou University Wuzhou China Guangxi Key Laboratory of Cryptography and Information Security School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Guangxi Colleges and Universities Key Laboratory of Industry Software Technology WuZhou University Wuzhou China

With the rapid development of blockchain technology in the financial sector, the security of blockchain is being put to the test due to an increase in phishing fraud. Therefore, it is essential to study more effective measures and better solutions. Graph models have been proven to provide abundant information for downstream assignments. In this study, a graph-based embedding classification method is proposed for phishing detection on Ethereum by modeling its transaction records using subgraphs. Initially, the transaction data of normal addresses and an equal number of confirmed phishing addresses are collected through web crawling. Multiple subgraphs using the collected transaction records are constructed, with each subgraph containing a target address and its nearby transaction network. To extract features of the addresses, a modified Graph2Vec model called imgraph2vec is designed, which considers block height, timestamp, and amount of transactions. Finally, the Extreme Gradient Boosting (XGBoost) algorithm is employed to detect phishing and normal addresses. The experimental results show that the proposed method achieves good performance in phishing detection, indicating the effectiveness of imgraph2vec in feature acquisition of transaction networks compared to existing models. © 2023 The Authors. IET Blockchain published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and technology.

关键词： Graphic methods

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DeepGCN based on variable multi‐graph and multimodal data for ASD diagnosis

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CAAI Transactions on intelligence technology 2024年第4期9卷 879-893页

作者： Shuaiqi Liu Siqi Wang Chaolei Sun Bing Li Shuihua Wang Fei Li College of Electronic and Information Engineering Hebei UniversityBaodingChina Machine Vision Technology Innovation Center of Hebei Province BaodingChina The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijingChina School of Computer Science and Technology Henan Polytechnic UniversityJiaozuoChina

Diagnosing individuals with autism spectrum disorder(ASD)accurately faces great chal-lenges in clinical practice,primarily due to the data's high heterogeneity and limited sample *** tackle this issue,the authors constructed a deep graph convolutional network(GCN)based on variable multi‐graph and multimodal data(VMM‐DGCN)for ASD ***,the functional connectivity matrix was constructed to extract primary ***,the authors constructed a variable multi‐graph construction strategy to capture the multi‐scale feature representations of each subject by utilising convolutional filters with varying kernel ***,the authors brought the non‐imaging in-formation into the feature representation at each scale and constructed multiple population graphs based on multimodal data by fully considering the correlation between *** extracting the deeper features of population graphs using the deep GCN(DeepGCN),the authors fused the node features of multiple subgraphs to perform node classification tasks for typical control and ASD *** proposed algorithm was evaluated on the Autism Brain Imaging Data Exchange I(ABIDE I)dataset,achieving an accuracy of 91.62%and an area under the curve value of 95.74%.These results demon-strated its outstanding performance compared to other ASD diagnostic algorithms.

关键词： machine learning medical image processing medical signal processing

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Multi-task Collaborative Network for Image-Text Retrieval 1

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30th International Conference on MultiMedia Modeling, MMM 2024

作者： Qin, Xueyang Li, Lishuang Hao, Jing Ge, Meiling Huang, Jiayi Pang, Guangyao School of Computer Science and Technology Dalian University of Technology Dalian116024 China School of Computer Engineering Weifang University Weifang261061 China Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Wuzhou543002 China

ISBN: (数字)9783031533112

ISBN: (纸本)9783031533105

Image-text retrieval aims to capture semantic relevance between images and texts. Most existing approaches rely solely on the image-text pairs to learn visual-semantic representation through fine-grained alignments while neglecting the potential beneficial impact of unimodal tasks on cross-modal retrieval. To this end, we present a Multi-Task Collaborative Network (MTCN) that leverages the synergy between multiple tasks to enhance the performance of image-text retrieval. Specifically, we introduce three unimodal tasks, including text-text matching, image multi-label classification, and text multi-label classification, and train together with target task image-text retrieval from the perspective of semantic constraints. Additionally, we employ a modality interaction module for image-text retrieval to discover interrelationships between these two modalities. Subsequently, a cascaded graph convolutional network combined with a multi-layer perceptron is used to infer the correlation scores between images and texts. We conduct comprehensive experiments on two benchmark datasets, Flickr30K and MSCOCO, and the quantitative and qualitative experimental results demonstrate the effectiveness of the proposed method. The source code is available at https://***/FlyCuteBird/MTCN. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Semantics

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Hierarchical Policy Optimization for Cooperative Multi-Agent Reinforcement Learning

Hierarchical Policy Optimization for Cooperative Multi-Agent...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： He, Shunfan Zheng, Ronghao Zhang, Senlin Liu, Meiqin College of Electrical Engineering Zhejiang University Hangzhou310027 China Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China Xi'an Jiaotong University National Key Laboratory of Human-Machine Hybrid Augmented Intelligence Xi'an710049 China

ISBN: (纸本)9781665410205

To handle the non-stationarity of the environment and the curse of dimensionality issues in multi-agent reinforcement learning, gathering information through communication is a critical part. Existing frameworks have proposed centralized or distributed structures to deal with the problem. However, they either have problems of robustness or problems of high communication costs. This paper adopts a hierarchical zeroth-order policy optimization (HZOPO) algorithm for cooperative multi-agent reinforcement learning (MARL) problems. The agents are divided into different groups with high-and low-level entities. A hierarchical communication structure is implemented to reach global consensus. It is shown that the HZOPO algorithm can balance both convergence and communication efficiency in cooperative MARL environments. The convergence of the algorithm is also proved. © 2024 IEEE.

关键词： Adversarial machine learning

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Distributed Estimation of a Flow Field using Cooperative Underwater Vehicles 63

Distributed Estimation of a Flow Field using Cooperative Und...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： He, Yi Zheng, Ronghao Zhang, Senlin Liu, Meiqin Zhejiang University College of Electrical Engineering Hangzhou310027 China Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China Xi'an Jiaotong University The National Key Laboratory of Human-Machine Hybrid Augmented Intelligence Xi'an710049 China

ISBN: (纸本)9798350316339

This paper presents a distributed method of cooperative flow field estimation using a group of underwater vehicles in GPS-denied environments without directly measuring the ambient flow velocity. We consider that the vehicle can measure the relative positions of its neighbors and the absolute position when it surfaces. By formulating the measurements into the relative and absolute motion-integration error constraints, the flow field estimation problem is converted into an inverse problem that solves a determined system of nonlinear equations in a distributed way. We then propose a distributed consensus algorithm to solve the above equations, in which each vehicle first shares local flow estimate with its neighbors, and then updates the estimate using local constraints. The convergence of the proposed algorithm is strictly guaranteed, and simulations are provided to validate its effectiveness. © 2024 IEEE.

关键词： Inverse problems

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Multi-agent evaluation for energy management by practically scalingα-rank

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Frontiers of Information technology & Electronic Engineering 2024年第7期25卷 1003-1016页

作者： Yiyun SUN Senlin ZHANG Meiqin LIU Ronghao ZHENG Shanling DONG Xuguang LAN National Key Laboratory of Industrial Control Technology Zhejiang UniversityHangzhou 310027China College of Electrical Engineering Zhejiang UniversityHangzhou 310027China National Key Laboratory of Human–Machine Hybrid Augmented Intelligence Xi’an Jiaotong UniversityXi’an 710049China

Currently,decarbonization has become an emerging trend in the power system ***,the increasing number of photovoltaic units distributed into a distribution network may result in voltage issues,providing challenges for voltage regulation across a large-scale power grid *** learning based intelligent control of smart inverters and other smart building energy management(EM)systems can be leveraged to alleviate these *** achieve the best EM strategy for building microgrids in a power system,this paper presents two large-scale multi-agent strategy evaluation methods to preserve building occupants’comfort while pursuing systemlevel *** EM problem is formulated as a general-sum game to optimize the benefits at both the system and building ***α-rank algorithm can solve the general-sum game and guarantee the ranking theoretically,but it is limited by the interaction complexity and hardly applies to the practical power system.A new evaluation algorithm(TcEval)is proposed by practically scaling theα-rank algorithm through a tensor complement to reduce the interaction ***,considering the noise prevalent in practice,a noise processing model with domain knowledge is built to calculate the strategy payoffs,and thus the TcEval-AS algorithm is proposed when noise *** evaluation algorithms developed in this paper greatly reduce the interaction complexity compared with existing approaches,including ResponseGraphUCB(RG-UCB)andαInformationGain(α-IG).Finally,the effectiveness of the proposed algorithms is verified in the EM case with realistic data.

关键词： Energy management Multi-agent deep reinforcement learning Strategy evaluation Power grid system

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A Mixed Intrusion Detection System utilizing K-means and Extreme Gradient Boosting 2

A Mixed Intrusion Detection System utilizing K-means and Ext...

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2022 2nd International Conference on Communication technology and Information technology, ICCTIT 2022

作者： Lv, Haifeng Ji, Xiaoyu Ding, Yong Guangxi Key Laboratory of Machine Vision and Intelligent Control WuZhou University Wuzhou China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Guangxi Colleges and Universities Key Laboratory of Industry Software Technology Wuzhou University Wuzhou China

The intrusion detection system (IDS) plays an important part because it offers an efficient way to prevent and mitigate cyber attacks. Numerous deep learning methods for intrusion anomaly detection have been developed as a result of recent advances in artificial intelligence (AI) in order to strengthen internet security. The balance among the high detection rate (DR), the low false alarm rate (FAR) and disaster of dimensionality is the crucial apprehension while devising an effective IDS. For the binary classification of intrusion detection systems, we present in this study a mixed model called K-means-XGBoost consisting of K-means and (Extreme Gradient Boosting, XGBoost) algorithms. The distributed computation of our method is achieved in Spark platform to rapidly separate normal events and anomaly events. In phrases of accuracy, DR, F1-score, recall, precision, and error indices FAR, the proposed model's performance is measured via the well-known dataset of NSL-KDD. The experimental outcomes indicate that our method is outstandingly better among accuracy, DR, F1-score, training time, and processing speed, compared to other models which are recently created. In particular, the accuracy, F1-score, and DR of the proposed model can achieve as high as 93.28%, 94.39%, and 99.22% in the NSL-KDD dataset, respectively. © Published under licence by IOP Publishing Ltd.

关键词： Network security

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