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A Novel Negative-Transfer-Resistant Fuzzy Clustering Model With a Shared Cross-Domain Transfer Latent Space and its Application to Brain CT image Segmentation

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021年第1期18卷 40-52页

作者： Jiang, Yizhang Gu, Xiaoqing Wu, Dongrui Hang, Wenlong Xue, Jing Qiu, Shi Lin, Chin-Teng Jiangnan Univ Jiangsu Key Lab Media Design & Software Technol Wuxi 214122 Jiangsu Peoples R China Jiangnan Univ Sch Digital Media Wuxi 214122 Jiangsu Peoples R China Changzhou Univ Sch Informat Sci & Engn Changzhou 213164 Jiangsu Peoples R China Huazhong Univ Sci & Technol Sch Automat Key Lab Minist Educ Image Proc & Intelligent Control Wuhan 430074 Hubei Peoples R China Nanjing Tech Univ Sch Comp Sci & Technol Nanjing 211816 Jiangsu Peoples R China Nanjing Med Univ Sch Comp Sci & Technol Wuxi 214023 Jiangsu Peoples R China Nanjing Med Univ Dept Nephrol Affiliated Wuxi Peoples Hosp Wuxi 214023 Jiangsu Peoples R China Chinese Acad Sci Xian Inst Opt & Precis Mech Key Lab Spectral Imaging Technol CAS Xian 710119 Shaanxi Peoples R China Univ Technol Sydney Fac Engn & Informat Technol Ctr Artificial Intelligence Ultimo NSW 2007 Australia

Traditional clustering algorithms for medical image segmentation can only achieve satisfactory clustering performance under relatively ideal conditions, in which there is adequate data from the same distribution, and the data is rarely disturbed by noise or outliers. However, a sufficient amount of medical images with representative manual labels are often not available, because medical images are frequently acquired with different scanners (or different scan protocols) or polluted by various noises. Transfer learning improves learning in the target domain by leveraging knowledge from related domains. Given some target data, the performance of transfer learning is determined by the degree of relevance between the source and target domains. To achieve positive transfer and avoid negative transfer, a negative-transfer-resistant mechanism is proposed by computing the weight of transferred knowledge. Extracting a negative-transfer-resistant fuzzy clustering model with a shared cross-domain transfer latent space (called NTR-FC-SCT) is proposed by integrating negative-transfer-resistant and maximum mean discrepancy (MMD) into the framework of fuzzy c-means clustering. Experimental results show that the proposed NTR-FC-SCT model outperformed several traditional non-transfer and related transfer clustering algorithms.

关键词： Medical image segmentation fuzzy clustering transfer learning negative transfer

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Minimal-time Deadbeat Consensus and Individual Disagreement Degree Prediction for High-order Linear Multi-agent Systems

arXiv

引用

arXiv 2023年

作者： Hu, Fu-Long Zhang, Hai-Tao Xu, Bowen Hu, Zhe Ren, Wei School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control The State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan430074 China School of Artificial Intelligence Optics and Electronics Northwestern Polytechnical University Xi’an710072 China Department of Electrical and Computer Engineering University of California Riverside92521 United States

In this paper, a Hankel matrix-based fully distributed algorithm is proposed to address a minimal-time deadbeat consensus prediction problem for discrete-time high-order multi-agent systems (MASs). Therein, each agent can predict the consensus value with the minimum number of observable historical outputs of its own. Accordingly, compared to most existing algorithms only yielding asymptotic convergence, the present method can attain deadbeat consensus instead. Moreover, based on the consensus value prediction, instant individual disagreement degree value of MASs can be calculated in advance as well. Sufficient conditions are derived to guarantee both the minimal-time deadbeat consensus and the instant individual disagreement degree prediction. Finally, both the effectiveness and superiority of the proposed deadbeat consensus algorithm are substantiated by numerical simulations. Copyright © 2023, The Authors. All rights reserved.

关键词： Multi agent systems

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Research and application of artificial intelligence based webshell detection model: A literature review

arXiv

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arXiv 2024年

作者： Ma, Mingrui Han, Lansheng Zhou, Chunjie Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China The Key Laboratory of Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Hubei Wuhan430074 China

Webshell, as the"culprit" behind numerous network attacks, is one of the research hotspots in the field of cybersecurity. However, the complexity, stealthiness, and confusing nature of webshells pose significant challenges to the corresponding detection schemes. With the rise of Artificial Intelligence (AI) technology, researchers have started to apply different intelligent algorithms and neural network architectures to the task of webshell detection. However, the related research still lacks a systematic and standardized methodological process, which is confusing and redundant. Therefore, following the development timeline, we carefully summarize the progress of relevant research in this field, dividing it into three stages: Start Stage, Initial Development Stage, and In-depth Development Stage. We further elaborate on the main characteristics and core algorithms of each stage. In addition, we analyze the pain points and challenges that still exist in this field and predict the future development trend of this field from our point of view. To the best of our knowledge, this is the first review that details the research related to AI-based webshell detection. It is also hoped that this paper can provide detailed technical information for more researchers interested in AI-based webshell detection tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

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Face recognition algorithm based on improved facial model

Face recognition algorithm based on improved facial model

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International Conference on Natural Computation (ICNC)

作者： Chengyun Liu Zhenxue Chen Faliang Chang Kaifang Wang Key Laboratory of Intelligent Computing & Signal Processing Anhui University Hefei P. R. China School of Control Science & Engineering Shandong University Jinan P. R. China Jiangsu Key Laboratory of Image and Video Understanding for Social Safety Nanjing University of Science and Technology Nanjing P. R. China

ISBN: (纸本)9781479951529

Face recognition is one of typical biometric identification method, which has a great prospect in secure authentication system, file management, human-computer interaction and social security. This paper proposes gray-scale characteristics and creates facial templates to recognize faces method based on a given number of samples. Firstly, it selects the method of building template according to the number of samples to create the facial template image; then, it will compare the difference of first-order edge entropy between recognition image and the template image and find the best match result; finally, the recognition result is output. Experimental results show that the proposed algorithm has good recognition effect on face recognition under non-constraint conditions.

关键词： Face recognition Face Entropy Lighting image edge detection

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Pattern phase transitions in 3D minimal Vicsek model

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EPL 2021年第5期134卷 50004-50004页

作者： Wu, Yue Li, Jiawei Chen, Duxin Li, Xiang Zhang, Hai-Tao Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China Southeast Univ Jiangsu Key Lab Networked Collect Intelligence Sch Math Nanjing 210096 Peoples R China Fudan Univ Sch Informat Sci & Engn Adapt Networks & Control Lab Shanghai 200433 Peoples R China Fudan Univ Sch Informat Sci & Engn Ctr Smart Networks & Syst Shanghai 200433 Peoples R China

A large part of biological collective behaviors involves motional formation patterns like migration, vortex, aggregation, etc., in three-dimensional space, but the phase transition principles among these patterns still remain a dilemma. In this letter, by tweaking the vision range of the particles in a minimal collective motion model, phase transitions are observed among gas-like, crystal-like, and liquid-sphere-like patterns. Significantly, the increase of noise magnitudes will destroy the moving direction synchronization of gas-like and crystal-like phases, but not the liquid-sphere-like phase. By contrast, three sub-phases of migration liquid-sphere, vortex liquid-sphere and aggregation liquid-sphere emerge by slightly increasing noise magnitudes. This study sheds some light onto the forming mechanisms of 3D motional patterns of abundant biological groups. These insights could be expected to enable tuning the collective motional formations of multi-unmanned systems or robots by simply tweaking a few dynamical parameters as well. Copyright (C) 2021 EPLA

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Short-Term Hybrid Prognostics of Fuel Cells: A Comparative and Improvement Study

SSRN

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SSRN 2024年

作者： Sheng, Chuang Fu, Jun Zu, YanMin Deng, ZhongHua Li, Xi School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control Education Ministry Huazhong University of Science and Technology Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518055 China School of Electronic Information Engineering Jingchu University of Technology Jingmen448000 China

Accurately predicting the performance degradation trend of fuel cells helps take measures in advance and prolong the service life of the stack, which led to the creation of a novel hybrid forecasting approach. The first grey model prediction method based on residual exponential smoothing optimization (ES-R-GM) can capture the voltage deterioration trend. The integration of two different ES techniques, specifically the double ES (ES2) and cubic ES (ES3), is explored to investigate their effect on improving the predictive accuracy of the GM model. The second method of the adaptive network fuzzy inference system (ANFIS) can characterize local nonlinear behaviour. The simulated annealing (SA) algorithm is utilized to optimize ANFIS results under different fuzzy rule selection strategies. The outcomes of the two types of prediction methods mentioned above are combined to create a hybrid prediction using the data fusion method and the moving window technique. Different hybrid methods are evaluated under general conditions and further detailed optimization. The proposed hybrid framework is verified by the data obtained from the experimental platform. Moreover, when applied to consistent public datasets, the hybrid approach maintains its robustness and accuracy compared with other prognostic methods. © 2024, The Authors. All rights reserved.

关键词： Simulated annealing

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Research and application of Transformer based anomaly detection model: A literature review

arXiv

引用

arXiv 2024年

作者： Ma, Mingrui Han, Lansheng Zhou, Chunjie Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Hubei Wuhan430074 China The Key Laboratory of Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Hubei Wuhan430074 China

Transformer, as one of the most advanced neural network models in Natural Language processing (NLP), exhibits diverse applications in the field of anomaly detection. To inspire research on Transformer-based anomaly detection, this review offers a fresh perspective on the concept of anomaly detection. We explore the current challenges of anomaly detection and provide detailed insights into the operating principles of Transformer and its variants in anomaly detection tasks. Additionally, we delineate various application scenarios for Transformer-based anomaly detection models and discuss the datasets and evaluation metrics employed. Furthermore, this review highlights the key challenges in Transformer-based anomaly detection research and conducts a comprehensive analysis of future research trends in this domain. The review includes an extensive compilation of over 100 core references related to Transformer-based anomaly detection. To the best of our knowledge, this is the first comprehensive review that focuses on the research related to Transformer in the context of anomaly detection. We hope that this paper can provide detailed technical information to researchers interested in Transformer-based anomaly detection tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Anomaly detection

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Front-end Replication Dynamic Window (FRDW) for Online Motor imagery Classification

arXiv

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arXiv 2024年

作者： Chen, Xinru An, Jiayu Wu, Huanyu Li, Siyang Liu, Bin Wu, Dongrui The Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China The Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen China Beijing Fenghuo Wanjia Technology Co. Ltd. Beijing100026 China

Motor imagery (MI) is a classical paradigm in electroencephalogram (EEG) based brain-computer interfaces (BCIs). Online accurate and fast decoding is very important to its successful applications. This paper proposes a simple yet effective front-end replication dynamic window (FRDW) algorithm for this purpose. Dynamic windows enable the classification based on a test EEG trial shorter than those used in training, improving the decision speed;front-end replication fills a short test EEG trial to the length used in training, improving the classification accuracy. Within-subject and cross-subject online MI classification experiments on three public datasets, with three different classifiers and three different data augmentation approaches, demonstrated that FRDW can significantly increase the information transfer rate in MI decoding. Additionally, FR can also be used in training data augmentation. FRDW helped win national champion of the China BCI Competition in 2022. © 2024, CC BY.

关键词： Brain computer interface

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Sparse Prior Is Not All You Need: When Differential Directionality Meets Saliency Coherence for Infrared Small Target Detection

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2024年 73卷 1页

作者： Zhou, Fei Fu, Maixia Qian, Yulei Yang, Jian Dai, Yimian Henan Univ Technol Key Lab Grain Informat Proc & Control Minist Educ Zhengzhou 450001 Peoples R China Henan Univ Technol Henan Key Lab Grain Photoelect Detect & Control Zhengzhou 450001 Peoples R China Henan Univ Technol Coll Informat Sci & Engn Zhengzhou 450001 Peoples R China Nanjing Marine Radar Inst Nanjing 210000 Peoples R China Nanjing Univ Sci & Technol Sch Comp Sci & Engn PCA Lab Key Lab Intelligent Percept & Syst High Dimens Inf Nanjing 210000 Peoples R China Nanjing Univ Sci & Technol Sch Comp Sci & Engn Jiangsu Key Lab Image & Video Understanding Social Nanjing 210000 Peoples R China

The infrared small target detection is crucial for the efficacy of infrared search and tracking systems. Current tensor decomposition methods emphasize representing small targets with sparsity but struggle to separate targets from complex backgrounds due to insufficient use of intrinsic directional information and reduced target visibility during decomposition. To address these challenges, this study introduces a sparse differential directionality prior (SDD) framework. SDD leverages the distinct directional characteristics of targets to differentiate them from the background, applying mixed sparse constraints on the differential directional images and continuity difference matrix of the temporal component, both derived from Tucker decomposition. We further enhance the target detectability with a saliency coherence strategy that intensifies target contrast against the background during hierarchical decomposition. A proximal alternating minimization (PAM)-based algorithm efficiently solves our proposed model. Experimental results on several real-world datasets validate our method's effectiveness, outperforming ten state-of-the-art methods in target detection and clutter suppression. Our code is available at: https://***/GrokCV/SDD.

关键词： Tensors Object detection Clutter Matrix decomposition Sparse matrices Correlation Coherence Vectors Target tracking Sparse approximation Proximal optimization saliency map spatial-temporal regularization target detection tensor decomposition

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Hop Count Distribution for Minimum Hop-Count Routing in Finite Ad Hoc Networks

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2022年第7期21卷 5317-5332页

作者： Li, Silan Hu, Xiaoya Jiang, Tao Zhang, Rongqing Yang, Liuqing Hu, Hui Huazhong Univ Sci & Technol Wuhan Natl Lab Optoelect Sch Cyber Sci & Engn Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sixth Generat Mobile Commun 6G Res Ctr Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Minist Educ Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Minist Educ Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Tongji Univ Sch Software Engn Shanghai 201804 Peoples R China Univ Minnesota Dept Elect & Comp Engn Minneapolis MN 55455 USA Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China

Hop count distribution (HCD), generally formulated as a discrete probability distribution of the hop count, constitutes an attractive tool for performance analysis and algorithm design. This paper devotes to deriving an analytical HCD expression for a finite ad hoc network under the minimum hop-count routing protocols. Formulating the node distribution with binomial point process, the network is provided as a bounded area with all nodes randomly and uniformly distributed. Considering an arbitrary pair of source node (SN) and destination node, an innovative and straightforward definition is presented for HCD. In order to derive HCD out, an original mathematical framework, named as the equivalent area replacement method (EARM), is proposed and verified. Under the EARM, HCD is derived by first considering the special case where SN locates at the network center and then extending to the general case where SN is randomly distributed. For each case, the accuracy of our HCD model is evaluated by simulation comparison. Results show that our model matches well with the simulation results over a wide range of parameters. Particularly, the derived HCD outperforms the existing formulations in terms of the Kullback Leibler divergence, especially when SN is randomly distributed.

关键词： Routing Ad hoc networks Graphical models Distribution functions Mathematical models Routing protocols Spread spectrum communication Ad hoc networks hop count distribution minimum hop-count route binomial point process random coverage of multiple circles

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