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162 results on '"Wang, Yingxu"'

10. Channel Attentional Correlation Filters Learning With Second-Order Difference for UAV Tracking.

Author

Zhang, Yang, Yu, Yu-Feng, Huang, Ke-Kun, and Wang, Yingxu

Abstract

Unmanned aerial vehicle (UAV) visual tracking has been a hot research topic in the field of remote sensing. Many filter-based UAV trackers have achieved excellent performance. However, existing methods do not distinguish the importance of different feature channels with semantic information and background information, which may hinder the tracker’s ability to adapt to changing environments. To deal with this problem, we propose a channel attentional correlation filters (CACFs) learning model. Specifically, we introduce the fuzzy C-means (FCMs) algorithm to preclassify the extracted features and then perform weight penalty to feature channels with different membership degrees. In addition, the filter can adapt more effectively to the background’s rapid changes during the UAV tracking process by learning the second-order difference between adjacent three frame features. Finally, the comparative experiments are conducted on three mainstream UAV datasets, including DTB70, UAV123@10fps, and UAVDT. The experimental results demonstrate the effectiveness of the proposed method. The tracking performance of CACF surpasses that of other state-of-the-art trackers. [ABSTRACT FROM AUTHOR]

Published
2023
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14. IEEE ICCI*CC Series in Year 20: Latest Advances in Cognitive Informatics and Cognitive Computing towards General AI (Plenary Panel Report-I)

Author

Wang, Yingxu, primary, Widrow, Bernard, additional, Pedrycz, Witold, additional, Berwick, Robert C., additional, Soda, Paolo, additional, Kwong, Sam, additional, Kaynak, Okyay, additional, Regazzoni, Carlo S., additional, Chan, Christine, additional, Gavrilova, Marina, additional, and Wang, Guoyin, additional

Published
2021
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25. A Tripartite Theory of Trustworthiness for Autonomous Systems

Author

Wang, Yingxu, primary, Yanushkevich, Svetlana, additional, Hou, Ming, additional, Plataniotis, Konstantinos, additional, Coates, Mark, additional, Gavrilova, Marina, additional, Hu, Yaoping, additional, Karray, Fakhri, additional, Leung, Henry, additional, Mohammadi, Arash, additional, Kwong, Sam, additional, Tunstel, Edward, additional, Trajkovic, Ljiljana, additional, Rudas, Imre J., additional, and Kacprzyk, Janusz, additional

Published
2020
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31. Transfer Collaborative Fuzzy Clustering in Distributed Peer-to-Peer Networks.

Author

Dang, Bozhan, Wang, Yingxu, Zhou, Jin, Wang, Rongrong, Chen, Long, Chen, C. L. Philip, Zhang, Tong, Han, Shiyuan, Wang, Lin, and Chen, Yuehui

Subjects
FUZZY algorithms, DATA extraction, PEER-to-peer architecture (Computer networks), PARALLEL algorithms, COMPUTER network security, ALGORITHMS
Abstract

The traditional collaborative fuzzy clustering can effectively perform data clustering in distributed peer-to-peer networks, which is an impossible task to complete for the centralized clustering methods due to privacy and security requirements or network transmission technology constraints. But it will increase the number of clustering iterations and lead to lower efficiency of the clustering. Moreover, the collaborative mechanism hidden in the iterative process of clustering cannot be well revealed and explained. In this article, a novel series of transfer collaborative fuzzy clustering algorithms are proposed to solve these issues. In the first basic algorithm, the transfer learning among neighbor nodes vividly expresses the collaborative mechanism and enhances the information collaboration to accelerate the convergence of fuzzy clustering. Meanwhile, neighbor nodes can learn the knowledge from each other to further promote their respective clustering performance. Then, an improved version, with the learning-rate-adjustable strategy instead of fixed values, is designed to highlight the different influence between neighbor nodes, and the appropriate learning rates between neighbor nodes are achieved to ensure the stable clustering accuracy. Finally, two extended versions with the attribute-weight-entropy regularization technique are presented for the clustering of high dimensional sparse data and the extraction of important subspace features. Experiments show the efficiency of the proposed algorithms compared with the related prototype-based clustering methods. [ABSTRACT FROM AUTHOR]

Published
2022
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32. Tensor-Based Robust Principal Component Analysis With Locality Preserving Graph and Frontal Slice Sparsity for Hyperspectral Image Classification.

Author

Wang, Yingxu, Li, Tianjun, Chen, Long, Yu, Yufeng, Zhao, Yinping, and Zhou, Jin

Subjects
*PRINCIPAL components analysis, *LOW-rank matrices
Abstract

Tensor-based robust principal component analysis (PCA) methods are efficient to discover the low-rank part of a hyperspectral image for reducing redundant information and guarantee good classification results. However, current methods cannot remove noise adequately, and the residual noise remaining in the low-rank image limits the further improvement of classification performance. Thus, enhancing the robustness to noise is important and helpful for tensor-based robust PCA (RPCA) methods to process hyperspectral images. To this end, we propose a tensor-based RPCA method with a locality preserving graph and frontal slice sparsity (LPGTRPCA) for hyperspectral image classification. Specifically, a tensor $l_{2,2,1}$ norm that requires the frontal slice sparsity of a tensor is defined to extract the noise in the hyperspectral image from the frontal direction. What is more, a position-based Laplacian graph that preserves the local structures of a tensor according to the spatial position is designed for relieving the impact of the residual noise remaining in the low-rank image. Based on the tensor nuclear norm, the tensor $l_{2,2,1}$ norm, and the position-based Laplacian graph, LPGTRPCA efficiently separates the low-rank part with little noise from a raw hyperspectral image and achieves more robust classification results than current methods. LPGTRPCA is optimized by the alternative direction multiplier method (ADMM), and the convergence of solutions is experimentally demonstrated. In the experiments conducted on Indian Pines, Pavia University, and Salinas datasets, LPGTRPCA outperformed various state-of-the-art and classical tensor-based RPCA methods in terms of average class classification accuracy (AA), overall classification accuracy (OA), and kappa coefficient (KC). [ABSTRACT FROM AUTHOR]

Published
2022
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35. Diagnosis/Prognosis of COVID-19 Chest Images via Machine Learning and Hypersignal Processing: Challenges, opportunities, and applications.

Author

Mohammadi, Arash, Wang, Yingxu, Enshaei, Nastaran, Afshar, Parnian, Naderkhani, Farnoosh, Oikonomou, Anastasia, Rafiee, Javad, Rodrigues de Oliveira, Helder, Yanushkevich, Svetlana, and Plataniotis, Konstantinos N.

Abstract

The novel coronavirus disease, COVID-19, has rapidly and abruptly changed the world as we knew it in 2020. It has become the most unprecedented challenge to analytic epidemiology (AE) in general and signal processing (SP) theories specifically. In this regard, medical imaging plays an important role for the management of COVID-19. SP and deep learning (DL) models can assist in the development of robust radiomics solutions for the diagnosis/prognosis, severity assessment, treatment response, and monitoring of COVID-19 patients. [ABSTRACT FROM AUTHOR]

Published
2021
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36. Design of Gibbon-Like Crawling Robot for High Voltage Transmission Line Inspection

Author

Qichen Zhang, Xiaodong Zhang, Wang Yingxu, Guangzhong Cao, Pengcheng Zhu, and Aibin Zhu

Subjects
Robot kinematics, Mechanism design, Bionics, Computer science, Line (geometry), Robot, Mobile robot, Kinematics, Swing, Simulation
Abstract

Aiming at the existing problems of high-voltage transmission line inspection robot, such as large size, low obstacle crossing efficiency, and inability to cross over obstacles such as diversion lines and insulators, this paper presents a two-arm swinging inspection robot based on primates by observing the movement characteristics of gibbons and combining with the principle of bionics. The robot can swing cantilever on the high-voltage line to achieve obstacle crossing. After kinematics analysis of the bionic model of the inspection robot, the 3d model of the robot is established in SolidWorks. Simulation analysis of the robot is carried out through the built-in motion plug-in to ensure that the robot can achieve the desired motion effect, which verifies the stability and rationality of the mechanism design.

Published
2019

37. Control of Lower Limb Rehabilitation Exoskeleton Robot Based on CPG Neural Network

Author

Pengcheng Zhu, Xiaodong Zhang, Aibin Zhu, Wang Yingxu, Hongling Wu, and Guang-Zhong Cao

Subjects
0209 industrial biotechnology, Rehabilitation, Artificial neural network, Computer science, media_common.quotation_subject, medicine.medical_treatment, 02 engineering and technology, Adaptability, Exoskeleton, 020901 industrial engineering & automation, Gait (human), Hebbian theory, 0202 electrical engineering, electronic engineering, information engineering, Trajectory, medicine, Robot, 020201 artificial intelligence & image processing, human activities, Simulation, media_common
Abstract

In view of the difficulties in modeling, large external interference, weak adaptability and other problems in the control strategy adopted by the lower limb exoskeleton robot for medical rehabilitation at the present stage. This paper applies the bionic control method based on CPG to the exoskeleton control of lower limb rehabilitation. By adopting Dynamic Hebbian learning algorithm to improve the Hopf oscillator. And build a CPG oscillator network, realize the gait signal study, and eventually to improve lower limb exoskeleton robot movement performance and enhance its adaptability. Through the patient's wear-wearing test. It is proved that CPG bionic control exoskeleton can be matched with the control signal of the limb produced by the human body in the case of the human body motion cycle, and it can effectively control the exoskeleton of the lower extremity and to perform rehabilitation exercises.

Published
2019

38. On Autonomous Systems: From Reflexive, Imperative and Adaptive Intelligence to Autonomous and Cognitive Intelligence

Author

Wang, Yingxu, primary, Plataniotis, Konstantinos N., additional, Kwong, Sam, additional, Leung, Henry, additional, Yanushkevich, Svetlana, additional, Karray, Fakhri, additional, Hou, Ming, additional, Howard, Newton, additional, Fiorini, Rodolfo A., additional, Soda, Paolo, additional, Tunstel, Edward, additional, Wang, Jianmin, additional, and Patel, Shushma, additional

Published
2019
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