IECE - Institute of Emerging and Computer Engineers

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    • yfengb@163.com
    Academic Editor
    Author
    Contributions by role
    Author 2
    Editor 1
    ORCID
    0000-0002-9087-5796
    Fengbao Yang
    School of Information and Communication Engineering, North University of China, Taiyuan 030051, China
    Summary
    Fengbao Yang received his Ph.D. degree in measurement technology and instrument from North University of China, Taiyuan, China, in 2003. From 2004 to2007, he was a post-doctoral research fellow with the Beijing Institute of Technology. He is a full professor at North University of China. He is a Fellow of the information fusion branch of the China Society of Aeronautics and Astronautics. Now, he is the leader of the Institute of Information Fusion and Recognition Technology at the North University of China. He has published approximately 100 papers in the information fusion and image processing journals and eight books on uncertain information reasoning methods and infrared technology. His current research interests include information fusion, performance detection and evaluation of complex systems, and information fusion theory of uncertainty.
    Edited Journals
    Chinese Journal of Information Fusion
    IECE Contributions
    Articles
    2
    Edited
    1
    Free Access | Review Article | 30 September 2024
    Complex Evidence Theory for Multisource Data Fusion
    by
    Fuyuan Xiao
    ,
    Junhao Wen
    ,
    Witold Pedrycz
    ,
    Masayoshi Aritsugi
    Chinese Journal of Information Fusion | Volume 1, Issue 2: 134-159, 2024 | DOI:10.62762/CJIF.2024.999646
    Abstract
    Data fusion is a prevalent technique for assembling imperfect raw data coming from multiple sources to capture reliable and accurate information. Dempster–Shafer evidence theory is one of useful methodologies in the fusion of uncertain multisource information. The existing literature lacks a thorough and comprehensive review of the recent advances of Dempster– Shafer evidence theory for data fusion. Therefore, the state of the art has to be surveyed to gain insight into how Dempster–Shafer evidence theory is beneficial for data fusion and how it evolved over time. In this paper, we first provide a comprehensive review of data fusion methods based on Dempster–Shafer evidence theory an... More >
    Graphical Abstract
    Complex Evidence Theory for Multisource Data Fusion