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    Volume 1, Issue 3 - Table of Contents
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    Cover Story: Forthcoming
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    Code (Data) Available | Free Access | Research Article | 31 December 2024
    DMFuse: Diffusion Model Guided Cross-Attention Learning for Infrared and Visible Image Fusion
    by
    Wuqiang Qi
    ,
    Zhuoqun Zhang
    ,
    Zhishe Wang
    Chinese Journal of Information Fusion | Volume 1, Issue 3: 226-241, 2024 | DOI:10.62762/CJIF.2024.655617
    Abstract
    Image fusion aims to integrate complementary information from different sensors into a single fused output for superior visual description and scene understanding. The existing GAN-based fusion methods generally suffer from multiple challenges, such as unexplainable mechanism, unstable training, and mode collapse, which may affect the fusion quality. To overcome these limitations, this paper introduces a diffusion model guided cross-attention learning network, termed as DMFuse, for infrared and visible image fusion. Firstly, to improve the diffusion inference efficiency, we compress the quadruple channels of the denoising UNet network to achieve more efficient and robust model for fusion tas... More >
    Graphical Abstract
    DMFuse: Diffusion Model Guided Cross-Attention Learning for Infrared and Visible Image Fusion
    Free Access | Research Article | 30 December 2024
    Robust Distributed State Estimation in Power Systems: A Multi-Estimator Data Fusion Approach to Counteract Cyber-Attacks
    by
    Shiyu Jin
    ,
    Wen Yang
    ,
    Hongbo Yuan
    ,
    Wenjie Ding
    ,
    Han Wu
    ,
    Jie Wang
    Chinese Journal of Information Fusion | Volume 1, Issue 3: 212-225, 2024 | DOI:10.62762/CJIF.2024.740709
    Abstract
    Cyber security in power systems has become increasingly critical with the rise of network attacks such as Denial-of-Service (DoS) attacks and False Data Injection (FDI) attacks. These threats can severely compromise the integrity and reliability of state estimation, which are fundamental to the operation and control of power systems. In this manuscript, an estimation algorithm based on the fusion of information from multiple estimators is proposed to ensure that state estimation at critical buses can function properly in case of attacks. Our approach leverages a network of estimators that can dynamically adjust to maintain system stability and accuracy. Furthermore, a new detector is adopted... More >
    Graphical Abstract
    Robust Distributed State Estimation in Power Systems: A Multi-Estimator Data Fusion Approach to Counteract Cyber-Attacks
    Code (Data) Available | Free Access | Review Article | 15 December 2024
    A Comprehensive Survey on Emerging Techniques and Technologies in Spatio-Temporal EEG Data Analysis
    by
    Pengfei Wang
    ,
    Huanran Zheng
    ,
    Silong Dai
    ,
    Yiqiao Wang
    ,
    Xiaotian Gu
    ,
    Yuanbin Wu
    ,
    Xiaoling Wang
    Chinese Journal of Information Fusion | Volume 1, Issue 3: 183-211, 2024 | DOI:10.62762/CJIF.2024.876830
    Abstract
    In recent years, the field of electroencephalography (EEG) analysis has witnessed remarkable advancements, driven by the integration of machine learning and artificial intelligence. This survey aims to encapsulate the latest developments, focusing on emerging methods and technologies that are poised to transform our comprehension and interpretation of brain activity. The structure of this paper is organized according to the categorization within the machine learning community, with representation learning as the foundational concept that encompasses both discriminative and generative approaches. We delve into self-supervised learning methods that enable the robust representation of brain sig... More >
    Graphical Abstract
    A Comprehensive Survey on Emerging Techniques and Technologies in Spatio-Temporal EEG Data Analysis
    Free Access | Research Article | 07 December 2024
    Basic Belief Assignment Determination Based on Radial Basis Function Network
    by
    Wei Li
    ,
    Deqiang Han
    ,
    Jean Dezert
    ,
    Yi Yang
    Chinese Journal of Information Fusion | Volume 1, Issue 3: 175-182, 2024 | DOI:10.62762/CJIF.2024.841250
    Abstract
    In Dempster-Shafer evidence theory (DST), the determination of basic belief assignment (BBA) is an important yet challenging issue. The rational mass determination of compound focal elements is crucial for fully taking advantage of DST, i.e., the ability to represent the ambiguity. In this paper, for the compound focal elements, we select and construct the \enquote{compound-class samples} with ambiguous class membership. Then, we use these samples to construct an end-to-end model called Evidential Radial Basis Function Network (E-RBFN), with the input as the sample and the output as the corresponding BBA. The E-RBFN can directly determine the mass values for all focal elements (including the... More >
    Graphical Abstract
    Basic Belief Assignment Determination Based on Radial Basis Function Network