Chinese Journal of Information Fusion

Code (Data) Available | Free Access | Review Article | 15 December 2024

A Comprehensive Survey on Emerging Techniques and Technologies in Spatio-Temporal EEG Data Analysis

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Chinese Journal of Information Fusion | Volume 1, Issue 3: 183-211, 2024 | DOI:10.62762/CJIF.2024.876830

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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 >

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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

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Chinese Journal of Information Fusion | Volume 1, Issue 3: 175-182, 2024 | DOI:10.62762/CJIF.2024.841250

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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 >

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Basic Belief Assignment Determination Based on Radial Basis Function Network

Free Access | Research Article | 30 September 2024

Unsupervised Industrial Anomaly Detection Based on Feature Mask Generation and Reverse Distillation

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Pei Qi

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Lin Chai

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Xinyu Ye

Chinese Journal of Information Fusion | Volume 1, Issue 2: 160-174, 2024 | DOI:10.62762/CJIF.2024.734267

Abstract

In the realm of industrial defect detection, unsupervised anomaly detection methods draw considerable attention as a result of their exceptional accomplishments. Among these, knowledge distillation-based methods have emerged as a prominent research focus, favored for their streamlined architecture, precision, and efficiency. However, the challenge of characterizing the variability in anomaly samples hinders the accuracy of detection. To address this issue, our research presents a novel approach for anomaly detection and localization, leveraging the concept of inverse knowledge distillation as its cornerstone. We employ the encoder as the guiding teacher model and designate the decoder as the... More >

Graphical Abstract

Unsupervised Industrial Anomaly Detection Based on Feature Mask Generation and Reverse Distillation

Free Access | Review Article | Feature Paper | 30 September 2024 | Cited: 1

Complex Evidence Theory for Multisource Data Fusion

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Chinese Journal of Information Fusion | Volume 1, Issue 2: 134-159, 2024 | DOI:10.62762/CJIF.2024.999646

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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 >

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Complex Evidence Theory for Multisource Data Fusion

Free Access | Research Article | 29 September 2024

Explainable Classification of Remote Sensing Ship Images Based on Graph Network

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Chinese Journal of Information Fusion | Volume 1, Issue 2: 126-133, 2024 | DOI:10.62762/CJIF.2024.932552

Abstract

Remote sensing image plays an important role in maritime surveillance, and as a result there is increasingly becoming a prominent focus on the detection and recognition of maritime objects. However, most existing studies in remote sensing image classification pay more attention on the performance of model, thus neglecting the transparency and explainability in it. To address the issue, an explainable classification method based on graph network is proposed in the present study, which seeks to make use of the relationship between objects' regions to infer the category information. First, the local visual attention module is designed to focus on different but important regions of the object. T... More >

Graphical Abstract

Explainable Classification of Remote Sensing Ship Images Based on Graph Network

Free Access | Research Article | 27 September 2024

A High-Efficiency Two-Layer Path Planning Method for UAVs in Vast Airspace

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Tongyao Yang

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Fengbao Yang

Chinese Journal of Information Fusion | Volume 1, Issue 2: 109-125, 2024 | DOI:10.62762/CJIF.2024.596648

Abstract

In response to the challenges associated with the inefficiency and poor quality of 3D path planning for Unmanned Aerial Systems (UAS) operating in vast airspace, a novel two-layer path planning method is proposed based on a divide-and-conquer methodology. This method segregates the solution process into two distinct stages: heading planning and path planning, thereby ensuring the planning of both efficiency and path quality. Firstly, the path planning phase is formulated as a multi-objective optimization problem, taking into account the environmental constraints of the UAV mission and path safety. Subsequently, the multi-dimensional environmental data is transformed into a two-dimensional pr... More >

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A High-Efficiency Two-Layer Path Planning Method for UAVs in Vast Airspace

Free Access | Research Article | 23 September 2024

Convolutional Neural Network for Ellipse Extended Target Tracking

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Binchao Bian

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Hui Chen

Chinese Journal of Information Fusion | Volume 1, Issue 2: 93-108, 2024 | DOI:10.62762/CJIF.2024.160538

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In the field of extended target tracking, constrained by the sparse measurement set from radar, the target contour is commonly estimated as an elliptical shape. This paper uses convolutional neural networks to estimate the size and orientation information of extended targets. First, by establishing a systematic model for elliptical extended targets and modeling its measurement information, data normalization, and length equalization operations were conducted to provide reliable measurement data for subsequent neural network processing. Subsequently, through the construction of a convolutional neural network model, accurate estimation of the contour parameters of elliptical extended targets w... More >

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Convolutional Neural Network for Ellipse Extended Target Tracking

Free Access | Review Article | 12 June 2024 | Cited: 2

Bridging Modalities: A Survey of Cross-Modal Image-Text Retrieval

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Chinese Journal of Information Fusion | Volume 1, Issue 1: 79-92, 2024 | DOI:10.62762/CJIF.2024.361895

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The rapid advancement of Internet technology, driven by social media and e-commerce platforms, has facilitated the generation and sharing of multimodal data, leading to increased interest in efficient cross-modal retrieval systems. Cross-modal image-text retrieval, encompassing tasks such as image query text (IqT) retrieval and text query image (TqI) retrieval, plays a crucial role in semantic searches across modalities. This paper presents a comprehensive survey of cross-modal image-text retrieval, addressing the limitations of previous studies that focused on single perspectives such as subspace learning or deep learning models. We categorize existing models into single-tower, dual-tower,... More >

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Bridging Modalities: A Survey of Cross-Modal Image-Text Retrieval

Free Access | Research Article | Feature Paper | 10 June 2024 | Cited: 2

Extraction of Motion Information from Occupancy Grid Map Using Keystone Transform

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Chinese Journal of Information Fusion | Volume 1, Issue 1: 63-78, 2024 | DOI:10.62762/CJIF.2024.361892

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Considering the tractability of OGM (Occupancy Grid Map) and its wide use in the dynamic environment representation of mobile robotics, the extraction of motion information from successive OGMs are very important for many tasks, such as SLAM (Simultaneously Localization And Mapping) and DATMO (Detection and Tracking of Moving Object). In this paper, we propose a novel motion extraction method based on the signal transform, called as S-KST (Spatial Keystone Transform), for the motion detection and estimation from successive noisy OGMs. It extends the KST in radar imaging or motion compensation to 1D spatial case (1DS-KST) and 2D spatial case (2DS-KST) combined multiple hypotheses about poss... More >

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Extraction of Motion Information from Occupancy Grid Map Using Keystone Transform

Free Access | Research Article | 08 June 2024

GPS Tracking Based on Stacked-Serial LSTM Network

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Chinese Journal of Information Fusion | Volume 1, Issue 1: 50-62, 2024 | DOI:10.62762/CJIF.2024.361889

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Maneuvering target tracking is widely used in unmanned vehicles, missile navigation, underwater ships, etc. Due to the uncertainty of the moving characteristics of maneuvering targets and the low sensor measurement accuracy, trajectory tracking has always been an open research problem and challenging work. This paper proposes a trajectory estimation method based on LSTM neural network for uncertain motion characteristics. The network consists of two LSTM networks with stacked serial relationships, one of which is used to predict the movement dynamics, and the other is used to update the track's state. Compared with the classical Kalman filter based on the maneuver model, the method proposed... More >

Graphical Abstract

GPS Tracking Based on Stacked-Serial LSTM Network

A Mimic Fusion Algorithm for Dual Channel Video Based on Possibility Distribution Synthesis Theory

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Xiaoming Guo

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Fengbao Yang

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Linna Ji

Chinese Journal of Information Fusion | Volume 1, Issue 1: 33-49, 2024 | DOI:10.62762/CJIF.2024.361886

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In response to the current practical fusion requirements for infrared and visible videos, which often involve collaborative fusion of difference feature information, and model cannot dynamically adjust the fusion strategy according to the difference between videos, resulting in poor fusion performance, a mimic fusion algorithm for infrared and visible videos based on the possibility distribution synthesis theory is proposed. Firstly, quantitatively describe the various difference features and their attributes of the region of interest in each frame of the dual channel video sequence, and select the main difference features corresponding to each frame. Secondly, the pearson correlation coeffi... More >

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Author's Talk

A Mimic Fusion Algorithm for Dual Channel Video Based on Possibility Distribution Synthesis Theory

Free Access | Research Article | Feature Paper | 27 May 2024 | Cited: 5

Simultaneous Spatiotemporal Bias Compensation and Data Fusion for Asynchronous Multisensor Systems

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Gongjian Zhou

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Shizhe Bu

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Thiagalingam Kirubarajan

Chinese Journal of Information Fusion | Volume 1, Issue 1: 16-32, 2024 | DOI:10.62762/CJIF.2024.361881

Abstract

Bias estimation of sensors is an essential prerequisite for accurate data fusion. Neglect of temporal bias in general real systems prevents the existing algorithms from successful application. In this paper, both spatial and temporal biases in asynchronous multisensor systems are investigated and two novel methods for simultaneous spatiotemporal bias compensation and data fusion are presented. The general situation that the sensors sample at different times with different and varying periods is explored, and unknown time delays may exist between the time stamps and the true measurement times. Due to the time delays, the time stamp interval of the measurements from different sensors may be di... More >

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Simultaneous Spatiotemporal Bias Compensation and Data Fusion for Asynchronous Multisensor Systems

Free Access | Research Article | 25 May 2024 | Cited: 4

Research on A Ship Trajectory Classification Method Based on Deep Learning

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Chinese Journal of Information Fusion | Volume 1, Issue 1: 3-15, 2024 | DOI:10.62762/CJIF.2024.361873

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The unrestricted development and utilization of marine resources have resulted in a series of practical problems, such as the destruction of marine ecology. The wide application of radar, satellites and other detection equipment has gradually led to a large variety of large-capacity marine spatiotemporal trajectory data from a vast number of sources. In the field of marine domain awareness, there is an urgent need to use relevant information technology means to control and monitor ships and accurately classify and identify ship behavior patterns through multisource data fusion analysis. In addition, the increase in the type and quantity of trajectory data has produced a corresponding increa... More >

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Research on A Ship Trajectory Classification Method Based on Deep Learning

Open Access | Editorial | 23 May 2024

Inaugural Editorial of the Chinese Journal of Information Fusion

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You He

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Jun Lu

Chinese Journal of Information Fusion | Volume 1, Issue 1: 1-2, 2024 | DOI:10.62762/CJIF.2024.100001

Abstract

Presents the introductory editorial for this issue of the publication. More >

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