Forthcoming Articles

International Journal of Modelling, Identification and Control

International Journal of Modelling, Identification and Control (IJMIC)

Forthcoming articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

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International Journal of Modelling, Identification and Control (3 papers in press)

Regular Issues

  • Outlier detection algorithm based on deviation characteristic   Order a copy of this article
    by Yong Wang, Hongbin Wang, Pengcheng Sun, Xinliang Yin 
    Abstract: Outlier mining focuses on researching rare events through detection and analysis to dig out the valuable knowledge from them. In the static data set environment, the traditional LOF algorithm calculates the local outlier factor through the whole data set and requires a lot of computing time. To solve this problem, the algorithm divides the data space into grids, and calculates the local outlier factor based on the centroids of the grids. Since the grid number is less than data point number, the time complexity is obviously reduced under acceptable error. When the new data points are added, it can rapidly detect outliers. The contrast experiment results show that the new algorithm can reduce the computation time and improve the efficiency, while achieving comparable accuracy.
    Keywords: outlier detection; local outlier factor; deviation characteristic; fast LOF detection algorithm.

  • A lightweight attention mechanism and self-supervised denoising approach for robust vehicle detection in adverse weather conditions   Order a copy of this article
    by Lina Sun 
    Abstract: Vehicle detection under adverse weather conditions remains a major challenge due to severe image degradation and complex noise. Traditional denoising methods often fail to retain essential features, while lightweight models typically trade accuracy for efficiency, limiting real-time application. This paper presents a self-supervised denoising framework tailored for foggy and rainy scenarios. It includes three modules: a global perception mask mapper for identifying noise regions, a denoising network that separates clean and noisy components, and a regularised re-visibility loss to enhance performance in blind spots. To ensure deployment feasibility, a lightweight attention module based on the general meta-mobile block is introduced, balancing speed and accuracy. Experiments on benchmark datasets demonstrate notable gains in image clarity and vehicle detection accuracy. The framework offers a robust, efficient solution for real-world systems like autonomous vehicles, and lays the groundwork for future research on adaptive denoising in low-visibility environments.
    Keywords: self-supervised learning; image denoising; vehicle detection; attention mechanism; model lightweighting.
    DOI: 10.1504/IJMIC.2025.10072537
     
  • Industrial robotic arm pose tracking based on tightly-coupled visual-inertial fusion   Order a copy of this article
    by Shixing Liu 
    Abstract: Industrial robotic arms play a pivotal role in automation, executing precise tasks with efficiency and accuracy However, achieving robust and accurate pose tracking for these arms remains a challenge, particularly in dynamic environments where external disturbances are prevalent This paper proposes a novel approach utilizing tightly-coupled visual-inertial fusion for industrial robotic arm pose tracking By integrating visual data from cameras with inertial measurements, our method aims to enhance the resilience of pose estimation against environmental uncertainties and occlusions We present a comprehensive framework that fuses information from both modalities in a tightly-coupled manner, leveraging their complementary strengths to achieve superior tracking performance Additionally, we address the challenges associated with real-time implementation and scalability to different robotic arm configurations Experimental results demonstrate the effectiveness of the proposed approach in accurately estimating the pose of industrial robotic arms in various scenarios, including cluttered environments and rapid motion conditions. Overall, our method shows promise in advancing the capabilities of industrial robotic systems, paving the way for enhanced automation in manufacturing and other domains.
    Keywords: industrial robotic arm; pose tracking; inertial measurement unit; IMU; visual-inertial fusion; dynamic environments with external disturbances.
    DOI: 10.1504/IJMIC.2025.10074327
     

 

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