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AI Applications in Modern Industrial Systems
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AI Applications in Modern Industrial Systems

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 September 2026 | Viewed by 1555

Special Issue Editor

Prof. Dr. Degang Xu

E-Mail Website
Guest Editor
School of Automation, Central South University, Changsha 410010, China
Interests: artificial intelligence; machine vision; process control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Current artificial intelligence has penetrated into every corner of the industrial field at an astonishing speed, profoundly changing the face of modern industry. The application of artificial intelligence in the industrial field covers various aspects such as production, management, and research and development. From optimizing production processes to improving product quality, and from innovating equipment maintenance to innovating supply chain management, AI technology has shown tremendous potential, leading the industry towards intelligent development.

This Special Issue aims to report the latest methods of intelligent perception, system modeling, online detection, process control applications of artificial intelligence in modern industry. We invite researchers and practitioners from academia and industry to publish papers on novel or innovative aspects of related advanced algorithms. Some examples are listed below, but the topics are not restricted to these:

  • New techniques for machine vision in industry.
  • System modeling based on big data of industry process.
  • Process parameter detection based on artificial intelligence technology.
  • Innovative cases of industrial process control with artificial intelligence methods.

Prof. Dr. Degang Xu
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • machine vision
  • system modeling
  • parameter detection
  • big data
  • process control

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Published Papers (4 papers)

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Research

21 pages, 3371 KB  
Article
An Implicit-Explicit Diffusion Model for Industrial Data Imputation
by Yishun Liu, Changyong Zhu, Lingsong Liu and Wenfeng Deng
Appl. Sci. 2026, 16(8), 3826; https://doi.org/10.3390/app16083826 - 14 Apr 2026
Viewed by 237
Abstract
High-quality process data are essential for modern manufacturing processes to enable advanced control techniques, fault detection, and predictive maintenance. However, real-world industrial datasets often contain missing values due to sensor failures, power outages, and equipment maintenance. This paper proposes a novel implicit–explicit diffusion [...] Read more.
High-quality process data are essential for modern manufacturing processes to enable advanced control techniques, fault detection, and predictive maintenance. However, real-world industrial datasets often contain missing values due to sensor failures, power outages, and equipment maintenance. This paper proposes a novel implicit–explicit diffusion model that jointly utilizes both hidden and visible properties for industrial data imputation. The model employs a dual-branch architecture: one branch uses multi-scale dilated causal convolutions to capture hierarchical periodic patterns inherent in industrial time series, while the other branch leverages structured state space (S4) models to learn long-term dependencies. A gated fusion mechanism adaptively combines these complementary representations. Extensive experiments on Debutanizer and Sulfur Recovery Unit (SRU) datasets demonstrate that the proposed method achieves the best root mean squared error (RMSE) across all tested missing rates (20–80%) on both datasets, and exhibits particularly strong advantages in high-missingness scenarios (60–80%), where the proposed multi-scale and long-range modeling capabilities prove most beneficial. Full article
(This article belongs to the Special Issue AI Applications in Modern Industrial Systems)
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19 pages, 49221 KB  
Article
Deep Reinforcement Learning for Navigation via Multi-Modal Belief State Representation from LiDAR and Depth Sensors
by Degang Xu, Haiou Wang and Yizhi Wang
Appl. Sci. 2026, 16(8), 3787; https://doi.org/10.3390/app16083787 - 13 Apr 2026
Viewed by 306
Abstract
This paper presents a deep reinforcement learning framework for autonomous navigation based on multi-modal belief state representation learned from LiDAR and depth sensors. To address the challenges posed by partial observability and sensor-specific uncertainty, we propose a probabilistic representation module that models belief [...] Read more.
This paper presents a deep reinforcement learning framework for autonomous navigation based on multi-modal belief state representation learned from LiDAR and depth sensors. To address the challenges posed by partial observability and sensor-specific uncertainty, we propose a probabilistic representation module that models belief states as Gaussian distributions over latent environmental features. Sensor-specific encoders extract structured features from raw LiDAR and depth inputs, which are fused using a Q-value-guided weighting scheme derived from the policy critic. A motion-prediction pretraining strategy and a cross-modal coherence loss are introduced to enhance the alignment and reliability of the learned belief states. The resulting representation is integrated into a Soft Actor–Critic (SAC) framework to enable policy-driven decision-making under uncertainty. Extensive experiments in simulated environments demonstrate that the proposed method improves success rate, navigation efficiency, and generalization. Real-world experiments further validate these findings, with the proposed method outperforming a classical navigation baseline by reducing average travel time by 16% and path length by 4%. These results support the use of probabilistic multi-modal belief modeling for autonomous navigation under partial observability. Full article
(This article belongs to the Special Issue AI Applications in Modern Industrial Systems)
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22 pages, 20655 KB  
Article
Center Prior Guided Multi-Feature Fusion for Salient Object Detection in Metallurgical Furnace Images
by Lin Pan, Haisheng Zhong, Zhikun Qi, Xiaofang Chen and Denghui Wu
Appl. Sci. 2026, 16(6), 2668; https://doi.org/10.3390/app16062668 - 11 Mar 2026
Viewed by 231
Abstract
This paper proposes a novel salient object detection method for operational hole localization in metallurgical furnaces, addressing challenging industrial conditions including extreme illumination variations and strong electromagnetic interference to enable two-level measurement in aluminum electrolysis cells and impact position recognition of the front-of-furnace [...] Read more.
This paper proposes a novel salient object detection method for operational hole localization in metallurgical furnaces, addressing challenging industrial conditions including extreme illumination variations and strong electromagnetic interference to enable two-level measurement in aluminum electrolysis cells and impact position recognition of the front-of-furnace operation robot. It employs a multi-feature fusion framework combining foreground and background saliency maps with center prior maps. Foreground saliency maps are generated through spatial compactness and local contrast computations, enhancing discriminative features while suppressing shared foreground–background characteristics. Background saliency maps are constructed via sparse reconstruction to exploit redundant features. Then method integrates edge extraction and density clustering to generate center prior maps that emphasize foreground target centroids and mitigate background noise. Comprehensive evaluations on both a specialized operational hole dataset and six public datasets demonstrate superior performance compared to other methods. On the specialized dataset, it achieves a precision of 0.8954, a maximum F-measure of 0.8994, and an S-measure of 0.8662. While maintaining operational robustness, the method offers a practical solution for furnace monitoring and robotic operation guidance in metallurgical processes. Full article
(This article belongs to the Special Issue AI Applications in Modern Industrial Systems)
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23 pages, 2666 KB  
Article
A Study on ACCC Surface Defect Classification Method Using ResNet18 with Integrated SE Attention Mechanism
by Wenlong Xiao and Rui Chen
Appl. Sci. 2026, 16(4), 1899; https://doi.org/10.3390/app16041899 - 13 Feb 2026
Viewed by 348
Abstract
Surface defect detection in aluminum-based composite core conductors (ACCC) via X-ray imaging has long been constrained by challenges such as small sample sizes, class imbalance, model redundancy, and inadequate adaptation to single-channel industrial images. To address this, this paper proposes SE-ResNet18, a lightweight [...] Read more.
Surface defect detection in aluminum-based composite core conductors (ACCC) via X-ray imaging has long been constrained by challenges such as small sample sizes, class imbalance, model redundancy, and inadequate adaptation to single-channel industrial images. To address this, this paper proposes SE-ResNet18, a lightweight classification model synergistically designed for industrial single-channel X-ray images. The model features a co-adapted architecture where a single-channel input layer (preserving native image information and eliminating RGB conversion overhead) is coupled with a channel attention mechanism (to amplify subtle defect features), all within a globally optimized lightweight framework. With targeted data augmentation and robust training strategies, the model achieves superior performance on the ACCC defect dataset: classification accuracy reaches 98.39%, while excelling in lightweight design (12.0 million parameters) and real-time capability (0.44 ms/image inference speed). The experiments demonstrate that the proposed model exhibits high classification accuracy in testing while offering superior lightweight characteristics and inference efficiency. This provides a feasible solution for achieving high-precision detection and real-time processing in industrial scenarios, showcasing potential for ACCC online detection applications. Full article
(This article belongs to the Special Issue AI Applications in Modern Industrial Systems)
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