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Processes
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Hybrid Artificial Intelligence for Smart Process Control
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Hybrid Artificial Intelligence for Smart Process Control

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Automation Control Systems".

Deadline for manuscript submissions: 30 November 2025 | Viewed by 889

Special Issue Editors

Prof. Dr. Ján Piteľ

E-Mail Website
Guest Editor
Department of Industrial Engineering and Informatics, Faculty of Manufacturing Technologies with a Seat in Presov, Technical University of Kosice, 080 01 Presov, Slovakia
Interests: monitoring and control of machines; mechatronic systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Haruki Ueno

E-Mail Website
Guest Editor
Principles of Informatics Research Division, National Institute of Informatics, Tokyo 101-8430, Japan
Interests: artificial intelligence; robotics; R&D strategy

Special Issue Information

Dear Colleagues,

The rapid advancements in artificial intelligence (AI) are transforming smart process control, enabling adaptive, autonomous, and reliable industrial operations. This Special Issue aims to explore the cutting-edge research and practical implementations of hybrid AI systems in smart process control. It seeks contributions that highlight how knowledge-based AI (symbolic reasoning and expert systems), deep learning-based AI (data-driven machine learning), and agent-based AI (autonomous systems) can be effectively combined to address challenges in industrial automation, predictive maintenance, fault diagnosis, and autonomous systems.

We invite researchers, practitioners, and industry experts to contribute original research articles, reviews, and case studies that address the following themes:

  • Hybrid AI in Process Control: Exploring integrated frameworks combining symbolic, neural, and agent-based AI for intelligent, robust, and adaptive process control systems.
  • Predictive Maintenance using Hybrid AI: Combining sensor data analytics and expert knowledge to forecast equipment failures and optimize maintenance scheduling in industrial systems.
  • Edge AI and Cloud System for Real-Time Process Control: Implementing lightweight hybrid AI models on edge devices for fast, on-site decision-making in industrial automation environments.
  • AI-Driven Control Loop Optimization: Optimizing feedback and feedforward control loops with hybrid AI for enhanced system responsiveness and operational stability.
  • Sustainability and Environmental Impact: Analyzing the contributions of hybrid AI in promoting sustainable practices and reducing the environmental footprint of industrial processes.

This Special Issue seeks to provide a comprehensive overview of the current state of the art in hybrid AI within smart process control, fostering discussions on future trends and challenges. We encourage submissions that not only advance academic knowledge but also offer practical insights and solutions for industry implementation.

Prof. Dr. Ján Piteľ
Prof. Dr. Haruki Ueno
Guest Editors

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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Processes is an international peer-reviewed open access monthly 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 (AI)
  • Machine Learning (ML)
  • smart process control
  • industrial automation
  • data-driven process control
  • predictive analytics
  • deep learning
  • real-time data processing
  • control systems
  • Internet of Things (IoT)

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Published Papers (1 paper)

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Research

18 pages, 10978 KB  
Article
A Lightweight Infrared and Visible Light Multimodal Fusion Method for Object Detection in Power Inspection
by Linghao Zhang, Junwei Kuang, Yufei Teng, Siyu Xiang, Lin Li and Yingjie Zhou
Processes 2025, 13(9), 2720; https://doi.org/10.3390/pr13092720 - 26 Aug 2025
Viewed by 651
Abstract
Visible and infrared thermal imaging are crucial techniques for detecting structural and temperature anomalies in electrical power system equipment. To meet the demand for multimodal infrared/visible light monitoring of target devices, this paper introduces CBAM-YOLOv4, an improved lightweight object detection model, which leverages [...] Read more.
Visible and infrared thermal imaging are crucial techniques for detecting structural and temperature anomalies in electrical power system equipment. To meet the demand for multimodal infrared/visible light monitoring of target devices, this paper introduces CBAM-YOLOv4, an improved lightweight object detection model, which leverages a novel synergistic integration of the Convolutional Block Attention Module (CBAM) with YOLOv4. The model employs MobileNet-v3 as the backbone to reduce parameter count, applies depthwise separable convolution to decrease computational complexity, and incorporates the CBAM module to enhance the extraction of critical optical features under complex backgrounds. Furthermore, a feature-level fusion strategy is adopted to integrate visible and infrared image information effectively. Validation on public datasets demonstrates that the proposed model achieves an 18.05 frames per second increase in detection speed over the baseline, a 1.61% improvement in mean average precision (mAP), and a 2 MB reduction in model size, substantially improving both detection accuracy and efficiency through this optimized integration in anomaly inspection of electrical equipment. Validation on a representative edge device, the NVIDIA Jetson Nano, confirms the model’s practical applicability. After INT8 quantization, the model achieves a real-time inference speed of 40.8 FPS with a high mAP of 80.91%, while consuming only 5.2 W of power. Compared to the standard YOLOv4, our model demonstrates a significant improvement in both processing efficiency and detection accuracy, offering a uniquely balanced and deployable solution for mobile inspection platforms. Full article
(This article belongs to the Special Issue Hybrid Artificial Intelligence for Smart Process Control)
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