Complex Process Modeling and Control Based on AI Technology

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: 1 April 2025 | Viewed by 483

Special Issue Editors


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Guest Editor
School of Automation, China University of Geosciences, Wuhan 430074, China
Interests: process control; intelligent control; computational intelligence

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Guest Editor
Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
Interests: multi-agent cooperative control; high-precision control of electromechanical systems; anti-disturbance control; modern robust control; control theory and applications; repetitive control

Special Issue Information

Dear Colleagues,

In recent years, with the rapid development of big data and artificial intelligence technology, how to apply these new technologies to complex systems has attracted the attention of many scholars. The aim of this Special Issue is to explore complex process modeling, optimization and control, as well as the important support for the application of advanced methods and techniques of machine learning and artificial intelligence to complex processes.

This Special Issue will focus on complex process modeling and control based on AI technology and will present considerable novelty in both theoretical background and practical design. Papers should provide original ideas and new approaches, and should clearly indicate progress made in problem formulation, methodology, or application. Research areas may include (but are not limited to) the following:

  • Hybrid intelligent modeling techniques;
  • Data-driven modelling techniques;
  • Modeling and optimization of complex industrial processes;
  • Online measurement, process control, and optimization for cyber–physical systems;
  • Data mining and management methods for massive volumes of data;
  • Machine learning applications to manufacturing automation.

Prof. Dr. Jie Hu
Prof. Dr. Sheng Du
Dr. Pan Yu
Guest Editors

Manuscript Submission Information

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Keywords

  • data-driven modeling
  • artificial intelligence
  • process control
  • dynamical systems modelling
  • machine learning

Published Papers (1 paper)

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Research

13 pages, 1279 KiB  
Article
Fault Distance Measurement in Distribution Networks Based on Markov Transition Field and Darknet-19
by Haozhi Wang, Wei Guo and Yuntao Shi
Mathematics 2024, 12(11), 1665; https://doi.org/10.3390/math12111665 - 27 May 2024
Viewed by 264
Abstract
The modern distribution network system is gradually becoming more complex and diverse, and traditional fault location methods have difficulty in quickly and accurately locating the fault location after a single-phase ground fault occurs. Therefore, this study proposes a new solution based on the [...] Read more.
The modern distribution network system is gradually becoming more complex and diverse, and traditional fault location methods have difficulty in quickly and accurately locating the fault location after a single-phase ground fault occurs. Therefore, this study proposes a new solution based on the Markov transfer field and deep learning to predict the fault location, which can accurately predict the location of a single-phase ground fault in the distribution network. First, a new phase-mode transformation matrix is used to take the fault current of the distribution network as the modulus 1 component, avoiding complex calculations in the complex field; then, the extracted modulus 1 component of the current is transformed into a Markov transfer field and converted into an image using pseudo-color coding, thereby fully exploiting the fault signal characteristics; finally, the Darknet-19 network is used to automatically extract fault features and predict the distance of the fault occurrence. Through simulations on existing models and training and testing with a large amount of data, the experimental results show that this method has good stability, high accuracy, and strong anti-interference ability. This solution can effectively predict the distance of ground faults in distribution networks. Full article
(This article belongs to the Special Issue Complex Process Modeling and Control Based on AI Technology)
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