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Mathematics
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Complex Process Modeling and Control Based on AI Technology
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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 4995

Special Issue Editors

Prof. Dr. Jie Hu

E-Mail Website
Guest Editor
School of Automation, China University of Geosciences, Wuhan 430074, China
Interests: process control; intelligent control; computational intelligence
Prof. Dr. Sheng Du

E-Mail Website
Guest Editor
School of Automation, China University of Geosciences, Wuhan 430074, China
Interests: process control; intelligent control; intelligent optimization; computational intelligence; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Pan Yu

E-Mail Website
Guest Editor
Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
Interests: multi-agent coopera-tive control; high-precision control of electromechanical systems; an-ti-disturbance control; modern robust control; control theory and ap-plications; repetitive control
Special Issues, Collections and Topics in MDPI journals

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

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. Mathematics 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 2600 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

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

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Published Papers (6 papers)

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Research

19 pages, 2126 KiB  
Article
A Dual-Path Neural Network for High-Impedance Fault Detection
by Keqing Ning, Lin Ye, Wei Song, Wei Guo, Guanyuan Li, Xiang Yin and Mingze Zhang
Mathematics 2025, 13(2), 225; https://doi.org/10.3390/math13020225 - 10 Jan 2025
Viewed by 286
Abstract
High-impedance fault detection poses significant challenges for distribution network maintenance and operation. We propose a dual-path neural network for high-impedance fault detection. To enhance feature extraction, we use a Gramian Angular Field algorithm to transform 1D zero-sequence voltage signals into 2D images. Our [...] Read more.
High-impedance fault detection poses significant challenges for distribution network maintenance and operation. We propose a dual-path neural network for high-impedance fault detection. To enhance feature extraction, we use a Gramian Angular Field algorithm to transform 1D zero-sequence voltage signals into 2D images. Our dual-branch network simultaneously processes both representations: the CNN extracts spatial features from the transformed images, while the GRU captures temporal features from the raw signals. To optimize model performance, we integrate the Crested Porcupine Optimizer (CPO) algorithm for the adaptive optimization of key network hyperparameters. The experimental results demonstrate that our method achieves a 99.70% recognition accuracy on a dataset comprising high-impedance faults, capacitor switching, and load connections. Furthermore, it maintains robust performance under various test conditions, including different noise levels and network topology changes. Full article
(This article belongs to the Special Issue Complex Process Modeling and Control Based on AI Technology)
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18 pages, 3370 KiB  
Article
Start Time Planning for Cyclic Queuing and Forwarding in Time-Sensitive Networks
by Daqian Liu, Zhewei Zhang, Yuntao Shi, Yingying Wang, Jingcheng Guo and Zhenwu Lei
Mathematics 2024, 12(21), 3382; https://doi.org/10.3390/math12213382 - 29 Oct 2024
Viewed by 772
Abstract
Time-sensitive networking (TSN) is a kind of network communication technology applied in fields such as industrial internet and intelligent transportation, capable of meeting the application requirements for precise time synchronization and low-latency deterministic forwarding. In TSN, cyclic queuing and forwarding (CQF) is a [...] Read more.
Time-sensitive networking (TSN) is a kind of network communication technology applied in fields such as industrial internet and intelligent transportation, capable of meeting the application requirements for precise time synchronization and low-latency deterministic forwarding. In TSN, cyclic queuing and forwarding (CQF) is a traffic shaping mechanism that has been extensively discussed in the recent literature, which allows the delay of time-triggered (TT) flow to be definite and easily calculable. In this paper, two algorithms are designed to tackle the start time planning issue with the CQF mechanism, namely the flow–path–offset joint scheduling (FPOJS) algorithm and congestion-aware scheduling algorithm, to improve the scheduling success ratio of TT flows. The FPOJS algorithm, which adopts a novel scheduling object—a combination of flow, path, and offset—implements scheduling in descending order of a well-designed priority that considers the resource capacity and resource requirements of ports. The congestion-aware scheduling algorithm identifies and optimizes congested ports during scheduling and substantially improves the scheduling success ratio by dynamically configuring port resources. The experimental results demonstrate that the FPOJS algorithm achieves a 39% improvement in the scheduling success ratio over the naive algorithm, 13% over the Tabu-ITP algorithm, and 10% over the MSS algorithm. Moreover, the algorithm exhibits a higher scheduling success ratio under large-scale TSN. Full article
(This article belongs to the Special Issue Complex Process Modeling and Control Based on AI Technology)
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14 pages, 2724 KiB  
Article
Improved Real-Time Detection Transformer-Based Rail Fastener Defect Detection Algorithm
by Wei Song, Bin Liao, Keqing Ning and Xiaoyu Yan
Mathematics 2024, 12(21), 3349; https://doi.org/10.3390/math12213349 - 25 Oct 2024
Viewed by 814
Abstract
To address the issues of the Real-Time DEtection TRansformer (RT-DETR) object detection model, including poor defect feature extraction in the task of rail fastener defect detection, inefficient use of computational resources, and suboptimal channel attention in the self-attention mechanism, the following improvements were [...] Read more.
To address the issues of the Real-Time DEtection TRansformer (RT-DETR) object detection model, including poor defect feature extraction in the task of rail fastener defect detection, inefficient use of computational resources, and suboptimal channel attention in the self-attention mechanism, the following improvements were made. Firstly, a Super-Resolution Convolutional Module (SRConv) was designed as a separate component and integrated into the Backbone network, which enhances the image details and clarity while preserving the original image structure and semantic content. This integration improves the model’s ability to extract defect features. Secondly, a channel attention mechanism was integrated into the self-attention module of RT-DETR to enhance the focus on feature map channels, addressing the problem of sparse attention maps caused by the lack of channel attention while saving computational resources. Finally, the experimental results show that compared to the original model, the improved RT-DETR-based rail fastener defect detection algorithm, with an additional 0.4 MB of parameters, achieved a higher accuracy, with a 2.8 percentage point increase in the Mean Average Precision (mAP) across IoU thresholds from 0.5 to 0.9 and a 1.7 percentage point increase in the Average Recall (AR) across the same thresholds. Full article
(This article belongs to the Special Issue Complex Process Modeling and Control Based on AI Technology)
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12 pages, 483 KiB  
Article
Anti-Disturbance Bumpless Transfer Control for a Switched Systems via a Switched Equivalent-Input-Disturbance Approach
by Jiawen Wu, Qian Liu and Pan Yu
Mathematics 2024, 12(15), 2307; https://doi.org/10.3390/math12152307 - 23 Jul 2024
Viewed by 539
Abstract
This paper concentrates on the issue of anti-disturbance bumpless transfer (ADBT) control design for switched systems. The ADBT control design problem refers to designing a continuous controller and a switching rule to ensure the switched system satisfies the ADBT property. First, the concept [...] Read more.
This paper concentrates on the issue of anti-disturbance bumpless transfer (ADBT) control design for switched systems. The ADBT control design problem refers to designing a continuous controller and a switching rule to ensure the switched system satisfies the ADBT property. First, the concept of the ADBT property is introduced. Then, via a switched equivalent-input-disturbance (EID) methodology, a switched EID estimator is formulated to estimate the impact of external disturbances within the switched system. Second, a bumpless transfer control is then constructed via a compensator integrating an EID estimation. Finally, the effectiveness of the presented control scheme is verified by controlling a switching resistor–inductor–capacitor circuit on the Matlab platform. Above all, a new configuration for ADBT control of switched systems is established via a switched EID methodology. Full article
(This article belongs to the Special Issue Complex Process Modeling and Control Based on AI Technology)
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12 pages, 955 KiB  
Article
Time-Series Prediction of Electricity Load for Charging Piles in a Region of China Based on Broad Learning System
by Liansong Yu and Xiaohu Ge
Mathematics 2024, 12(13), 2147; https://doi.org/10.3390/math12132147 - 8 Jul 2024
Cited by 1 | Viewed by 1063
Abstract
This paper introduces a novel electricity load time-series prediction model, utilizing a broad learning system to tackle the challenge of low prediction accuracy caused by the unpredictable nature of electricity load sequences in a specific region of China. First, a correlation analysis with [...] Read more.
This paper introduces a novel electricity load time-series prediction model, utilizing a broad learning system to tackle the challenge of low prediction accuracy caused by the unpredictable nature of electricity load sequences in a specific region of China. First, a correlation analysis with mutual information is utilized to identify the key factors affecting the electricity load. Second, variational mode decomposition is employed to obtain different mode information, and then a broad learning system is utilized to build a prediction model with different mode information. Finally, particle swarm optimization is used to fuse the prediction models under different modes. Simulation experiments using real data validate the efficiency of the proposed method, demonstrating that it offers higher accuracy compared to advanced modeling techniques and can assist in optimal electricity-load scheduling decision-making. Additionally, the R2 of the proposed model is 0.9831, the PRMSE is 21.8502, the PMAE is 17.0097, and the PMAPE is 2.6468. Full article
(This article belongs to the Special Issue Complex Process Modeling and Control Based on AI Technology)
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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
Cited by 2 | Viewed by 793
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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