Table of Content

Computing Methods for Industrial Artificial Intelligence

Submission Deadline: 31 December 2022 Submit to Special Issue

Guest Editors

Prof. Liang Gao, Huazhong University of Science and Technology, China
Prof. Dazhong Wu, University of Central Florida Orlando, USA
Prof. Long Wen, China University of Geosciences, China
Prof. Junliang Wang, Donghua University, China
Dr. Yiping Gao, Huazhong University of Science and Technology, China


Nowadays, with the rapid developments of smart technology, data can be collected more comprehensively and extensively than before in industry. Data-driven intelligent manufacturing has become the hot point and has been widely investigated worldwide. Artificial intelligence (AI) is the key technology, which can mine the valuable information from industrial data to help the analysis and optimization on the industrial manufacturing system.

Recently, various advanced AI techniques have been developed, such as swarm intelligence, intelligent computation and deep learning. These AI techniques have shown their potential to promote the efficiency and effectiveness for the industrial manufacturing system. The proposed Special Issue on Computing Methods in Industrial Artificial Intelligence will focus on the theories, methodologies and applications of the advanced AI techniques in intelligent manufacturing. The special issue is encouraging to use the advanced AI techniques to handle with the full life-cycle data in intelligent manufacturing with different application scenarios, such as workshop scheduling, quality control and intelligence operations. The purpose of this special issue is to reflect the latest developments of AI techniques and their application in intelligent manufacturing.


Potential topics include but are not limited to the following:

• Advanced industrial AI theories and methodologies

• AI-based industrial data preprocessing, modeling, analysis and decision-making

• AI-driven methods for optimization of the manufacturing system

• AI-driven methods for intelligent equipment operation

• AI-driven methods for product quality control

• AI-driven methods for full life-cycle product design

• AI-driven methods for imbalanced data in intelligent manufacturing

• AI-driven methods for small-scale samples in intelligent manufacturing


Artificial intelligence; intelligent manufacturing; industrial data analysis; deep learning; workshop scheduling and optimization

Published Papers

  • Open Access


    An Edge-Fog-Cloud Computing-Based Digital Twin Model for Prognostics Health Management of Process Manufacturing Systems

    Jie Ren, Chuqiao Xu, Junliang Wang, Jie Zhang, Xinhua Mao, Wei Shen
    CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.1, pp. 599-618, 2023, DOI:10.32604/cmes.2022.022415
    (This article belongs to this Special Issue: Computing Methods for Industrial Artificial Intelligence)
    Abstract The prognostics health management (PHM) from the systematic view is critical to the healthy continuous operation of process manufacturing systems (PMS), with different kinds of dynamic interference events. This paper proposes a three leveled digital twin model for the systematic PHM of PMSs. The unit-leveled digital twin model of each basic device unit of PMSs is constructed based on edge computing, which can provide real-time monitoring and analysis of the device status. The station-leveled digital twin models in the PMSs are designed to optimize and control the process parameters, which are deployed for the manufacturing execution on the fog server.… More >

  • Open Access


    A New Childhood Pneumonia Diagnosis Method Based on Fine-Grained Convolutional Neural Network

    Yang Zhang, Liru Qiu, Yongkai Zhu, Long Wen, Xiaoping Luo
    CMES-Computer Modeling in Engineering & Sciences, Vol.133, No.3, pp. 873-894, 2022, DOI:10.32604/cmes.2022.022322
    (This article belongs to this Special Issue: Computing Methods for Industrial Artificial Intelligence)
    Abstract Pneumonia is part of the main diseases causing the death of children. It is generally diagnosed through chest X-ray images. With the development of Deep Learning (DL), the diagnosis of pneumonia based on DL has received extensive attention. However, due to the small difference between pneumonia and normal images, the performance of DL methods could be improved. This research proposes a new fine-grained Convolutional Neural Network (CNN) for children’s pneumonia diagnosis (FG-CPD). Firstly, the fine-grained CNN classification which can handle the slight difference in images is investigated. To obtain the raw images from the real-world chest X-ray data, the YOLOv4… More >

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