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Computing Methods for Industrial Artificial Intelligence

Submission Deadline: 01 May 2023 (closed)

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

Summary

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


Keywords

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

Published Papers


  • Open Access

    ARTICLE

    Digital Twin Modeling and Simulation Optimization of Transmission Front and Middle Case Assembly Line

    Xianfeng Cao, Meihua Yao, Yahui Zhang, Xiaofeng Hu, Chuanxun Wu
    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3233-3253, 2024, DOI:10.32604/cmes.2023.030773
    (This article belongs to the Special Issue: Computing Methods for Industrial Artificial Intelligence)
    Abstract As the take-off of China’s macro economy, as well as the rapid development of infrastructure construction, real estate industry, and highway logistics transportation industry, the demand for heavy vehicles is increasing rapidly, the competition is becoming increasingly fierce, and the digital transformation of the production line is imminent. As one of the most important components of heavy vehicles, the transmission front and middle case assembly lines have a high degree of automation, which can be used as a pilot for the digital transformation of production. To ensure the visualization of digital twins (DT), consistent control More >

    Graphic Abstract

    Digital Twin Modeling and Simulation Optimization of Transmission Front and Middle Case Assembly Line

  • Open Access

    ARTICLE

    Strategic Contracting for Software Upgrade Outsourcing in Industry 4.0

    Cheng Wang, Zhuowei Zheng
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1563-1592, 2024, DOI:10.32604/cmes.2023.031103
    (This article belongs to the Special Issue: Computing Methods for Industrial Artificial Intelligence)
    Abstract The advent of Industry 4.0 has compelled businesses to adopt digital approaches that combine software to enhance production efficiency. In this rapidly evolving market, software development is an ongoing process that must be tailored to meet the dynamic needs of enterprises. However, internal research and development can be prohibitively expensive, driving many enterprises to outsource software development and upgrades to external service providers. This paper presents a software upgrade outsourcing model for enterprises and service providers that accounts for the impact of market fluctuations on software adaptability. To mitigate the risk of adverse selection due… More >

  • Open Access

    ARTICLE

    Optimization of Engine Control Strategies for Low Fuel Consumption in Heavy-Duty Commercial Vehicles

    Shuilong He, Yang Liu, Shanchao Wang, Liangying Hu, Fei Xiao, Chao Li
    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2693-2714, 2023, DOI:10.32604/cmes.2023.028631
    (This article belongs to the Special Issue: Computing Methods for Industrial Artificial Intelligence)
    Abstract The reduction of fuel consumption in engines is always considered of vital importance. Along these lines, in this work, this goal was attained by optimizing the heavy-duty commercial vehicle engine control strategy. More specifically, at first, a general first principles model for heavy-duty commercial vehicles and a transient fuel consumption model for heavy-duty commercial vehicles were developed and the parameters were adjusted to fit the empirical data. The accuracy of the proposed model was demonstrated from the stage and the final results. Next, the control optimization problem resulting in low fuel consumption in heavy commercial… More >

    Graphic Abstract

    Optimization of Engine Control Strategies for Low Fuel Consumption in Heavy-Duty Commercial Vehicles

  • Open Access

    ARTICLE

    A Novel Collaborative Evolutionary Algorithm with Two-Population for Multi-Objective Flexible Job Shop Scheduling

    Cuiyu Wang, Xinyu Li, Yiping Gao
    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 1849-1870, 2023, DOI:10.32604/cmes.2023.028098
    (This article belongs to the Special Issue: Computing Methods for Industrial Artificial Intelligence)
    Abstract Job shop scheduling (JS) is an important technology for modern manufacturing. Flexible job shop scheduling (FJS) is critical in JS, and it has been widely employed in many industries, including aerospace and energy. FJS enables any machine from a certain set to handle an operation, and this is an NP-hard problem. Furthermore, due to the requirements in real-world cases, multi-objective FJS is increasingly widespread, thus increasing the challenge of solving the FJS problems. As a result, it is necessary to develop a novel method to address this challenge. To achieve this goal, a novel collaborative More >

  • Open Access

    ARTICLE

    An Effective Neighborhood Solution Clipping Method for Large-Scale Job Shop Scheduling Problem

    Sihan Wang, Xinyu Li, Qihao Liu
    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 1871-1890, 2023, DOI:10.32604/cmes.2023.028339
    (This article belongs to the Special Issue: Computing Methods for Industrial Artificial Intelligence)
    Abstract The job shop scheduling problem (JSSP) is a classical combinatorial optimization problem that exists widely in diverse scenarios of manufacturing systems. It is a well-known NP-hard problem, when the number of jobs increases, the difficulty of solving the problem exponentially increases. Therefore, a major challenge is to increase the solving efficiency of current algorithms. Modifying the neighborhood structure of the solutions can effectively improve the local search ability and efficiency. In this paper, a genetic Tabu search algorithm with neighborhood clipping (GTS_NC) is proposed for solving JSSP. A neighborhood solution clipping method is developed and… More >

  • Open Access

    ARTICLE

    Multitarget Flexible Grasping Detection Method for Robots in Unstructured Environments

    Qingsong Fan, Qijie Rao, Haisong Huang
    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 1825-1848, 2023, DOI:10.32604/cmes.2023.028369
    (This article belongs to the Special Issue: Computing Methods for Industrial Artificial Intelligence)
    Abstract In present-day industrial settings, where robot arms perform tasks in an unstructured environment, there may exist numerous objects of various shapes scattered in random positions, making it challenging for a robot arm to precisely attain the ideal pose to grasp the object. To solve this problem, a multistage robotic arm flexible grasp detection method based on deep learning is proposed. This method first improves the Faster RCNN target detection model, which significantly improves the detection ability of the model for multiscale grasped objects in unstructured scenes. Then, a Squeeze-and-Excitation module is introduced to design a… More >

  • Open Access

    ARTICLE

    Rules Mining-Based Gene Expression Programming for the Multi-Skill Resource Constrained Project Scheduling Problem

    Min Hu, Zhimin Chen, Yuan Xia, Liping Zhang, Qiuhua Tang
    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 2815-2840, 2023, DOI:10.32604/cmes.2023.027146
    (This article belongs to the Special Issue: Computing Methods for Industrial Artificial Intelligence)
    Abstract The multi-skill resource-constrained project scheduling problem (MS-RCPSP) is a significant management science problem that extends from the resource-constrained project scheduling problem (RCPSP) and is integrated with a real project and production environment. To solve MS-RCPSP, it is an efficient method to use dispatching rules combined with a parallel scheduling mechanism to generate a scheduling scheme. This paper proposes an improved gene expression programming (IGEP) approach to explore newly dispatching rules that can broadly solve MS-RCPSP. A new backward traversal decoding mechanism, and several neighborhood operators are applied in IGEP. The backward traversal decoding mechanism dramatically More >

    Graphic Abstract

    Rules Mining-Based Gene Expression Programming for the Multi-Skill Resource Constrained Project Scheduling Problem

  • Open Access

    ARTICLE

    An Enhanced Adaptive Differential Evolution Approach for Constrained Optimization Problems

    Wenchao Yi, Zhilei Lin, Yong Chen, Zhi Pei, Jiansha Lu
    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 2841-2860, 2023, DOI:10.32604/cmes.2023.027055
    (This article belongs to the Special Issue: Computing Methods for Industrial Artificial Intelligence)
    Abstract Effective constrained optimization algorithms have been proposed for engineering problems recently. It is common to consider constraint violation and optimization algorithm as two separate parts. In this study, a pbest selection mechanism is proposed to integrate the current mutation strategy in constrained optimization problems. Based on the improved pbest selection method, an adaptive differential evolution approach is proposed, which helps the population jump out of the infeasible region. If all the individuals are infeasible, the top 5% of infeasible individuals are selected. In addition, a modified truncated ε-level method is proposed to avoid trapping in infeasible More >

    Graphic Abstract

    An Enhanced Adaptive Differential Evolution Approach for Constrained Optimization Problems

  • Open Access

    ARTICLE

    Image Semantic Segmentation for Autonomous Driving Based on Improved U-Net

    Chuanlong Sun, Hong Zhao, Liang Mu, Fuliang Xu, Laiwei Lu
    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 787-801, 2023, DOI:10.32604/cmes.2023.025119
    (This article belongs to the Special Issue: Computing Methods for Industrial Artificial Intelligence)
    Abstract Image semantic segmentation has become an essential part of autonomous driving. To further improve the generalization ability and the robustness of semantic segmentation algorithms, a lightweight algorithm network based on Squeeze-and-Excitation Attention Mechanism (SE) and Depthwise Separable Convolution (DSC) is designed. Meanwhile, Adam-GC, an Adam optimization algorithm based on Gradient Compression (GC), is proposed to improve the training speed, segmentation accuracy, generalization ability and stability of the algorithm network. To verify and compare the effectiveness of the algorithm network proposed in this paper, the trained network model is used for experimental verification and comparative test More >

  • Open Access

    ARTICLE

    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 the 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… More >

  • Open Access

    ARTICLE

    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 the 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… More >

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