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Table of Content

Machine Learning-Guided Intelligent Modeling with Its Industrial Applications

Submission Deadline: 31 August 2023 (closed)

Guest Editors

Prof. Xiong Luo, University of Science and Technology Beijing, China.
Dr. Yongqiang Cheng, University of Hull, UK.
Prof. Zhifang Liao, Central South University, China.

Summary

The industrial system is the nerve center widely used in the fields of critical infrastructure such as power, petroleum and petrochemical, water conservancy, transportation, and nuclear facilities. With the increasing demand for intelligent manufacturing, the essence of the modern industrial system has been transformed into human-cyber-physical systems. Therefore, the industrial system has become a complex system with many factors, and the modeling and design guided by machine learning (ML) are gradually applied to large-scale production with higher accuracy.


In recent years, industrial production has reached an unprecedented level. In the complex industrial system operation process, huge amounts of production, operation, control, and other kinds of data are generated, which can be generally characterized by massiveness, multi-source, heterogeneity, and high-dimension. The accumulation of big data not only promotes the development of production technology but also brings great challenges. Due to its limited representation ability, the traditional modeling method cannot fully extract the information contained in the big data of the industrial system, thus, intelligent modeling intends to fully explore the useful information in big data by constructing an appropriate intelligent modeling structure. Accordingly, ML-guided directed evolution has become a new paradigm for industrial design that enables the optimization of complex functions. Both structured and unstructured data from industrial systems can be applied to ML intelligent modeling use to predict how sequence maps function without requiring a detailed model of the underlying physics pathways. Then it can help the system to make more accurate intelligent decisions and promote the concept of digital twins.

 

The focus of this Special Issue is on the development of ML-guided intelligent modeling for solving problems in the fields of industrial. Articles submitted to this Special Issue can also be concerned with the intelligence algorithms for systematic modeling, simulation, and optimization of complex industrial systems. We invite researchers to contribute original research articles, as well as review articles, that will stimulate the continuing research effort on applications of data-enabled intelligence about complex industrial systems and computing techniques to assess/solve engineering problems.


Topics of interest include but are not restricted to:

-Industrial applications of complex system theory

-Machine learning and deep learning for complex system modeling

-Filter-aided methods for industrial processes

-Data-driven control of industrial systems

-Artificial intelligence for system optimization

-Neurodynamic analysis for industrial process

-Detection classification for complex industrial systems

-Distributed multi-agent modeling algorithms and its industrial applications

-Robust modeling methods for industrial process

-the other related topics


Keywords

Machine learning; Deep learning; Intelligent models; Data analysis; Industrial applications

Published Papers


  • Open Access

    ARTICLE

    CAW-YOLO: Cross-Layer Fusion and Weighted Receptive Field-Based YOLO for Small Object Detection in Remote Sensing

    Weiya Shi, Shaowen Zhang, Shiqiang Zhang
    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3209-3231, 2024, DOI:10.32604/cmes.2023.044863
    (This article belongs to this Special Issue: Machine Learning-Guided Intelligent Modeling with Its Industrial Applications)
    Abstract In recent years, there has been extensive research on object detection methods applied to optical remote sensing images utilizing convolutional neural networks. Despite these efforts, the detection of small objects in remote sensing remains a formidable challenge. The deep network structure will bring about the loss of object features, resulting in the loss of object features and the near elimination of some subtle features associated with small objects in deep layers. Additionally, the features of small objects are susceptible to interference from background features contained within the image, leading to a decline in detection accuracy. Moreover, the sensitivity of small… More >

  • Open Access

    ARTICLE

    Heterophilic Graph Neural Network Based on Spatial and Frequency Domain Adaptive Embedding Mechanism

    Lanze Zhang, Yijun Gu, Jingjie Peng
    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1701-1731, 2024, DOI:10.32604/cmes.2023.045129
    (This article belongs to this Special Issue: Machine Learning-Guided Intelligent Modeling with Its Industrial Applications)
    Abstract Graph Neural Networks (GNNs) play a significant role in tasks related to homophilic graphs. Traditional GNNs, based on the assumption of homophily, employ low-pass filters for neighboring nodes to achieve information aggregation and embedding. However, in heterophilic graphs, nodes from different categories often establish connections, while nodes of the same category are located further apart in the graph topology. This characteristic poses challenges to traditional GNNs, leading to issues of “distant node modeling deficiency” and “failure of the homophily assumption”. In response, this paper introduces the Spatial-Frequency domain Adaptive Heterophilic Graph Neural Networks (SFA-HGNN), which integrates adaptive embedding mechanisms for… More >

  • Open Access

    REVIEW

    A Survey of Knowledge Graph Construction Using Machine Learning

    Zhigang Zhao, Xiong Luo, Maojian Chen, Ling Ma
    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 225-257, 2024, DOI:10.32604/cmes.2023.031513
    (This article belongs to this Special Issue: Machine Learning-Guided Intelligent Modeling with Its Industrial Applications)
    Abstract Knowledge graph (KG) serves as a specialized semantic network that encapsulates intricate relationships among real-world entities within a structured framework. This framework facilitates a transformation in information retrieval, transitioning it from mere string matching to far more sophisticated entity matching. In this transformative process, the advancement of artificial intelligence and intelligent information services is invigorated. Meanwhile, the role of machine learning method in the construction of KG is important, and these techniques have already achieved initial success. This article embarks on a comprehensive journey through the last strides in the field of KG via machine learning. With a profound amalgamation… More >

  • Open Access

    ARTICLE

    User Purchase Intention Prediction Based on Improved Deep Forest

    Yifan Zhang, Qiancheng Yu, Lisi Zhang
    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 661-677, 2024, DOI:10.32604/cmes.2023.044255
    (This article belongs to this Special Issue: Machine Learning-Guided Intelligent Modeling with Its Industrial Applications)
    Abstract Widely used deep neural networks currently face limitations in achieving optimal performance for purchase intention prediction due to constraints on data volume and hyperparameter selection. To address this issue, based on the deep forest algorithm and further integrating evolutionary ensemble learning methods, this paper proposes a novel Deep Adaptive Evolutionary Ensemble (DAEE) model. This model introduces model diversity into the cascade layer, allowing it to adaptively adjust its structure to accommodate complex and evolving purchasing behavior patterns. Moreover, this paper optimizes the methods of obtaining feature vectors, enhancement vectors, and prediction results within the deep forest algorithm to enhance the… More >

  • Open Access

    ARTICLE

    Research on Condenser Deterioration Evolution Trend Based on ANP-EWM Fusion Health Degree

    Hong Qian, Haixin Wang, Guangji Wang, Qingyun Yan
    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 679-698, 2024, DOI:10.32604/cmes.2023.043377
    (This article belongs to this Special Issue: Machine Learning-Guided Intelligent Modeling with Its Industrial Applications)
    Abstract This study presents a proposed method for assessing the condition and predicting the future status of condensers operating in seawater over an extended period. The aim is to address the problems of scaling and corrosion, which lead to increased loss of cold resources. The method involves utilising a set of multivariate feature parameters associated with the condenser as input for evaluation and trend prediction. This methodology offers a precise means of determining the optimal timing for condenser cleaning, with the ultimate goal of improving its overall performance. The proposed approach involves the integration of the analytic network process with subjective… More >

  • Open Access

    ARTICLE

    A Degradation Type Adaptive and Deep CNN-Based Image Classification Model for Degraded Images

    Huanhua Liu, Wei Wang, Hanyu Liu, Shuheng Yi, Yonghao Yu, Xunwen Yao
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 459-472, 2024, DOI:10.32604/cmes.2023.029084
    (This article belongs to this Special Issue: Machine Learning-Guided Intelligent Modeling with Its Industrial Applications)
    Abstract Deep Convolutional Neural Networks (CNNs) have achieved high accuracy in image classification tasks, however, most existing models are trained on high-quality images that are not subject to image degradation. In practice, images are often affected by various types of degradation which can significantly impact the performance of CNNs. In this work, we investigate the influence of image degradation on three typical image classification CNNs and propose a Degradation Type Adaptive Image Classification Model (DTA-ICM) to improve the existing CNNs’ classification accuracy on degraded images. The proposed DTA-ICM comprises two key components: a Degradation Type Predictor (DTP) and a Degradation Type… More >

  • Open Access

    ARTICLE

    Role Dynamic Allocation of Human-Robot Cooperation Based on Reinforcement Learning in an Installation of Curtain Wall

    Zhiguang Liu, Shilin Wang, Jian Zhao, Jianhong Hao, Fei Yu
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 473-487, 2024, DOI:10.32604/cmes.2023.029729
    (This article belongs to this Special Issue: Machine Learning-Guided Intelligent Modeling with Its Industrial Applications)
    Abstract A real-time adaptive roles allocation method based on reinforcement learning is proposed to improve human-robot cooperation performance for a curtain wall installation task. This method breaks the traditional idea that the robot is regarded as the follower or only adjusts the leader and the follower in cooperation. In this paper, a self-learning method is proposed which can dynamically adapt and continuously adjust the initiative weight of the robot according to the change of the task. Firstly, the physical human-robot cooperation model, including the role factor is built. Then, a reinforcement learning model that can adjust the role factor in real… More >

    Graphic Abstract

    Role Dynamic Allocation of Human-Robot Cooperation Based on Reinforcement Learning in an Installation of Curtain Wall

  • Open Access

    ARTICLE

    Improved RRT Algorithm for Automatic Charging Robot Obstacle Avoidance Path Planning in Complex Environments

    Chong Xu, Hao Zhu, Haotian Zhu, Jirong Wang, Qinghai Zhao
    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2567-2591, 2023, DOI:10.32604/cmes.2023.029152
    (This article belongs to this Special Issue: Machine Learning-Guided Intelligent Modeling with Its Industrial Applications)
    Abstract A new and improved RRT algorithm has been developed to address the low efficiency of obstacle avoidance planning and long path distances in the electric vehicle automatic charging robot arm. This algorithm enables the robot to avoid obstacles, find the optimal path, and complete automatic charging docking. It maintains the global completeness and path optimality of the RRT algorithm while also improving the iteration speed and quality of generated paths in both 2D and 3D path planning. After finding the optimal path, the B-sample curve is used to optimize the rough path to create a smoother and more optimal path.… More >

    Graphic Abstract

    Improved RRT<sup>∗</sup> Algorithm for Automatic Charging Robot Obstacle Avoidance Path Planning in Complex Environments

  • Open Access

    ARTICLE

    Code Reviewer Intelligent Prediction in Open Source Industrial Software Project

    Zhifang Liao, Bolin Zhang, Xuechun Huang, Song Yu, Yan Zhang
    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 687-704, 2023, DOI:10.32604/cmes.2023.027466
    (This article belongs to this Special Issue: Machine Learning-Guided Intelligent Modeling with Its Industrial Applications)
    Abstract Currently, open-source software is gradually being integrated into industrial software, while industry protocols in industrial software are also gradually transferred to open-source community development. Industrial protocol standardization organizations are confronted with fragmented and numerous code PR (Pull Request) and informal proposals, and different workflows will lead to increased operating costs. The open-source community maintenance team needs software that is more intelligent to guide the identification and classification of these issues. To solve the above problems, this paper proposes a PR review prediction model based on multi-dimensional features. We extract 43 features of PR and divide them into five dimensions: contributor,… More >

  • Open Access

    ARTICLE

    STPGTN–A Multi-Branch Parameters Identification Method Considering Spatial Constraints and Transient Measurement Data

    Shuai Zhang, Liguo Weng
    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 2635-2654, 2023, DOI:10.32604/cmes.2023.025405
    (This article belongs to this Special Issue: Machine Learning-Guided Intelligent Modeling with Its Industrial Applications)
    Abstract Transmission line (TL) Parameter Identification (PI) method plays an essential role in the transmission system. The existing PI methods usually have two limitations: (1) These methods only model for single TL, and can not consider the topology connection of multiple branches for simultaneous identification. (2) Transient bad data is ignored by methods, and the random selection of terminal section data may cause the distortion of PI and have serious consequences. Therefore, a multi-task PI model considering multiple TLs’ spatial constraints and massive electrical section data is proposed in this paper. The Graph Attention Network module is used to draw a… More >

    Graphic Abstract

    STPGTN–A Multi-Branch Parameters Identification Method Considering Spatial Constraints and Transient Measurement Data

  • Open Access

    ARTICLE

    LF-CNN: Deep Learning-Guided Small Sample Target Detection for Remote Sensing Classification

    Chengfan Li, Lan Liu, Junjuan Zhao, Xuefeng Liu
    CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.1, pp. 429-444, 2022, DOI:10.32604/cmes.2022.019202
    (This article belongs to this Special Issue: Machine Learning-Guided Intelligent Modeling with Its Industrial Applications)
    Abstract Target detection of small samples with a complex background is always difficult in the classification of remote sensing images. We propose a new small sample target detection method combining local features and a convolutional neural network (LF-CNN) with the aim of detecting small numbers of unevenly distributed ground object targets in remote sensing images. The k-nearest neighbor method is used to construct the local neighborhood of each point and the local neighborhoods of the features are extracted one by one from the convolution layer. All the local features are aggregated by maximum pooling to obtain global feature representation. The classification… More >

  • Open Access

    ARTICLE

    Machine Learning Enhanced Boundary Element Method: Prediction of Gaussian Quadrature Points

    Ruhui Cheng, Xiaomeng Yin, Leilei Chen
    CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.1, pp. 445-464, 2022, DOI:10.32604/cmes.2022.018519
    (This article belongs to this Special Issue: Machine Learning-Guided Intelligent Modeling with Its Industrial Applications)
    Abstract This paper applies a machine learning technique to find a general and efficient numerical integration scheme for boundary element methods. A model based on the neural network multi-classification algorithm is constructed to find the minimum number of Gaussian quadrature points satisfying the given accuracy. The constructed model is trained by using a large amount of data calculated in the traditional boundary element method and the optimal network architecture is selected. The two-dimensional potential problem of a circular structure is tested and analyzed based on the determined model, and the accuracy of the model is about 90%. Finally, by incorporating the… More >

  • Open Access

    ARTICLE

    Transferable Features from 1D-Convolutional Network for Industrial Malware Classification

    Liwei Wang, Jiankun Sun, Xiong Luo, Xi Yang
    CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.2, pp. 1003-1016, 2022, DOI:10.32604/cmes.2022.018492
    (This article belongs to this Special Issue: Machine Learning-Guided Intelligent Modeling with Its Industrial Applications)
    Abstract With the development of information technology, malware threats to the industrial system have become an emergent issue, since various industrial infrastructures have been deeply integrated into our modern works and lives. To identify and classify new malware variants, different types of deep learning models have been widely explored recently. Generally, sufficient data is usually required to achieve a well-trained deep learning classifier with satisfactory generalization ability. However, in current practical applications, an ample supply of data is absent in most specific industrial malware detection scenarios. Transfer learning as an effective approach can be used to alleviate the influence of the… More >

  • Open Access

    ARTICLE

    A Fast Small-Sample Modeling Method for Precision Inertial Systems Fault Prediction and Quantitative Anomaly Measurement

    Hongqiao Wang, Yanning Cai
    CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.1, pp. 187-203, 2022, DOI:10.32604/cmes.2022.018000
    (This article belongs to this Special Issue: Machine Learning-Guided Intelligent Modeling with Its Industrial Applications)
    Abstract Inertial system platforms are a kind of important precision devices, which have the characteristics of difficult acquisition for state data and small sample scale. Focusing on the model optimization for data-driven fault state prediction and quantitative degree measurement, a fast small-sample supersphere one-class SVM modeling method using support vectors pre-selection is systematically studied in this paper. By theorem-proving the irrelevance between the model's learning result and the non-support vectors (NSVs), the distribution characters of the support vectors are analyzed. On this basis, a modeling method with selected samples having specific geometry character from the training sets is also proposed. The… More >

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