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Artificial Intelligence Algorithm for Industrial Operation Application

Submission Deadline: 30 September 2023 (closed)

Guest Editors

Dr. Tao Ye, China University of Mining and Technology-Beijing, China.
Dr. Xiao-Zhi Gao, University of Eastern Finland, Finland.
Dr. Naiming Xie, Nanjing University of Aeronautics & Astronautics, China.
Dr. Xianyu Yu, Nanjing University of Aeronautics & Astronautics, China.
Dr. Hanyu E, University of Alberta, Canada.

Summary

Artificial intelligence algorithm is a set of instructions to be followed in calculations or other operations based on creating machines, which can think and make decisions independently of human intervention. The current artificial intelligence algorithms include artificial neural network, machine learning, genetic algorithm, simulated annealing algorithm, cluster intelligent ant colony algorithm and support vector machines and so on. With the rapid development of intelligent demand in industry, the artificial intelligence algorithm research for various industrial operations such as logistics optimization, mineral mining, and production scheduling has become a hot research field that has attracted widespread attention from scholars.

This special issue aims to find new research of advanced artificial intelligence algorithms to improve the performance of solving industrial operation problems in different fields, such as logistics optimization, Intelligent Traffic System, Intelligent mine, and production scheduling, pattern recognition and other industrial optimization problems. This issue welcomes the new research ideas and developments in artificial intelligence algorithms relevant to industrial operation, including foundation, systems, smart applications, and other research contributions. Original research and review articles are both welcome.

Keywords

Artificial intelligence algorithm for transportation optimization
Artificial intelligence algorithm for vehicle scheduling
Artificial intelligence algorithm for production scheduling
Application of artificial intelligence in mining
Intelligent Traffic System
Artificial intelligence algorithm for pattern recognition
Optimization of industrial production scheduling
Evolutionary Algorithms in various fields
Natural language processing and decision models
Machine Learning and deep learning
Artificial intelligence algorithm research in other fields

Published Papers

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Open Access

ARTICLE

A Reference Vector-Assisted Many-Objective Optimization Algorithm with Adaptive Niche Dominance Relation
Fangzhen Ge, Yating Wu, Debao Chen, Longfeng Shen
Intelligent Automation & Soft Computing, DOI:10.32604/iasc.2024.042841
（This article belongs to the Special Issue: Artificial Intelligence Algorithm for Industrial Operation Application)
Abstract It is still a huge challenge for traditional Pareto-dominated many-objective optimization algorithms to solve many-objective optimization problems because these algorithms hardly maintain the balance between convergence and diversity and can only find a group of solutions focused on a small area on the Pareto front, resulting in poor performance of those algorithms. For this reason, we propose a reference vector-assisted algorithm with an adaptive niche dominance relation, for short MaOEA-AR. The new dominance relation forms a niche based on the angle between candidate solutions. By comparing these solutions, the solution with the best convergence is found to be the non-dominated… More >

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ARTICLE

A Hybrid Manufacturing Process Monitoring Method Using Stacked Gated Recurrent Unit and Random Forest
Chao-Lung Yang, Atinkut Atinafu Yilma, Bereket Haile Woldegiorgis, Hendrik Tampubolon, Hendri Sutrisno
Intelligent Automation & Soft Computing, DOI:10.32604/iasc.2024.043091
（This article belongs to the Special Issue: Artificial Intelligence Algorithm for Industrial Operation Application)
Abstract This study proposed a new real-time manufacturing process monitoring method to monitor and detect process shifts in manufacturing operations. Since real-time production process monitoring is critical in today’s smart manufacturing. The more robust the monitoring model, the more reliable a process is to be under control. In the past, many researchers have developed real-time monitoring methods to detect process shifts early. However, these methods have limitations in detecting process shifts as quickly as possible and handling various data volumes and varieties. In this paper, a robust monitoring model combining Gated Recurrent Unit (GRU) and Random Forest (RF) with Real-Time Contrast… More >

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Open Access

ARTICLE

Deep Neural Network Architecture Search via Decomposition-Based Multi-Objective Stochastic Fractal Search
Hongshang Xu, Bei Dong, Xiaochang Liu, Xiaojun Wu
Intelligent Automation & Soft Computing, Vol.38, No.2, pp. 185-202, 2023, DOI:10.32604/iasc.2023.041177
（This article belongs to the Special Issue: Artificial Intelligence Algorithm for Industrial Operation Application)
Abstract Deep neural networks often outperform classical machine learning algorithms in solving real-world problems. However, designing better networks usually requires domain expertise and consumes significant time and computing resources. Moreover, when the task changes, the original network architecture becomes outdated and requires redesigning. Thus, Neural Architecture Search (NAS) has gained attention as an effective approach to automatically generate optimal network architectures. Most NAS methods mainly focus on achieving high performance while ignoring architectural complexity. A myriad of research has revealed that network performance and structural complexity are often positively correlated. Nevertheless, complex network structures will bring enormous computing resources. To cope… More >

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172
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ARTICLE

A New Method for Image Tamper Detection Based on an Improved U-Net
Jie Zhang, Jianxun Zhang, Bowen Li, Jie Cao, Yifan Guo
Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2883-2895, 2023, DOI:10.32604/iasc.2023.039805
（This article belongs to the Special Issue: Artificial Intelligence Algorithm for Industrial Operation Application)
Abstract With the improvement of image editing technology, the threshold of image tampering technology decreases, which leads to a decrease in the authenticity of image content. This has also driven research on image forgery detection techniques. In this paper, a U-Net with multiple sensory field feature extraction (MSCU-Net) for image forgery detection is proposed. The proposed MSCU-Net is an end-to-end image essential attribute segmentation network that can perform image forgery detection without any pre-processing or post-processing. MSCU-Net replaces the single-scale convolution module in the original network with an improved multiple perceptual field convolution module so that the decoder can synthesize the… More >

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ARTICLE

A Sensor Network Coverage Planning Based on Adjusted Single Candidate Optimizer
Trong-The Nguyen, Thi-Kien Dao, Trinh-Dong Nguyen
Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 3213-3234, 2023, DOI:10.32604/iasc.2023.041356
（This article belongs to the Special Issue: Artificial Intelligence Algorithm for Industrial Operation Application)
Abstract Wireless sensor networks (WSNs) are widely used for various practical applications due to their simplicity and versatility. The quality of service in WSNs is greatly influenced by the coverage, which directly affects the monitoring capacity of the target region. However, low WSN coverage and uneven distribution of nodes in random deployments pose significant challenges. This study proposes an optimal node planning strategy for network coverage based on an adjusted single candidate optimizer (ASCO) to address these issues. The single candidate optimizer (SCO) is a metaheuristic algorithm with stable implementation procedures. However, it has limitations in avoiding local optimum traps in… More >

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ARTICLE

Competitive and Cooperative-Based Strength Pareto Evolutionary Algorithm for Green Distributed Heterogeneous Flow Shop Scheduling
Kuihua Huang, Rui Li, Wenyin Gong, Weiwei Bian, Rui Wang
Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2077-2101, 2023, DOI:10.32604/iasc.2023.040215
（This article belongs to the Special Issue: Artificial Intelligence Algorithm for Industrial Operation Application)
Abstract This work aims to resolve the distributed heterogeneous permutation flow shop scheduling problem (DHPFSP) with minimizing makespan and total energy consumption (TEC). To solve this NP-hard problem, this work proposed a competitive and cooperative-based strength Pareto evolutionary algorithm (CCSPEA) which contains the following features: 1) An initialization based on three heuristic rules is developed to generate a population with great diversity and convergence. 2) A comprehensive metric combining convergence and diversity metrics is used to better represent the heuristic information of a solution. 3) A competitive selection is designed which divides the population into a winner and a loser swarms… More >

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ARTICLE

A Multi-Object Genetic Algorithm for the Assembly Line Balance Optimization in Garment Flexible Job Shop Scheduling
Junru Liu, Yonggui Lv
Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2421-2439, 2023, DOI:10.32604/iasc.2023.040262
（This article belongs to the Special Issue: Artificial Intelligence Algorithm for Industrial Operation Application)
Abstract Numerous clothing enterprises in the market have a relatively low efficiency of assembly line planning due to insufficient optimization of bottleneck stations. As a result, the production efficiency of the enterprise is not high, and the production organization is not up to expectations. Aiming at the problem of flexible process route planning in garment workshops, a multi-object genetic algorithm is proposed to solve the assembly line balance optimization problem and minimize the machine adjustment path. The encoding method adopts the object-oriented path representation method, and the initial population is generated by random topology sorting based on an in-degree selection mechanism.… More >

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ARTICLE

Prediction-Based Thunderstorm Path Recovery Method Using CNN-BiLSTM
Xu Yang, Ling Zhuang, Yuqiang Sun, Wenjie Zhang
Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1637-1654, 2023, DOI:10.32604/iasc.2023.039879
（This article belongs to the Special Issue: Artificial Intelligence Algorithm for Industrial Operation Application)
Abstract The loss of three-dimensional atmospheric electric field (3DAEF) data has a negative impact on thunderstorm detection. This paper proposes a method for thunderstorm point charge path recovery. Based on the relationship between a point charge and 3DAEF, we derive corresponding localization formulae by establishing a point charge localization model. Generally, point charge movement paths are obtained after fitting time series localization results. However, AEF data losses make it difficult to fit and visualize paths. Therefore, using available AEF data without loss as input, we design a hybrid model combining the convolutional neural network (CNN) and bi-directional long short-term memory (BiLSTM)… More >

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Artificial Intelligence Algorithm for Industrial Operation Application

Guest Editors

Summary

Keywords

Published Papers

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200

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23

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249

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39

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354

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172

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510

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278

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1

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455

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191

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679

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404

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640

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357

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1

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631

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347

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