A machine learning and genetic algorithm-based method for predicting width deviation of hot-rolled strip in steel production systems

Ji, Yingjun; Liu, Shixin; Zhou, MengChu; Zhao, Ziyan; Guo, Xiwang; Qi, Liang

doi:10.1016/j.ins.2021.12.063

Cited by 97 publications

(16 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to better present the effect of the college English education model, this section also uses the original GA (genetic algorithm) [18,19] and BSA (backtracking search optimization algorithm) [20,21] foretell similar 15 groups of test set information in the sample information for comparative analysis. The evaluation outcomes of GA and BSA are displayed in Figures 4 and 5.…”

Section: Network Training and Experimental Analysismentioning

confidence: 99%

College English Audio-Visual-Oral Teaching Mode from the Perspective of Artificial Intelligence

Ling

2022

Advances in Multimedia

View full text Add to dashboard Cite

The “New Era Artificial Brain Power Improvement Plan” issued by the State Council and the “Computer Reasoning Development Activity Plan for Colleges and Universities” issued by the Ministry of Education clearly pointed out that it is necessary to attach importance to the combination of modern education and artificial intelligence. As a global language, English is becoming more and more important in global trade. Under the premise of educational plan changes in the new environment, English teaching is gradually reflected in the cultivation of college students’ English application ability. This research studies the current college English audio-visual-oral teaching mode from the perspective of artificial intelligence and analyzes the current situation of traditional English teaching by explaining the meaning of artificial intelligence, which mainly includes four stages: literature research, model construction, empirical research, and result analysis. In light of BP neural network, the showing assessment model of school English audio-visual-oral is laid out, and the change technique of showing method of school English audio-visual-oral under AI is advanced. Experiments show that this model achieves better teaching quality than the models constructed by traditional GA algorithm and BSA algorithm and is more suitable for promotion in college English teaching.

show abstract

Section: Network Training and Experimental Analysismentioning

confidence: 99%

College English Audio-Visual-Oral Teaching Mode from the Perspective of Artificial Intelligence

Ling

2022

Advances in Multimedia

View full text Add to dashboard Cite

show abstract

“…Although the accuracy of the model is improved through various optimization algorithms, the interpretability of the model becomes poor, and the model with more variables will generally be less robust, which brings great constraints to the actual production and application [ 49 ]. Ji et al [ 61 ] proposed a hybrid method based on machine learning and GA to obtain the prediction model of strip width deviation after hot rolling. The model can consider both prediction accuracy and interpretability.…”

Section: The State Of the Art Of Intelligent Optimization In The Plas...mentioning

confidence: 99%

A Review of the Intelligent Optimization and Decision in Plastic Forming

Tang

Wang²,

Deng³

et al. 2022

Materials

View full text Add to dashboard Cite

The plastic forming process involves many influencing factors and has some inevitable disturbance factors, rendering the multi-objective collaborative optimization difficult. With the rapid development of big data and artificial intelligence (AI) technology, intelligent process optimization has become one of the critical technologies for plastic forming. This paper elaborated on the research progress on the intelligent optimization of plastic forming and the data-driven process planning and decision-making system in plastic forming process optimization. The development trend in intelligent optimization of the plastic forming process was researched. This review showed that the intelligent optimization algorithm has great potential in controlling forming quality, microstructure, and performance in plastic forming. It is a general trend to develop an intelligent optimization model of the plastic forming process with high integration, versatility, and high performance. Future research will take the data-driven expert system and digital twin system as the carrier, integrate the optimization algorithm and model, and realize the multi-scale, high-precision, high-efficiency, and real-time optimization of the plastic forming process.

show abstract

“…In order to deal with these problems, more effective methods are needed for the prediction, detection, decision planning, and parameter optimization of disassembly lines [23,24]. The characteristics of disassembly lines determine that a solution is needed with fast learning speed, high intelligence, strong expandability, to quickly adjust the constraints.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning for Disassembly System Optimization Problems: A Survey

Guo

Bi²,

Wang³

et al. 2023

IJNDI

Self Cite

View full text Add to dashboard Cite

Survey/review study Reinforcement Learning for Disassembly System Optimization Problems: A Survey Xiwang Guo 1,2,*, Zhiliang Bi 2, Jiacun Wang 1, Shujin Qin 3, Shixin Liu 4, and Liang Qi 5 1 Department of Computer Science and Software Engineering, Monmouth University, New Jersey 07710, USA 2 Department of Information Control, Liaoning Petrochemical University, Fushun 113005, China 3 Department of Economic Management, Shangqiu Normal University, Shangqiu 476000, China 4 Department of Information Science and Engineering, Northeast University, Shenyang 110819, China 5 Department of Computer Intelligence Science and Technology, Shandong University of Science and Technology, Qingdao 266590, China * Correspondence: x.w.guo@163.com Received: 16 October 2022 Accepted: 28 November 2022 Published: Abstract: The disassembly complexity of end-of-life products increases continuously. Traditional methods are facing difficulties in solving the decision-making and control problems of disassembly operations. On the other hand, the latest development in reinforcement learning makes it more feasible to solve such kind of complex problems. Inspired by behaviorism psychology, reinforcement learning is considered as one of the most promising directions to achieve universal artificial intelligence (AI). In this context, we first review the basic ideas, mathematical models, and various algorithms of reinforcement learning. Then, we introduce the research progress and application subjects in the field of disassembly and recycling, such as disassembly sequencing, disassembly line balancing, product transportation, disassembly layout, etc. In addition, the prospects, challenges and applications of reinforcement learning based disassembly and recycling are also comprehensively analyzed and discussed.

show abstract

A machine learning and genetic algorithm-based method for predicting width deviation of hot-rolled strip in steel production systems

Cited by 97 publications

References 42 publications

College English Audio-Visual-Oral Teaching Mode from the Perspective of Artificial Intelligence

College English Audio-Visual-Oral Teaching Mode from the Perspective of Artificial Intelligence

A Review of the Intelligent Optimization and Decision in Plastic Forming

Reinforcement Learning for Disassembly System Optimization Problems: A Survey

Contact Info

Product

Resources

About