Mathematical Problems in Engineering

Computational Intelligence in Data-Driven Modelling and Its Engineering Applications 2020


Publishing date
01 Dec 2020
Status
Closed
Submission deadline
24 Jul 2020

Lead Editor

1Liverpool John Moores University, Liverpool, UK

2Queen Mary University of London, London, UK

This issue is now closed for submissions.
More articles will be published in the near future.

Computational Intelligence in Data-Driven Modelling and Its Engineering Applications 2020

This issue is now closed for submissions.
More articles will be published in the near future.

Description

Modern engineering systems show increasing complexity due to their high nonlinearity and large disturbances and uncertainties introduced by them. In many cases, conventional mathematical models, such as differential equations that can accurately describe the complex systems and can be exploited in real-life applications, do not exist. However, with the fast development of advanced sensing, measurement, and data collection technologies, large amounts of data that represent input-output relationships of the systems become available. This makes data-driven modelling (DDM) possible and practical.

Data-driven modelling aims at information extraction from data and is normally used to elicit numerical predictive models with good generalization ability, which can be viewed as regression problems in mathematics. It analyses the data that characterize a system to find relationships among the system state variables (input, internal, and output variables) without taking into account explicit knowledge about physical behaviors. Many paradigms utilized in DDM have been established based on statistics and/or computational intelligence. For instance, artificial neural networks (ANNs) and fuzzy rule-based systems (FRBSs) serve as fundamental model frameworks, which are alternatives to statistical inference methods, while evolutionary algorithms (EAs), swarm intelligence (SI), and machine learning (ML) methods provide learning and optimization abilities for calibrating and improving the intelligent or statistical models. In recent years, DDM has found widespread applications, ranging across machinery, manufacturing, materials, processing, power and energy systems, transport, and so on.

This Special Issue intends to bring together the state-of-the-art research, applications, and reviews of DDM techniques. It aims at not only stimulating deep insights into computational intelligence approaches in DDM but also promoting their potential applications in complex engineering problems.

Potential topics include but are not limited to the following:

  • The use of computational intelligence techniques, such as ANNs, deep neural networks, FRBSs, neurofuzzy systems, genetic programming, evolutionary programming, EAs, SI, nature-inspired metaheuristics, ML in data-driven modelling for:
    • Materials processing and metal processing
    • Manufacturing and instrumentation
    • Robotics and industrial automation
    • Transport and logistics systems
    • Power and energy systems
    • Sports engineering

Articles

  • Special Issue
  • - Volume 2020
  • - Article ID 9098709
  • - Research Article

Modeling a Thermochemical Reactor of a Solar Refrigerator by BaCl2-NH3 Sorption Using Artificial Neural Networks and Mathematical Symmetry Groups

Onesimo Meza-Cruz | Isaac Pilatowsky | ... | Mauricio A. Sanchez
  • Special Issue
  • - Volume 2020
  • - Article ID 9356165
  • - Research Article

A Machine-Learning Based Nonintrusive Smart Home Appliance Status Recognition

Liston Matindife | Yanxia Sun | Zenghui Wang
  • Special Issue
  • - Volume 2020
  • - Article ID 2946980
  • - Research Article

Analysing and Data-Driven Modelling the Handling Performance of Rugby Balls under Wet Conditions

Jiaojiao Liu | Yunan Liu | ... | Chao Liu
  • Special Issue
  • - Volume 2020
  • - Article ID 8071810
  • - Research Article

A Novel Hybrid CNN-LSTM Scheme for Nitrogen Oxide Emission Prediction in FCC Unit

Wei He | Jufeng Li | ... | Huaqing Liang
  • Special Issue
  • - Volume 2020
  • - Article ID 5735496
  • - Research Article

Data-Driven Model for Rockburst Prediction

Hongbo Zhao | Bingrui Chen
  • Special Issue
  • - Volume 2020
  • - Article ID 3589198
  • - Research Article

Financial Trading Strategy System Based on Machine Learning

Yanjun Chen | Kun Liu | ... | Mingyu Hu
  • Special Issue
  • - Volume 2020
  • - Article ID 9863936
  • - Research Article

Health Assessment of High-Speed Train Running Gear System under Complex Working Conditions Based on Data-Driven Model

Chao Cheng | Ming Liu | ... | Wanxiu Teng
  • Special Issue
  • - Volume 2020
  • - Article ID 8916028
  • - Research Article

A Scene Text Detector for Text with Arbitrary Shapes

Weijia Wu | Jici Xing | ... | Hong Zhou
  • Special Issue
  • - Volume 2020
  • - Article ID 3871897
  • - Research Article

Texts as Lines: Text Detection with Weak Supervision

Weijia Wu | Jici Xing | ... | Hong Zhou
Mathematical Problems in Engineering
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Acceptance rate27%
Submission to final decision64 days
Acceptance to publication34 days
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Impact Factor1.305
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Article of the Year Award: Outstanding research contributions of 2020, as selected by our Chief Editors. Read the winning articles.