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Mathematics
Special Issues
Applied Computing and Artificial Intelligence, 2nd Edition
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Applied Computing and Artificial Intelligence, 2nd Edition

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Computational and Applied Mathematics".

Deadline for manuscript submissions: 28 February 2025 | Viewed by 1715

Special Issue Editors

Dr. Xiang Li

grade E-Mail Website
Guest Editor
School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China
Interests: industrial artificial intelligence; industrial big data; deep learning; fault diagnosis; prognosis; intelligent maintenance
Special Issues, Collections and Topics in MDPI journals
Dr. Shuo Zhang

E-Mail Website
Guest Editor
School of mathematics and statistics, Northwestern Polytechnical University, Xi'an 710072, China
Interests: applied mathematics; nonlinear dynamics; control; information science; neural network; complex network system
Special Issues, Collections and Topics in MDPI journals
Dr. Wei Zhang

E-Mail Website1 Website2
Guest Editor
School of Aerospace Engineering, Shenyang Aerospace University, Shenyang 110136, China
Interests: rotor dynamics; squeeze film damper; fault diagnosis; deep learning; mechanical engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Jin Qian

E-Mail Website
Guest Editor
College of Science, Northeastern University, Shenyang 110004, China
Interests: discrete combinatorial optimization; scheduling; applied mathematics; intelligent algorithm; optimization algorithm

Special Issue Information

Dear Colleagues,

We are pleased to announce this Special Issue of the journal Mathematics entitled “Applied Computing and Artificial Intelligence, 2nd Edition”. This initiative focuses on advances in algorithmic research and practical applications of applied computing and artificial intelligence methods, which have been attracting growing interest in recent years, due to their effectiveness in solving technical problems. The recent developments in applied mathematics have largely led to benefits in many industrial tasks in different fields, including the aerospace industry, manufacturing, transportation, energy, robotics, materials, informatics, etc. A large number of practical industrial problems have been well addressed, such as system condition monitoring, parameter identification, time-series data prediction, fault diagnosis, signal processing, dynamics analysis, system optimization, data-driven modeling, etc.

This Special Issue invites high-quality original research or review papers on recent advanced methods in applied computing and artificial intelligence to address the practical challenges in the related areas. The topics of interest of this Special Issue include, but are not limited to, the following:

  • Applied computing
  • Applied mathematics
  • Neural networks
  • Deep learning
  • Parameter identification
  • Dynamics
  • System optimization
  • Machine learning
  • Control engineering
  • Intelligent maintenance
  • Computational methods
  • Fault diagnosis
  • Signal processing
  • Rotor dynamics
  • Prognosis
  • Remaining useful life prediction
  • Data mining
  • Fractional differential equation
  • Complex network system
  • Stochastic dynamics
  • Structural health monitoring
  • Nonlinear dynamics and control
  • Spacecraft dynamics.

Dr. Xiang Li
Dr. Shuo Zhang
Dr. Wei Zhang
Dr. Jin Qian
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • applied computing
  • applied mathematics
  • neural networks
  • deep learning
  • parameter identification
  • dynamics
  • system optimization
  • machine learning
  • control engineering
  • intelligent maintenance
  • computational methods
  • fault diagnosis
  • signal processing
  • rotor dynamics
  • prognosis
  • remaining useful life prediction
  • data mining
  • fractional differential equation
  • complex network system
  • stochastic dynamics
  • structural health monitoring
  • non-linear dynamics and control
  • spacecraft dynamics

Published Papers (2 papers)

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Research

21 pages, 339 KiB  
Article
Scheduling with Group Technology, Resource Allocation, and Learning Effect Simultaneously
by Ming-Hui Li, Dan-Yang Lv, Yuan-Yuan Lu and Ji-Bo Wang
Mathematics 2024, 12(7), 1029; https://doi.org/10.3390/math12071029 - 29 Mar 2024
Viewed by 364
Abstract
This paper studies the single-machine group scheduling problem with convex resource allocation and learning effect. The number of jobs in each group is different, and the corresponding common due dates are also different, where the processing time of jobs follows a convex function [...] Read more.
This paper studies the single-machine group scheduling problem with convex resource allocation and learning effect. The number of jobs in each group is different, and the corresponding common due dates are also different, where the processing time of jobs follows a convex function of resource allocation. Under common due date assignment, the objective is to minimize the weighted sum of earliness, tardiness, common due date, resource consumption, and makespan. To solve the problem, we present the heuristic, simulated annealing, and branch-and-bound algorithms. Computational experiments indicate that the proposed algorithms are effective. Full article
(This article belongs to the Special Issue Applied Computing and Artificial Intelligence, 2nd Edition)
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17 pages, 5226 KiB  
Article
Novel Creation Method of Feature Graphics for Image Generation Based on Deep Learning Algorithms
by Ying Li and Ye Tang
Mathematics 2023, 11(7), 1644; https://doi.org/10.3390/math11071644 - 29 Mar 2023
Viewed by 900
Abstract
In this paper, we propose a novel creation method of feature graphics by deep learning algorithms based on a channel attention module consisting of a separable deep convolutional neural network and an SENet network. The main innovation of this method is that the [...] Read more.
In this paper, we propose a novel creation method of feature graphics by deep learning algorithms based on a channel attention module consisting of a separable deep convolutional neural network and an SENet network. The main innovation of this method is that the image feature of sample images is extracted by convolution operation and the key point matrix is obtained by channel weighting calculation to create feature graphics within the channel attention module. The main problem of existing image generation methods is that the complex network training and calculation process affects the accuracy and efficiency of image generation. It greatly reduced the complexity of image generation and improved the efficiency when we trained the image generation network with the feature graphic maps. To verify the superiority of this method, we conducted a comparative experiment with the existing method. Additionally, we explored the influence on the accuracy and efficiency of image generation of the channel number of the weighting matrix based on the test experiment. The experimental results demonstrate that this method highlights the image features of geometric lines, simplifies the complexity of image generation and improves the efficiency. Based on this method, images with more prominent line features are generated from the description text and dynamic graphics are created for the display of the images generated, which can be applied in the construction of smart museums. Full article
(This article belongs to the Special Issue Applied Computing and Artificial Intelligence, 2nd Edition)
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