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Electronics
Special Issues
Modeling, Optimization, and Automation for Complex Manufacturing System
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Modeling, Optimization, and Automation for Complex Manufacturing System

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Systems & Control Engineering".

Deadline for manuscript submissions: 15 July 2024 | Viewed by 2486

Special Issue Editors

Dr. Dong Yang

E-Mail Website
Guest Editor
School of Electrical Engineering and Automation, Anhui University, Hefei 230601, China
Interests: modelling and optimization for complex manufacturing systems; industrial robot technology; CAD/CAE/CAM/CAPP
Prof. Dr. Teng Li

E-Mail Website
Guest Editor
School of Electrical Engineering and Automation, Anhui University, Hefei 230601, China
Interests: computer vision; pattern recognition; multimedia computing
Special Issues, Collections and Topics in MDPI journals
Dr. Yali Zhi

E-Mail Website
Guest Editor
School of Electrical Engineering and Automation, Anhui University, Hefei 230601, China
Interests: time-delay systems; singular systems; neural network; T-S fuzzy systems; robust control

Special Issue Information

Dear Colleagues,

Complex manufacturing systems have received widespread attention in fields such as automotive manufacturing, chip manufacturing, and robotics due to their advantages of automation, intellectualization and customizable operation. Modeling and optimization provide a clear picture on how to improve overall performance of complex manufacturing systems. How to design the optimization strategy, and how to apply automation methods in order to achieve desired target have attracted widespread attention. This special issue provides a forum for the publication and dissemination of recent ideas/strategies which contributes to greater scientific understanding of the main disciplines underpinning the electrical engineering, artificial intelligence, mechanical engineering, control engineering and computer sciences, etc.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but not limited to) the following: Intelligent system, Systems & control engineering, Fault-tolerant control of Time-delay systems, Singular systems, T-S fuzzy systems, Circuit and signal processing, Networks, Event-driven, Fault diagnosis, Neural network, Pattern recognition and computer vision, Deep learning, Electrical and Autonomous Vehicles, Multimedia computing, Artificial Intelligence Circuits and Systems (AICAS), Reinforcement learning and Cooperative game, etc.

I look forward to receiving your contributions.

Dr. Dong Yang
Prof. Dr. Teng Li
Dr. Yali Zhi
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. Electronics 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 2400 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

  • modeling
  • optimization
  • automation
  • complex manufacturing system

Published Papers (2 papers)

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Research

23 pages, 4297 KiB  
Article
A Data Hierarchical Encryption Scheme Based on Attribute Hiding under Multiple Authorization Centers
by Caimei Wang, Jianzhong Pan, Jianhao Lu and Zhize Wu
Electronics 2024, 13(1), 125; https://doi.org/10.3390/electronics13010125 - 28 Dec 2023
Viewed by 563
Abstract
The data hierarchical Ciphertext-Policy Attribute-Based Encryption (CP-ABE) scheme implements multiple hierarchical data encryption of a single access policy, which reduces the computation and storage overhead. However, existing data hierarchical CP-ABE schemes have some problems, such as the leakage of personal privacy information through [...] Read more.
The data hierarchical Ciphertext-Policy Attribute-Based Encryption (CP-ABE) scheme implements multiple hierarchical data encryption of a single access policy, which reduces the computation and storage overhead. However, existing data hierarchical CP-ABE schemes have some problems, such as the leakage of personal privacy information through access policies or user attributes in plaintext form, and these schemes grant enough privileges to a single authorization center. If the authorization center is untrusted or attacked, keys can be used to illegally access data, which is the key escrow problem. To solve these problems, we propose an Attribute Hiding and Multiple Authorization Centers-based Data Hierarchical Encryption Scheme (AH-MAC-DHE). Firstly, we propose an Attribute Convergence Hiding Mechanism (ACHM). This mechanism solves the problem of personal privacy information leakage by hiding access policies and user attributes. Secondly, we design Privilege-Dispersed Multiple Authorization Centers (PD-MAC). PD-MAC solves the problem of key escrow by dispersing the privileges of the single authorization center to the user authorization center and attribute authorization center. Finally, we prove that AH-MAC-DHE is secure under the decisional q-parallel Bilinear Diffie-Hellman Exponent (BDHE) assumption, which also satisfies anti-collusion and privacy security. The experimental results indicate that compared with existing schemes, AH-MAC-DHE performs well. Full article
(This article belongs to the Special Issue Modeling, Optimization, and Automation for Complex Manufacturing System)
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13 pages, 3745 KiB  
Article
Quantitative Analysis of Steel Alloy Elements Based on LIBS and Deep Learning of Multi-Perspective Features
by Yanhong Gu, Zhiwei Chen, Hao Chen and Fudong Nian
Electronics 2023, 12(12), 2566; https://doi.org/10.3390/electronics12122566 - 06 Jun 2023
Cited by 1 | Viewed by 1299
Abstract
The Si and Mn contents in steel alloys are important characteristic indexes that influence the plasticity and welding properties of these alloys. In this work, the quantitative analysis methods for trace elements under complex alloy matrices by laser-induced breakdown spectroscopy (LIBS) are studied, [...] Read more.
The Si and Mn contents in steel alloys are important characteristic indexes that influence the plasticity and welding properties of these alloys. In this work, the quantitative analysis methods for trace elements under complex alloy matrices by laser-induced breakdown spectroscopy (LIBS) are studied, which provide a foundation for utilizing LIBS technology in the rapid online detection of steel alloy properties. To improve the quantitative analysis accuracy of LIBS, deep learning algorithm methods are introduced. Given the characteristics of LIBS spectra, we explore multi-perspective feature extraction and backward differential methods to extract the spatio-temporal characteristics of LIBS spectra. The Text Convolutional Neural Network (TextCNN) model, combined with multi-perspective feature extraction, displays good stability and lower average relative errors (6.988% for Si, 6.280% for Mn) in the test set compared to the traditional quantitative analysis method and deep neural network (DNN) model. Finally, the backward differential method is employed to optimize the two-dimensional LIBS spectral input matrix, and the results indicate that the average relative errors of Si and Mn elements in the test set decrease to 5.139% and 3.939%, respectively. The method proposed in this work establishes a theoretical basis and technical support for precise prediction and online quality monitoring. Full article
(This article belongs to the Special Issue Modeling, Optimization, and Automation for Complex Manufacturing System)
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