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Intelligent Manufacturing and Informatization
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Intelligent Manufacturing and Informatization

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (1 March 2024) | Viewed by 19112

Special Issue Editors

Dr. Xianfeng Li

E-Mail Website
Guest Editor
International Institute for Next Generation Internet, Macau University of Science and Technology, Macao 999078, China
Interests: computer architecture; networked embedded systems; high-performance networking; edge intelligence (Edge AI); Smart Internet of Things (AIoT)
Dr. Zhi Zheng

E-Mail Website
Guest Editor
School of Communication and Information Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: information and communication engineering; integrated circuits and systems for communication and signal processing; wireless and mobile communication

Special Issue Information

Dear Colleagues,

The manufacturing industry as a whole is attempting, through intelligent manufacturing and scientific management of industrial upgrading, to solve the problem of integration of labor and equipment. Robotics, automation, information technology, the Internet of Things, sensors, and other components are used to reduce production costs, improve the quality of this enterprise’s products, and improve the level of digital information technology of industry, combined with the means of scientific management and the future development of enterprises to provide the brain of intelligent manufacturing.

This Special Issue will also present extended versions of selected papers presented at the 2022 4th International Conference on Information Technology and Computer Communications (ITCC 2022). This conference is meant for researchers from academia, industries, and research and development organizations all over the globe interested in the areas of Information Technology and Computer Communications. It will put special emphasis on the participation of PhD students, postdoctoral fellows, and other young researchers from all over the world. It would be beneficial to bring together a group of experts from diverse fields to discuss recent progress and to share ideas on open questions. The conference will feature world-class keynote speakers in the main areas. Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works, and industrial experiences that describe significant advances in information technology and computer communications.

Dr. Xianfeng Li
Dr. Zhi Zheng
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. Information is an international peer-reviewed open access monthly 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 1600 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

  • Decision analysis and methods
  • E-business and e-commerce
  • Human factors
  • Quality control and management
  • Systems modeling and simulation
  • Technology and knowledge management
  • Production planning and control
  • Supply chain management

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

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Research

11 pages, 1781 KiB  
Article
Deep Learning-Based Semantic Segmentation Methods for Pavement Cracks
by Yu Zhang, Xin Gao and Hanzhong Zhang
Information 2023, 14(3), 182; https://doi.org/10.3390/info14030182 - 15 Mar 2023
Cited by 2 | Viewed by 2149
Abstract
As road mileage continues to expand, the number of disasters caused by expanding pavement cracks is increasing. Two main methods, image processing and deep learning, are used to detect these cracks to improve the efficiency and quality of pavement crack segmentation. The classical [...] Read more.
As road mileage continues to expand, the number of disasters caused by expanding pavement cracks is increasing. Two main methods, image processing and deep learning, are used to detect these cracks to improve the efficiency and quality of pavement crack segmentation. The classical segmentation network, UNet, has a poor ability to extract target edge information and small target segmentation, and is susceptible to the influence of distracting objects in the environment, thus failing to better segment the tiny cracks on the pavement. To resolve this problem, we propose a U-shaped network, ALP-UNet, which adds an attention module to each encoding layer. In the decoding phase, we incorporated the Laplacian pyramid to make the feature map contain more boundary information. We also propose adding a PAN auxiliary head to provide an additional loss for the backbone to improve the overall network segmentation effect. The experimental results show that the proposed method can effectively reduce the interference of other factors on the pavement and effectively improve the mIou and mPA values compared to the previous methods. Full article
(This article belongs to the Special Issue Intelligent Manufacturing and Informatization)
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35 pages, 1274 KiB  
Article
Driving Factors of Industry 4.0 Readiness among Manufacturing SMEs in Malaysia
by Annie Pooi Hang Wong and Daisy Mui Hung Kee
Information 2022, 13(12), 552; https://doi.org/10.3390/info13120552 - 23 Nov 2022
Cited by 15 | Viewed by 9643
Abstract
Industry 4.0 increases the production efficiency and competitiveness of companies. However, Industry 4.0 implementation is comparatively low in developing countries, while Malaysian manufacturing Small and Medium Enterprises (SMEs) Industry 4.0 adoption is still in its infancy stage. This quantitative study aimed to broaden [...] Read more.
Industry 4.0 increases the production efficiency and competitiveness of companies. However, Industry 4.0 implementation is comparatively low in developing countries, while Malaysian manufacturing Small and Medium Enterprises (SMEs) Industry 4.0 adoption is still in its infancy stage. This quantitative study aimed to broaden the knowledge of the driving factors that significantly strengthen Malaysian manufacturing SMEs’ readiness for the digital revolution. Based on the Resource-Based View theory, the study built a research framework to govern the investigation of organizational capabilities, SME institutional support, perceived advantage, and market factors as the driving factors of Industry 4.0 readiness, while firm size as the moderating variable. The data were collected by conducting an online survey with the owners and managers of Malaysian-owned manufacturing SMEs located throughout Peninsular Malaysia, where the firms have received some form of government assistance. The analysis of the study indicated that organizational capabilities, SME institutional support, and market factors positively correlate with Industry 4.0 readiness. It was determined that firm size only moderates the relationship between SME institutional support and Industry 4.0 readiness. This study’s findings benefit industry practitioners and policymakers who wish to drive the future of Malaysia’s SMEs business ecosystem and contribute to Industry 4.0 literature. Full article
(This article belongs to the Special Issue Intelligent Manufacturing and Informatization)
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14 pages, 5131 KiB  
Article
Tool Wear Prediction Based on a Multi-Scale Convolutional Neural Network with Attention Fusion
by Qingqing Huang, Di Wu, Hao Huang, Yan Zhang and Yan Han
Information 2022, 13(10), 504; https://doi.org/10.3390/info13100504 - 18 Oct 2022
Cited by 8 | Viewed by 2584
Abstract
Compared with traditional machine learning algorithms, the convolutional neural network (CNN) has an excellent automatic feature learning ability and can complete the nonlinear representation from original data input to output by itself. However, the CNN does not sufficiently mine the tool wear information [...] Read more.
Compared with traditional machine learning algorithms, the convolutional neural network (CNN) has an excellent automatic feature learning ability and can complete the nonlinear representation from original data input to output by itself. However, the CNN does not sufficiently mine the tool wear information contained in the multi-sensor data due to disregard of the differences in the contribution of different features when extracting features. In this paper, a tool wear prediction method based on a multi-scale convolutional neural network with attention fusion is proposed, which fuses the tool wear degradation information collected by different types of sensors. In the multi-scale convolution module, convolution kernels with different sizes are used to extract the degradation information of different scales in the wear information, and then the attention fusion module is constructed to fuse the multi-scale feature information. Finally, the mapping between tool wear and multi-sensor data is realized through the feature information obtained by residual connection and full connection layer. By comparing the multi-scale convolutional neural network with different attention mechanisms, the experiments demonstrated the effectiveness and superiority of the proposed method. Full article
(This article belongs to the Special Issue Intelligent Manufacturing and Informatization)
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31 pages, 68508 KiB  
Article
Optimal Energy Management Scheme of Battery Supercapacitor-Based Bidirectional Converter for DC Microgrid Applications
by Srinivas Punna, Sujatha Banka and Surender Reddy Salkuti
Information 2022, 13(7), 350; https://doi.org/10.3390/info13070350 - 21 Jul 2022
Cited by 3 | Viewed by 3114
Abstract
Because of the splendid front of sustainable energy reassets in a DC Microgrid, it is profoundly willing to variances in energy age. A hybrid energy storage system (HESS) which includes a battery and a supercapacitor (SC) is used to decrease in-built fluctuations. The [...] Read more.
Because of the splendid front of sustainable energy reassets in a DC Microgrid, it is profoundly willing to variances in energy age. A hybrid energy storage system (HESS) which includes a battery and a supercapacitor (SC) is used to decrease in-built fluctuations. The two different characteristics of the battery and supercapacitor make it a great match for HESS applications. The HESS is connected to the DC Microgrid through a bidirectional converter, which allows energy to be exchanged between the battery and supercapacitor. This paper discusses a converter presenting an approach for a double-input bidirectional converter. Related to this, a regulator was designed for use as a voltage regulation in a DC Microgrid. The designed controllers accelerated PV generation and load disturbance DC link voltage restoration, in addition to effective power balancing among the battery and the SC. The conventional PI, proposed PI, and predictive PI control techniques are effectively validated using MATLAB Simulink. Experimental findings with low power have been used to validate the operation of the predictive PI control technique. The DC grid voltage profile showed substantial improvement while using the predictive PI control in comparison with the proposed and conventional PI control techniques in terms of setting time and maximum peak overshoot. Full article
(This article belongs to the Special Issue Intelligent Manufacturing and Informatization)
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