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Processes
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Applications of Artificial Intelligence Technologies in Energy, Manufacturing and Automatic...
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Applications of Artificial Intelligence Technologies in Energy, Manufacturing and Automatic Control Processes

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Advanced Digital and Other Processes".

Deadline for manuscript submissions: 25 October 2024 | Viewed by 346

Special Issue Editors

Dr. Honggang Chen

E-Mail Website
Guest Editor
College of Electronics and Information Engineering, Sichuan University, Chengdu 610065, China
Interests: artificial intelligence; signal processing; pattern recognition
Special Issues, Collections and Topics in MDPI journals
Dr. Yuan Li

E-Mail Website
Guest Editor
College of Electrical Engineering, Sichuan University, Chengdu 610065, China
Interests: electrical engineering; high-voltage and insulation technology; power transmission and distribution; energy storage
Dr. Junyu Guo

E-Mail Website
Guest Editor
School of Mechatronic Engineering, Southwest Petroleum University, Chengdu 610500, China
Interests: reliability assessments; condition monitoring; fault diagnosis; residual life prediction

Special Issue Information

Dear Colleagues,

The integration of artificial intelligence (AI) technologies into the fields of energy, manufacturing, and automatic control processes is transforming these industries by enhancing efficiency, accuracy, and innovation. As the volume and complexity of data grow, AI's role in these sectors becomes increasingly vital.

This Special Issue focuses on showcasing cutting-edge research where AI technologies are applied to optimize energy systems, revolutionize manufacturing processes, and refine automatic control mechanisms. Contributions to this Special Issue will highlight how AI not only improves operational efficiencies but also drives the evolution of these crucial sectors toward a more innovative and sustainable future.

The topics covered may include, but are not limited to, the following:

  • AI for condition monitoring;
  • AI for fault diagnosis;
  • AI for decision support;
  • AI for risk prediction;
  • AI for process management.

Dr. Honggang Chen
Dr. Yuan Li
Dr. Junyu Guo
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. Processes 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 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

  • artificial intelligence
  • deep learning
  • machine learning
  • energy
  • manufacturing
  • automatic control

Published Papers (1 paper)

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Research

14 pages, 3261 KiB  
Article
A Lightweight Safety Helmet Detection Algorithm Based on Receptive Field Enhancement
by Changpeng Ji, Zhibo Hou and Wei Dai
Processes 2024, 12(6), 1136; https://doi.org/10.3390/pr12061136 - 31 May 2024
Viewed by 178
Abstract
Wearing safety helmets is an important way to ensure the safety of workers’ lives. To address the challenges associated with low accuracy, large parameter values, and slow detection speed of existing safety helmet detection algorithms, we propose a receptive field-enhanced lightweight safety helmet [...] Read more.
Wearing safety helmets is an important way to ensure the safety of workers’ lives. To address the challenges associated with low accuracy, large parameter values, and slow detection speed of existing safety helmet detection algorithms, we propose a receptive field-enhanced lightweight safety helmet detection algorithm called YOLOv5s-CR. First, we use a lightweight backbone, a high-resolution feature fusion network, and a small object detection layer to improve the detection accuracy of small objects while substantially decreasing the model parameters. Next, we embed a coordinate attention mechanism into the feature extraction network to improve the localization accuracy of the detected object. Finally, we propose a new receptive field enhancement module (RFEM) to substitute the SPPF module in the original network, enabling the model to acquire features under multiple receptive fields, thereby enhancing the detection precision of multi-scale objects. Using the Safety Helmet Detection dataset for validation, in contrast to the initial YOLOv5s, the parameters of the improved algorithm were reduced by 62.8% to 2.61 M, and P, R, and mAP0.5 were increased by 1.5%, 1.2%, and 2.0%, respectively. The detection speed can reach 149FPS on the RTX3070 GPU, which satisfies the accuracy and real-time requirements for detecting safety helmets. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence Technologies in Energy, Manufacturing and Automatic Control Processes)
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