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Sustainability and Automation: Intelligent Control and Its Applications

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Engineering and Science".

Deadline for manuscript submissions: closed (20 March 2024) | Viewed by 1797

Special Issue Editors


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Guest Editor
School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
Interests: adaptive control; intelligent control; CAM of CNC

Special Issue Information

Dear Colleagues,

Sustainability and automation are the two main trends of our society and its economy development. Automation, especially, intelligent automation, is a supporting technology in sustainable development all over the world, in applications such as production systems (processes), transportation systems (automobile, plane, train, etc.), and power systems (power generation, power transmission, and power consumption). This Special Issue focusing on the latest advances in intelligent automation technologies aims to promote scholarly communications among related researchers.

The aim of this Special Issue is to explore recent technological development, including artificial intelligence, neural networks, machine learning, etc., in automation-related fields (robotics, manufacturing, transportation, power systems, etc.).

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Intelligent sensing;
  • Intelligent control;
  • Intelligent manufacturing;
  • Intelligent diagnosis;
  • Intelligent signal processing.

We look forward to receiving your contributions.

Dr. Weicun Zhang
Prof. Dr. Quanmin Zhu
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. Sustainability 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

  • intelligent sensing
  • intelligent control
  • intelligent manufacturing
  • intelligent diagnosis
  • intelligent signal processing

Published Papers (1 paper)

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Research

17 pages, 8755 KiB  
Article
Improved Multi-Person 2D Human Pose Estimation Using Attention Mechanisms and Hard Example Mining
by Lixin Zhang, Wenteng Huang, Chenliang Wang and Hui Zeng
Sustainability 2023, 15(18), 13363; https://doi.org/10.3390/su151813363 - 06 Sep 2023
Viewed by 889
Abstract
In recent years, human pose estimation, as a subfield of computer vision and artificial intelligence, has achieved significant performance improvements due to its wide applications in human-computer interaction, virtual reality, and smart security. However, most existing methods are designed for single-person scenes and [...] Read more.
In recent years, human pose estimation, as a subfield of computer vision and artificial intelligence, has achieved significant performance improvements due to its wide applications in human-computer interaction, virtual reality, and smart security. However, most existing methods are designed for single-person scenes and suffer from low accuracy and long inference time in multi-person scenes. To address this issue, increasing attention has been paid to developing methods for multi-person pose estimation, such as utilizing Partial Affinity Field (PAF)-based bottom-up methods to estimate 2D poses of multiple people. In this study, we propose a method that addresses the problems of low network accuracy and poor estimation of flexible joints. This method introduces the attention mechanism into the network and utilizes the joint point extraction method based on hard example mining. Integrating the attention mechanism into the network improves its overall performance. In contrast, the joint point extraction method improves the localization accuracy of the flexible joints of the network without increasing the complexity. Experimental results demonstrate that our proposed method significantly improves the accuracy of 2D human pose estimation. Our network achieved a notably elevated Average Precision (AP) score of 60.0 and outperformed competing methods on the standard benchmark COCO test dataset, signifying its exceptional performance. Full article
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