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Electronics
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Advances in AI Engineering: Exploring Machine Learning Applications
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Advances in AI Engineering: Exploring Machine Learning Applications

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 15 July 2024 | Viewed by 1700

Special Issue Editors

Prof. Dr. Quan Qian

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Guest Editor
Department of Machine Intelligence, School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
Interests: machine learning; materials informatics; AI-related engineering applications; data security; privacy-preserving machine learning, etc.
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Xing Wu

E-Mail
Guest Editor
Department of Machine Intelligence, School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
Interests: machine learning with multimodal data; materials informatics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As we enter an era where artificial intelligence (AI) evolves from a supplementary tool to a primary catalyst for innovation across diverse scientific and industrial sectors, our foremost objective is to spotlight the inventive machine learning (ML) applications. We seek to accelerate diverse methods by which AI and ML are utilized to address intricate challenges, unlocking untapped potential in the process. Spanning a broad spectrum of subjects, it explores AI’s role not only in electronics but also various other industrial scenarios. We especially welcome submissions that demonstrate creative applications of AI techniques in problem-solving, predictive analysis, and the improvement of efficiency across various fields.

We are eager to publish both original research articles and reviews in this Special Issue. Areas of research may encompass, but are not limited to: AI applications, AI in industry, AI for electronics, AI for materials, AI for engineering, etc.

We look forward to receiving your contributions.

Prof. Dr. Quan Qian
Prof. Dr. Xing Wu
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

  • AI applications
  • AI in industry
  • AI for electronic
  • AI for materials

Published Papers (2 papers)

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Research

20 pages, 3258 KiB  
Article
AI for Automating Data Center Operations: Model Explainability in the Data Centre Context Using Shapley Additive Explanations (SHAP)
by Yibrah Gebreyesus, Damian Dalton, Davide De Chiara, Marta Chinnici and Andrea Chinnici
Electronics 2024, 13(9), 1628; https://doi.org/10.3390/electronics13091628 - 24 Apr 2024
Viewed by 381
Abstract
The application of Artificial Intelligence (AI) and Machine Learning (ML) models is increasingly leveraged to automate and optimize Data Centre (DC) operations. However, the interpretability and transparency of these complex models pose critical challenges. Hence, this paper explores the Shapley Additive exPlanations (SHAP) [...] Read more.
The application of Artificial Intelligence (AI) and Machine Learning (ML) models is increasingly leveraged to automate and optimize Data Centre (DC) operations. However, the interpretability and transparency of these complex models pose critical challenges. Hence, this paper explores the Shapley Additive exPlanations (SHAP) values model explainability method for addressing and enhancing the critical interpretability and transparency challenges of predictive maintenance models. This method computes and assigns Shapley values for each feature, then quantifies and assesses their impact on the model’s output. By quantifying the contribution of each feature, SHAP values can assist DC operators in understanding the underlying reasoning behind the model’s output in order to make proactive decisions. As DC operations are dynamically changing, we additionally investigate how SHAP can capture the temporal behaviors of feature importance in the dynamic DC environment over time. We validate our approach with selected predictive models using an actual dataset from a High-Performance Computing (HPC) DC sourced from the Enea CRESCO6 cluster in Italy. The experimental analyses are formalized using summary, waterfall, force, and dependency explanations. We delve into temporal feature importance analysis to capture the features’ impact on model output over time. The results demonstrate that model explainability can improve model transparency and facilitate collaboration between DC operators and AI systems, which can enhance the operational efficiency and reliability of DCs by providing a quantitative assessment of each feature’s impact on the model’s output. Full article
(This article belongs to the Special Issue Advances in AI Engineering: Exploring Machine Learning Applications)
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22 pages, 10096 KiB  
Article
Development of Autonomous Mobile Robot with 3DLidar Self-Localization Function Using Layout Map
by Minoru Sasaki, Yuki Tsuda and Kojiro Matsushita
Electronics 2024, 13(6), 1082; https://doi.org/10.3390/electronics13061082 - 14 Mar 2024
Viewed by 1068
Abstract
In recent years, there has been growing interest in autonomous mobile robots equipped with Simultaneous Localization and Mapping (SLAM) technology as a solution to labour shortages in production and distribution settings. SLAM allows these robots to create maps of their environment using devices [...] Read more.
In recent years, there has been growing interest in autonomous mobile robots equipped with Simultaneous Localization and Mapping (SLAM) technology as a solution to labour shortages in production and distribution settings. SLAM allows these robots to create maps of their environment using devices such as Lidar, radar, and sonar sensors, enabling them to navigate and track routes without prior knowledge of the environment. However, the manual operation of these robots for map construction can be labour-intensive. To address this issue, this research aims to develop a 3D SLAM autonomous mobile robot system that eliminates the need for manual map construction by utilizing existing layout maps. The system includes a PC for self-position estimation, 3DLidar, a camera for verification, a touch panel display, and the mobile robot itself. The proposed SLAM method extracts stable wall point cloud information from 3DLidar, matches it with the wall surface information in the layout map, and uses a particle filter to estimate the robot’s position. The system also includes features such as route creation, tracking, and obstacle detection for autonomous movement. Experiments were conducted to compare the proposed system with conventional 3D SLAM methods. The results showed that the proposed system significantly reduced errors in self-positioning and enabled accurate autonomous movement on specified routes, even in the presence of slight differences in layout maps and obstacles. Ultimately, this research demonstrates the effectiveness of a system that can transport goods without the need for manual environment mapping, addressing labour shortages in such environments. Full article
(This article belongs to the Special Issue Advances in AI Engineering: Exploring Machine Learning Applications)
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