Industrial Artificial Intelligence: Innovations and Challenges

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

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 1449

Special Issue Editors


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Guest Editor
School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: AI in industry; PHM; reliability; monitoring and diagnosis
School of Mechanics, Civil Engineering and Architecture, Northwestern Polytechnical University, Xi’an 710129, China
Interests: reliability engineering; system reliability analysis
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Special Issue Information

Dear Colleagues,

This Special Issue (SI) encourages the submission of present research achievements concerning novel theories, methods, algorithms and applications in industrial artificial intelligence (AI), including innovations and challenges. Industrial AI focuses on research regarding novel methods and their applications in practical engineering. Various AI theories and methods have been proposed to solve issues relevant to various fields. However, most of the challenges arising from artificial intelligence still require further research, regardless of the practical application, as many research questions remain. Many limitations exist in various application environments, and many researchers are looking for solutions to these problems. We look forward to receiving submissions of the latest research findings suggesting theories and practical solutions for various practical applications in industries based on artificial intelligence.

Authors are encouraged to submit contributions regarding the latest theories, methods, algorithms and applications of AI in:

  • Controllable nuclear fusion;
  • Nuclear power plants;
  • Fluid machinery;
  • Face recognition;
  • Image and video recognition;
  • Surface defect detection;
  • Structural health monitoring;
  • The quality inspection of the semiconductor industry;
  • Industry monitoring based on fiber sensors;
  • Other related areas.

Dr. Zhang-Chun Tang
Dr. Feng Zhang
Guest Editors

Manuscript Submission Information

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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

  • controllable nuclear fusion
  • nuclear power plants
  • fluid machinery
  • face recognition
  • image and video recognition
  • surface defect detection
  • structural health monitoring
  • the quality inspection of the semiconductor industry
  • industry monitoring based on fiber sensors
  • other related areas

Published Papers (1 paper)

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Research

14 pages, 396 KiB  
Article
Short Text Classification Based on Hierarchical Heterogeneous Graph and LDA Fusion
by Xinlan Xu, Bo Li, Yuhao Shen, Bing Luo, Chao Zhang and Fei Hao
Electronics 2023, 12(12), 2560; https://doi.org/10.3390/electronics12122560 - 6 Jun 2023
Cited by 2 | Viewed by 1117
Abstract
The proliferation of short texts resulting from the rapid advancements of social networks, online communication, and e-commerce has created a pressing need for short text classification in various applications. This paper presents a novel approach for short text classification, which combines a hierarchical [...] Read more.
The proliferation of short texts resulting from the rapid advancements of social networks, online communication, and e-commerce has created a pressing need for short text classification in various applications. This paper presents a novel approach for short text classification, which combines a hierarchical heterogeneous graph with latent Dirichlet allocation (LDA) fusion. Our method first models the short text dataset as a hierarchical heterogeneous graph, which incorporates more syntactic and semantic information through a word graph, parts-of-speech (POS) tag graph, and entity graph. We then connected the representation of these three feature maps to derive a comprehensive feature vector for the text. Finally, we used the LDA topic model to adjust the feature weight, enhancing the effectiveness of short text extension. Our experiments demonstrated that our proposed approach has a promising performance in English short text classification, while in Chinese short text classification, although slightly inferior to the LDA + TF-IDF method, it still achieved promising results. Full article
(This article belongs to the Special Issue Industrial Artificial Intelligence: Innovations and Challenges)
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