Advancements in Artificial Intelligence (AI) for Engineering Applications

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 15 April 2025 | Viewed by 957

Special Issue Editor


E-Mail Website
Guest Editor
Faculty of Computer Science and Technology, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland
Interests: artificial intelligence; optimization techniques; fuzzy logic; natural language processing; reinforcement learning

Special Issue Information

Dear Colleagues,

We are excited to issue a call for papers for a Special Issue of Electronics focusing on the intersection of artificial intelligence (AI) with engineering applications. This Special Issue aims to explore the latest advancements and practical implementations within the realms of swarm intelligence, optimization, fuzzy logic, natural language processing (NLP), computer vision, and reinforcement learning.

The overall focus, scope, and purpose of this Special Issue are delineated as follows:

(a) Focus: The Special Issue will concentrate on exploring innovative methodologies and algorithms within the fields of swarm intelligence, optimization, fuzzy logic, NLP, computer vision, and reinforcement learning, with a specific emphasis on their application in engineering domains.

(b) Scope: We welcome contributions that present novel research findings, methodologies, case studies, and applications related to the aforementioned AI fields in engineering contexts. Topics of interest include, but are not limited to, the following:

  • Development of advanced AI-based optimization techniques for engineering problems;
  • Integration of fuzzy logic principles into engineering systems to enhance adaptability and decision-making;
  • Utilization of NLP for improving human–computer interaction in engineering applications;
  • Application of computer vision techniques for object recognition, image analysis, and visual perception in engineering tasks;
  • Implementation of reinforcement learning algorithms for autonomous decision-making and control in engineering systems.

(c) Purpose: The purpose of this Special Issue is to provide a platform for researchers to disseminate their latest findings, exchange insights, and foster collaborations for advancing the state of the art in AI-driven engineering applications. By showcasing practical implementations and case studies, we aim to bridge the gap between theoretical advancements in AI and their real-world applications in engineering.

This Special Issue will complement the existing literature by taking the following steps:

  • Offering a comprehensive overview of the latest trends and advancements in AI techniques as applied to engineering problems;
  • Providing in-depth discussions and analyses of practical case studies and applications, thereby offering valuable insights for both academia and industry practitioners;
  • Stimulating further research and innovation in this field by identifying emerging challenges and potential areas for future exploration.

We encourage researchers from academia, industry, and other relevant sectors to contribute their original research articles for publication in this Special Issue.

We eagerly anticipate your contributions to this Special Issue and advance our collective understanding of AI-driven engineering applications.

Dr. Hubert Zarzycki
Guest Editor

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

  • artificial intelligence
  • swarm intelligence
  • optimization
  • fuzzy logic
  • natural language processing (NLP)
  • computer vision
  • reinforcement learning
  • engineering applications

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

23 pages, 770 KiB  
Article
Computationally Efficient Inference via Time-Aware Modular Control Systems
by Dmytro Shchyrba and Hubert Zarzycki
Electronics 2024, 13(22), 4416; https://doi.org/10.3390/electronics13224416 - 11 Nov 2024
Viewed by 532
Abstract
Control in multi-agent decision-making systems is an important issue with a wide variety of existing approaches. In this work, we offer a new comprehensive framework for distributed control. The main contributions of this paper are summarized as follows. First, we propose PHIMEC (physics-informed [...] Read more.
Control in multi-agent decision-making systems is an important issue with a wide variety of existing approaches. In this work, we offer a new comprehensive framework for distributed control. The main contributions of this paper are summarized as follows. First, we propose PHIMEC (physics-informed meta control)—an architecture for learning optimal control by employing a physics-informed neural network when the state space is too large for reward-based learning. Second, we offer a way to leverage impulse response as a tool for system modeling and control. We propose IMPULSTM, a novel approach for incorporating time awareness into recurrent neural networks designed to accommodate irregular sampling rates in the signal. Third, we propose DIMAS, a modular approach to increasing computational efficiency in distributed control systems via domain-knowledge integration. We analyze the performance of the first two contributions on a set of corresponding benchmarks and then showcase their combined performance as a domain-informed distributed control system. The proposed approaches show satisfactory performance both individually in their respective applications and as a connected system. Full article
Show Figures

Figure 1

Back to TopTop