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Eng
Special Issues
Artificial Intelligence for Engineering Applications
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Artificial Intelligence for Engineering Applications

A special issue of Eng (ISSN 2673-4117).

Deadline for manuscript submissions: 31 March 2025 | Viewed by 377

Special Issue Editors

Prof. Dr. Juvenal Rodriguez-Resendiz

E-Mail Website
Guest Editor
Facultad de Ingeniería, Universidad Autónoma de Querétaro, Querétaro 76010, Mexico
Interests: robotics; biomedical applications; instrumentation and measurement; signal processing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Marco Antonio Aceves-Fernandez

E-Mail Website
Guest Editor
Facultad de Ingeniería, Universidad Autónoma de Querétaro, Cerro de las Campanas, Las Campanas, Queretaro 76010, Mexico
Interests: machine learning; neural networks and artificial intelligence; air pollution; particulate matter
Special Issues, Collections and Topics in MDPI journals
Dr. Akos Odry

E-Mail Website
Guest Editor
Department of Mechatronics and Automation, Faculty of Engineering, University of Szeged, 6275 Szeged, Hungary
Interests: intelligent control; sensor fusion; robotics; kalman filtering; industrial robotics; soft computing; localization; SLAM
Special Issues, Collections and Topics in MDPI journals
Dr. José Manuel Álvarez-Alvarado

E-Mail Website
Guest Editor
Facultad de Ingeniería, Universidad Autónoma de Queretaro, Queretaro 76010, Mexico
Interests: solar energy; power generation; waste heat recovery; control techniques; renewable energy technologies; solar radiation; energy engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to invite you to contribute to our Special Issue focused on the advances in artificial intelligence applied to comprehensive engineering solutions. These techniques range from machine learning models that enable accurate prediction and decision making to image processing that improves visual analysis and pattern detection.

In an increasingly technologically advanced world, integrating artificial intelligence into engineering has offered prominent results. This has motivated efforts to create comprehensive solutions by optimizing processes and improving the design and functionality of electronics to enhance different systems. From the health applications of engineering, such as biomedical technology, to the efficiency of energy systems, AI has been fundamental to revolutionizing these areas. This is why our SI aims to compile AI advances applied to innovative, technological, and scientific solutions in the engineering field.

The main areas of engineering that our Special Issue focuses on are as follows:

  • Automation;
  • Electronics;
  • Electric power;
  • Sustainability;
  • Biomedical;
  • Mechatronic;
  • Computer systems;
  • Multidisciplinary engineering.

Prof. Dr. Juvenal Rodriguez-Resendiz
Prof. Dr. Marco Antonio Aceves-Fernandez
Dr. Akos Odry
Dr. José Manuel Álvarez-Alvarado
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. Eng is an international peer-reviewed open access quarterly 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 1200 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

  • image processing
  • AI in embedded systems
  • optimization algorithms
  • autonomous robotics
  • system control
  • computational optimization
  • neural networks for engineering
  • IoT

Published Papers (1 paper)

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Research

29 pages, 4864 KiB  
Article
Comparative Analysis of Deep Learning Models for Optimal EEG-Based Real-Time Servo Motor Control
by Dimitris Angelakis, Errikos C. Ventouras, Spiros Kostopoulos and Pantelis Asvestas
Eng 2024, 5(3), 1708-1736; https://doi.org/10.3390/eng5030090 - 2 Aug 2024
Viewed by 252
Abstract
This study harnesses EEG signals to enable the real-time control of servo motors, utilizing the OpenBCI Community Dataset to identify and assess brainwave patterns related to motor imagery tasks. Specifically, the dataset includes EEG data from 52 subjects, capturing electrical brain activity while [...] Read more.
This study harnesses EEG signals to enable the real-time control of servo motors, utilizing the OpenBCI Community Dataset to identify and assess brainwave patterns related to motor imagery tasks. Specifically, the dataset includes EEG data from 52 subjects, capturing electrical brain activity while participants imagined executing specific motor tasks. Each participant underwent multiple trials for each motor imagery task, ensuring a diverse and comprehensive dataset for model training and evaluation. A deep neural network model comprising convolutional and bidirectional long short-term memory (LSTM) layers was developed and trained using k-fold cross-validation, achieving a notable accuracy of 98%. The model’s performance was further compared against recurrent neural networks (RNNs), multilayer perceptrons (MLPs), and Τransformer algorithms, demonstrating that the CNN-LSTM model provided the best performance due to its effective capture of both spatial and temporal features. The model was deployed on a Python script interfacing with an Arduino board, enabling communication with two servo motors. The Python script predicts actions from preprocessed EEG data to control the servo motors in real-time. Real-time performance metrics, including classification reports and confusion matrices, demonstrate the seamless integration of the LSTM model with the Arduino board for precise and responsive control. An Arduino program was implemented to receive commands from the Python script via serial communication and control the servo motors, enabling accurate and responsive control based on EEG predictions. Overall, this study presents a comprehensive approach that combines machine learning, real-time implementation, and hardware interfacing to enable the precise and real-time control of servo motors using EEG signals, with potential applications in the human–robot interaction and assistive technology domains. Full article
(This article belongs to the Special Issue Artificial Intelligence for Engineering Applications)
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