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Advances in Sensors-Based Machine Learning for Intelligent Engineering Systems and Applications III

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: closed (10 July 2023) | Viewed by 6794

Special Issue Editors

Dr. Anastasios Doulamis

E-Mail Website
Guest Editor
Photogrammetry and Computer Vision Laboratory, National Technical University of Athens, 15773 Athens, Greece
Interests: image processing; computer vision; robotic systems; deep machine learning; machine learning; Markovian models; signal processing and pattern analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Nikolaos Doulamis

E-Mail Website
Guest Editor
School of Rural and Surveying Engineering, National Technical University of Athens, Athens, Greece
Interests: pattern recognition; machine learning; signal processing; image/hyper-spectral sensors
Special Issues, Collections and Topics in MDPI journals
Dr. Athanasios Voulodimos

E-Mail Website
Guest Editor
Department of Informatics and Computer Engineering, University of West Attica, Agiou Spiridonos 28, 122 43 Egaleo, Greece
Interests: machine learning; artificial intelligence; multimedia; intelligent systems; pervasive computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The latest advances in machine learning have contributed to various developments in many areas of interest to the engineering community. Data-driven or domain-oriented engineering applications are significantly benefitting from the latest developments in machine learning theories and methods (including deep, reinforcement, transfer, and extreme learning), but may also promote the development of learning algorithms, optimization approaches, fusion techniques for multimodal data, novel hardware, and network architectures. The rapid development in these fields has also stimulated new research on sensors and sensor networks.

The purpose of this Special Issue is to provide a forum for engineers, data scientists, researchers, and practitioners to present new academic research and industrial developments on machine learning for engineering applications. The Special Issue gathers original research papers in the field, covering new theories, algorithms, systems, as well as new implementations and applications incorporating state-of-the-art machine-learning techniques. Emphasis will be placed on systems that incorporate new sensors and their configuration. Review articles and works on performance evaluation and benchmark datasets are also solicited.

Topics of interest for the Special Issue include:

  • Research on sensors for new critical engineering applications;
  • Sensor networks and drones to survey critical infrastructure;
  • Software and hardware architectures for new sensorial systems in managing critical infrastructure;
  • Electrical and mechanical engineering, production management and optimization, manufacturing, failure detection, energy management, and smart grids;
  • Robotics and automation, computer vision and pattern recognition applications, critical infrastructure protection;
  • Civil engineering, construction management and optimization, structural health monitoring, earthquake engineering, urban planning;
  • Transportation, hydraulics, water power and environmental engineering;
  • Surveying and geospatial engineering, spatial planning, and remote sensing;
  • Materials science and engineering;
  • Biomedical engineering.

Dr. Anastasios Doulamis
Dr. Nikolaos Doulamis
Dr. Athanasios Voulodimos
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. Sensors 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 2600 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.

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

Related Special Issue

Published Papers (2 papers)

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Research

13 pages, 3525 KiB  
Article
Vision Transformers (ViT) for Blanket-Penetrating Sleep Posture Recognition Using a Triple Ultra-Wideband (UWB) Radar System
by Derek Ka-Hei Lai, Zi-Han Yu, Tommy Yau-Nam Leung, Hyo-Jung Lim, Andy Yiu-Chau Tam, Bryan Pak-Hei So, Ye-Jiao Mao, Daphne Sze Ki Cheung, Duo Wai-Chi Wong and James Chung-Wai Cheung
Sensors 2023, 23(5), 2475; https://doi.org/10.3390/s23052475 - 23 Feb 2023
Cited by 15 | Viewed by 3694
Abstract
Sleep posture has a crucial impact on the incidence and severity of obstructive sleep apnea (OSA). Therefore, the surveillance and recognition of sleep postures could facilitate the assessment of OSA. The existing contact-based systems might interfere with sleeping, while camera-based systems introduce privacy [...] Read more.
Sleep posture has a crucial impact on the incidence and severity of obstructive sleep apnea (OSA). Therefore, the surveillance and recognition of sleep postures could facilitate the assessment of OSA. The existing contact-based systems might interfere with sleeping, while camera-based systems introduce privacy concerns. Radar-based systems might overcome these challenges, especially when individuals are covered with blankets. The aim of this research is to develop a nonobstructive multiple ultra-wideband radar sleep posture recognition system based on machine learning models. We evaluated three single-radar configurations (top, side, and head), three dual-radar configurations (top + side, top + head, and side + head), and one tri-radar configuration (top + side + head), in addition to machine learning models, including CNN-based networks (ResNet50, DenseNet121, and EfficientNetV2) and vision transformer-based networks (traditional vision transformer and Swin Transformer V2). Thirty participants (n = 30) were invited to perform four recumbent postures (supine, left side-lying, right side-lying, and prone). Data from eighteen participants were randomly chosen for model training, another six participants’ data (n = 6) for model validation, and the remaining six participants’ data (n = 6) for model testing. The Swin Transformer with side and head radar configuration achieved the highest prediction accuracy (0.808). Future research may consider the application of the synthetic aperture radar technique. Full article
(This article belongs to the Special Issue Advances in Sensors-Based Machine Learning for Intelligent Engineering Systems and Applications III)
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18 pages, 1472 KiB  
Article
Constructing Physics-Informed Neural Networks with Architecture Based on Analytical Modification of Numerical Methods by Solving the Problem of Modelling Processes in a Chemical Reactor
by Dmitriy Tarkhov, Tatiana Lazovskaya and Galina Malykhina
Sensors 2023, 23(2), 663; https://doi.org/10.3390/s23020663 - 6 Jan 2023
Cited by 2 | Viewed by 2582
Abstract
A novel type of neural network with an architecture based on physics is proposed. The network structure builds on a body of analytical modifications of classical numerical methods. A feature of the constructed neural networks is defining parameters of the governing equations as [...] Read more.
A novel type of neural network with an architecture based on physics is proposed. The network structure builds on a body of analytical modifications of classical numerical methods. A feature of the constructed neural networks is defining parameters of the governing equations as trainable parameters. Constructing the network is carried out in three stages. In the first step, a neural network solution to an equation corresponding to a numerical scheme is constructed. It allows for forming an initial low-fidelity neural network solution to the original problem. At the second stage, the network with physics-based architecture (PBA) is further trained to solve the differential equation by minimising the loss function, as is typical in works devoted to physics-informed neural networks (PINNs). In the third stage, the physics-informed neural network with architecture based on physics (PBA-PINN) is trained on high-fidelity sensor data, parameters are identified, or another task of interest is solved. This approach makes it possible to solve insufficiently studied PINN problems: selecting neural network architecture and successfully initialising network weights corresponding to the problem being solved that ensure rapid convergence to the loss function minimum. It is advisable to use the devised PBA-PINNs in the problems of surrogate modelling and modelling real objects with multi-fidelity data. The effectiveness of the approach proposed is demonstrated using the problem of modelling processes in a chemical reactor. Experiments show that subsequent retraining of the initial low-fidelity PBA model based on a few high-accuracy data leads to the achievement of relatively high accuracy. Full article
(This article belongs to the Special Issue Advances in Sensors-Based Machine Learning for Intelligent Engineering Systems and Applications III)
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