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Energy-Efficient AI in Smart Sensors
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Energy-Efficient AI in Smart Sensors

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

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 1597

Special Issue Editors

Dr. Vidas Raudonis

E-Mail Website
Guest Editor
Department of Automation, Kaunas University of Technology, 51367 Kaunas, Lithuania
Interests: robotics; AI; computer vision
Dr. Agne Paulauskaite-Taraseviciene

E-Mail Website
Guest Editor
Artificial Intelligence Centre, Kaunas University of Technology, 51423 Kaunas, Lithuania
Interests: AI; computer vision; machine learning

Special Issue Information

Dear Colleagues,

Recently, global society is facing energy crisis that generates growing interest in energy efficient usage of AI-driven solutions. This energy crisis brings new opportunity to rethink the design and structure of AI models making them as lighter as possible and energy efficient. Recent advantages in edge computing, IoT technologies and informatics engineering have made possible to make dedicated AI solution that can be applied into low-power and energy-efficient computing devices. Edge device can be wearable, kept in the pocket or fixated on the wall.

This Special Issue therefore aims to put together original research and review papers on recent advances, solutions, applications, and new challenges in the field of energy-efficient AI.

Potential topics include but are not limited to:

  • Automatic decision making on edge devices
  • Medical diagnostics in the pocket (AI-driven applications on the smart phone)
  • Energy efficient machine learning (ML) and artificial intelligence (AI)
  • Self-learning smart sensors
  • Autonomous image understanding using limited data set
  • Anomaly detection in N-dimensional data
  • Data fusion and features extraction
  • Computer vision of autonomous robotics

Dr. Vidas Raudonis
Dr. Agne Paulauskaite-Taraseviciene
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.

Published Papers (1 paper)

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Research

17 pages, 11817 KiB  
Article
Deep Learning-Based Subsurface Damage Localization Using Full-Field Surface Strains
by Ashish Pal, Wei Meng and Satish Nagarajaiah
Sensors 2023, 23(17), 7445; https://doi.org/10.3390/s23177445 - 26 Aug 2023
Viewed by 953
Abstract
Structures in their service life are often damaged as a result of aging or extreme events such as earthquakes or storms. It is essential to detect damage in a timely fashion to ensure the safe operation of the structure. If left unchecked, subsurface [...] Read more.
Structures in their service life are often damaged as a result of aging or extreme events such as earthquakes or storms. It is essential to detect damage in a timely fashion to ensure the safe operation of the structure. If left unchecked, subsurface damage (SSD) can cause significant internal damage and may result in premature structural failure. In this study, a Convolutional Neural Network (CNN) has been developed for SSD detection using surface strain measurements. The adopted network architecture is capable of pixel-level image segmentation, that is, it classifies each location of strain measurement as damaged or undamaged. The CNN which is fed full-field strain measurements as an input image of size 256 × 256 projects the SSD onto an output image of the same size. The data for network training is generated by numerical simulation of aluminum bars with different damage scenarios, including single damage and double damage cases at a random location, direction, length, and thickness. The trained network achieves an Intersection over Union (IoU) score of 0.790 for the validation set and 0.794 for the testing set. To check the applicability of the trained network on materials other than aluminum, testing is performed on a numerically generated steel dataset. The IoU score is 0.793, the same as the aluminum dataset, affirming the network’s capability to apply to materials exhibiting a similar stress–strain relationship. To check the generalization potential of the network, it is tested on triple damage cases; the IoU score is found to be 0.764, suggesting that the network works well for unseen damage patterns as well. The network was also found to provide accurate predictions for real experimental data obtained from Strain Sensing Smart Skin (S4). This proves the efficacy of the network to work in real-life scenarios utilizing the full potential of the novel full-field strain sensing methods such as S4. The performance of the proposed network affirms that it can be used as a non-destructive testing method for subsurface crack detection and localization. Full article
(This article belongs to the Special Issue Energy-Efficient AI in Smart Sensors)
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