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Study on Few-Shot Fault Diagnosis Method for Marine Fuel Systems Based on DT-SViT-KNN.
Sensors (Basel)
December 2024
School of Automation, Jiangsu University of Science and Technology, Zhenjiang 212100, China.
The fuel system serves as the core component of marine diesel engines, and timely and effective fault diagnosis is the prerequisite for the safe navigation of ships. To address the challenge of current data-driven fault-diagnosis-based methods, which have difficulty in feature extraction and low accuracy under small samples, this paper proposes a fault diagnosis method based on digital twin (DT), Siamese Vision Transformer (SViT), and K-Nearest Neighbor (KNN). Firstly, a diesel engine DT model is constructed by integrating the mathematical, mechanism, and three-dimensional physical models of the Medium-speed diesel engines of 6L21/31 Marine, completing the mapping from physical entity to virtual entity.
View Article and Find Full Text PDFEstablishing bio-logging data collections as dynamic archives of animal life on Earth.
Nat Ecol Evol
January 2025
Centre for Biological Diversity, School of Biology, University of St Andrews, St Andrews, UK.
Rapid growth in bio-logging-the use of animal-borne electronic tags to document the movements, behaviour, physiology and environments of wildlife-offers opportunities to mitigate biodiversity threats and expand digital natural history archives. Here we present a vision to achieve such benefits by accounting for the heterogeneity inherent to bio-logging data and the concerns of those who collect and use them. First, we can enable data integration through standard vocabularies, transfer protocols and aggregation protocols, and drive their wide adoption.
View Article and Find Full Text PDFArtificial Intelligence-Guided Inverse Design of Deployable Thermo-Metamaterial Implants.
ACS Appl Mater Interfaces
January 2025
Department of Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States.
Current limitations in implant design often lead to trade-offs between minimally invasive surgery and achieving the desired post-implantation functionality. Here, we present an artificial intelligence inverse design paradigm for creating deployable implants as planar and tubular thermal mechanical metamaterials (thermo-metamaterials). These thermo-metamaterial implants exhibit tunable mechanical properties and volume change in response to temperature changes, enabling minimally invasive and personalized surgery.
View Article and Find Full Text PDFX-Ray Image-Based Real-Time COVID-19 Diagnosis Using Deep Neural Networks (CXR-DNNs).
J Imaging
December 2024
Institute of Oceanic Engineering Research, University of Malaga, 29010 Malaga, Spain.
On 11 February 2020, the prevalent outbreak of COVID-19, a coronavirus illness, was declared a global pandemic. Since then, nearly seven million people have died and over 765 million confirmed cases of COVID-19 have been reported. The goal of this study is to develop a diagnostic tool for detecting COVID-19 infections more efficiently.
View Article and Find Full Text PDFTea grading, blending, and matching based on computer vision and deep learning.
J Sci Food Agric
December 2024
School of Food and Biological Engineering, Jiangsu University, Jiangsu, People's Republic of China.
Background: Accurate tea blending assessment and sample matching are critical in the tea production process. Traditional methods face efficiency and accuracy challenges, which can be addressed by advances in computer vision and deep learning. This study developed an efficient and non-destructive method for fast tea grading classification, blending ratio evaluation, and sample matching.
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