Cargando…
SSVM: An Ultra-Low-Power Strain Sensing and Visualization Module for Long-Term Structural Health Monitoring
Structural health monitoring (SHM) is crucial for quantitative behavioral analysis of structural members such as fatigue, buckling, and crack propagation identification. However, formerly developed approaches cannot be implemented effectively for long-term infrastructure monitoring, owing to power i...
Autores principales: | Khan, Suleman, Won, Jongbin, Shin, Junsik, Park, Junyoung, Park, Jong-Woong, Kim, Seung-Eock, Jang, Yun, Kim, Dong Joo |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8004234/ https://www.ncbi.nlm.nih.gov/pubmed/33809847 http://dx.doi.org/10.3390/s21062211 |
Ejemplares similares
-
Development of Low-Cost Wireless Sensing System for Smart Ultra-High Performance Concrete
por: Le, Huy-Viet, et al.
Publicado: (2021) -
Development of a Reference-Free Indirect Bridge Displacement Sensing System
por: Won, Jongbin, et al.
Publicado: (2021) -
BLESeis: Low-Cost IoT Sensor for Smart Earthquake Detection and Notification
por: Won, Jongbin, et al.
Publicado: (2020) -
Tendon Stress Estimation from Strain Data of a Bridge Girder Using Machine Learning-Based Surrogate Model
por: Khayam, Sadia Umer, et al.
Publicado: (2023) -
Scale space and variational methods in computer vision: 7th international conference, SSVM 2019, Hofgeismar, Germany, June 30 - July 4, 2019, proceedings
por: Lellmann, Jan, et al.
Publicado: (2019)