Cargando…
Learning Damage Representations with Sequence-to-Sequence Models
Natural hazards have caused damages to structures and economic losses worldwide. Post-hazard responses require accurate and fast damage detection and assessment. In many studies, the development of data-driven damage detection within the research community of structural health monitoring has emerged...
Autores principales: | Yang, Qun, Shen, Dejian |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8781882/ https://www.ncbi.nlm.nih.gov/pubmed/35062411 http://dx.doi.org/10.3390/s22020452 |
Ejemplares similares
-
Learning meaningful representations of protein sequences
por: Detlefsen, Nicki Skafte, et al.
Publicado: (2022) -
Representation learning applications in biological sequence analysis
por: Iuchi, Hitoshi, et al.
Publicado: (2021) -
Identification of bacteriophage genome sequences with representation learning
por: Bai, Zeheng, et al.
Publicado: (2022) -
Skill learning strengthens cortical representations of motor sequences
por: Wiestler, Tobias, et al.
Publicado: (2013) -
Time course of learning sequence representations in action imagery practice
por: Dahm, Stephan F., et al.
Publicado: (2023)