A Modified Fully Convolutional Network for Crack Damage Identification Compared with Conventional Methods
Large-scale structural health monitoring and damage detection of concealed underwater structures are always the urgent and state-of-art problems to be solved in the field of civil engineering. With the development of artificial intelligence especially the combination of deep learning and computer vi...
Guardado en:
Autores principales: | Meng Meng, Kun Zhu, Keqin Chen, Hang Qu |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Hindawi Limited
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/d1dcb83a9cf44400ae6ab9deb7285889 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Novel Convolutional Restricted Boltzmann Machine manifold learning inspired dynamic user clustering hybrid precoding for millimeter-wave massive multiple-input multiple-output systems
por: Xiaoping Zhou, et al.
Publicado: (2021) -
D Modified KNN-LVQ for Stairs Down Detection Based on Digital Image
por: Ahmad Wali Satria Bahari Johan, et al.
Publicado: (2021) -
Cooperation Mechanism in Blockchain by Evolutionary Game Theory
por: Jinxin Zhang, et al.
Publicado: (2021) -
A Modified TODIM Based on Compromise Distance for MAGDM with q-Rung Orthopair Trapezoidal Fuzzy Numbers
por: Benting Wan, et al.
Publicado: (2021) -
Sustainable Development of the Innovation Ecosystem from the Perspective of T-O-V
por: Ruixue Yan, et al.
Publicado: (2021)