Convolutional sparse coding‐based deep random vector functional link network for distress classification of road structures

Author:

Maeda Keisuke1,Takahashi Sho2,Ogawa Takahiro1,Haseyama Miki1

Affiliation:

1. Faculty of Information Science and TechnologyHokkaido University Hokkaido Japan

2. Faculty of EngineeringHokkaido University Hokkaido Japan

Funder

Global Institution for Collaborative Research and Education at Hokkaido University and JSPS KAKENHI

Publisher

Wiley

Subject

Computational Theory and Mathematics,Computer Graphics and Computer-Aided Design,Computer Science Applications,Civil and Structural Engineering

Reference77 articles.

1. Feature extraction and classification techniques for health monitoring of structures;Amezquita‐Sanchez J.;Scientia Iranica: Transaction A, Civil Engineering,2015

2. Synchrosqueezed wavelet transform-fractality model for locating, detecting, and quantifying damage in smart highrise building structures

3. Data Analysis for Condition‐Based Railway Infrastructure Maintenance

4. Bristow H. Eriksson A. &Lucey S.(2013).Fast convolutional sparse coding.Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 391–398.

5. Extreme Learning Machines [Trends & Controversies]

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