Earticle

초록

영어

Most health problems of building structures are accumulative damages which are difficult to detect, and it is more difficult to monitor the structure health due to the complexity of the practical structure and the environment noise, and the existing methods need lots of data for model training but it is very complicated to mark the data in practice. In order to solve above problems, the wireless sensor network is configured and the sparse encoding method is adopted to monitor the bridge structure health, and meanwhile the sparse encoding algorithm is adopted for training on the basis of the characteristic extraction of many unlabeled instances, thus to compress data dimensionality and preprocess unlabeled data. Then, the deep learning algorithm is adopted to predict the bridge structure health monitoring type, and meanwhile Hessian optimization is improved on the basis of the linear conjugate gradient in order to replace uncertain Hessian matrix by positive semidefinite Gaussian - Newton curvature matrix for secondary objective combination, thus to improve the efficiency of the deep learning algorithm. The experiment result shows that the security detection of the bridge structure based on deep learning algorithm can monitor the high-accuracy structure health conditions under the sparse encoding of the environment noise.

목차

Abstract
 1. Introduction
 2. Sparse encoding deep learning
  2.1. Sparse encoding learning
  2.2. Deep learning algorithm
  2.3. Algorithm process description
 3. Experimental analysis
  3.1. Experiment setting
  3.2. Classification accuracy index
 4. Conclusion
 Reference

키워드

저자

• Dr. Gaurav Bansod [ Pune Institute of Computer Technology ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Security and Its Applications

• 간기

  격월간

• pISSN

  1738-9976

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605