Structural health monitoring of tall buildings with numerical integrator and convex-concave hull classification
type: Item
instance of: scholarly article
title: Structural health monitoring of tall buildings with numerical integrator and convex-concave hull classification
zbMATH Open document ID: 1264.74167
DOI: 10.1155/2012/212369
author: Suresh Thenozhi
author: Wen Yu
author: Asdrúbal López Chau
author: Xiaoou Li
published in: Mathematical Problems in Engineering
publication date: 2013-06-11T00:00:00Z
review text: Summary: An important objective of health monitoring systems for tall buildings is to diagnose the state of the building and to evaluate its possible damage. In this paper, we use our prototype to evaluate our data-mining approach for the fault monitoring. The offset cancellation and high-pass filtering techniques are combined effectively to solve common problems in numerical integration of acceleration signals in real-time applications. The integration accuracy is improved compared with other numerical integrators. Then we introduce a novel method for support vector machine (SVM) classification, called convex-concave hull. We use the Jarvis march method to decide the concave (nonconvex) hull for the inseparable points. Finally the vertices of the convex-concave hull are applied for SVM training.
Mathematics Subject Classification ID: 74M05
Mathematics Subject Classification ID: 94A12
zbMATH DE Number: 6173098
Wikidata QID: Q58449872
describes a project that uses: LIBSVM
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2012/212369
OpenAlex ID: W2152884277
cites work: An efficient algorithm for determining the convex hull of a finite planar set
cites work: On the identification of the convex hull of a finite set of points in the plane
cites work: Convex hulls of finite sets of points in two and three dimensions
cites work: The complexity of incremental convex hull algorithms in \(R^ d\)
cites work: A New Convex Hull Algorithm for Planar Sets
cites work: An introduction to support vector machines and other kernel-based learning methods.
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description: scientific article; zbMATH DE number 6173098