Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Data-Driven Approach for Linear and Nonlinear Damage Detection Using
  Variational Mode Decomposition and GARCH Model   In this article, an original data-driven approach is proposed to detect both
linear and nonlinear damage in structures using output-only responses. The
method deploys variational mode decomposition (VMD) and a generalised
autoregressive conditional heteroscedasticity (GARCH) model for signal
processing and feature extraction. To this end, VMD decomposes the response
signals into intrinsic mode functions (IMFs). Afterwards, the GARCH model is
utilised to represent the statistics of IMFs. The model coefficients of IMFs
construct the primary feature vector. Kernel-based principal component analysis
(PCA) and linear discriminant analysis (LDA) are utilised to reduce the
redundancy of the primary features by mapping them to the new feature space.
The informative features are then fed separately into three supervised
classifiers, namely support vector machine (SVM), k-nearest neighbour (kNN),
and fine tree. The performance of the proposed method is evaluated on two
experimentally scaled models in terms of linear and nonlinear damage
assessment. Kurtosis and ARCH tests proved the compatibility of the GARCH
model.

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The categories of this paper are: atom-ph
27
The categories of this paper are: comp-gas
18
The categories of this paper are: econ
35
The categories of this paper are: mtrl-th
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The categories of this paper are: acc-phys
16
The categories of this paper are: stat
10
The categories of this paper are: q-bio
7
The categories of this paper are: hep-th
37
The categories of this paper are: supr-con
23
The categories of this paper are: solv-int
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The categories of this paper are: quant-ph
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The categories of this paper are: q-fin
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The categories of this paper are: patt-sol
3
The categories of this paper are: physics
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The categories of this paper are: funct-an
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The categories of this paper are: plasm-ph