Comparative Study of Feature Extraction Using Different Transform Techniques in Frequency Domain

Devendrappa, Deepak M.; Karthik, P; Kumar, Sandeep; Deepak, N. A.

doi:10.1007/978-981-15-8221-9_265

Cited by 2 publications

(2 citation statements)

References 29 publications

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“…Subsequently, features are extracted to facilitate the training of the machine learning models. In this regard, potential features that can capture the trends and recognize the patterns, such as the FFT and WT coefficients, are used [43]. To address the high dimensionality of these coefficients and enhance the training speed while reducing the computational demands, an unsupervised PCA method is implemented [44].…”

Section: Damage Detection Research Design and Dataset Description 21 ...mentioning

confidence: 99%

Damage Detection with Data-Driven Machine Learning Models on an Experimental Structure

Alemu,

Lahmer,

Walther

2024

Eng

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Various techniques have been employed to detect damage in civil engineering structures. Apart from the model-based approach, which demands the frequent updating of its corresponding finite element method (FEM)-built model, data-driven methods have gained prominence. Environmental and operational effects significantly affect damage detection due to the presence of damage-related trends in their analyses. Time-domain approaches such as autoregression and metrics such as the Mahalanobis squared distance have been utilized to mitigate these effects. In the realm of machine learning (ML) models, their effectiveness relies heavily on the type and quality of the extracted features, making this aspect a focal point of attention. The objective of this work is therefore to deploy and observe potential feature extraction approaches used as input in training fully data-driven damage detection machine learning models. The most damage-sensitive segment (MDSS) feature extraction technique, which potentially treats signals under multiple conditions, is also proposed and deployed. It identifies potential segments for each feature coefficient under a defined criterion. Therefore, 680 signals, each consisting of 8192 data points, are recorded using accelerometer sensors at the Los Alamos National Laboratory in the USA. The data are obtained from a three-story 3D building frame and are utilized in this research for a mainly data-driven damage detection task. Three approaches are implemented to replace four missing signals with the generated ones. In this paper, multiple fast Fourier and wavelet-transformed features are employed to evaluate their performance. Most importantly, a power spectral density (PSD)-based feature extraction approach that considers the maximum variability criterion to identify the most sensitive segments is developed and implemented. The performance of the MDSS selection technique, proposed in this work, surpasses that of all 18 trained neural networks (NN) and recurrent neural network (RNN) models, achieving more than 80% prediction accuracy on an unseen prediction dataset. It also significantly reduces the feature dimension. Furthermore, a sensitivity analysis is conducted on signal segmentation, overlapping, the treatment of a training dataset imbalance, and principal component analysis (PCA) implementation across various combinations of features. Binary and multiclass classification models are employed to primarily detect and additionally locate and identify the severity class of the damage. The collaborative approach of feature extraction and machine learning models effectively addresses the impact of environmental and operational effects (EOFs), suppressing their influences on the damage detection process.

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Section: Damage Detection Research Design and Dataset Description 21 ...mentioning

confidence: 99%

Damage Detection with Data-Driven Machine Learning Models on an Experimental Structure

Alemu,

Lahmer,

Walther

2024

Eng

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“…Finally, it should also accept the criterion for halting. The CoSaMP [27]- [29] is a greedy matching pursuit algorithm.…”

Section: Orthonormal Basesmentioning

confidence: 99%

Comparison of different sparse dictionaries for compressive sampling

Devendrappa¹,

Karthik²,

A.³

et al. 2022

Bulletin EEI

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Compressive sampling/compressed sensing (CS) is building on the observation that most of the signals in nature are sparse or compressible concerning some transform domain. And by converse, the same can be reconstructed with high accuracy by making use of far fewer samples than what is required by violating Shannon-Nyquist theorem. Some of the transform techniques like discrete cosine transform, fast fourier transforms discrete wavelet transform, discrete fourier transforms. In this paper, novel CS techniques like FFTCoSAMP, DCTCoSaMP, and DWTCoSaMP are introduced and compared on different sparse transforms for CS in magnetic resonance (MR) images based on sparse signal sequences/dictionaries by means of transform techniques with respect to objective quality assessment algorithms like PSNR, SSIM and RMSE, where CoSaMP stands for compressive sampling matching pursuit. DWTCoSaMP is giving the PSNR values of 37.16 (DB4), 38.12 (Coif3), 38.5 (Sym8), for DCTCoSaMP and FFTCoSaMP, it’s 36.33 and 36.01 respectively. For DWTCoSaMP, SSIM value is 0.81, and for DCTCoSaMP and FFTCoSaMP, it’s 0.73 and 0.7 respectively. And finally, for DWTCoSaMP, RMSE value is 0.66, and for DCTCoSaMP and FFTCoSaMP, it’s 0.53 and 0.41 respectively. DWTCoSaMP reveals the best than rest of the methods and traditional CS techniques by the detailed comparison and analysis.

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Comparative Study of Feature Extraction Using Different Transform Techniques in Frequency Domain

Cited by 2 publications

References 29 publications

Damage Detection with Data-Driven Machine Learning Models on an Experimental Structure

Damage Detection with Data-Driven Machine Learning Models on an Experimental Structure

Comparison of different sparse dictionaries for compressive sampling

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