Seismic Intensity Feature Construction Based on the Hilbert–Huang Transform

Alvanitopoulos, Petros-Fotios; Papavasileiou, M.; Andreadis, Ioannis; Elenas, Anaxagoras

doi:10.1109/tim.2011.2161934

Cited by 17 publications

(6 citation statements)

References 36 publications

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“…Therefore, the maximum value of the signal's power spectrum, its frequency, the peak energy ratio, the area under the spectrum, and the maximum value of the spectrum at the first‐mode period of the structure are also included. Moreover, a high correlation between the indices extracted from the Hilbert spectrum of the ground motion and structural damage has been identified (Alvanitopoulos et al., 2009; Alvanitopoulos, Papavasileiou, Andreadis, & Elenas, 2011). Therefore, in this study, the average

HH {normalT}_{normalavg}

and the maximum value

HH {normalT}_{normalavg}

of the Hilbert spectrum in a frequency band between

0.83 {normalT}_{1}

and

1.25 {normalT}_{1}

, are also added to the earthquake indices.…”

Section: Informative Variables In Structural Seismic Damage Assessmentmentioning

confidence: 99%

The development of Gaussian process regression for effective regional post‐earthquake building damage inference

Sheibani

2020

Computer aided Civil Eng

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Post‐earthquake reconnaissance survey of structural damage is an effective way of documenting and understanding the impact of earthquakes on structures. This article aims at providing an efficient data‐based framework that reduces the required time for reconnaissance missions and predicts the damage intensities for every building in the affected region. We hypothesize that a joint selection of necessary structural and earthquake parameters along with sparse damage observations are sufficient to train a supervised learning algorithm and accurately infer the damage for other buildings in the region. Gaussian process regression is employed to prove the hypothesis for probabilistic inference of different damage indices. The algorithm performs efficiently by selecting a set of diverse and representative buildings for damage observations using K‐medoids clustering. To validate the hypothesis and the proposed method, the algorithm framework is implemented on two severe earthquake simulation testbeds. The impacts of different building and ground motion variables on the damage inference performance are discussed. Furthermore, the effectiveness of observation sampling by clustering in the post‐earthquake damage inference is compared with random sampling.

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HH {normalT}_{normalavg}

and the maximum value

HH {normalT}_{normalavg}

of the Hilbert spectrum in a frequency band between

0.83 {normalT}_{1}

and

1.25 {normalT}_{1}

, are also added to the earthquake indices.…”

Section: Informative Variables In Structural Seismic Damage Assessmentmentioning

confidence: 99%

The development of Gaussian process regression for effective regional post‐earthquake building damage inference

Sheibani

2020

Computer aided Civil Eng

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show abstract

“…In Section 3.1, the all-pass filters are given in forms of continuous transfer functions as shown in (4). In practice, most control systems are discrete systems.…”

Section: Influence Of Changing Speedmentioning

confidence: 99%

“…This technique is widely used in many fields. For example, it is used in decomposition of speech signals in [1], mechanical fault diagnosis in [2], disease diagnosis in [3], seismic waveform analysis in [4], power system currents analysis in [5] etc.…”

Section: Introductionmentioning

confidence: 99%

Instantaneous harmonic decomposition technique for three‐phase current based on multiple reference coordinates

Wang

Sun

2018

IET Electric Power Applications

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Current harmonic decomposition is important to power systems. Current signal is classified into stationary and nonstationary signal according to whether the parameters change with time. This study focuses on the instantaneous harmonic decomposition of three-phase permanent magnet synchronous motor currents in non-stationary process. First, based on the definition of the instantaneous frequency in non-stationary process, the concept of the spatial frequency based on the electrical angle is proposed. Under this definition, the spatial frequency no longer varies with the rotational speed. A new instantaneous harmonic decomposition method based on multiple reference coordinates is then proposed. To verify the effectiveness of the new method, it is used to analyse constructed currents and experimental currents. Finally, the new method is compared with other analysis methods and proved to be the most accurate and effective.

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“…It has been proved a powerful tool for extracting intrinsic components from nonlinear/nonstationary signal and has found substantial applications in various areas, including science and engineering [51], [52]. Versatile as EMD is, one of its essential pitfalls is mode mixing caused by signal intermittency [53].…”

Section: Eemdmentioning

confidence: 99%

Optimized Ensemble EMD-Based Spectral Features for Hyperspectral Image Classification

Shen

Wang

et al. 2014

IEEE Trans. Instrum. Meas.

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Extracting essential features from massive bands is an important yet challenging issue in hyperspectral image (HSI) classification. Plenty of feature extraction techniques can be found in the literature but most of these methods rely on linear/stationary assumptions. This paper proposes an alternative methodology inspired by the ensemble empirical mode decomposition (EEMD) to gain spectral features of the HSI. To this end, two major aspects are involved: 1) the optimization problems are formulated in each sifting process and solved by the alternating direction method of multipliers (ADMM) algorithm to enhance the benefits of EEMD; 2) the intrinsic mode functions (IMFs) extracted by the optimized EEMD (OEEMD) are summed with appropriate weights automatically gained from the local Fisher discriminant analysis (LFDA). As a consequence, the constructed features (i.e., sum of the IMFs) can then be significantly classified by the state-of-the-art classifiers, i.e., k-nearest neighbor (k-NN) or support vector machine (SVM). Experiments on two benchmark HSIs validate that the extracted new features achieve higher classification rates as well as greater robustness to the choice of training samples compared with several generally acknowledged methods.Index Terms-Alternating direction method of multipliers (ADMM), classification, ensemble empirical mode decomposition (EEMD), hyperspectral image (HSI), local Fisher discriminant analysis (LFDA).

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Seismic Intensity Feature Construction Based on the Hilbert–Huang Transform

Cited by 17 publications

References 36 publications

The development of Gaussian process regression for effective regional post‐earthquake building damage inference

The development of Gaussian process regression for effective regional post‐earthquake building damage inference

Instantaneous harmonic decomposition technique for three‐phase current based on multiple reference coordinates

Optimized Ensemble EMD-Based Spectral Features for Hyperspectral Image Classification

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