• Journal of the Institute of Electronics Engineers of Korea SC
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• v.44 no.5
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• pp.45-54
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• 2007

Time/frequency analysis has been extensively used in biomedical signal processing. By extracting some essential features from the electro-physiological signals, these methods are able to determine the clinical pathology mechanisms of some diseases. However, this method assumes that the signal should be stationary, which limits its application in non-stationary system. In this paper, we develop a new signal processing method using Hilbert-Huang Transform to perform analysis of the nonlinear and non-stationary ventricular fibrillation(VF). Hilbert-Huang Transform combines two major analytical theories: Empirical Mode Decomposition(EMD) and the Hilbert Transform. Hilbert-Huang Transform can be used to decompose natural data into independent Intrinsic Mode Functions using the theories of EMD. Furthermore, Hilbert-Huang Transform employs Hilbert Transform to determine instantaneous frequency and amplitude, and therefore can be used to accurately describe the local behavior of signals. This paper studied for Return Of Spontaneous Circulation(ROSC) and non-ROSC prediction performance by Support Vector Machine and three parameters(EMD-IF, EMD-FFT) extracted from ventricular fibrillation ECG waveform using Hilbert-Huang transform. On the average results of sensitivity and specificity were 87.35% and 76.88% respectively. Hilbert-Huang Transform shows that it enables us to predict the ROSC of VF more precisely.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2011.10a
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• pp.106-106
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• 2011

• Journal of Advanced Marine Engineering and Technology
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• v.36 no.2
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• pp.258-266
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• 2012

Recently, Acoustic Emission (AE) technique is widely applied to develop the early fault detection system, and the problem about a signal processing method for AE signal is mainly focused on. In the signal processing method, envelope analysis is a useful method to evaluate the rolling element bearing problems and Wavelet transform is a powerful method to detect faults occurred on gearboxes. However, exact method for AE signal is not developed yet. Therefore, in this paper, two methods, which is Hilbert transforms (HT) and Hilbert-Huang transforms (HHT), will be compared for development a signal processing method for early fault detection system by using AE. AE signals were measured through a fatigue test. HHT has better advantages than HT because HHT can show the time-frequency domain result. But, HHT needs long time to process a signal, which has a lot of data, and has a disadvantage in de-noising filter.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.33 no.12
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• pp.1442-1448
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• 2009

Transient characteristics of a signal can be effectively exhibited in time-frequency domain. Hilbert-Huang Transform (HHT) is one of the time-frequency domain analysis methods. HHT is known for its several advantages over other signal analysis methods. The capability of analyzing non-stationary or nonlinear characteristics of a signal is the primary advantage of HHT. Moreover, it is known that HHT can provide fine resolution in high frequency region and handle large size data efficiently. In this study, the effectiveness of Hilbert-Huang transform is illustrated by employing structural systems undergoing impact. A simple discrete system and an axially oscillating cantilever beam undertaking periodic impulsive force are chosen to show the effectiveness of HHT.

• Journal of Ocean Engineering and Technology
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• v.22 no.1
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• pp.30-36
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• 2008

Underwater ambient noise originating from geophysical, biological, and man-made acoustic sources contains information on the source and the ocean environment. Such noise affectsthe performance of sonar equipment. In this paper, three steps are used to identify the ambient noise source, detection, feature extraction, and similarity measurement. First, we use the zero-crossing rate to detect the ambient noisesource from background noise. Then, a set of feature vectors is proposed forthe ambient noise source using the Hilbert-Huang transform and the Karhunen-Loeve transform. Finally, the Euclidean distance is used to measure the similarity between the standard feature vector and the feature vector of the unknown ambient noise source. The developed algorithm is applied to the observed ocean data, and the results are presented and discussed.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2012.05a
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• pp.555-561
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• 2012

The Hilbert Huang Transform (HHT) technigue with Empirical Mode Decomposition (EMD) is one of the time-frequency domain analysis methods and it has several advantages such that analyzing non-stationary and nonlinear signal is possible. However, there are shortcomings in detecting near-range of frequencies and added noise signals. In this paper, to analyze characteristics of each method, HHT and Short-Time Fourier Transform (STFT) effective in dealing with stationary signals are compared. And with thermoacoustic instabilities signals from a Rijke tube test, HHT and the improved HHT with Ensemble Empirical Mode Decomposition (EEMD) are compared. The results show that the improved HHT is more appropriate than the original HHT due to the relative insensitivity to noise. Therefore it will result in more accurate analysis.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2008.04a
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• pp.484-490
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• 2008

For CSLDV, the Chebyshev demodulation (or polynomial) technique and Hilbert transform approach have been used for mode shape reconstruction with harmonic excitation. In this paper, the Hilbert-Huang transform approach was applied as an alternative to impact excitation cases in terms of a numerical approach. The vibration of the tested structure is modeled using impulse response functions. In order to verify this technique, a simply supported beam was chosen as the test rig. With additional innovative steps which are the ideal-band pass filter and the nodal point determination, Hilbert-Huang transformation can be used for a good mode shape reconstruction even in the impact excitation case.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2002.03a
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• pp.159-168
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• 2002

A recently developed filbert-Huang transform (HHT) technique is applied to detect damage locations of bridge structures. The HHT may be used to identify the locations of damages which exhibit nonlinear and nonstationary behavior, since the HHT can show the instantaneous frequency characteristics of the signal. A series of numerical simulations were conducted for bridge pier systems with damages under a controlled load with sweeping frequency. The results of the numerical simulation study indicate that the HHT method can reasonably identify damage locations using a limited number of acceleration sensors under severe measurement noise condition.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.35 no.2
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• pp.106-112
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• 2012

Real-life time series characteristic data has significant amount of non-stationary components, especially periodic components in nature. Extracting such components has required many ad-hoc techniques with external parameters set by users in case-by-case manner. In our study, we evaluate whether Hilbert-Huang Transform, a new tool of time-series analysis can be used for effective analysis of such data. It is divided into two points : 1) how effective it is in finding periodic components, 2) whether we can use its results directly in detecting values outside control limits, for which a traditional method such as ARIMA had been used. We use glass furnace temperature data to illustrate the method.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.37 no.4
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• pp.35-41
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• 2014

Real-life time series characteristic data has significant amount of non-stationary components, especially periodic components in nature. Extracting such components has required many ad-hoc techniques with external parameters set by users in a case-by-case manner. In this study, we used Empirical Mode Decomposition Method from Hilbert-Huang Transform to extract them in a systematic manner with least number of ad-hoc parameters set by users. After the periodic components are removed, the remaining time-series data can be analyzed with traditional methods such as ARIMA model. Then we suggest a different way of setting control chart limits for characteristic data with periodic components in addition to ARIMA components.