• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.15 no.2 s.95
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• pp.192-198
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• 2005

Empirical mode decomposition(EMD) method has been recently proposed to analyze non-linear and non-stationary data. This method allows the decomposition of one-dimensional signals into intrinsic mode functions(IMFs) and is used to calculate a meaningful multi-component instantaneous frequency. In this paper, it is assumed that each mode of damped vibration signal could be well separated in the form of IMF by EMD. In this case, we can have a new powerful method to calculate natural frequencies and dampings from damped vibration signal which usually has multiple modes. This proposed method has been verified by both simulation and experiment. The results by EMD method whichhas used only output vibration data are almost identical to the results by FRF method which has used both input and output data, thereby proving usefulness and accuracy of the proposed method.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2009.10a
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• pp.99-105
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• 2009

Crack detection method of a rotating blade was suggested in this paper. A rotating blade was modeled with a cantilever beam connected to a hub undergoing rotating motion. The existence and the location of crack were able to be recognized from the vertical response of end tip of a rotating cantilever beam by employing Discrete Hidden Markov Model (DHMM) and Empirical Mode Decomposition (EMD). DHMM is a famous stochastic method in the field of speech recognition. However, in recent researches, it has been proved that DHMM can also be used in machine health monitoring. EMD is the method suggested by Huang et al. that decompose a random signal into several mono component signals. EMD was used in this paper as the process of extraction of feature vectors which is the important process to developing DHMM. It was found that developed DHMMs for crack detection of a rotating blade have shown good crack detection ability.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.25 no.6
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• pp.700-708
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• 2014

Jet engine modulation(JEM) is a frequency modulation phenomenon of the radar signal induced by electromagnetic scattering from a rotating jet engine turbine. Although JEM can be used as a representative radar target recognition method by providing unique information on the target, its recognition performance may be degraded in the observation range of weakly present JEM. Hence, this paper presents a method for extracting the JEM component by decomposing the radar signal into intrisic mode functions(IMFs) via complex empirical mode decomposition(CEMD) and by combining them based on signal eccentricity. Its application to various signals demonstrated that the proposed method improved the clarity of JEM analysis and could extend the effective observation range of JEM.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.25 no.6
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• pp.690-699
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• 2014

Jet engine modulation(JEM) is a technique used to identify the jet engine type from the radar target signature modulated by periodic rotation of the jet engine mounted on the aircraft target. As a new approach of JEM, this paper proposes an automatic algorithm for extracting the jet engine information. First, the rotation period of the jet engine is yielded from auto-correlation of the JEM signal preprocessed by complex empirical mode decomposition(CEMD). Then, the final blade number is estimated by introducing the DM(Divisor-Multiplier) rule and the 'Scoring' concept into JEM spectral analysis. Application results of the simulated and measured JEM signals demonstrated that the proposed algorithm is effective in accurate and automatic extraction of the jet engine information.

• 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.

• The Korean Journal of Nuclear Medicine Technology
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• v.12 no.3
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• pp.222-228
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• 2008

Purpose: The objectives of this study were to compare the left ventricle ejection fraction (LVEF) from gated cardiac blood pool scan (GCBP) for analysis auto-drawing region of interest mode (AROI) and manual-drawing region of interest mode (MROI), respectively. To evaluation the relationships between values produced by both ROI modes. Materials and Methods: Gated cardiac blood pool scan using in vivo method Tc-99m Red Blood Cell were performed for 33 patients (mean age: $53.2{\pm}13.2\;y$) with objective of chemotherapy using single head gamma camera (ADAC Laboratories, Milpitas, CA). Left ventricular ejection fraction was automatically and manually measured, respectively. Results: There was significant difference statistically between AROI and MROI ($LVEF^{AROI}$: $71.4{\pm}12.4%$ vs. $LVEF^{MROI}$: $65.8{\pm}5.9%$, p=0.003). Intra-observer agreements in AROI was higher than MROI ($\gamma^{AROI}=0.964$, Cronbach's $\alpha^{AROI}=0.986$ vs. $\gamma^{MROI}=0.793$, Cronbach's $\alpha^{MROI}=0.911$), either. Additionally, there was no significant difference statistically at best septal view (${\Delta}LVEF^{BSV}=0.7{\pm}2.3%$, p=0.233), however statistically significant difference was found at badly separated septal view (${\Delta}LVEF=10.9{\pm}11.4%$, p=0.001). Moreover, Intra-observer agreements in best septal view was higher than badly separated septal view ($\gamma^{BSV}=0.939$, Cronbach's $\alpha^{BSV}=0.978$; $\gamma=0.948$, Cronbach's $\alpha=0.981$ at AROI, $\gamma^{BSV}=0.836$, Cronbach's $\alpha^{BSV}=0.936$; $\gamma=0.748$, Cronbach's $\alpha=0.888$ at MROI). Conclusion: When best septal view was acquired, LVEF by AROI and MROI indicated not different. Comparing Intra-observer agreements with AROI and MROI, the AROI tended to show higher. Therefore, it is considered that the AROI than MROI is valuable in reproducibility and objective when ROI analysis by acquire left ventricular of best septal view.