• Communications for Statistical Applications and Methods
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• v.16 no.6
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• pp.891-901
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• 2009

In this paper, we investigate the approach to estimate VaR under the transformed GARCH model. The time series are transformed to approximate to the underlying distribution of error terms and then the parameters and the one-sided prediction interval are estimated with the transformed data. The back-transformation is applied to compute the VaR in the original data scale. The analyses on the asset returns of KOSPI and KOSDAQ are presented to verify the accuracy of the coverage probabilities of the proposed VaR.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.05a
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• pp.1142-1147
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• 2002

For the evaluation of the DC motor noise and vibration, usually it is rely on human feeling because some kinds of noise are not definitely represented by measurement Instrument such as sound meter. But when we consider time signal of the noise and vibration. It is possible to represent them. And in this paper. it is suggested to study output current shape of the motor because it Is the source to make speed and torque variation of the motor. If the current shape is not stable. it makes operating state of the motor unstable and produces noise and vibration. By analyzing signal at time and frequency of noise and vibration and current shape. it is possible to automation of the noise and vibration measurement in the Production line.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.7
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• pp.3050-3065
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• 2016

Neural networks (NNs) are extensively used in applications requiring signal classification and regression analysis. In this paper, a NN based threshold selection algorithm for 60 GHz millimeter wave (MMW) time of arrival (TOA) estimation using an energy detector (ED) is proposed which is based on the skewness, kurtosis, and curl of the received energy block values. The best normalized threshold for a given signal-to-noise ratio (SNR) is determined, and the influence of the integration period and channel on the performance is investigated. Results are presented which show that the proposed NN based algorithm provides superior precision and better robustness than other ED based algorithms over a wide range of SNR values. Further, it is independent of the integration period and channel model.

• The Mathematical Education
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• v.41 no.1
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• pp.109-126
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• 2002

In this thesis. we estimated the good reliability coefficient ${\beta}$$\sub$k/ that is unbiased, consistent and more efficient than Cronbach's ${\alpha}$$\sub$k/ in splitting of a test into several formats of subtests and several properties of ${\beta}$$\sub$k/ are also represented. The tables of coefficients of skewness and kurtosis are represented to test the significance of departures from normality. We got the cumulative normal plots of z'from the distribution which is departed from normality using the Bock's approximation procedure and we finally enumerated the transformed standardized scores z'and a new raw score X' which enable us to proceed further evaluation procedures depending on our assessment policy.

• Communications for Statistical Applications and Methods
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• v.10 no.1
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• pp.11-18
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• 2003

Quite often normality assumptions are not satisfied in practical applications. In this paper, an estimated generalized least squares(EGLS) analysis are considered in two way mixed linear models with arbitrary types of distributions for random effects. We investigate the power performance of EGLS analysis based on Henderson's method III, ML, REML and MINQUE(1). The power performances depend on the imbalance of design, on the actual values of ratio of variance components, and on the skewness and kurtosis parameters of the underlying distributions slightly. Results of our limited simulation study suggest that the EGLS F-statistics using four estimators and arbitrary distributions produce similar type I error rates and power performance.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.16 no.5 s.110
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• pp.495-502
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• 2006

A fault diagnosis method is developed in this study using automaton through quantization of vibration signals for normal and faulty conditions, respectively. Automaton is a kind of qualitative model which describes the system behaviour at the level of abstraction. The system behavior was extracted from the probability of the output sequence of vibration signals. The sequence was made as vibration levels by reconstructing the originally measured vibration signals. As an example, a fault diagnosis for the bearing of ATM machine was done, which detected the bearing fault with confident level compared to any other existing methods of kurtosis or spectrum analysis.

• Transactions of the Korean Society of Automotive Engineers
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• v.7 no.5
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• pp.268-277
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• 1999

Impulsive acoustic and vibration signals within gear are often induced by impacting of fault tooths in gear. Thus the detection of these impulses can be useful for fault diagnosis. Recently there is an increasing trend towards the use of higher order statistics for fault detection within mechanical systems based on the observation that impulsive signals then to increase the kurtosis values. We show that the fourth order Wigner Moment Spectrum, called the Wigner Trispectrum, has found superior detection performance to second order Wigner distribution for typical impulsive signals in a condition monitoring application. These methods are also applied to data sets measured within an industrial gear box.

• Journal of the Korean Society of Industry Convergence
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• v.7 no.1
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• pp.41-46
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• 2004

This paper proposes a separation of mixed signals by using the robust independent component analysis(RICA) based on neural networks. RICA is based on the temporal correlations and the second order statistics of signal. This method e is applied for improving the analysis rate and speed in which the sources have very small or zero kurtosis. The proposed method has been applied for separating the 10 mixed finger prints of $256{\times}256$-pixel and the 4 mixed images of $512{\times}512$-pixel, respectively. The simulation results show that RICA has the separating rate and speed better than those using the conventional FP algorithm based on Newton method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.12A
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• pp.1177-1184
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• 2008

Non-line-of-sight (NLOS) propagation can severely weaken the accuracy of ranging and localization in wireless location systems. NLOS bias mitigation techniques have recently been proposed to relieve the NLOS effects, but positively rely on the capability to accurately distinguish between LOS and NLOS propagation scenarios. This paper proposes an energy-capture-based NLOS identification method for LDR-UWB systems, based on the analysis of the characteristics of the channel impulse response (CIR). With this proposed energy capture method, the probability of successfully identifying NLOS is much improved than the existing methods, such as the kurtosis method, the strongest path compare method, etc. This NLOS identification method can be employed in adaptive modulation scheme to decrease bit error ratio (BER) level for certain signal-to-noise ratio (SNR). The BER performance with the adaptive modulation can be significantly enhanced by selecting proper modulation method with the knowledge of channel information from the proposed NLOS identification method.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.50 no.3
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• pp.121-129
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• 2001

In this paper, a new efficient feature extraction method based on Daubechies discrete wavelet transform is presented. This paper especially deals with the assessment of process statistical parameter using the features extracted from the wavelet coefficients of measured acoustic emission signals. Since the parameter assessment using all wavelet coefficients will often turn out leads to inefficient or inaccurate results, we selected that level-3 stage of multi decomposition in discrete wavelet transform. We make use of the feature extraction parameter namely, maximum value of acoustic emission signal, average value, dispersion, skewness, kurtosis, etc. The effectiveness of this new method has been verified on ability a diagnosis transformer go through feature extraction in stage of aging(the early period, the middle period, the last period)