• Journal of the Korean Data and Information Science Society
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• v.7 no.1
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• pp.21-35
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• 1996

In this paper, estimation and prediction procedures are discussed for grneral situation in which the failure time follows the independent density $f_{i}({\varepsilon}_{i})$ for the accelerated life testing under Type II censoring. In the context of accelerated life test experiment, procedures are given for estimating the parameters in the Eyring model, and for estimating mean life at a given future stress level. The procedures given are conditional confidence interval procedures, obtained by conditioning on ancillary statistics. A comparison is made of these procedures and procedures based on asymptotic properties of the maximum, likelihood estimates.

• Journal of the Korean Society of Combustion
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• v.8 no.4
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• pp.15-23
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• 2003

The zone conditional two-fluid equations are derived and validated against DNS database of a premixed turbulent flame. The conditional statistics of major flow variables are investigated to understand the mechanism of flame generated turbulence. The flow field in burned zone shows substantially increased turbulent kinetic energy, which is highly anisotropic due to reaction kinematics across thin f1amelets. The transverse component may be larger than the axial component for a distributed pdf of the flamelet orientation angle, while the opposite occurs due to redistribution of turbulent kinetic energy and flamelet orientation normal to the flow at the end of a flame brush. The major source or sink terms of turbulent kinetic energy are the interfacial transfer by the mean reaction rate and the work terms by fluctuating pressure and velocity on a flame surface. Ad hoc modeling of some interfacial terms may be required for further application of the two-fluid model in turbulent combustion simulations.

• 한국연소학회:학술대회논문집
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• 2003.12a
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• pp.49-56
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• 2003

Mathematical formulation of the zone conditional two-fluid model is established to consider flame-generated turbulence in premixed turbulent combustion. The conditional statistics of major flow variables are investigated to understand the mechanism of flame generated turbulence. The flow field in burned zone shows substantially increased turbulent kinetic energy, which is highly anisotropic due to reaction kinematics across thin flamelets. The transverse component of rms velocities in burned zone become larger than axial component in the core of turbulent flame brush. The major source or sink terms of turbulent kinetic energy are the interfacial transfer by the mean reaction rate and the work terms by fluctuating pressure and velocity on a flame surface.

• Journal of the Korean Data and Information Science Society
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• v.22 no.1
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• pp.89-97
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• 2011

The aims of this study were to propose a method of estimation for mean residual life function (MRLF) from conditional survival function using the Buckley and James's (1979) pseudo random variables, and then to assess the performance of the proposed method through the simulation studies. The mean squared error (MSE) of proposed method were less than those of the Cox's proportional hazard model (PHM) and Beran's nonparametric method for non-PHM case. Futhermore in the case of PHM, the MSE's of proposed method were similar to those of Cox's PHM. Finally, to evaluate the appropriateness of practical use, we applied the proposed method to the gastric cancer data. The data set consist of the 1, 192 patients with gastric cancer underwent surgery at the Department of Surgery, K-University Hospital.

• Communications for Statistical Applications and Methods
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• v.7 no.1
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• pp.87-96
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• 2000

Nonlinear time series prediction is derived and compared between statistic of modeling and neural network method. In particular mean squared errors of predication are obtained in generalized random coefficient model and generalized autoregressive conditional heteroscedastic model and compared with them by neural network forecasting.

• 한국연소학회:학술대회논문집
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• 2004.06a
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• pp.75-82
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• 2004

The conditional moment closure(CMC) model has been implemented in context with the unstructured-grid finite-volume method which efficiently handle the physically and geometrically complex turbulent reacting flows. The validation cases include a turbulent nonpremixed $CO/H_2/N_2$ Jet flame and a turbulent nonpremixed $H_2/CO$ flame stabilized on an axisymmetric bluff-body burner. In terms of mean flame field, minor species and NO formation, numerical results has the overall agreement with expermental data. The detailed discussion has been made for the turbulence-chemistry interaction and NOx formation characteristics as well as the comparative performance for CMC and flamelet model.

• Proceedings of the Acoustical Society of Korea Conference
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• 1998.08a
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• pp.225-230
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• 1998

In this paper, a Kalman filter approach for enhancing speech signals degraded by statistically independent additive nonstationary noise is developed. The autoregressive hidden markov model is used for modeling the statistical characteristics of both the clean speech signal and the nonstationary noise process. In this case, the speech enhancement comprises a weighted sum of conditional mean estimators for the composite states of the models for the speech and noise, where the weights equal to the posterior probabilities of the composite states, given the noisy speech. The conditional mean estimators use a smoothing spproach based on two Kalmean filters with Markovian switching coefficients, where one of the filters propagates in the forward-time direction with one frame. The proposed method is tested against the noisy speech signals degraded by Gaussian colored noise or nonstationary noise at various input signal-to-noise ratios. An app개ximate improvement of 4.7-5.2 dB is SNR is achieved at input SNR 10 and 15 dB. Also, in a comparison of conventional and the proposed methods, an improvement of the about 0.3 dB in SNR is obtained with our proposed method.

• Communications for Statistical Applications and Methods
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• v.26 no.3
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• pp.295-304
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• 2019

This article deals with threshold-asymmetric volatility models for over-dispersed and zero-inflated time series of count data. We introduce various threshold integer-valued autoregressive conditional heteroscedasticity (ARCH) models as incorporating over-dispersion and zero-inflation via conditional Poisson and negative binomial distributions. EM-algorithm is used to estimate parameters. The cholera data from Kolkata in India from 2006 to 2011 is analyzed as a real application. In order to construct the threshold-variable, both local constant mean which is time-varying and grand mean are adopted. It is noted via a data application that threshold model as an asymmetric version is useful in modelling count time series volatility.

• International Area Studies Review
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• v.20 no.3
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• pp.123-144
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• 2016

This paper investigates the linkages between returns both in foreign exchange and stock markets, and uncertainties in two markets using daily data for the period of 16 July 2004 to 30 June 2014 in Nepalese economy. Four hypotheses are tested about how uncertainty influences the stock index and exchange rates. From the empirical results, a bivariate EGARCH-M model is the best to explain the volatility in the two markets. There is a negative relationship from the exchange rates return to stock price return. Empirical results do provide strong empirical confirmation that negative effect of stock index uncertainty and positive effect of exchange rates uncertainty on average stock index. GARCH-in-mean variables in AR modeling are significant and shows that there is positive effect of exchange rates uncertainty and negative effect of stock index uncertainty on average exchange rates. Stock index shocks have longer lived effects on uncertainty in the stock market than exchange rates shock have on uncertainly in the foreign exchange market. The effect of the last period's shock, volatility is more sensitive to its own lagged values.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.2 s.314
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• pp.93-101
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• 2007

To jointly optimize the spatial registration and the exposure compensation, an iterative registration algorithm, the Lucas-Kanade algorithm, is combined with an exposure compensation algorithm, which is based on the histogram transformation function. Based on a simple regression model, a nonparametric estimator, the empirical conditional mean, and its polynomial fitting are used as histogram transformation functions for the exposure compensation. Since the proposed algorithm is composed of separable optimization phases, the proposed algorithm is more advantageous than the joint approaches of Mann and Candocia in the aspect of implementation flexibility. The proposed algorithm performs a better registration for real images than the case of registration that does not consider the exposure difference.