• Communications for Statistical Applications and Methods
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• 제21권5호
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• pp.387-393
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• 2014

Recurrent gap times are analyzed with diverse methods under several assumptions such as a marginal model or a frailty model. Several resampling techniques have been recently suggested to estimate the covariate effect; however, these approaches can be applied with a time-fixed covariate. According to simulation results, these methods result in biased estimates for a time-varying covariate which is often observed in a longitudinal study. In this paper, we extend a resampling method by incorporating new weights and sampling scheme. Simulation studies are performed to compare the suggested method with previous resampling methods. The proposed method is applied to estimate the effect of an educational program on traffic conviction data where a program participation occurs in the middle of the study.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.521-521
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• 2000

This paper investigates the problem of robust H$_{\infty}$ filter with FIR(Finite Impulse Response) structure for linear continuous time-varying systems with sampled-data measurements. It is assumed that the system is subject to real time-varying uncertainty which is represented by the state-space model having parameter uncertainty. The robust H$_{\infty}$ FIR filter is proposed for the continuous-time linear parameter uncertain systems. It is also derived from the equivalence relationship between the robust linear H$_{\infty}$ FIR filter and the robust linear H$_{\infty}$ filter with sampled-data measurements.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2008년도 추계학술대회A
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• pp.1331-1336
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• 2008

This paper suggests a method using Bayesian inference to estimate the parameters of Weibull distribution and acceleration parameters under the condition that the stresses are time-dependent functions. A Bayesian model based on the discrete time approximation is formulated to infer the parameters of interest from the failure data of the virtual tests and a statistical analysis is considered to decide the most probable mean values of the parameters for reasoning of the failure data.

• Communications for Statistical Applications and Methods
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• 제24권2호
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• pp.115-127
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• 2017

This paper proposes a Bayesian test for the equivalence of survival functions in multiple groups. Proposed Bayesian test use the model of Cox's regression with time-varying coefficients. B-spline expansions are used for the time-varying coefficients, and the proposed test use only the partial likelihood, which provides easier computations. Various simulations of the proposed test and typical tests such as log-rank and Fleming and Harrington tests were conducted. This result shows that the proposed test is consistent as data size increase. Specifically, the power of the proposed test is high despite the existence of crossing hazards. The proposed test is based on a Bayesian approach, which is more flexible when used in multiple tests. The proposed test can therefore perform various tests simultaneously. Real data analysis of Larynx Cancer Data was conducted to assess applicability.

• Journal of the Korean Statistical Society
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• 제33권4호
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• pp.367-379
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• 2004

This article deals with the nonparametric analysis of longitudinal data when there exist possible correlations among repeated measurements for a given subject. We consider a quasi-likelihood regression model where a transformation of the regression function through a link function is linear in time-varying coefficients. We investigate the local polynomial approach to estimate the time-varying coefficients, and derive the asymptotic distribution of the estimators in this quasi-likelihood context. A real data set is analyzed as an illustrative example.

• 한국철도학회논문집
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• 제12권4호
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• pp.465-471
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• 2009

본 논분에서는 보다 적은 실험으로 고속철도차량의 윤축에서 발생하는 소음과 진동의 동적 특성을 알기 위해 불규칙 신호처리와 시간-주파수 해석을 이용하여 분석하였다. 윤축의 소음과 진동은 대부분 차륜과 궤도의 비정상상호작용이 원인이다. 이를 분석하기 위해 마이크로폰과 가속도계를 이용하여 데이터를 수집하고 붙규칙 신호처리와 시간-주파수 분석을 이용하여 분석하였다. 분석한 결과, 이 방법들은 고속철도차량의 윤축에서 발생하는 소음과 진동의 동적특성을 분석하는데 유용함을 확인하였다.

• 전기학회논문지
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• 제66권1호
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• pp.171-178
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• 2017

This paper studies the state estimation problem for static neural networks with time-varying delay. Unlike other studies, the controller scheme, which involves time-varying sampling and uncertainties, is first employed to design the state estimator for delayed static neural networks. Based on Lyapunov functional approach and linear matrix inequality technique, the non-fragile sampled-data estimator is designed such that the resulting estimation error system is globally asymptotically stable with $H_{\infty}$ performance. Finally, the effectiveness of the developed results is demonstrated by a numerical example.

• The Journal of Asian Finance, Economics and Business
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• 제7권9호
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• pp.95-104
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• 2020

In financial economics studies, the autoregressive model has been a workhorse for a long time. However, the model has a fixed value on every parameter and requires the stationarity assumptions. Time-varying coefficient autoregressive model that we use in this paper offers some desirable benefits over the traditional model such as the parameters are allowed to be varied over-time and can be applies to non-stationary financial data. This paper provides the Monte Carlo simulation studies which show that the model can capture the dynamic movement of parameters very well, even though, there are some sudden changes or jumps. For the daily data from January 1, 2015 to February 12, 2020, our paper provides the empirical studies that Thailand, Taiwan and Tokyo Stock market Index can be explained very well by the time-varying coefficient autoregressive model with lag order one while South Korea's stock index can be explained by the model with lag order three. We show that the model can unveil the non-linear shape of the estimated mean. We employ GJR-GARCH in the condition variance equation and found the evidences that the negative shocks have more impact on market's volatility than the positive shock in the case of South Korea and Tokyo.

• 한국컴퓨터정보학회논문지
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• 제12권3호
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• pp.85-93
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• 2007

본 논문에서는 시간에 따라서 변하는 삼차원 볼륨의 집합인 볼륨 비디오 데이터의 압축 기법을 제안하고 각각의 프레임을 실시간으로 압축 해제하며 가시화하는 방법을 설명하였다. 시변 볼륨 데이터는 한 프레임의 크기 조차도 매우 크기 때문에 그 크기를 줄이는 압축 기법을 수행할 필요가 있다. 그리고 실행 과정에서 일어나는 압축 해제는 시변 볼륨 데이터를 실시간으로 가시화 하는데 있어 병목 현상의 원인이 될 수 있다. 우리는 실행중의 압축 해제 속도와 데이터의 압축률을 높이기 위하여 삼차원 볼륨 데이터를 작은 블록으로 분해하고 충분히 많이 변하는 블록들을 갱신하는 방법을 제안하였다. 이 방법을 구현한 결과 본 논문에서 제안된 압축 및 압축 해제 기법이 압축 해제 속도와 압축에서 복원된 데이터의 정확성, 그리고 압축률의 정도를 조절하여 대용량 시변 볼륨데이터의 대화형 가시화를 가능하게 함을 알 수 있다.

• 열처리공학회지
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• 제36권6호
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• pp.374-381
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• 2023

This study develops and evaluates a deep learning model for predicting oxide and nitride layers based on plasma process data. We introduce a novel deep learning-based Varying Coefficient Regressor (VCR) by adapting the VCR, which previously relied on an existing unique function. This model is employed to forecast the oxide and nitride layers within the plasma. Through comparative experiments, the proposed VCR-based model exhibits superior performance compared to Long Short-Term Memory, Random Forest, and other methods, showcasing its excellence in predicting time series data. This study indicates the potential for advancing prediction models through deep learning in the domain of plasma processing and highlights its application prospects in industrial settings.