• 한국추진공학회지
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• 제20권2호
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• pp.56-66
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• 2016

모델기반 FDI 과정에서 모델오차와 센서잡음은 피할 수 없으므로 견실성은 모델기반 FDI에서 매우 중요하다. 본 연구에서는 이러한 선형모델 오차 및 신호잡음으로 인하여 고장진단 과정에서 발생하는 결함판단 오류들을 비선형 NARX (Nonlinear Auto Regressive eXogenous) 모델과 칼만추정기를 적용하여 개선하는 방법을 제안하였다. 최종 고장판단은 퍼지로직을 이용하여 발생하는 오차의 추이에 대한 확률로 결정하여 순간적인 신호잡음에 강인하도록 설계하였다. 시뮬레이션을 통하여 운용 환경조건에서 엔진제어기의 고장허용에 따른 성능을 확인하였다.

• 한국윤활학회:학술대회논문집
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• 한국윤활학회 2002년도 proceedings of the second asia international conference on tribology
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• pp.259-260
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• 2002

In this paper, electrical discharge machined (EDM) surfaces machined with various machining parameters are characterized and simulated. Three-dimensional surface topography of EDM surfaces are measured by a stylus instrument. Surface topography is characterized with auto-correlation coefficient and height probability density functions. Then, EDM surfaces are modeled and computer-simulated by using the non-causal 2-D auto-regressive model. Simulation results show that EDM surfaces are characterized well by a few parameters.

• 산업경영시스템학회지
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• 제19권40호
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• pp.1-14
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• 1996

We develop a dynamic demand forecasting model compared to regression analysis model and AutoRegressive Integrated Moving Average(ARIMA) model. The dynamic model can apply to the current dynamic data to forecasts through introducing state equation. A multiple regression model and ARIMA model using given data are designed via the model analysis. The forecasting fitness evaluation between the designed models and the dynamic model is compared with the criterion of sum of squared error.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.342-345
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• 2005

This paper describes dynamics analysis of a small training boat and a new type of ship's autopilot not only to keep her course but also to reduce her roll motion. Firstly, statistical analysis through multi-variate auto regressive model is carried out using the real data collected from the sea trial on an actual small training boat Sazanami after the navigational system of the boat was upgraded. It is shown that the roll motion is strongly influenced by the rudder motion and it is suggested that there is a possibility of reducing the roll motion by controlling the rudder order properly. Based on this observation, a new type of ship's autopilot that takes the roll motion into account is designed using the muti-variate modern control theory. Lastly, digital simulations by white noise are carried out in order to evaluate the proposed system and a typical result is demonstrated. As results of simulations, the proposed autopilot had good performance compared with the original data.

• Journal of Positioning, Navigation, and Timing
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• 제9권2호
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• pp.89-98
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• 2020

Prediction of clock behaviors is necessary to generate very high stable system time which is essential for a satellite navigation system. For the purpose, we applied the Auto-Regressive Integrated Moving Average (ARIMA) model to the prediction of two hydrogen masers' behaviors with respect to the rapid Coordinated Universal Time (UTCr). Using the packaged programming language R, we made an analysis and prediction of time series data of [UTCr - clocks]. The maximum variation width of the residuals which were obtained by the difference between the predicted and measured values, was 6.2 ns for 106 days. This variation width was just one-sixth of [UTCr-UTC (KRIS)] published by the BIPM for the same period. Since the two hydrogen masers were found to be strongly correlated, we applied the Vector Auto-Regressive Moving Average (VARMA) model for more accurate prediction. The result showed that the prediction accuarcy was improved by two times for one hydrogen maser.

• Smart Structures and Systems
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• 제17권6호
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• pp.1031-1053
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• 2016

The health conditions of in-service civil infrastructures can be evaluated by employing structural health monitoring technology. A reliable health evaluation result depends heavily on the quality of the data collected from the structural monitoring sensor network. Hence, the problem of sensor fault diagnosis has gained considerable attention in recent years. In this paper, an innovative sensor fault diagnosis method that focuses on fault detection and isolation stages has been proposed. The dynamic or auto-regressive characteristic is firstly utilized to build a multivariable statistical model that measures the correlations of the currently collected structural responses and the future possible ones in combination with the canonical correlation analysis. Two different fault detection statistics are then defined based on the above multivariable statistical model for deciding whether a fault or failure occurred in the sensor network. After that, two corresponding fault isolation indices are deduced through the contribution analysis methodology to identify the faulty sensor. Case studies, using a benchmark structure developed for bridge health monitoring, are considered in the research and demonstrate the superiority of the new proposed sensor fault diagnosis method over the traditional principal component analysis-based and the dynamic principal component analysis-based methods.

• Communications for Statistical Applications and Methods
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• 제26권3호
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• pp.261-272
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• 2019

In this paper, we propose a procedure to build a prediction interval of the sum of dependent binary random variables over a graph to account for the dependence among binary variables. Our main interest is to find a prediction interval of the weighted sum of dependent binary random variables indexed by a graph. This problem is motivated by the prediction problem of various elections including Korean National Assembly and US presidential election. Traditional and popular approaches to construct the prediction interval of the seats won by major parties are normal approximation by the CLT and Monte Carlo method by generating many independent Bernoulli random variables assuming that those binary random variables are independent and the success probabilities are known constants. However, in practice, the survey results (also the exit polls) on the election are random and hardly independent to each other. They are more often spatially correlated random variables. To take this into account, we suggest a spatial auto-regressive (AR) model for the surveyed success probabilities, and propose a residual based bootstrap procedure to construct the prediction interval of the sum of the binary outcomes. Finally, we apply the procedure to building the prediction intervals of the number of legislative seats won by each party from the exit poll data in the $19^{th}$ and $20^{th}$ Korea National Assembly elections.

• 한국정보통신학회논문지
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• 제25권12호
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• pp.1756-1761
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• 2021

2019년 12월경 후베이 우한시에서 발생한 코로나19 바이러스가 점차 줄어드는 듯 보였으나, 2020년 11월, 2021년 6월 기준으로 점차 늘어나고 있으며, 전세계적으로 총 1억 9천 2백만명, 대한민국 기준 총 확진자는 대략 18만4천명으로 추정된다. 이에 따른 대책으로 중앙재난안전대책본부는 사회적 거리두기 4단계를 시행하면서 강력한 대응책을 내고있지만, 델타바이러스등 전염성이 강한 코로나 변이 바이러스가 기승을 부리면서 국내 일일 확진자 수는 1800명대 까지 증가하게 되었다. 그에따라 코로나바이러스의 심각성을 강조하고자 코로나 누적 확진자 수를 ARIMA 알고리즘을 이용해 예측한다. 그 과정에서 추세와 계절성을 제거하기 위해서 차분을 이용하고, MA, AR, 자기상관함수와 편자기상관함수를 이용해 ARIMA에서 p,d,q값을 결정하고 예측한다. 마지막으로 예측값과 실제값을 비교해 얼마나 잘 예측되었는지 평가한다.

• 한국윤활학회:학술대회논문집
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• 한국윤활학회 2002년도 proceedings of the second asia international conference on tribology
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• pp.249-250
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• 2002

In this paper, the relationship among the 3-D surface topography parameters are studied. Several surface topography parameters that are important in tribology are calculated against various surface topography data. 3-D surface data with desired properties are generated by using the non-causal 2-D auto-regressive (AR) model. The non-causal 2-D AR model is a random 3-D surface topography model that can generate 3-D surface topography data with specified parameters.

• Journal of the Korean Data and Information Science Society
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• 제19권2호
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• pp.413-420
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• 2008

This paper suggests a marginal logit mixed-effects for analyzing repeated binary response data. Since binary repeated measures are obtained over time from each subject, observations will have a certain covariance structure among them. As a plausible covariance structure, 1st order auto-regressive correlation structure is assumed for analyzing data. Generalized estimating equations(GEE) method is used for estimating fixed effects in the model.