• 대기
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• 제24권4호
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• pp.491-502
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• 2014

As a part of the KIAPS (Korea Institute of Atmospheric Prediction Systems) Package for Observation Processing (KPOP), we have developed the modules for Advanced Microwave Sounding Unit-A (AMSU-A) pre-processing and its bias correction. The KPOP system calculates the airmass bias correction coefficients via the method of multiple linear regression in which the scan-corrected innovation and the thicknesses of 850~300, 200~50, 50~5, and 10~1 hPa are respectively used for dependent and independent variables. Among the four airmass predictors, the multicollinearity has been shown by the Variance Inflation Factor (VIF) that quantifies the severity of multicollinearity in a least square regression. To resolve the multicollinearity, we adopted simple linear regression and Principal Component Regression (PCR) to calculate the airmass bias correction coefficients and compared the results with those from the multiple linear regression. The analysis shows that the order of performances is multiple linear, principal component, and simple linear regressions. For bias correction for the AMSU-A channel 4 which is the most sensitive to the lower troposphere, the multiple linear regression with all four airmass predictors is superior to the simple linear regression with one airmass predictor of 850~300 hPa. The results of PCR with 95% accumulated variances accounted for eigenvalues showed the similar results of the multiple linear regression.

• 한국정밀공학회지
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• 제25권5호
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• pp.52-59
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• 2008

The accuracy of the machine tools is degraded because of thermal error of structure due to thermal variation. To improve the accuracy of a machine tools, measurement and prediction of thermal error is very important. The main part of thermal source is spindle due to high speed with friction. The thermal error of spindle is very important because it is over 10% in total thermals errors. In this paper, the suitable thermal error prediction technology for machine tools with open architecture controller is developed and implemented to machine tools. Two thermal error prediction technologies, neural network and multi-linear regression, are investigated in several methods. The multi-linear regression method is more effective for implementation to CNC. The developed thermal error prediction technology is implemented on the internal function of CNC.

• 한국전자파학회논문지
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• 제20권12호
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• pp.1287-1296
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• 2009

채널의 상태가 시간에 따라 수시로 변하는 전송 환경에서 수신된 신호에 대한 잡음 비를 추정하는 것이 중요하다. 대부분의 SNR 추정기는 MF(Matched Filter) 후 수신된 샘플로 추정이 이루어진다. 하지만 이런 기법들은 무선 통신에서 채널의 상태에 민감한 특성을 갖는다. 하지만 수신기의 front-end에서 모아진 데이터들을 이용하는 선형 예측(LP: Linear Prediction) 기법을 기반으로 하는 신호 대 잡음 비 추정 알고리즘은 이에 비해 안정된 성능을 보인다. 본 논문에서는 LP 기반의 SNR 추정기를 소개하고, 기존의 LP 기법 기반으로 하는 SNR 추정 알고리즘의 계산 복잡도를 줄이기 위한 새로운 기법을 제안한다. 본 논문에서 제안하는 알고리즘은 Linear Prediction 오차를 구하는 과정에서 순방향 오차와 그 conjugate 값을 이용하여 SNR 추정 과정을 보다 간단하게 한다.

• Journal of the Korean Data and Information Science Society
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• 제14권2호
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• pp.265-274
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• 2003

Time series data are influenced by the external events such as holiday, strike, oil shock, and political change, so the external events cause a sudden change to the time series data. We regard the observation as outlier that occurred as a result of external events. In general, it is called intervention if we know the period and the reason of external events, and it makes an analyst difficult to establish a time series model. Therefore, it is important that we analyze the styles and effects of intervention. In this paper, we considered the linear time series model with invention and compared with nonlinear time series models such as ARCH, GARCH model and also we compared with the combination prediction method that Tong(1990) introduced. In the practical case study, we compared prediction power with RMSE among linear, nonlinear time series model with intervention and combination prediction method.

• 반도체디스플레이기술학회지
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• 제19권4호
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• pp.59-64
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• 2020

In this paper, we found the solution using data based machine learning regression method to check the pore shape, to solve the problem of the experiment quantity occurring when producing scaffold with the 3d printer. Through experiments, we learned secured each print condition and pore shape. We have produced the scaffold from scaffold pore shape defect prediction model using multiple linear regression method. We predicted scaffold pore shapes of unsecured print condition using the manufactured scaffold pore shape defect prediction model. We randomly selected 20 print conditions from various predicted print conditions. We print scaffold five times under same print condition. We measured the pore shape of scaffold. We compared printed average pore shape with predicted pore shape. We have confirmed the prediction model precision is 99 %.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 ITC-CSCC -1
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• pp.102-105
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• 2002

A noise reduction technique to reduce background noise in corrupted voice is proposed. The proposed method is based on linear prediction and takes advantages of periodicity of voiced speech. A voiced sound is regarded as a periodic stationary signal in short time interval. Therefore, the current voice signal is correlated with the voice signal delayed by a pitch period. A linear predictor can estimate only the current signal correlated with the delayed signal. Therefore, the enhanced voice can be obtained as output of the linear predictor. Simulation results show that the proposed method is able to reduce the background noise.

• Wind and Structures
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• 제31권6호
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• pp.549-560
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• 2020

Methods for stochastic simulation of non-Gaussian wind pressure have increasingly addressed the efficiency and accuracy contents to offer an accurate description of the extreme value estimation of the long-span and high-rise structures. This paper presents a linear prediction and z-transform (LPZ) based Cumulative distribution function (CDF) mapping algorithm for the simulation of multivariate non-Gaussian fluctuating wind pressure. The new algorithm generates realizations of non-Gaussian with prescribed marginal probability distribution function (PDF) and prescribed spectral density function (PSD). The inverse linear prediction and z-transform function (ILPZ) is deduced. LPZ is improved and applied to non-Gaussian wind pressure simulation for the first time. The new algorithm is demonstrated to be efficient, flexible, and more accurate in comparison with the FFT-based method and Hermite polynomial model method in two examples for transverse softening and longitudinal hardening non-Gaussian wind pressures.

• 정보처리학회논문지B
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• 제9B권4호
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• pp.421-428
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• 2002

지금까지 삽입(Embedding)백터를 이용한 국소적예측방법은 고차미분방정식으로부터 생성된 카오스 시계열을 예측할 때, 파라메타 $\tau$의 추정이 정확하지 않으면 예측성능은 떨어졌다. 지금까지 지연시간 ($\tau$)의 값을 추정하는 방법은 많이 제안되어있지만 실제로 고차원미분방정식부터 생성되어진 수많은 시계열에 모두 적용 가능한 방법은 아직 없다. 이것을 기울기 백터를 이용한 기울기 선형모델을 도입하는 것에 의해 정확한 지연시간 ($\tau$)의 값을 추정하지 않아도 예측성능에 만족할 수 있는 결과를 표시했다. 이것을 이론뿐이 아니고 경제시계열에도 적용해서 종래의 예측방법과 비교해서 그 유효성을 표시했다.

• Nuclear Engineering and Technology
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• 제44권4호
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• pp.355-362
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• 2012

The aim of this study was to develop a bundle position-wise linear model (BPLM) to predict Pressure Tube (PT) diametral creep employing the previously measured PT diameters and operating conditions. There are twelve bundles in a fuel channel, and for each bundle a linear model was developed by using the dependent variables, such as the fast neutron fluences and the bundle coolant temperatures. The training data set was selected using the subtractive clustering method. The data of 39 channels that consist of 80 percent of a total of 49 measured channels from Units 2, 3, and 4 of the Wolsung nuclear plant in Korea were used to develop the BPLM. The data from the remaining 10 channels were used to test the developed BPLM. The BPLM was optimized by the maximum likelihood estimation method. The developed BPLM to predict PT diametral creep was verified using the operating data gathered from Units 2, 3, and 4. Two error components for the BPLM, which are the epistemic error and the aleatory error, were generated. The diametral creep prediction and two error components will be used for the generation of the regional overpower trip setpoint at the corresponding effective full power days. The root mean square (RMS) errors were also generated and compared to those from the current prediction method. The RMS errors were found to be less than the previous errors.

• 한국정밀공학회지
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• 제19권6호
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• pp.77-85
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• 2002

An analysis method which calculates corrective machining information for improving the motion accuracy of linear motion guide Is proposed in this paper. The method is composed of two algorithms. One is the algorithm fur prediction of the motion errors from rail form error. The other is the algorithm for prediction of rail form error from the motion errors of table. Transfer function is utilized in each algorithm, which represents the ratio of bearing reaction force variation to unit magnitude of spatial frequencies of raid from error. As the corrective machining information is acquired from the measured motion errors of table, the method has a merit not to measure rail form error directly. Validity of the method is verified both theoretically and experimentally. By applying the method, linear motion error of test equipment is reduced from 5.97$\mu$m to 0.58$\mu$m, and reduced from 32.78arcsec to 6.21 arcsec in case of angular motion error. From the results, it is confirmed that the method is very effective to improve the motion accuracy of linear motion guide.