• Publications of The Korean Astronomical Society
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• v.4 no.1
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• pp.53-62
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• 1989

낱별의 광전측광 관측을 위하여 두 가지 컴퓨터 프로그램을 개발하였다. 하나는 자료수집 (data aquisition), 자료의 표시, 통계처리, 오차추정, 대기상태의 모니터링 등을 다루는 실시간(實時間, real time) 측광제어 프로그램이고, 다른 하나는 하룻밤 관측의 도중 또는 관측 종료 직후에 수집된 자료 전체를 입력 파일로 만들어 주면 대기소광계수 결정과 표준계 변환 및 오차 추정을 해 주는 자료분석(資料分析, data reduction) 프로그램이다. 두 프로그램은 모두 임의의 다색측광계(多色測光系)에 대해서 입력 과일에 필터의 갯수, 색지 수의 정의 등 필요한 자료만 써주면 수행 가능하도록 일반화되어 있다.

• Korean Journal of Remote Sensing
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• v.34 no.2_1
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• pp.191-201
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• 2018

In this present study, we, for the first time, retrieved total column of ozone ($O_3$) and tropospheric ozone vertical profile using the Optimal Estimation (OE) method based on the MAX-DOAS measurement at the Yonsei University in Seoul, Korea. The optical density fitting is carried out using the OE method to calculate ozone columns. The optical density between the MAX-DOAS data obtained by dividing the measured intensities for each viewing elevated angle by those at the zenith angle. The retrieved total columns of the ozone are 375.4 and 412.6 DU in the morning (08:13) and afternoon (17:55) on 23 May, 2017, respectively. In addition, under 10 km altitude, the $O_3$ vertical profile was retrieved with about 5% of retrieval uncertainty. However, above 10 km altitude, the $O_3$ vertical profile retrieval uncertainty was increased (>10%). The spectral fitting errors are 16.8% and 19.1% in the morning and afternoon, respectively. The method suggested in this present study can be useful to measure the total ozone column using the ground-based hyper-spectral UV sensors.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.17 no.1
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• pp.106-112
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• 2008

In this paper, It does the profile measurement of the cam of diesel engine SOHC of the actual object. It uses the measurement of run-out method. This method is that the surface of the object is measured by the sensor when the object rotate, and calculated and displayed by the computer the signal which acquired by sensor. When we acquire the signals, we have two error because of motion and contacting between cam and probe. In this paper, we compensate the motion error while simply liner equation. And we have a solution that we change the figure of probe when we have a contacting error. We compared the data measuring on developed automatic cam profile measuring system with the data measured on CMM.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.29 no.6
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• pp.279-285
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• 2017

In general, the power law and logarithmic profile are commonly used as flow vertical velocity profile model. However, since the parameters of profile vary with characteristics of coastal environment, it is necessary to estimate these values from measured data using regression analysis. In this paper, we estimated the power law exponent (n), friction velocity ($u^*$) and roughness length ($z_0$) of logarithmic profile by analyzing measured tidal current data that are averaged at a interval of 30 min. In the results of analysis, power law exponent (n) was estimated to be about 10.75 during flood and about 9.3 during ebb. Meanwhile, $u^*$ of logarithmic profile was estimated to be about 0.084 m/s, 0.105 m/s during flood and ebb, respectively. Also, $z_0$ was estimated to be 0.004 m and 0.006 m, respectively.

• Progress in Medical Physics
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• v.19 no.1
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• pp.80-88
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• 2008

Recent radiotherapy dose planning system (RTPS) generally adapted the kernel beam using the convolution method for computation of tissue dose. To get a depth and profile dose in a given depth concerened a given photon beam, the energy spectrum was reconstructed from the attenuation dose of transmission of filter through iterative numerical analysis. The experiments were performed with 15 MV X rays (Oncor, Siemens) and ionization chamber (0.125 cc, PTW) for measurements of filter transmitted dose. The energy spectrum of 15MV X-rays was determined from attenuated dose of lead filter transmission from 0.51 cm to 8.04 cm with energy interval 0.25 MeV. In the results, the peak flux revealed at 3.75 MeV and mean energy of 15 MV X rays was 4.639 MeV in this experiments. The results of transmitted dose of lead filter showed within 0.6% in average but maximum 2.5% discrepancy in a 5 cm thickness of lead filter. Since the tissue dose is highly depend on the its energy, the lateral dose are delivered from the lateral spread of energy fluence through flattening filter shape as tangent 0.075 and 0.125 which showed 4.211 MeV and 3.906 MeV. In this experiments, analyzed the energy spectrum has applied to obtain the percent depth dose of RTPS (XiO, Version 4.3.1, CMS). The generated percent depth dose from $6{\times}6cm^2$ of field to $30{\times}30cm^2$ showed very close to that of experimental measurement within 1 % discrepancy in average. The computed dose profile were within 1% discrepancy to measurement in field size $10{\times}10cm$, however, the large field sizes were obtained within 2% uncertainty. The resulting algorithm produced x-ray spectrum that match both quality and quantity with small discrepancy in this experiments.

• Journal of the Korea Convergence Society
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• v.8 no.5
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• pp.185-192
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• 2017

Machine tool errors can be divided into geometric error, thermal deformation error, and machining error. In this study, the influence of each error on the total error and the relative size of each error are quantitatively analyzed in 2D machining. The thermal deformation error and the machining error caused a relatively large error compared to the geometric error, which is directly related to the machining accuracy. In order to eliminate the error factors, the possibility of error compensation was examined by analyzing the measured error profile shape. As a result, about 40 ~ 50% error compensation was achieved for each error factor. Through this study, it is possible to construct a basic data base on machining, and it is expected that it will be able to compensate the machining error from the viewpoint of users.

• Korean Journal of Agricultural and Forest Meteorology
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• v.17 no.3
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• pp.227-235
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• 2015

Using eddy covariance method, net ecosystem exchange (NEE) of $CO_2$ ($F_{CO_2}$), $H_2O$ (LE), and sensible heat (H) can be approximated as the sum of eddy flux ($F_c$) and storage flux term ($F_s$). Depending on strength and distribution of sink/source of scalars and magnitude of vertical turbulence mixing, the rates of changes in scalars are different with height. In order to calculate $F_s$ accurately, the differences should be considered using scalar profile measurement. However, most of flux sites for agricultural lands in Asia do not operate profile system and estimate $F_s$ using single-level scalars from eddy covariance system under the assumption that the rates of changes in scalars are constant regardless of the height. In this study, we measured $F_c$ and $F_s$ of $CO_2$, $H_2O$, and air temperature ($T_a$) using eddy covariance and profile system (i.e., the multi-level measurement system in scalars from eddy covariance measurement height to the land surface) at the Chengmicheon farmland site in Korea (CFK) in order to quantify the differences between $F_s$ calculated by single-level measurements ($F_s_{-single}$ i.e., $F_s$ from scalars measured by profile system only at eddy covariance system measurement height) and $F_s$ calculated by profile measurements and verify the errors of NEE caused by $F_s_{-single}$. The rate of change in $CO_2$, $H_2O$, and Ta were varied with height depending on the magnitudes and distribution of sink and source and the stability in the atmospheric boundary layer. Thus, $F_s_{-single}$ underestimated or overestimated $F_s$ (especially 21% underestimation in $F_s$ of $CO_2$ around sunrise and sunset (0430-0800 h and 1630-2000 h)). For $F_{CO_2}$, the errors in $F_s_{-single}$ generated 3% and 2% underestimation of $F_{CO_2}$ during nighttime (2030-0400 h) and around sunrise and sunset, respectively. In the process of nighttime correction and partitioning of $F_{CO_2}$, these differences would cause an underestimation in carbon balance at the rice paddy. In contrast, there were little differences at the errors in LE and H caused by the error in $F_s_{-single}$, irrespective of time.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.5
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• pp.819-826
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• 2020

The UHF band wind profiler radars of the Korea Meteorological Administration (KMA), which produces the vertical profile of the wind, need to be calibrated for better performance. The capabilities of the radar in detecting even light precipitation were used for the calibration of which reference takes the hourly series of ground rainfall rate measured by a rain gauge at the radar site. This calibration must be renewed regularly according to the methodology implemented in this work since errors occur on the wind vectors in the clear sky without reflectivity calibration. Comparing the wind by wind profiler with that by radiosonde, the optimal radar constant contributed to the improvement of wind accuracy.

• The Korean Journal of Applied Statistics
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• v.35 no.6
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• pp.755-764
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• 2022

This paper is concerned with power transformations in estimating GARCH volatility. To handle a semi-parametric case for which the exact likelihood is not known, quasi-likelihood (QL) rather than maximum-likelihood method is investigated to best estimate GARCH via maximizing the information criteria. A power transformation is introduced in the innovation generating QL estimating functions and then optimum power is selected by maximizing the profile information. A combination of two different power transformations is also studied in order to increase the parameter estimation efficiency. Nine domestic stock prices data are analyzed to order to illustrate the main idea of the paper. The data span includes Covid-19 pandemic period in which financial time series are really volatile.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.24 no.5
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• pp.326-332
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• 2012

In this paper, error analyses on the calculation of offshore wind speed have been conducted using HeMOSU-1 data to develop offshore wind energy in Yeonggwang sea of Korea and onshore observed wind data in Buan, Gochang and Yeonggwang for 2011. Offshore wind speed data at 98.69 m height above M.S.L is estimated using relational expression induced by linear regression analysis between onshore and offshore wind data. In addition, estimated offshore wind speed data is set at 87.65 m above M.S.L using power law wind profile model with power law exponent(0.115) and its results are compared with the observed data. As a result, the spatial adjustment error are 1.6~2.2 m/s and the altitude adjustment error is approximately 0.1 m/s. This study shows that the altitude adjustment error is about 5% of the spatial adjustment error. Thus, long term observed data are needed when offshore wind speed was estimated by onshore wind speed data. because the conversion of onshore wind data lead to large error.