• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2006년도 International Symposium on GPS/GNSS Vol.2
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• pp.41-45
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• 2006

To get accurate positions of GPS antennas, one should apply phase center variations (PCV) corrections in the data processing. Until recently, relative calibrations, originally proposed by National Geodetic Survey of United States, were the international standard. However, in late 2006, International GNSS Service will switch to absolute calibration methods. In this study, we compared the position differences caused by different PCV models, and their effects on the calculations of Precipitable Water Vapor (PWV) in the atmosphere. Data from ${\sim}40$ permanent GPS stations in Korea were processed and we found that the vertical position differences reach up to 5 cm, depending on the model selected. Also the PWV values varied quite significantly: the maximum bias in the computed PWV values was ${\sim}4$ mm.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.772-777
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• 2003

A vision-based tool position estimation system for the inspection and maintenance manipulator working inside the steam generator bowl of nuclear power plants can help human operators ensure that the inspection probe or plug are inserted to the targeted tube. Some previous research proposed a simplified tube position verification system that counts the tubes passed through during the motion and displays only the position of the tool. In this paper, by using a general camera calibration approach, tool orientation is also estimated. In order to reduce the computation time and avoid the parameter bias problem in an ellipse fitting, a small number of edge points are collected around the large section of the ellipse boundary. Experiment results show that the camera calibration parameters, detected ellipses, and estimated tool position are appropriate.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2006년도 International Symposium on GPS/GNSS Vol.2
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• pp.598-601
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• 2006

The radio signal in GNSS was intentionally designed with two frequencies in order to combat the dispersion error caused by trans-ionospheric propagation. By measuring the path delay independently at the two, widely spaced GPS frequencies, L1 & L2, the TEC along the path from satellite to receiver can be measured directly. The issue with dual frequency measurement of the ionosphere is the calibration of L1/L2 interfrequency biases. L1/L2 interfrequency biases are generated because physical electric signal paths of L1 and L2 circuits are different from each other for both satellites and receiver. Conventionally L1/L2 interfrequency bias is estimated and broadcasted by 2D ionospheric model. In this paper, we estimated IFB (interfrequency bias) by 2D & 3D ionospheric models including real time filter methods and compared the result of those and concluded the merit of 3D tomography model to recover the problem of 2D thin shell model. We confirmed our conclusion by experimental data.

• 대기
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• 제25권1호
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• pp.19-30
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• 2015

The Ka-band cloud radar (KCR) has been operated by the National Institute of Meteorological Research (NIMR) of Korea Meteorological Administration (KMA) at Boseong National Center for Intensive Observation of severe weather since 2013. Evaluation of data quality is an essential process to further analyze cloud information. In this study, we estimate the measurement error and the sampling uncertainty to evaluate data quality. By using vertically pointing data, the statistical uncertainty is obtained by calculating the standard deviation of each radar parameter. The statistical uncertainties decrease as functions of sampling number. The statistical uncertainties of horizontal and vertical reflectivities are identical (0.28 dB). On the other hand, the statistical uncertainties of Doppler velocity (spectrum width) are 2.2 times (1.6 times) larger at the vertical channel. The reflectivity calibration of KCR is also performed using X-band vertically pointing radar (VertiX) and 2-dimensional video disdrometer (2DVD). Since the monitoring of calibration values is useful to evaluate radar condition, the variation of calibration is monitored for five rain events. The average of calibration bias is 10.77 dBZ and standard deviation is 3.69 dB. Finally, the statistical characteristics of cloud properties have been investigated during two months in autumn using calibrated reflectivity. The percentage of clouds is about 26% and 16% on September to October. However, further analyses are required to derive general characteristics of autumn cloud in Korea.

• 농촌계획
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• 제21권4호
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• pp.177-186
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• 2015

The main objective of this study was to assess the applicability of IoT (Internet of Things)-based flood management under climate change by developing intelligent water level monitoring platform based on IoT. In this study, Arduino Uno was selected as the development board, which is an open-source electronic platform. Arduino Uno was designed to connect the ultrasonic sensor, temperature sensor, and data logger shield for implementing IoT. Arduino IDE (Integrated Development Environment) was selected as the Arduino software and used to develop the intelligent algorithm to measure and calibrate the real-time water level automatically. The intelligent water level monitoring platform consists of water level measurement, temperature calibration, data calibration, stage-discharge relationship, and data logger algorithms. Water level measurement and temperature calibration algorithm corrected the bias inherent in the ultrasonic sensor. Data calibration algorithm analyzed and corrected the outliers during the measurement process. The verification of the intelligent water level measurement algorithm was performed by comparing water levels using the tape and ultrasonic sensor, which was generated by measuring water levels at regular intervals up to the maximum level. The statistics of the slope of the regression line and $R^2$ were 1.00 and 0.99, respectively which were considered acceptable. The error was 0.0575 cm. The verification of data calibration algorithm was performed by analyzing water levels containing all error codes in a time series graph. The intelligent platform developed in this study may contribute to the public IoT service, which is applicable to intelligent flood management under climate change.

• 대한원격탐사학회지
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• 제37권1호
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• pp.169-176
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• 2021

The Global Space-based Inter-Calibration System (GSICS) is an international partnership sponsored by World Meteorological Organization (WMO) to continue and improve climate monitoring and to ensure consistent accuracy between observation data from meteorological satellites operating around the world. The objective for GSICS is to inter-calibration from pairs of satellites observations, which includes direct comparison of collocated Geostationary Earth Orbit (GEO)-Low Earth Orbit (LEO) observations. One of the GSICS inter-calibration methods, the Ray-matching technique, is a surrogate approach that uses matched, co-angled and co-located pixels to transfer the calibration from a well calibrated satellite sensor to another sensor. In Korea, the first GEO satellite, Communication Ocean and Meteorological Satellite (COMS), is used to participate in the GSICS program. The National Meteorological Satellite Center (NMSC), which operated COMS/MI, calculated the Radiative Transfer Model (RTM)-based GSICS coefficient coefficients. The L1P reproduced through GSICS correction coefficient showed lower RMSE and Bias than L1B without GSICS correction coefficient applied. The calculation cycles of the GSICS correction coefficients for COMS/MI visible channel are provided annual and diurnal (2, 5, 10, 14-day), but long-term evaluation according to these cycles was not performed. The purpose of this paper is to perform evaluation depending on the annual/diurnal cycles of COMS/MI GSICS correction coefficients based on the ray-matching technique using Suomi-NPP/Visible Infrared Imaging Radiometer Suite (VIIRS) data as reference data. As a result of evaluation, the diurnal cycle had a higher coincidence rate with the reference data than the annual cycle, and the 14-day diurnal cycle was the most suitable for use as the GSICS correction coefficient.

• Journal of Radiation Protection and Research
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• 제47권3호
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• pp.158-166
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• 2022

Background: The effects of radiation on the health of radiation workers who are constantly susceptible to occupational exposure must be assessed based on an accurate and reliable reconstruction of organ-absorbed doses that can be calculated using personal dosimeter readings measured as H_p(10) and dose conversion coefficients. However, the data used in the dose reconstruction contain significant biases arising from the lack of reality and could result in an inaccurate measure of organ-absorbed doses. Therefore, this study quantified the biases involved in organ dose reconstruction and calculated the bias-corrected H_p(10)-to-organ-absorbed dose coefficients for the use in epidemiological studies of Korean radiation workers. Materials and Methods: Two major biases were considered: (a) the bias in H_p(10) arising from the difference between the dosimeter calibration geometry and the actual exposure geometry, and (b) the bias in air kerma-to-H_p(10) conversion coefficients resulting from geometric differences between the human body and slab phantom. The biases were quantified by implementing personal dosimeters on the slab and human phantoms coupled with a Monte Carlo method and considered to calculate the bias-corrected H_p(10)-to-organ-absorbed dose conversion coefficients. Results and Discussion: The bias in H_p(10) was significant for large incident angles and low energies (e.g., 0.32 for right lateral at 218 keV), whereas the bias in dose coefficients was significant for the posteroanterior (PA) geometry only (e.g., 0.79 at 218 keV). The bias-corrected H_p(10)-to-organ-absorbed dose conversion coefficients derived in this study were up to 3.09- fold greater than those from the International Commission on Radiological Protection publications without considering the biases. Conclusion: The obtained results will aid future studies in assessing the health effects of occupational exposure of Korean radiation workers. The bias-corrected dose coefficients of this study can be used to calculate organ doses for Korean radiation workers based on personal dose records.

• Communications for Statistical Applications and Methods
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• 제22권6호
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• pp.655-664
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• 2015

A nonresponse adjusted raking ratio estimator that consists of weighting adjustment using estimated response probability and raking procedure is often used to reduce the nonresponse bias and keep the calibration property of the estimator. We investigated asymptotic properties of nonresponse adjusted raking ratio estimator and proposed a variance estimator. A simulation study is used to examine the performance of suggested estimators.

• 대한전자공학회논문지SD
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• 제48권9호
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• pp.13-21
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• 2011

본 논문에서는 MEMS 용량형 각속도 센서용 프로그래머블 CMOS 인터페이스 회로를 제작하고, 이를 MEMS 센싱 엘리먼트와 결합하여 평가하였다. 본 회로는 10 bit 프로그래머블 캐패시터 어레이 를 이용한 전하 증폭기, 오프셋 미세 조정을 위한 9 비트 DAC, 출력 민감도의 미세 조정을 위한 10 비트 PGA를 내장하여, 오프셋 및 민감도 오차를 정밀 조정할 수 있다. 제작 결과 자동 이득 제어 회로를 포함한 자가 발진 루프의 정상 동작을 확인하였다. 오프셋 오차와 민감도 오차는 각각 0.36%FSO 와 0.19%FSO 로 측정되었으며, 잡음 등가 해상도와 바이어스 불안정도는 각각 0.016 deg/sec 와 0.012 deg/sec 으로 평가되었다. 본 회로의 조정 기능을 이용하여 MEMS 용량형 각속도 센서의 기생 용량으로 인하여 발생되는 출력 오프셋 및 출력 민감도의 산포를 감소시킬 수 있으며, 이는 센서의 양산성 및 수율 향상에 크게 기여할 수 있을 것으로 기대된다.

• 한국작물학회지
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• 제61권1호
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• pp.64-69
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• 2016

This study was conducted to develop a fast and efficient screening method to determine the quantity of fatty acid in peanut oil for high oleate breeding program. A total of 329 peanut samples were used in this study, 227 of which were considered in the calibration equation development and 102 were utilized for validation, using near infrared reflectance spectroscopy (NIRS). The NIRS equations for all the seven fatty acids had low standard error of calibration (SEC) values, while high R2 values of 0.983 and 0.991 were obtained for oleic and linoleic acids, respectively in the calibration equation. Furthermore, the predicted means of the two main fatty acids in the calibration equation were very similar to the means based on gas chromatography (GC) analysis, ranging from 36.7 to 77.1% for oleic acid and 7.1 to 42.7% for linoleic acid. Based on the standard error of prediction (SEP), bias values, and $R^2$ statistics, the NIRS fatty acid equations were accurately predicted the concentrations of oleic and linoleic acids of the validation sample set. These results suggest that NIRS equations of oleic and linoleic acid can be used as a rapid mass screening method for fatty acid content analysis in peanut breeding program.