• Journal of Institute of Control, Robotics and Systems
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• v.20 no.5
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• pp.578-583
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• 2014

Optical pick-up is a core component for data read/write operations in optical disc drives, and an optical pick-up performance evaluator is an instrument used to analyze the overall performance of an optical pick-up. Due to inevitable errors in an analog measurement circuit, resultant evaluation data is not guaranteed to be accurate. In this paper, a calibration method for an optical pick-up performance evaluator is proposed to ensure evaluation accuracy. Measured data is corrected by a 1st order correction function, and a calibration process based on least-square method is utilized to obtain correction coefficients of the correction function. The proposed calibration method is applied to experiments, and enhanced accuracy is presented with resultant evaluation data.

• International Journal of Control, Automation, and Systems
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• v.6 no.5
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• pp.746-754
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• 2008

This paper proposes a method to detect calibration patterns for accurate camera calibration under complicated backgrounds and uneven lighting conditions of industrial fields. Required to measure object dimensions, the preprocessing of camera calibration must be able to extract calibration points from a calibration pattern. However, industrial fields for visual inspection rarely provide the proper lighting conditions for camera calibration of a measurement system. In this paper, a probabilistic criterion is proposed to detect a local set of calibration points, which would guide the extraction of other calibration points in a cluttered background under irregular lighting conditions. If only a local part of the calibration pattern can be seen, input data can be extracted for camera calibration. In an experiment using real images, we verified that the method can be applied to camera calibration for poor quality images obtained under uneven illumination and cluttered background.

• Journal of the military operations research society of Korea
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• v.36 no.1
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• pp.29-38
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• 2010

In Korea weapon system acquisition processes, it's required a cost estimation report obtained from a commercial cost model. The PRICE model is generally used as a cost estimation model in Korea. However, the model uses American historical R&D data and it's output cost component is different from our cost component of defense accounting system. Also, we found that estimating results show about 10% of difference when we comparing with actual costs in 44 finished weapon acquisition projects. There are some limitations in calibration to increase an accuracy of the PRICE model because it's difficult obtain good real input data, detailed cost and technical data in low level WBS. So, only 8% of the defense R&D projects are calibrated and validation of calibration results is more difficult. Therefore, we studied the standard calibration process and performed the calibration about the MCPLXS/E parameters of the PRICE model based on actual cost data. In order to obtain a good calculation result, we collected the actual material costs from the defense industry companies. Our results can be used for an reference in similar weapon system R&D and production cost estimation cases.

• Proceedings of the KSRS Conference
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• v.1
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• pp.184-187
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• 2006

The radiometric correction is prerequisite to derive both land and ocean surface properties from optical remote sensing data. Radiometric calibration of remotely sensed data has traditionally been accomplished by means of vicarious ground calibration techniques. The purpose of this study is to calibrate the radiometric characteristic of Airborne Multispectral Scanner (AMS) by field campaign. In order to calibrate the AMS data, four different spectral tarps which are 3.5%, 23%, 35%, and 53% were validated by GER-3700 that is the surface reflectance measurement equipment and were utilized. After validation of the spectral tarps, each reflectance from the spectral tarps was compared with Digital Number (DN) value of AMS. There was very high correlation between tarp reflectance and DN value of AMS so that radiometric calibration of AMS data has been accomplished by those results. The calibrated AMS data were validated with in-situ measured reflectance data from artificial and natural target. Also QuickBird image data were used for verifying the results of AMS radiometric calibration. This presentation discusses the results of the above tests.

• Journal of Sensor Science and Technology
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• v.28 no.3
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• pp.182-186
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• 2019

When a camera is employed for 3D sensing, accurate camera calibration is vital as it is a prerequisite for the subsequent steps of the sensing process. Camera calibration is usually performed by complex mathematical modeling and geometric analysis. On the other contrary, data learning using an artificial neural network can establish a transformation relation between the 3D space and the 2D camera image without explicit camera modeling. However, a neural network requires a large amount of accurate data for its learning. A significantly large amount of time and work using a precise system setup is needed to collect extensive data accurately in practice. In this study, we propose a two-step neural calibration method that is effective when only a small amount of learning data is available. In the first step, the camera projection transformation matrix is determined using the limited available data. In the second step, the transformation matrix is used for generating a large amount of synthetic data, and the neural network is trained using the generated data. Results of simulation study have shown that the proposed method as valid and effective.

• Journal of the Optical Society of Korea
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• v.5 no.1
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• pp.9-15
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• 2001

The ocean Scanning Multi-spectral Imager (OSMI) is a payload on the KOrea Multi-Purpose SATellite (KOMPSAT) to perform worldwide ocean color monitoring f the study of biological oceanography. OSMI performs solar and dark calibrations for on-orbit instrument calibration. The purpose of the solar calibration is to monitor the degradation of imaging performance for each pixel of 6 spectral bands and to correct the degradation effect on OSMI image during the ground station date processing. The design, the operation concept, and the radiometric characteristics of the solar calibration are investigated. A linear model of image response and a solar calibration radiance model are proposed to study the instrument characteristics using the solar calibration data. The performance of spectral responsivity and spatial response uniformity. The first solar calibration data and the analysis results are important references for further study on the on-orbit stability of OSMI response during its lifetime.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.4
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• pp.291-302
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• 2017

Radiometric calibration is a prerequisite to quantitative remote sensing, and its accuracy has a direct impact on the reliability and accuracy of the quantitative application of remotely sensed data. This paper presents absolute radiometric calibration of the KOMPSAT-3 (KOrea Multi Purpose SATellite-3) and cross calibration using the Landsat-8 OLI (Operational Land Imager). Absolute radiometric calibration was performed using a reflectance-based method. Correlations between TOA (Top Of Atmosphere) radiances and the spectral band responses of the KOMPSAT-3 sensors in Goheung, South Korea, were significant for multispectral bands. A cross calibration method based on the Landsat-8 OLI was also used to assess the two sensors using near simultaneous image pairs over the Libya-4 PICS (Pseudo Invariant Calibration Sites). The spectral profile of the target was obtained from EO-1 (Earth Observing-1) Hyperion data over the Libya-4 PICS to derive the SBAF (Spectral Band Adjustment Factor). The results revealed that the TOA radiance of the KOMPSAT-3 agree with Landsat-8 within 5.14% for all bands after applying the SBAF. The radiometric coefficient presented here appears to be a good standard for maintaining the optical quality of the KOMPSAT-3.

• Journal of Biosystems Engineering
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• v.33 no.4
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• pp.260-268
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• 2008

Optical diffuse reflectance sensing has potential for rapid and reliable on-site estimation of soil properties. For good results, proper calibration to measured soil properties is required. One issue is whether it is necessary to develop calibrations using samples from the specific area or areas (e.g., field, soil series) in which the sensor will be applied, or whether a general "factory" calibration is sufficient. A further question is if specific calibration is required, how many sample points are needed. In this study, these issues were addressed using data from 42 paddy fields representing 14 distinct soil series accounting for 74% of the total Korean paddy field area. Partial least squares (PLS) regression was used to develop calibrations between soil properties and reflectance spectra. Model evaluation was based on coefficient of determination ($R^2$) root mean square error of prediction (RMSEP), and RPD, the ratio of standard deviation to RMSEP. When sample data from a soil series were included in the calibration stage (full information calibration), RPD values of prediction models were increased by 0.03 to 3.32, compared with results from calibration models not including data from the test soil series (calibration without site-specific information). Higher $R^2$ values were also obtained in most cases. Including some samples from the test soil series (hybrid calibration) generally increased RPD rapidly up to a certain number of sample points. A large portion of the potential improvement could be obtained by adding about 8 to 22 points, depending on the soil properties to be estimated, where the numbers were 10 to 18 for pH, 18-22 for EC, and 8 to 22 for total C. These results provide guidance on sampling and calibration requirements for NIR soil property estimation.

• Asia-Pacific Journal of Atmospheric Sciences
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• v.54 no.4
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• pp.639-648
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• 2018

The pyranometer for observing the solar radiation reaching the surface of the earth is manufactured by various companies around the world. The sensitivity of the pyranometer at the observatory is required to be properly controlled based on the reference value of the World Radiometric Center (WRC) and the observatory environment; otherwise, the observational data may be subject to a large error. Since the sensitivity of the pyranometer can be calibrated in an indoor or outdoor calibration, this study used a CSTMUSS-4000C Integrating Sphere by Labsphere Inc. (USA) to calibrate the sensitivity of CMP22 pyranometer by Kipp&Zonen Inc. (Netherlands). Consequently, the factory sensitivity of CMP22 was corrected from $8.68{\mu}V{\cdot}(Wm^{-2})^{-1}$ to $8.98{\mu}V{\cdot}(Wm^{-2})^{-1}$, and the result from the outdoor calibration according to the observatory environment was $8.90{\mu}V{\cdot}(Wm^{-2})^{-1}$. After the indoor calibration of the pyranometer sensitivity, the root mean square error (RMSE) of the observational data at the observatory on a clear day without clouds (July 13, 2017) was $7.11Wm^{-2}$ in comparison to the reference pyranometer. After the outdoor calibration of the pyranometer sensitivity based on these results, the RMSE of the observational data was $1.74Wm^{-2}$ on the same day. Periodic inspections are required because the decrease of sensitivity over time is inevitable in the pyranometer data produced at the observatory. The initial sensitivity after indoor calibration ($8.98{\mu}V{\cdot}(Wm^{-2})^{-1}$) is important, and the sensitivity after outdoor calibration ($8.90{\mu}V{\cdot}(Wm^{-2})^{-1})$ can be compared to the data at the Baseline Surface Radiation Network (BSRN) or can be used for various studies and daily applications.

• Journal of applied mathematics & informatics
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• v.11 no.1_2
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• pp.327-340
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• 2003

For motion analysis electromagnetic sensors are often used to measure positions and orientations of human subjects. It is observed from several experiments of the Ergonomics Research group that there exist systematic errors and unexpected serious distortions due to some metal masses in the test area. A calibration process is necessary to fix these errors. In this article three models are proposed to correct position measurement errors based on observations from calibration experiments.