• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.42 no.4
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• pp.284-290
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• 2014

To successfully explore the moon by lunar lander, it is essential to guarantee the safe landing of lunar lander. Therefore, efficient shock absorption system of lunar lander should be designed in order to reduce landing impact force. Also, for more practical design of lunar lander, it is important to consider the effect of lunar regolith. In the line of thought, finite element model of lunar lander considering the effect of lunar regolith is developed. To reduce landing impact force, optimization of shock absorption system for lunar lander has been carried out. In optimization, sequential approximate optimization method based on meta-model is used. Through the result of optimization, it is verified that landing impact force on lunar lander can be efficiently reduced by the present optimization procedure.

• Journal of the Korean Geotechnical Society
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• v.36 no.8
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• pp.17-25
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• 2020

Korea Institute of Civil Engineering and Building Technology has constructed a large scale Dust Thermal Vacuum Chamber to simulate extreme lunar terrestrial environments and to study the Moon as an outposts for space development and exploration. Although a large amount of KLS-1 (Korean Lunar Simulant-1) is required for research, its massive production is practically difficult. This paper describes semi-automatic manufacturing system for massive production of KLS-1 in detail, which is seven times more efficient than manual production. In addition, to increase the similarity with lunar regolith, hydrogen reduction reaction using ilmenite which is one of the minerals was also conducted to simulate nanophase Fe(0) which is the unique property of lunar regolith. As a result, it was found that np-Fe(0) was formed at a temperature of 700℃ or higher, and increased in proportion to the temperature until 900℃.

• Journal of Satellite, Information and Communications
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• v.9 no.4
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• pp.127-133
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• 2014

In this paper, we present the design, functions and performance analysis of the virtual machine (VM) based on the Lua interpreter for On-Board Control Procedure Execution Environment (OEE). The development of the OEE has been required in order to operate the lunar explorer mission autonomously which is planned by Korea Aerospace Research Institute (KARI) autonomously. The concept of On-Board Control Procedure (OBCP) is already being applied to the deep space missions with a long propagation delay and a limited data transmission capacity since it ensure he autonomy of the mission without the ground intervention. The interpreter is the execution engine in the VM and it interpreters high-level programming codes line by line and executes the VM instructions. So the execution speed is very more slower than that of natively compiled codes. In order to overcome it, we design and implement OEE using register-based Lua interpreter for execution engine in OEE. We present experimental results on a range of additional hardware configurations such as usages of cache and floating point unit. We expect those to utilized to the OBCP scheduling policy and the system with Lua interpreter.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.531-535
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• 2015

본 연구에서는 한반도 관측 공백지역인 북한지역에 대하여 레이더와 위성 원격탐사자료를 활용하여 강수량과 토양수분 등 수문기상정보를 생산 및 검증하고 효율적인 수문 모니터링 및 수문 기상 재해 감시와 평가 방안을 수립하고자 한다. 또한, 북한지역의 수문 기상 정보 수집 및 통합 DB를 마련하고 북한 수문기상 포털시스템을 구축함으로써 부처 간 자료를 공유할 수 있는 매개체를 마련하여 일관된 정책 수립과 효율적인 물관리를 도모하고자 한다. WPMM(Window Probability Matching Method)방법을 기반으로 구성된 RAD-RAR(Rain rate system) 산정 알고리즘(Rosenfeld et al., 1993)을 활용하여 산출된 합성 강우장 데이터의 정확성을 비교 분석하기 위해 접경지역 AWS 강수량과 세계기상통신망(GTS)기반 강수량을 산출하여 각각 레이더 강수량과 검증분석을 실시하였다. 연구기간은 2012년과 2013년 여름철 기간 중 5개의 기간을 선별하였다. 연구 기간 동안의 RAR 합성 강우장 데이터를 이용하여, 기간 중 1시간 동안 누적된 강수량을 산출하고 접경지역 AWS 강수량과 비교하였고 12시간 누적 강수량을 산출하여 GTS 강수량과 비교 분석을 실시하였다. 전반적으로 레이더 강수량에 비해 AWS 강수량이 더 높게 나타났으며 마찬가지로 레이더 강수량과 GTS 강수량의 비를 통해 레이더 자료가 상대적으로 과소추정되고 있음을 확인 할 수 있었다. 미항공우주국(NASA)과 일본항공우주국(JAXA)을 중심으로 진행된 GPM(Global Precipitation Measurement)미션은 한 개의 핵심위성과 마이크로파 복사계를 탑재한 10여개의 보조위성으로 구성되어 있으며, 매 3시간 간격의 전구 강수량 자료 생산에 목적이 있다. 이는 홈페이지를 통해 Level 1, 2, 3의 GPM 데이터를 배포하고 있다. 특히 Level 2 데이터는 언급된 3시간 간격의 전구 강수량 데이터를 제공한다. 이 경우 복사량을 강수량으로 변환하는 번거로움을 덜 수 있으며 NASA가 제공하는 Panoply라는 프로그램을 이용하여 한반도 강수 자료 가시화가 가능하다.

• Atmosphere
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• v.28 no.4
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• pp.443-455
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• 2018

Nowcasting algorithms using weather radar data are mostly based on extrapolating the radar echoes. We estimate the echo motion vectors that are used to extrapolate the echo properly. Therefore, understanding the general characteristics of these motion vectors is important to improve the performance of nowcasting. General characteristics of radar-based motions are analyzed for warm season precipitation over Jeju region. Three-year summer season data (June~August, 2011~2013) from two radars (GSN, SSP) in Jeju are used to obtain echo motion vectors that are retrieved by Variational Echo Tracking (VET) method which is widely used in nowcasting. The highest frequency occurs in precipitation motion toward east-northeast with the speed of $15{\sim}16m\;s^{-1}$ during the warm season. Precipitation system moves faster and eastward in June-July while it moves slower and northeastward in August. The maximum frequency of speed appears in $10{\sim}20m\;s^{-1}$ and $5{\sim}10m\;s^{-1}$ in June~July and August respectively while average speed is about $14{\sim}15m\;s^{-1}$ in June~July and $8m\;s^{-1}$ in August. In addition, the direction of precipitation motion is highly variable in time in August. The speed of motion in Lee side of the island is smaller than that of the windward side.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.129-142
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• 2023

This study analyzed the ratio of carbon monoxide (CO) and carbon dioxide (CO₂), CO and nitrogen dioxide (NO₂) for cities and regionsin Korea and China using column-averaged carbon dioxide dry-air mole fraction (XCO₂) of the Orbiting Carbon Observatory-2/3, CO and NO₂ vertical column density (named XCO, XNO₂ in thisstudy) of TROPOspheric monitoring instrument from April 2018 to April 2022, and presented the relationship between socioeconomic indicators (population, number of vehicles, Gross Regional Domestic Product) and ratio, and differences in characteristics between Korea and China. First, CO₂ and CO were analyzed after calculating ΔXCO₂ and ΔXCO removing the background value and trend line due to the difference in atmospheric residence time of three gaseous substances (CO₂, CO, and NO₂). Comparing the three values by regions, ΔXCO and ΔXCO₂ were relatively higher in China and XNO₂ were higher in Korea and the ratio of both values (ΔXCO/ΔXCO₂, ΔXCO/XNO₂) was higher in China than in Korea. ΔXCO/ΔXCO₂, ΔXCO/XNO₂ and socioeconomic indicators have a positive correlation suggesting that the concentration of air pollutants and greenhouse gases is higher as the city is large and the economic activity is active. Regarding the differences in the ratio characteristics of Korea and China, the relationship between ΔXCO and ΔXCO₂ showed a negative correlation in Korea and a positive correlation in China. When the relationship between ΔXCO and XNO₂ was examined for summer and winter, the change of ΔXCO by season was not significant in Korea, whereasthe change of ΔXCO and XNO₂ by season waslarge in China resulting in the relationship between two countries appeared differently. These results suggest that seasonal variability and national emission characteristics should be considered in the process of analyzing the ratio of greenhouse gases to air pollutants.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.207-221
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• 2023

This study suggests deep neural network models for estimating air temperature with Level 1B (L1B) datasets of GEO-KOMPSAT-2A (GK-2A). The temperature at 1.5 m above the ground impact not only daily life but also weather warnings such as cold and heat waves. There are many studies to assume the air temperature from the land surface temperature (LST) retrieved from satellites because the air temperature has a strong relationship with the LST. However, an algorithm of the LST, Level 2 output of GK-2A, works only clear sky pixels. To overcome the cloud effects, we apply a deep neural network (DNN) model to assume the air temperature with L1B calibrated for radiometric and geometrics from raw satellite data and compare the model with a linear regression model between LST and air temperature. The root mean square errors (RMSE) of the air temperature for model outputs are used to evaluate the model. The number of 95 in-situ air temperature data was 2,496,634 and the ratio of datasets paired with LST and L1B show 42.1% and 98.4%. The training years are 2020 and 2021 and 2022 is used to validate. The DNN model is designed with an input layer taking 16 channels and four hidden fully connected layers to assume an air temperature. As a result of the model using 16 bands of L1B, the DNN with RMSE 2.22℃ showed great performance than the baseline model with RMSE 3.55℃ on clear sky conditions and the total RMSE including overcast samples was 3.33℃. It is suggested that the DNN is able to overcome cloud effects. However, it showed different characteristics in seasonal and hourly analysis and needed to append solar information as inputs to make a general DNN model because the summer and winter seasons showed a low coefficient of determinations with high standard deviations.

• Journal of Astronomy and Space Sciences
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• v.24 no.3
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• pp.209-218
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• 2007

In this paper, we have presented the observational result for the photometric quality of main telescopes in Gimhae Astronomical Observatory. Also we performed the observation of searching new minor planets as competitive work in public observatories. The observation was carried out using 60cm telescope of Gimhae Astronomical Observatory on 2007 January 13. And, $Sch\ddot{u}ler$ BVI filters and 1K CCD camera (AP8p) were used. To define the quality of CCD photometry, we observed the region of well-known standard stars in the open cluster M67. From observed data, The transformation coefficients and airmass coefficients were obtained, and the accuracy of CCD photometry was investigated. From PSF photometry, we obtained the color-magnitude diagram of M67, and considered the useful magnitude limit and the physical properties of M67. This method can be successfully used to confirm the photometric quality of main telescope in public observatories. To investigate the detection possibility of unknown object as astroid, we observed the near area of the opposition in the ecliptic plane. And we discussed the result. Our result show that it can be possible to detect minor planets in solar system brighter than $V{\sim}18.3mag$. and it can carry out photometric study brighter than V 16mag. in Gimhae Astronomical Observatory. These results imply that the public observatories can make the research work.

• Journal of Broadcast Engineering
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• v.26 no.1
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• pp.26-38
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• 2021

In this paper, DLKF (Deep Learning Keypoint Filtering), the deep learning-based keypoint filtering method for the rapidization of the image registration method for remote sensing images is proposed. The complexity of the conventional feature-based image registration method arises during the feature matching step. To reduce this complexity, this paper proposes to filter only the keypoints detected in the artificial structure among the keypoints detected in the keypoint detector by ensuring that the feature matching is matched with the keypoints detected in the artificial structure of the image. For reducing the number of keypoints points as preserving essential keypoints, we preserve keypoints adjacent to the boundaries of the artificial structure, and use reduced images, and crop image patches overlapping to eliminate noise from the patch boundary as a result of the image segmentation method. the proposed method improves the speed and accuracy of registration. To verify the performance of DLKF, the speed and accuracy of the conventional keypoints extraction method were compared using the remote sensing image of KOMPSAT-3 satellite. Based on the SIFT-based registration method, which is commonly used in households, the SURF-based registration method, which improved the speed of the SIFT method, improved the speed by 2.6 times while reducing the number of keypoints by about 18%, but the accuracy decreased from 3.42 to 5.43. Became. However, when the proposed method, DLKF, was used, the number of keypoints was reduced by about 82%, improving the speed by about 20.5 times, while reducing the accuracy to 4.51.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1541-1551
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• 2023

Radiometric calibration is a fundamental step in ocean color remote sensing since the step to derive solar radiance spectrum in visible to near-infrared wavelengths from the sensor-observed electromagnetic signals. Generally, satellite sensor suffers from degradation over the mission period, which results in biases/uncertainties in radiometric calibration and the final ocean products such as water-leaving radiance, chlorophyll-a concentration, and colored dissolved organic matter. Therefore, the importance of radiometric calibration for the continuity of ocean color satellites has been emphasized internationally. This study introduces an approach to improve the radiometric calibration algorithm for the visible bands of the Geostationary Ocean Color Imager-II (GOCI-II) satellite with a focus on stability. Solar Diffuser (SD) measurements were employed as an on-orbit radiometric calibration reference, to obtain the continuous monitoring of absolute gain values. Time series analysis of GOCI-II absolute gains revealed seasonal variations depending on the azimuth angle, as well as long-term trends by possible sensor degradation effects. To resolve the complexities in gain variability, an azimuth angle correction model was developed to eliminate seasonal periodicity, and a sensor degradation correction model was applied to estimate nonlinear trends in the absolute gain parameters. The results demonstrate the effects of the azimuth angle correction and sensor degradation correction model on the spectrum of Top of Atmosphere (TOA) radiance, confirming the capability for improving the long-term stability of GOCI-II data.