• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.38 no.12
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• pp.1232-1239
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• 2010

A high-resolution electro-optical camera (EOS-C Ver.3.0), the mission payload of an Earth observation satellite, is under development in Satrec Initiative. We designed this system to give improved thermal performance compared with the EOS-C Ver.2.0 which is the main payload of DubaiSat-1 by optimizing the active and passive thermal control design. We developed the Structural-Thermal Model (STM) and verified the design margin by performing the qualification level thermal vacuum test. We also conducted the verification of its Thermal Mathematical Model (TMM) through the thermal balance test. As a result, it was confirmed that TMM faithfully represents the thermal characteristics of the EOS-C Ver.3.0.

• Proceedings of the KIPE Conference
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• 2020.08a
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• pp.49-51
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• 2020

본 논문에서는 전기자동차 급속 충전 시 배터리 팩의 온도 모사를 위한 열 모델링 기법을 제안한다. 배터리 등가 회로 모델을 기반으로 배터리 팩 내부 발열량을 계산하고, 배터리 열 모델 구성을 위한 파라미터 추출 실험 방안을 제안하다. 또한, 전기자동차 방열 시스템의 영향 등으로 인한 발열량 변화를 실시간으로 보정하여 온도 모사 정확도를 개선한다. 열 모델링 기법의 유효성 검증을 위하여 전기자동차용 배터리 팩 기반의 시뮬레이션 및 실험을 진행한다.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.41 no.11
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• pp.921-930
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• 2013

Satrec Initiative successfully developed and verified a high-resolution electro-optical camera system, EOS-D Ver.1.0. We designed this system to give improved spatial and radiometric resolution compared with EOS-C series systems. The thermal control subsystem (TCS) of the EOS-D Ver.1.0 uses heaters to meet the opto-mechanical requirements during in-orbit operation and uses different thermal coatings and multi-layer insulation (MLI) blankets to minimize the heater power consumption. Also, we designed and verified a refocusing mechanism to compensate the misalignment caused by moisture desorption from the metering structure. We verified the design margin and workmanship by conducting the qualification level thermal vacuum test. We also performed the verification of thermal math model (TMM) by comparing with thermal balance test results. As a result, we concluded that it faithfully represents the thermal characteristics of the EOS-D Ver.1.0.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.33 no.11
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• pp.106-114
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• 2005

This study addresses space simulation test results and thermal modelling verification of HAUSAT-2 nanosatellite STM (Structural-Thermal Model). The thermal modelling of the HAUSAT-2 has been modified in accordance with test results. Thermal analysis results were repeatedly compared with test results for modified thermal modelling. It is verified that the analysis results for modified thermal modelling agree well with test results. Some surface heaters were implemented to simulate solar illumination for HAUSAT-2 Thermal Vacuum/Balance Test. A low-cost and effective thermal test methodology, which is applicable to ultra-small satellite system, was proposed and verified by test results in this study.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.1773-1774
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• 2008

유동적 비선형 특성을 보이는 혼돈 시계열에 대한 정확한 예측을 위해 예측 입력으로 차분 데이터를 사용하면 보다 나은 예측이 가능하다. 그러므로 본 논문에서는 상관 해석에 기반한 데이터의 전처리를 통해 적절한 최적 차분 간격 후보군을 선정하고 이들 각각에 대한 TS 퍼지 예측기로 다중 모델을 구성하여 성능 지수 평가에 의해 최적의 퍼지 예측기를 선택하여 예측을 수행하도록 하였으며, TS 퍼지 규칙 후건부에서 결정되는 예측 출력에 상관 해석에 기반한 오차 보정 메거니즘을 추가함으로써 예측 성능을 더욱 향상시킬 수 있도록 하였다.

• Journal of the Korean Society of Marine Environment & Safety
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• v.26 no.4
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• pp.382-391
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• 2020

In this study, we used artificial intelligence algorithms for the prediction of dissolved oxygen in Jindong Bay. To determine missing values in the observational data, we used the Bidirectional Recurrent Imputation for Time Series (BRITS) deep learning algorithm, Auto-Regressive Integrated Moving Average (ARIMA), a widely used time series analysis method, and the Long Short-Term Memory (LSTM) deep learning method were used to predict the dissolved oxygen. We also compared accuracy of ARIMA and LSTM. The missing values were determined with high accuracy by BRITS in the surface layer; however, the accuracy was low in the lower layers. The accuracy of BRITS was unstable due to the experimental conditions in the middle layer. In the middle and bottom layers, the LSTM model showed higher accuracy than the ARIMA model, whereas the ARIMA model showed superior performance in the surface layer.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.12
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• pp.663-670
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• 2019

Corrective thermal performance analysis is required for power plants' turbine cycles to determine the performance status of the cycle and improve the economic operation of the power plant. We developed a sectional classification method for the main feed-water flow to make precise corrections for the performance analysis based on the Performance Test Code (PTC) of the American Society of Mechanical Engineers (ASME). The method was developed for the estimation of the turbine cycle performance in a classified section. The classification is based on feature identification of the correlation status of the main feed-water flow measurements. We also developed predictive algorithms for the corrected main feed-water through a Kernel Regression (KR) model for each classified feature area. The method was compared with estimation using an Artificial Neural Network (ANN). The feature classification and predictive model provided more practical and reliable methods for the corrective thermal performance analysis of a turbine cycle.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.43 no.9
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• pp.814-821
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• 2015

The STEP Cube Lab. classified as a pico-class satellite has been successfully developed as a flight model(FM) to be launched in 2015. Its mission objective is to perform the on-orbit verification of fundamental space core-technologies. In this study, a thermal design concept based on the passive method to achieve the mission objective is introduced. The effectiveness of the thermal design and performance of the satellite has been verified through the acceptance level thermal vacuum test. In addition, to improve the reliability of thermal mathematical model, correlation was performed using the results of thermal balance test. This paper describes a series of process for the thermal vacuum test on the STEP Cube Lab. FM.

• Journal of the Korean Association of Geographic Information Studies
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• v.9 no.2
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• pp.159-172
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• 2006

In this study, the series of Landsat TM/ETM+ images was acquired to extract land surface temperature for wide-area and executed geometric correction and radiometric correction. And the land surface temperature was extracted using NASA Model, and achieved the first correction by performing land coverage category for study area and applied characteristic emission rate. Land surface temperature which was acquired by the first correction was analyzed in correlation with Meteorological Administration's temperature data by regression analysis, and established correction formula. And I wished to improve accuracy of land surface temperature extraction using satellite image by second correcting deviations between two data using establishing correction formula. As a result, land surface temperature acquired by 1st and 2st correction could be corrected in mean deviation of about ${\pm}3.0^{\circ}C$ with Meteorological Administration data. Also, I could acquire land surface temperature about study area by higher accuracy by applying to other Landsat images for re-verification of study results.

• Journal of Energy Engineering
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• v.23 no.4
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• pp.263-271
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• 2014

Corrective thermal performance analysis is required for thermal power plants to determine performance status of turbine cycle. We developed classification method for main feed water flow to make precise correction for performance analysis based on ASME (American Society of Mechanical Engineers) PTC (Performance Test Code). The classification is based on feature identification of status of main water flow. Also we developed predictive algorithms for corrected main feed-water through Support Vector Machine (SVM) Model for each classified feature area. The results was compared to estimations using Neural Network(NN) and Kernel Regression(KR). The feature classification and predictive model of main feed-water flow provides more practical methods for corrective thermal performance analysis of turbine cycle.