• 한국신재생에너지학회:학술대회논문집
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• 2008.10a
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• pp.234-237
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• 2008

분류층 가스화기에서 가스화기 운전 온도는 슬래그의 원활한 배출과 가스화기 성능 등에 영향을 미친다. 가스화기 운전온도는 또한, 석탄 및 산소 소비량에도 영향을 미쳐 궁극적으로는 가스화 플랜트의 주요 설비 용량을 결정하는 주요 요인중의 하나이다. 가스화기 운전 온도가 일정수준 이상으로 증가할 경우 냉가스 효율이 저하되고 가스화 성능에 약 영향을 미친다. 본 논문에서는 Coal 및 Flux 공급장치, 슬래그 배출장치 당의 구성을 설명하고 Flux 투입량에 따른 슬래그 Tcv, 가스화기 성능 등을 예측하였다. 또한, 300MW IGCC 실증 가스화플랜트 엔지니어링을 위한 예비단계로 석회석 투입에 따른 Flux 공급장치를 포함한 Feeding 설비 용량, 슬래그처리설비 용량, 가스화기 내부 및 출구 적정온도를 예측하였다.

• Proceedings of the KIEE Conference
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• 2007.11b
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• pp.29-30
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• 2007

본 논문은 특수일 전력 수요 예측을 위해 온도 효과를 고려한 데이터 추출법을 이용하여 특수일 전력 수용 예측 오차율을 감소시키는 방법을 제시한다. 제안된 기법의 타당성을 확인하기 위해 논문에서는 통계학에서 사용되는 결정계수를 이용한다. 결정계수를 이용하여 온도효과의 고려 여부가 오차율에 미치는 영향을 분석하였다. 또한 제안된 기법은 1996년 특수일 오차율을 기존 논문의 결과와 비교 분석하여 기존 방식 대비 특수일 전력 수요예측 관련 우수성을 보였으며, 최근 데이터인 2006년 특수일 전력 수요 예측을 통하여 검증하였다.

• Korean Journal of Construction Engineering and Management
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• v.4 no.4 s.16
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• pp.155-163
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• 2003

There are a growing number of cases to expand balconies of apartments faced with open air in order to enhance functional satisfaction and efficiency of dwelling space. In case of the balcony expansion at the floor, however, it is difficult to exclude a possibility of bringing about internal condensation due to the difference of temperature between indoor air and outdoor air caused by the Inflow of outer low-temperature air through the upper part of ceilings by failure in completely putting together the outer composite wall panels on the aluminum curtain walls installed at outer walls This study is to forecast possible occurrence of internal condensation around parapets and H-beam located at the inside of balcony ceilings on the uppermost floor of super-high apartment buildings faced with open air in order to provide dwellers with more comfortable environment in the related space and get rid of their uneasiness about the condensation. In this study, we estimated internal condensation, which vary in accordance with humidity pressure distribution, at curtain walls, stone panels or lower parts of slabs that constitute outer space of the residence and are weak against heat, through temperature forecast and temperature distribution interpretation program at normal two-dimension temperature

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.49 no.5
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• pp.437-446
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• 2021

As flying vehicle's speed is getting faster, the magnitude of aerodynamic heating is getting bigger. High-speed vehicle's exterior skin is heated to hundreds of degrees, and electrical equipments inside the vehicle are heated, simultaneously. Since allowable temperature of electrical equipments is low, they are vulnerable to effect of aerodynamic heating. These days, lots of techniques are applied to estimate temperature of electrical equipments in flight condition, and to make them thermally safe from heating during flight. In this paper, new model building technique for thermal safety analysis is introduced. To understand internal thermal transient characteristic of electrical equipment, simple heating experiment was held. From the result of experiment, we used our new building technique to build thermal analysis model which reflects thermal transient characteristic of original equipment. This model can provide internal temperature differences of electrical equipment and temperature change of specific unit which is thermally most vulnerable part in the equipment. So, engineers are provided much more detailed thermal analysis data for thermal safety of electrical equipment through this technique.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1107-1108
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• 2011

본 논문에서는 전력용 변압기 온도상승을 예측하기 위해 CFD 상용 프로세서인 Fluent를 이용하였다. 온도상승의 원인이 되는 전력손실은 자계 상용 프로세서인 Maxwell을 이용하였으며, 자계해석에 의해 얻은 전력손실을 유체역학과 열전달을 동시에 고려한 열유동해석의 열원으로 적용하였다. 해석의 정확도를 향상시키기 위해 변압기 권선의 형상을 실제형상과 유사하게 모델링하였으며, 해석결과의 타당성을 검증하기 위해 온도 상승 시험을 통해 얻은 측정값과 비교하였다.

• ETRI Journal
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• v.9 no.3
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• pp.90-95
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• 1987

반도체 집적 기술의 개발과 실장 기술의 발전으로 전자장치는 점점 열밀도가 높아지고 있다. 여기서는 PBA가 수직으로 배치된 전자장치를 대상으로, 먼저 자연공냉과 PBA사이에서 발생하는 2차원 유동의 열적 특성을 고려한 후에, 최고 온도 상승률에 대하여 비대칭 열유속에서의 상한값과 대칭 열유속에서의 하한값을 구하는 과정을 기술하였다. 이것은 시스팀 설계 초기 단계에서 온도예측을 하여 시스팀 신뢰도를 향상시키도록 하기 위한 것이다.

• Korean Journal of Food Science and Technology
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• v.37 no.5
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• pp.850-855
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• 2005

Model was developed to predict the growth of Listeria monocytogenes in raw pork. Experiment condition for model development was full 5-by-7 factorial arrangements of temperature (0, 5, 10, 15, and $20^{\circ}C$) and time (0, 1, 2, 3, 18, 48, and 120 hr). Gompertz values A, C, B, and M, and growth kinetics, exponential growth rate (EGR), generation time (GT), lag phase duration (LPD), and maximum population density (MPD) were calculated based on growth increased data. GT and LPD values gradually decreased, whereas EGR value gradually increased with increasing temperature. Response surface analysis (RSA) was carried out using Gompertz B and M values, to formulate equation with temperature being main control factor. This equation was applied to Gompertz equation. Experimental and predictive values for GT, LPD, and EGR, compared using the model, showed no significant differences (p<0.01). Proposed model could be used to predict growth of microorganisms for exposure assessment of MRA, thereby allowing more informed decision-making on potential regulatory actions of microorganisms in raw pork.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.27 no.2
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• pp.49-70
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• 2022

Sea Surface Temperature (SST), one of the ocean features, has a significant impact on climate, marine ecosystem and human activities. Therefore, SST prediction has been always an important issue. Recently, deep learning has drawn much attentions, since it can predict SST by training past SST patterns. Compared to the numerical simulations, deep learning model is highly efficient, since it can estimate nonlinear relationships between input data. With the recent development of Graphics Processing Unit (GPU) in computer, large amounts of data can be calculated repeatedly and rapidly. In this study, Short-term SST will be predicted through Convolutional Neural Network (CNN)-based U-Net that can handle spatiotemporal data concurrently and overcome the drawbacks of previously existing deep learning-based models. The SST prediction performance depends on the seasonal and interannual SST variabilities around the southern coast of Korea. The predicted SST has a wide range of variance during spring and summer, while it has small range of variance during fall and winter. A wide range of variance also has a significant correlation with the change of the Pacific Decadal Oscillation (PDO) index. These results are found to be affected by the intensity of the seasonal and PDO-related interannual SST fronts and their intensity variations along the southern Korean seas. This study implies that the SST prediction performance using the developed deep learning model can be significantly varied by seasonal and interannual variabilities in SST.

• Journal of the Korea Concrete Institute
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• v.15 no.1
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• pp.87-94
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• 2003

Construction plan and strength control have limitations in construction production field because it is difficult to predict the form removal strength and development of specified concrete strength. However, we can have reasonable construction plan and strength control if prediction of concrete strength is available. In this study, firstly, the newly proposed strength prediction model with maturity method was compared with the logistic model to test the adaptability. Secondly, the determination of time of form removal was verified through the new strength prediction model. As the results, it is found that investigation of the activation energy that are used to calculate equivalent age is necessary, and new strength prediction model was proved to be more accurate in the strength prediction than logistic model in the early age. Moreover, the use of new model was more reasonable because it has low SSE and high decisive factor. If we adopt new strength prediction model at construction field, we can expect the reduced period of work through the reduced time of form removal.

• Journal of the Korean Data and Information Science Society
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• v.20 no.4
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• pp.649-654
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• 2009

Soil temperature is an important tool in predicting a change of climate and agricultural environment together with the change of atmospheric temperature. In this paper, we examine changing patterns of soil temperature measured in 0.5m under ground from 1932 to 1990 and atmospheric temperature from 1961 to 2008, and derive a relationship between atmospheric temperature and soil temperature. Using this model, we predict unmeasured soil temperature in Daegu area and soil temperature is found to be increasing about $0.028^{\circ}C$per a year. Prediction of soil temperature is an important indicator for climate change in Daegu and will be useful information to help us take precautions for global warming, etc.