• 대한원격탐사학회지
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• 제33권5_2호
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• pp.647-659
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• 2017

마늘과 양파 재배는 작물의 생육 조건과 주산지 기상에 영향을 받는다. 따라서 단수를 예측할 때에는 주산지의 작황과 기상을 고려할 필요가 있다. 본 연구에서는 2006년에서 2015년까지의 작물의 생육 조건을 반영한 MODIS NDVI와 7개 주산지의 기상요인을 다중 회귀 모형에 적용하여 주산지별 마늘 및 양파의 단수예측 모형을 개발하였다. 다중 회귀 모형에서 독립변수 채택은 단계적 선택방법을 이용하였다. 그 결과, 마늘과 양파 단수예측 모형은 2월의 MODIS NDVI가 중요한 독립변수로 채택되었다. 기상요인은 마늘의 경우, 평균온도(3월), 강우량(11월, 3월), 상대습도(4월), 최저온도(6월)가 채택되었으며, 양파는 강우량(11월), 일조시간(1월), 상대습도(4월), 최저온도(6월)가 독립변수로 채택되었다. MODIS NDVI와 기상요인을 이용한 단수예측 모형은 주산지별 마늘, 양파 평균 단수의 84.4%, 75.9% 설명력을 나타내었으며, RMSE는 각각 42.57 kg/10a, 340.29 kg/10a로 나타났다. 따라서 본 모형은 MODIS NDVI와 기상요인에 따른 마늘과 양파의 단수 변화특성을 잘 반영하고 있는 것으로 판단된다.

• KEPCO Journal on Electric Power and Energy
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• 제3권2호
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• pp.133-140
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• 2017

The performance prediction software developed in this paper is a process analysis tool that enables one to foretell the behavior of processes when certain conditions of operation are altered. The immediate objective of this research is to predict the process characteristics of combined heat and power plant under varying operating conditions. A cogeneration virtual power plant that mimics the mechanical performance of the actual plant was constructed and the performance of the power plant was predicted in the following varying atmospheric conditions: temperature, pressure and humidity. This resulted in a positive outcome where the performance of the power plant under changing conditions were correctly predicted as well as the calorific value of low calorific gas fuel such as shale gas and PNG. The performance prediction tool can detect the operation characteristics of the power plant through the performance index analysis and thus propose the operation method taking into consideration the changes in environmental conditions.

• 전기학회논문지
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• 제67권11호
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• pp.1506-1511
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• 2018

This study presents a human sensibility evaluation method using neural network and multiple-template method on electroencephalogram(EEG). We used a multi-layer perceptron type neural network as the sensibility classifier using EEG signal. For our research objective, 10-channel EEG signals are collected from the healthy subjects. After the necessary preprocessing is performed on the acquired signals, the various EEG parameters are estimated and their discriminating performance is evaluated in terms of pattern classification capability. In our study, Linear Prediction(LP) coefficients are utilized as the feature parameters extracting the characteristics of EEG signal, and a multi-layer neural network is used for indicating the degree of human sensibility. Also, the estimation for human comfortableness is performed by varying temperature and humidity environment factors and our results showed that the proposed scheme achieved good performances for evaluation of human sensibility.

• 대기
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• 제18권3호
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• pp.237-248
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• 2008

A thermal index which considers metabolic heat generation of human body is proposed for operational forecasting. The new thermal index, Perceived Temperature (PT), is forecasted using Weather Research and Forecasting (WRF) mesoscale model and validated. Forecasted PT shows the characteristics of diurnal variation and topographic and latitudinal effect. Statistical skill scores such as correlation, bias, and RMSE are employed for objective verification of PT and input meteorological variables which are used for calculating PT. Verification result indicates that the accuracy of air temperature and wind forecast is higher in the initial forecast time, while relative humidity is improved as the forecast time increases. The forecasted PT during 2007 summer is lower than PT calculated by observation data. The predicted PT has a minimum Root-Mean-Square-Error (RMSE) of $7-8^{\circ}C$ at 9-18 hour forecast. Spatial distribution of PT shows that it is overestimated in western region, while PT in middle-eastern region is underestimated due to strong wind and low temperature forecast. Underestimation of wind speed and overestimation of relative humidity have caused higher PT than observation in southern region. The predicted PT from the mesoscale model gives appropriate information as a thermal index forecast. This study suggests that forecasted PT is applicable to the prediction of health warning based on the relationship between PT and mortality.

• Advances in environmental research
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• 제4권4호
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• pp.219-231
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• 2015

In recent years, raising air pollutants has become as a big concern, especially in metropolitan cities such as Tehran. Therefore, forecasting the level of pollutants plays a significant role in air quality management. One of the forecasting tools that can be used is an artificial neural network which is able to model the complicated process of air pollution. In this study, we applied two different methods of artificial neural networks, the Multilayer Perceptron (MLP) and Radial Basis Function (RBF), to predict the hourly air concentrations of toluene in Tehran. Hourly temperature, wind speed, humidity and $NO_x$ were selected as inputs. Both methods had acceptable results; however, the RBF neural network produced better results. The coefficient of determination ($R^2$) between the observed and predicted data was 0.9642 and 0.99 for MLP and RBF neural networks, respectively. The results of the mean bias errors (MBE) were 0.00 and -0.014 for RBF and MLP, respectively which indicate the adequacy of the models. The index of agreement (IA) between the observed and predicted data was 0.999 and 0.994 in the RBF and the MLP, respectively which indicates the efficiency of the models. Finally, sensitivity analysis related to the MLP neural network determined that temperature was the most significant factor in air concentration of toluene in Tehran which may be due to the volatile nature of toluene.

• 한국군사과학기술학회지
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• 제11권5호
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• pp.84-91
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• 2008

The spectral radiance received by a remote sensor at a given temperature and wavelength region is consisted of the self-emitted component directly from the object surface, the reflected component of the solar irradiation at the object surface, and the scattered component by the atmosphere without ever reaching the object surface. The IR image of a ship is mainly affected by location, meteorological condition(atmosphere temperature, wind direction and velocity, humidity etc.), atmospheric transmittance, solar position and ship surface temperature etc. Computer simulations for prediction of the IR signatures of ships are very useful to examine the effects of various meteorological conditions. In this paper, we have acquired the IR signature for different meteorological conditions by using two different computer programs. The numerical results show that the IR image contrast as compared to the background sea considering the atmosphere temperature and wind velocity.

• 대한설비공학회:학술대회논문집
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• 대한설비공학회 2008년도 하계학술발표대회 논문집
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• pp.603-608
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• 2008

A simple equation to find a optimum speed of desiccant rotor is presented in this theoretical study. Usually the determination of optimum speed of desiccant rotor requires tedious and lengthy procedures by solving governing differential equations with many complicated parameters. The determining equation of optimal rotating speed is derivated from governing differential equations with three linearization assumptions, which simplify temperature profile linear along the desiccant rotor depth, psychrometric chart within a proper range, and relative humidity-sorption capacity relation. This study shows that the dominant parameters of optimal rotating speed of desiccant rotor are NTU, flow velocity, desiccant rotor depth, and temperature different between dehumidification and regeneration. The comparison shows the good agreement between complicated calculation results and simple theoretical equation prediction.

• Journal of the Korean Wood Science and Technology
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• 제47권6호
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• pp.732-750
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• 2019

천연건조 중 목재의 함수율 변화 예측모형을 제시하기 위하여 15본의 소나무 원목에 대한 천연건조를 수행하였다. 초기함수율이 68.7%인 6본의 소나무 원목에 대하여 여름철에 천연건조를 시작한 후 약 880일이 경과한 후의 최종함수율은 17.4%이었다. 초기함수율이 35.8%인 9본의 소나무 원목에 대하여 겨울철에 천연건조를 시작한 후 약 760일이 경과한 후의 최종함수율은 16.0%이었다. 소나무 원목의 말구지름, 온도, 상대습도, 풍속을 독립변수로 결정하고, 천연건조 중 감소한 함수율을 종속변수로 다중회귀분석을 진행한 결과, 결정계수 0.925의 회귀모형을 얻을 수 있었다. 소나무 원목의 특성인 초기함수율과 말구지름이 기상조건인 온도, 상대습도, 풍속에 비하여 천연건조 중 함수율 감소에 미치는 영향이 더 크게 나타났다. 천연건조 중 내부함수율의 분포 및 함수율 변화를 예측하기 위하여 2차원 물질전달 해석을 수행하였다. 건조일수를 서로 다르게 적용하고, 수분확산계수 및 표면방사계수를 결정하는 기상조건을 다르게 적용한 2가지의 예측모형을 제시하였다. 2가지 적용 방법의 오차는 0.1 - 0.8%의 범위였으며, 측정값과의 차이는 2.2 - 3.6%의 범위였다. 다양한 초기함수율과 말구지름의 소나무 원목에 대한 천연건조 중 내부함수율을 측정하고, 각각의 기상조건에 대한 목재 내 수분이동계수를 산출하면 예측모형의 오차를 감소시킬 수 있을 것으로 판단된다.

• 한국산업보건학회지
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• 제18권1호
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• pp.20-31
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• 2008

A respirator is useful to protect a worker from the harmful gases and vapors in the workplace, and the evaluation of respirator cartridge service life is important for the worker's health and safety. The performance of cartridge is effected by several factors such as concentration of gas and vapor, humidity, temperature, adsorbents and cartridge packing density. Adsorption model was applied to both sampling tube and respirator cartridge to predict the service life for organic vapors. The variables of the adsorption model were measured from the experiment with the sampling tube, and it was used to predict the service life of respirator cartridge. In the experiment, we used carbon tetrachloride as a organic vapor and activated carbon take out respirator cartridge as activated carbon. As a result, it was possible to predict the service life of respirator cartridge and predicted service life was quite correct. Breakthrough time decreased with increase of CCl4 concentration. In case of sampling tube, adsorbed amount of CCl4 was larger than respirator cartridge due to linear velocity. Also, rate constant of sampling tube was larger than respirator cartridge, because of, effect of flow rate, packing density. In the prediction of service life of respirator cartridge by using sampling tube, the time required for 50% contaminant breakthrough(${\tau}$) is more effective than the rate constant(k').

• Communications for Statistical Applications and Methods
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• 제29권6호
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• pp.709-719
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• 2022

Recently, as the importance of environmental protection has emerged, interest in new and renewable energy is also increasing worldwide. In particular, the solar energy sector accounts for the highest production rate among new and renewable energy in Korea due to its infinite resources, easy installation and maintenance, and eco-friendly characteristics such as low noise emission levels and less pollutants during power generation. However, although climate prediction is essential since solar power is affected by weather and climate change, solar radiation, which is closely related to solar power, is not currently forecasted by the Korea Meteorological Administration. Solar radiation prediction can be the basis for establishing a reasonable new and renewable energy operation plan, and it is very important because it can be used not only in solar power but also in other fields such as power consumption prediction. Therefore, this study was conducted for the purpose of improving the accuracy of solar radiation. Solar radiation was predicted by a total of three weather variables, temperature, humidity, and cloudiness, and solar radiation outside the atmosphere, and the results were compared using various models. The CatBoost model was best obtained by fitting and comparing the Boosting series (XGB, CatBoost) and RNN series (Simple RNN, LSTM, GRU) models. In addition, the results were further improved through Time series cross-validation.