• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.135-135
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• 2021

In water resources management, rainfall prediction with high accuracy is still one of controversial issues particularly in countries facing heavy rainfall during wet seasons in the monsoon climate. The aim of this study is to develop an artificial neural network (ANN) for predicting future six months of rainfall data (from April to September 2020) from daily meteorological data (from 1971 to 2019) such as rainfall, temperature, wind speed, and humidity at Seoul, Korea. After normalizing these data, they were trained by using a multilayer perceptron (MLP) as a class of the feedforward ANN with 15,000 neurons. The results show that the proposed method can analyze the relation between meteorological datasets properly and predict rainfall data for future six months in 2020, with an overall accuracy over almost 70% and a root mean square error of 0.0098. This study demonstrates the possibility and potential of MLP's applications to predict future daily rainfall patterns, essential for managing flood risks and protecting water resources.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.30 no.12 s.255
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• pp.1188-1195
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• 2006

This paper examines the critical air temperature at which frost formation does not occur on a cold plate in order to improve the efficiency of a heat pump. The critical air temperature on test points is obtained from numerical analysis and a correlation of the critical air temperature is derived as a function of frosting factors (air absolute humidity, air velocity, and cold plate temperature) by least square method. Experiments are carried out to verify the correlation proposed in this study, and the prediction obtained with the correlation agrees well with the experimental data. Based on the correlation, we propose the non-frosting regions according to frosting factors.

• 한국신재생에너지학회:학술대회논문집
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• 2011.11a
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• pp.63.1-63.1
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• 2011

To analyze the phenomenon of corrosion in the PV module, we experimented damp heat test at $85^{\circ}C$/85% relative humidity(RH) and $65^{\circ}C$/85% RH for 2,000 hours, respectively. We used 30 mini-modules designed of 6inch one cell. Despite of 2,000 hours test, measured $P_{max}$ is not reached failure which is defined less than 80% compared to initial $P_{max}$. Therefore, we calculate proper curve fitting over 2,000 hours. Data less than 80% $P_{max}$ is found and B10 lifetime is calculated by the number of failure specimens and weibull distribution. Using B10 lifetime that the point of failure rate 10% and Peck's model, the predictable equation of lifetime was derived under temperature and humidity condition.

• Journal of the Korea Institute of Military Science and Technology
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• v.13 no.2
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• pp.320-327
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• 2010

The IR signature data of a ship is mainly affected by location, meteorological conditions(atmosphere temperature, wind direction and velocity, humidity etc.), atmospheric transmittance, solar position and ship surface temperature etc. The IR signatures 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. Computer simulations for prediction of the IR signatures of ships are very useful to examine the effects of various sensor positions. In this paper, we have acquired the IR signature for different sensor positions by using computer program for prediction of the IR signatures. The numerical results show that the IR signature contrast as compared to the background sea considering the meteorological conditions, solar and sky irradiations.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2020.06a
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• pp.58-59
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• 2020

The change of temperature and humidity in early-age concrete has a great influence on the durability of the structure. In this study, a reliable wireless sensor network system and a concrete embedded type Compressive strength prediction sensor were designed using the Arduino platform. The accuracy of the compressive strength prediction sensor was verified through a mock-up experiment, and it was confirmed that the experiment had sufficient accuracy to be used in the field environment.

• Journal of the Korea Construction Safety Engineering Association
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• s.41
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• pp.88-101
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• 2007

Recently, the condensation of walls often occurring in domestic high-rise apartment buildings is an important problem. The main purpose of this study is to develop the prediction method for the surface condensation on curtain wall in high-rise apartment buildings. Therefore, in this study, we first analyzed exterior climate factors through the analysis of the Seoul climate data and predicted the change of indoor temperature by using Apache program to find the cause of the condensation state and to prevent condensation. Also, according to this result, exterior climate factors and interior factors, which caused the condensation, was examined. The thermal performance of the curtain wall and the range of potential condensation were analyzed to focus on high-rise apartment buildings through computer simulation programs. The results are as $follows;^1$) The frame edge of curtain wall has a higher U-value than in the center by $30%^2$) Because of stack effect, the rooms on the higher floor have a lower external ventilation rate resulting to a higher relative humidity3) Installing a ventilation system($20m^3$/h. person) makes it possible to have a higher external ventilation rate, resulting to a lower relative humidity.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.6
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• pp.1506-1514
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• 2015

In this paper, we proposed a forest fires prediction and detection system. It could provide a situation of fire prediction and detection methods using context awareness sensor. A fire occurs wide range of sensing a fire in a single camera sensor, it is difficult to detect the occurrence of a fire. In this paper, we propose an algorithm for real-time by using a temperature sensor, humidity, Co2, the flame presence information acquired and comparing the data based on multiple conditions, analyze and determine the weighting according to fire in complex situations. In addition, it is possible to differential management of intensive fire detection and prediction for required dividing the state of fire zone. Therefore we propose an algorithm to determine the prediction and detection from the fire parameters as an temperature, humidity, Co2 and the flame in real-time by using a context awareness sensor and also suggest algorithm that provide the path of fire diffusion and service the secure safety zone prediction.

• Korean Journal of Environment and Ecology
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• v.32 no.4
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• pp.413-424
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• 2018

The study analyzed the effects of topographic structures and altitude in mountainous parks in Mt. Namsan in Gyeongju on the generation of anions. The temperature was at ridge ($9.82^{\circ}C$) > valley ($8.44^{\circ}C$), the relative humidity valley (59.01 %) > ridge (58.64 %), the solar radiation ridge ($34.40W/m^2$) > valley($14.69W/m^2$), the wind speed ridge (0.63m/s) > valley(0.37m/s), and the negative ion valley($636.81ea/cm^3$) > ridge($580.04ea/cm^3$). In the valley, the correlation with altitude was verified for the temperature, relative humidity, solar radiation, and negative ion generation in the valley. The relative humidity, solar radiation, and negative ion indicated a positive correlation while the temperature had a negative correlation. In the ridge, the correlation with altitude was verified for the temperature, relative humidity, wind speed, solar radiation, and negative ion generation. The relative humidity, solar radiation, and negative ion generation indicated a positive correlation while the temperature and wind speed had a negative correlation. The regression analysis showed the prediction equation of y=-0.006x+9.663 (x=altitude, y=temperature) in the valley and y=-0.009x+11.595 (x=altitude, y=temperature) in the ridge for the temperature, y=0.027x+53.561 (x=altitude, y=relative humidity) in the valley and y=0.008x+56.646 (x=altitude, y=relative humidity) in the ridges for the relative humidity, and y=0.027x+53.561 (x=altitude, y=negative Ion generation) in the valley and y= 0.008x+56.646 (x=altitude, y=negative Ion generation) in the ridge for the negative ion generation.

• Atmosphere
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• v.28 no.3
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• pp.263-272
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• 2018

The high-resolution ocean surface wind vector produced by scatterometer was assimilated within the Local Ensemble Transform Kalman Filter (LETKF) in Korea Institute of Atmospheric Prediction Systems (KIAPS). The Advanced Scatterometer (ASCAT) on Metop-A/B wind data was processed in the KIAPS Package for Observation Processing (KPOP), and a module capable of processing surface wind observation was implemented in the LETKF system. The LETKF data assimilation cycle for evaluating the performance improvement due to ASCAT observation was carried out for approximately 20 days from June through July 2017 when Typhoon Nepartak was present. As a result, we have found that the performance of ASCAT wind vector has a clear and beneficial effect on the data assimilation cycle. It has reduced analysis errors of wind, temperature, and humidity, as well as analysis errors of lower troposphere wind. Furthermore, by the assimilation of the ASCAT wind observation, the initial condition of the model described the typhoon structure more accurately and improved the typhoon track prediction skill. Therefore, we can expect the analysis field of LETKF will be improved if the Scatterometer wind observation is added.

• Journal of Korean Society of Forest Science
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• v.94 no.6
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• pp.481-487
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• 2005

In this study, a GIS model to simulate the behavior of surface forest fires was developed on the basis of forest fire growth prediction algorithm. This model consists of three modules for data-handling, simulation and report writing. The data-handling module was designed to interpret such forest fire environment factors as terrain, fuel and weather and provide sets of data required in analyzing fire behavior. The simulation module simulates the fire and determines spread velocity, fire intensity and burnt area over time associated with terrain slope, wind, effective humidity and such fuel condition factors as fuel depth, fuel loading and moisture content for fire extinction. The module is equipped with the functions to infer the fuel condition factors from the information extracted from digital vegetation map sand the fuel moisture from the weather conditions including effective humidity, maximum temperature, precipitation and hourly irradiation. The report writer has the function to provide results of a series of analyses for fire prediction. A performance test of the model with the 2002 Chungyang forest fire showed the predictive accuracy of 61% in spread rate.