• Journal of Environmental Science International
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• v.18 no.7
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• pp.781-796
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• 2009

The current standard level of Heat Health Watch Warning System consider both daily maximum temperature and daily maximum heat index(HI), but current standard could not consider daily maximum HI due to the difficulties in forecasting when we consider both daily maximum temperature and daily maximum HI and no considering HI because relative humidity could not observed for some regions. So, Newly established standard level of Heat Health Watch Warning System is based on daily maximum temperature exceeding $30^{\circ}C$ for two consecutive days or daily minimum temperature exceeding $25^{\circ}C$ and daily maximum temperature exceeding $30^{\circ}C$. These days are called "extreme heat days". On extreme heat days, the standard of extreme heat advisory is based on daily maximum temperature among exceeding $32.7^{\circ}C$ and not exceeding $34.8^{\circ}C$, and extreme heat warning is based on daily maximum temperature exceeding $34.8^{\circ}C$. ANOVA analysis was carried out using the data of Seoul Metropolitan City in 1994 to check the robustness of the new standard level of Heat Health Watch Warning System from this study, in particular for mortality variable. The results reveal that the new standard specifies excess mortality well, showing significance level of 0.05 in the difference of excess mortality for each phase.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.3
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• pp.359-364
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• 2011

The study uses department of the sensitivity and fuzzy reasoning, one of artificial intelligence algorithms, so that develop LED lighting system based on fuzzy reasoning for systematical control of the LED color temperature. In the area of sensitivity engineering, by considering the relation between color and emotion expressed as an adjective word, the corresponding sensitivity word can be determined, By taking into consideration the relation between the brain wave measured from the human brain and the color temperature, the preferred lesson subject can be determined. From the decision of the sensitivity word and the lesson subject, we adjust the color temperature of RGB (Red, Green, Blue) LED. In addition, by using the information of the latitude and the longitude from GPS(Global Positioning System), we can calculate the on-line moving altitude of sun. By using the sensor information of both temperature and humidity, we can calculate the discomfort index. By considering the altitude of sun as well as the value of the discomfort index, the illumination of W(white) LED and the color temperature of RGB LED can be determined. The (LED) sensitivity lighting control system is bulit up by considering the sensitivity word, the lesson subject, the altitude of sun, and the discomfort index The developed sensitivity lighting control system leads to more suitable atmosphere and also the enhancement of the efficiency of lesson subjects as well as business affairs.

• Journal of the Korean Geographical Society
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• v.49 no.5
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• pp.779-793
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• 2014

This study was aimed at examining the recent and the future changes of tourism climatic conditions for Chiaksan national park using TCI(Tourism Climate Index). The distribution type of daily mean of TCI showed the bimodal-shoulder peaks for all periods of the past and the future. Therefore, Chiaksan national park showed affordable climate of touring in spring and autumn due to temperature and relative humidity constituting Cid and Cia. Summer tourism climatic conditions of Chiaksan national park has become worse recently. In the future, these trends are projected more stronger. Also, the lowest TCI in the year is projected to shift from winter to summer due to a decrease of Cid.

• Journal of the Korean Association of Geographic Information Studies
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• v.1 no.1
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• pp.99-108
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• 1998

In order to represent the surface characteristics in local meteorological model, soil type, vegetation index, surface roughness length, surface albedo and leaf area index should be prescribed on the surface process parameterization. In this study, the $1^{\circ}/1^{\circ}leaf$ area index, surface roughness length, and snow free surface albedo and fine mesh NDVI with seasonal variation derived from the satellite observation were applied to the land surface process parameterization. From comparison between with and without satellite data in the interactions between biosphere and atmosphere, land and atmosphere, the sensitivity of the simulated heat, energy and water vapor fluxes, ground temperature, wind, canopy water content, specific humidity, and precipitation fields were investigated.

• Korean Journal of Remote Sensing
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• v.28 no.2
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• pp.237-243
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• 2012

In this paper, an atmospheric effect of repetitive measurements of X-band (9.65 GHz) arc-rail type GB-SAR (ArcSAR) system was quantitatively analyzed. Four artificial triangular trihedral corner reflectors as stationary targets for getting stable back scattered signal during 43 hours continually. The results of the analysis showed that the phase of those stationary targets had changed maximum of 5 radian (12.4 mm) and total RMS error had was 1.62 radian (4 mm) during 65 repeated measuring time. The refractive index of air which was calculated using the temperature;humidity and pressure of atmosphere showed very close relationship with the phase difference. We could check the atmospheric correction was fulfilled by the correction of an atmospheric effect using refractive index during the selected 16 hours period showed that RMS error was dropped from 1.74 radian (4.3 mm) to 0.10 radian (0.24 mm).

• Journal of the Korea Society of Computer and Information
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• v.24 no.9
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• pp.83-95
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• 2019

Recently, a variety of IoT data is collected by attaching geosensors to many vehicles that are on the road. IoT data basically has time and space information and is composed of various data such as temperature, humidity, fine dust, Co2, etc. Although a certain sensor data can be retrieved using time, latitude and longitude, which are keys to the IoT data, advanced search engines for IoT data to handle high-level user queries are still limited. There is also a problem with searching large amounts of IoT data without generating indexes, which wastes a great deal of time through sequential scans. In this paper, we propose a unified spatial index model that handles both grid and trajectory queries using a cell-based space-filling curve method. also it presents a visualization method that helps user grasp intuitively. The Trajectory query is to aggregate the traffic of the trajectory cells passed by taxi on the road searched by the user. The grid query is to find the cells on the road searched by the user and to aggregate the fine dust. Based on the generated spatial index, the user interface quickly summarizes the trajectory and grid queries for specific road and all roads, and proposes a Web-based prototype system that can be analyzed intuitively through road and heat map visualization.

• Journal of the Korean Solar Energy Society
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• v.26 no.4
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• pp.9-16
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• 2006

Geothermal-energy has been getting popular as a natural energy source for green buildings these days. Public building with gross area more than $3000m^2$, planned after March, 2005, should spend about 5% of total building cost for equipment run by natural energy source (e.g. geothermal, solar heat, solar power, etc) according to renewable energy promotion law in Korea. As a result geothermal-energy using heat pump system is emerging as a effective alternative for realistic and economic plan although design guidelines and construction code for the system is in progress and technical data is far from sufficient. The quantitative analysis on the performance of geothermal-energy using heat pump system is insufficient for appropriate design of it. In this paper, cooling performance of geothermal-energy using heat pump system of residential and retail etc. mixed-use building has been analyzed on the basis of temperature comparison between inlet and outlet of heat exchangers of the operating system. Additionally, dry-bulb temperature and relative humidity have been measured and analyzed together as an index of indoor thermal environment.

• Journal of the Korean earth science society
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• v.31 no.5
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• pp.454-464
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• 2010

A probability prediction model for tropical cyclone (TC) genesis in the Northwestern Pacific area was developed using the logistic regression method. Total five predictors were used in this model: the lower-level relative vorticity, vertical wind shear, mid-level relative humidity, upper-level equivalent potential temperature, and sea surface temperature (SST). The values for four predictors except for SST were obtained from difference of spatial-averaged value between May and January, and the time average of Ni$\tilde{n}$o-3.4 index from February to April was used to see the SST effect. As a result of prediction for the TC genesis frequency from June to December during 1951 to 2007, the model was capable of predicting that 21 (22) years had higher (lower) frequency than the normal year. The analysis of real data indicated that the number of year with the higher (lower) frequency of TC genesis was 28 (29). The overall predictability was about 75%, and the model reliability was also verified statistically through the cross validation analysis method.

• Advances in environmental research
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• v.4 no.4
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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.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.41 no.8
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• pp.131-137
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• 2004

The aim of this paper is to find the weather conditions which make the abnormal propagation of a radar bear In order to analyze the weather conditions which cause superrefraction or ducting, the meteorological data of the west sea and the south sea of Korea are classified which are observed during three years from 2000. Atmospheric indexes of refraction with increasing altitude are calculated and the rate of variation of temperature and hmidity at the altitude where the index is very low are observed. It is found that unwanted radar echoes by anomalous propagation are showed up only when the atmospheric indexes of refraction at a altitude is less than -150/km and the reverse layer of temperature appears with a sudden drop of humidity at the altitude.