• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 2010.04a
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• pp.448-453
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• 2010

본 연구에서는 캐나다 연료 수분지수의 정확도를 보정하여, 산불발생의 예측 효율성을 높이기 위해, 기존 입력 값인 상대습도와 실효습도(Effective humidity)를 이용한 결과를 상호 비교 분석하였다. 캐나다 연료수분지수인 미세연료수분지수, 가뭄지수, 부식층수분지수의 결과를 비교한 결과 이에 따라 실효습도를 사용한 캐나다 기상지수의 민감도가 상대습도를 사용하여 산출된 지수보다 민감도가 떨어지는 것으로 조사되었다. 또한, 산불 발생 예측의 지표인 미세연료 수분 지수의 분석결과, 실효습도를 이용하여 산출된 미세연료 수분지수의 적중률이 떨어지는 것으로 조사되었다. 따라서, 미세연료 수분지수의 적용보다 상대습도의 적용이 효과적인 것으로 분석되었다.

• Journal of the Korean Association of Geographic Information Studies
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• v.19 no.4
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• pp.146-156
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• 2016

In this study, we developed a large fire forecasting system using critical weather conditions, such as strong winds and effective humidity. We incorporated information on forest type prior to large fires using an incident case study. The case study includes thirty-seven large fires covering more than 100 ha of damaged area over the last 20 years. Dangerous large fire regions were identified as areas of more than 30 ha of Korean red pine and the surrounding two kilometers. Large fires occur when wind speeds average 5.3 m/s with a maximum of 11.6 m/s and standard deviation of 2.5 m/s. Effective humidity for large fires average 30% with a minimum of 13% and standard deviation of 14.5%. In dangerous Korean red pine stand areas, the large fire 'Watch' level is issued when effective humidity is 30-45% for more than two days and average wind speed is 7-10 m/s. The 'Warning' level is issued when effective humidity is less than 30% for more than two days and average wind speed is more than 11 m/s. Therefore, from now on, the large fire forecasting system can be used effectively for forest fire prevention activities based on a selection and concentration strategy in dangerous large fire regions using severe weather conditions.

• Korean Journal of Agricultural and Forest Meteorology
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• v.18 no.4
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• pp.199-207
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• 2016

This study was conducted to develop a forest fire occurrence model using meteorological characteristics for practical forecasting of forest fire danger rate by reflecting the climate change for the time period of 2000yrs. Forest fire in South Korea is highly influenced by humidity, wind speed, temperature, and precipitation. To effectively forecast forest fire occurrence, we developed a forest fire danger rating model using weather factors associated with forest fire in 2000yrs. Forest fire occurrence patterns were investigated statistically to develop a forest fire danger rating index using times series weather data sets collected from 76 meteorological observation centers. The data sets were used for 11 years from 2000 to 2010. Development of the national forest fire occurrence probability model used a logistic regression analysis with forest fire occurrence data and meteorological variables. Nine probability models for individual nine provinces including Jeju Island have been developed. The results of the statistical analysis show that the logistic models (p<0.05) strongly depends on the effective and relative humidity, temperature, wind speed, and rainfall. The results of verification showed that the probability of randomly selected fires ranges from 0.687 to 0.981, which represent a relatively high accuracy of the developed model. These findings may be beneficial to the policy makers in South Korea for the prevention of forest fires.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.5 no.4
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• pp.278-284
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• 1993

A simplified TAC method was developed for the selection of design temperature and absolute humidity for peak cooling and heating load calculation with ETD method. And the design data of the 11 major cities in Korea were obtained. Based on the simplified TAC method, the design data for summer and autumn cooling season were selected by the TAC 5.0% of July through August and TAC 5.0% of October, respectively. But the design data for winter heating season were selected by the conventional TAC 2.5% of the full winter season.

• 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.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.04a
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• pp.1402-1405
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• 2009

무선 센서네트워크는 넓은 지역에 무선 네트워크로 설치된 센서들을 사용하여, 온도 습도 등의 환경을 감지하여 환경 감시, 대상 추적, 환자 모니터링, 군사적 목적 등 매우 다양한 분야의 서비스에 활용된다. 센서네트워크도 기존 네트워크와 마찬가지로 네트워크 보안 기능을 필요로 한다. 그러나 센서네트워크에 사용되는 장비가 사용할 수 있는 자원에 제약이 많아, 기존의 암호기술을 적용하는데 어려움이 있었다. 그러나, 최근의 연구결과들은 경량화 구현 기술을 적용하여 기존 네트워크에 적용하여 오던 보안 기술들을 센서네트워크에 적용하는 것이 실효성이 있다는 것을 보여준다. 본 논문에서는 대칭키 암호 기능과 비대칭키 암호 기능을 각각 소프트웨어와 하드웨어로 구현하여 성능을 측정한 결과를 비교한다.

• Korean Journal of Agricultural and Forest Meteorology
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• v.20 no.4
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• pp.348-356
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• 2018

Most of fires were human-caused fires in Korea, but meteorological factors are also big contributors to fire behavior and its spread. Thus, meteorological factors as well as topographical and forest factors were considered in the fire danger rating systems. This study aims to develop an advanced national integrated daily weather index(DWI) using weather data in the spring and fall to support forest fire prevention strategy in South Korea. DWI represents the meteorological characteristics, such as humidity (relative and effective), temperature and wind speed, and we integrated nine logistic regression models of the past into one national model. One national integrated model of the spring and fall is respectively $[1+{\exp}\{-(2.706+(0.088^*T_{mean})-(0.055^*Rh)-(0.023^*Eh)-(0.014^*W_{mean}))\}^{-1}]^{-1}$, $[1+{\exp}\{-(1.099+(0.117^*T_{mean})-(0.069^*Rh)-(0.182^*W_{mean}))\}^{-1}]^{-1}$ and all weather variables significantly (p<0.01) affected the probability of forest fire occurrence in the overall regions. The accuracy of the model in the spring and fall is respectively 71.7% and 86.9%. One integrated national model showed 10% higher accuracy than nine logistic regression models when it is applied weather data with 66 random sampling in forest fire event days. These findings would be necessary for the policy makers in the Republic of Korea for the prevention of forest fires.

• Korean Journal of Agricultural and Forest Meteorology
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• v.14 no.1
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• pp.1-10
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• 2012

Digital Forecast of the Korea Meteorological Administration (KMA) represents 5 km gridded weather forecast over the Korean Peninsula and the surrounding oceanic regions in Korean territory. Digital Forecast provides 12 weather forecast elements such as three-hour interval temperature, sky condition, wind direction, wind speed, relative humidity, wave height, probability of precipitation, 12 hour accumulated rain and snow, as well as daily minimum and maximum temperatures. These forecast elements are updated every three-hour for the next 48 hours regularly. The objective of this study was to construct Forest Fire Danger Rating Systems on the Korean Peninsula (FFDRS_KORP) based on the daily weather index (DWI) and to improve the accuracy using the digital forecast data. We produced the thematic maps of temperature, humidity, and wind speed over the Korean Peninsula to analyze DWI. To calculate DWI of the Korean Peninsula it was applied forest fire occurrence probability model by logistic regression analysis, i.e. $[1+{\exp}\{-(2.494+(0.004{\times}T_{max})-(0.008{\times}EF))\}]^{-1}$. The result of verification test among the real-time observatory data, digital forecast and RDAPS data showed that predicting values of the digital forecast advanced more than those of RDAPS data. The results of the comparison with the average forest fire danger rating index (sampled at 233 administrative districts) and those with the digital weather showed higher relative accuracy than those with the RDAPS data. The coefficient of determination of forest fire danger rating was shown as $R^2$=0.854. There was a difference of 0.5 between the national mean fire danger rating index (70) with the application of the real-time observatory data and that with the digital forecast (70.5).

• Journal of Advanced Navigation Technology
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• v.21 no.1
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• pp.126-131
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• 2017

This paper designs a smart safety measurement system with Bluetooth beacon sensor nodes that can provide risk detection and evacuation/countermeasure services. The Bluetooth beacon sensor nodes is easily able to be attached to old building wall or construction or civil structure with potential danger. The proposed smart safety measurement system transmits various sensor data such as acceleration, gyroscope, geomagnetic, pressure, altitude, temperature, humidity at the spot where Bluetooth beacon sensor nodes are installed, and we can use them for risk perception, prediction, and warning services. To verify the effectiveness of the proposed system, we performed filed tests which showed that measured displacement values of precast retaining walls were within the permitted displacement value of 38.5 mm.

• Korean Journal of Mineralogy and Petrology
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• v.34 no.1
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• pp.31-40
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• 2021

The hydrogen in nominally anhydrous mineral is known to be associated with lattice defects, but it also can exist in the form of water and hydroxyl groups on the large surface of the nanoscale particles. In this study, we investigate the effectiveness of 1H solid-state nuclear magnetic resonance (NMR) spectroscopy as a robust experimental method to quantify the hydrogen atomic environments of amorphous silica nanoparticles with varying relative humidity. Amorphous silica nanoparticles were packed into NMR rotors in a temperature-humidity controlled glove box, then stored in different atmospheric conditions with 25% and 70% relative humidity for 2~10 days until 1H NMR experiments, and a slight difference was observed in 1H NMR spectra. These results indicate that amount of hydrous species in the sample packed in the NMR rotor is rarely changed by the external atmosphere. The amount of hydrogen atom, especially the amount of physisorbed water may vary in the range of ~10% due to the temporal and spatial inhomogeneity of relative humidity in the glove box. The quantitative analysis of 1H NMR spectra shows that the amount of hydrogen atom in amorphous silica nanoparticles linearly increases as the relative humidity increases. These results imply that the sample sealing capability of the NMR rotor is sufficient to preserve the hydrous environments of samples, and is suitable for the quantitative measurement of water content of ultrafine nominally anhydrous minerals depending on the atmospheric relative humidity. We expect that 1H solid-state NMR method is suitable to investigate systematically the effect of surface area and crystallinity on the water content of diverse nano-sized nominally anhydrous minerals with varying relative humidity.