• 터널과지하공간
• /
• 제9권4호
• /
• pp.306-314
• /
• 1999

핵폐기물 저장공간과 같이 대규모 터널형 지하공간내에서의 환기량, 온/습도, 오염물질 농도 예측은 공간건설 및 운영을 위하여서 뿐만 아니라 화재등과 같은 비상시의 대처방안 강구를 위하여 반드시 필요하다. 본 연구에서는 압축성 가정하에서 autocompression의 영향을 고려한 자연환기압 계산 fan 및 regulator의 최적 위치 및 용량 결정, 암반 열물성 및 암반 표면으로부터의 증발, 응축에 의한 수분함유량 변화를 고려한 온/습도 계산, 이류확산에 기초한 오염물질 농도 분포 계산 기능을 갖춘 네트워크형 지하공간 환경예측 모델을 개발하였다. 온/습도 예측 모델을 군수물자 지하 저장공간에 적용한 결과 실측값과 상대오차는 건구온도 1.5~2.9%, 습도온도 0.6~6.1%로 나타났다. 도로터널 2개소를 대상으로한 실험결과 외부 입기만에 의한 확산계수는 9.78과 17.35$m^2$/s 큰 편이었으나 차량 운행과 환기설비의 작동시에는 이류확산만을 고려한 경우 CO 및 매연 농도의 상대오차가 5.88과 6.62%로 비교적 작게 나타났다. 이는 대부분의 터널형 지하공간에서의 농도 분포는 이류확산만에 의하여 추정이 가능함을 의미한다.

• International Journal of Advanced Culture Technology
• /
• 제6권4호
• /
• pp.190-194
• /
• 2018

The runway visual range is one of the important factors that decide the possibility of taking offs and landings of the airplane at local airports. The runway visual range is affected by weather conditions like fog, wind, etc. The pilots and aviation related workers check a local weather forecast such as runway visual range for safe flight. However there are several local airfields at which no other forecasting functions are provided due to realistic problems like the deterioration, breakdown, expensive purchasing cost of the measurement equipment. To this end, this study proposes a prediction model of runway visual range for a local airport by applying convolutional neural network that has been most commonly used for image/video recognition, image classification, natural language processing and so on to the prediction of runway visual range. For constituting the prediction model, we use the previous time series data of wind speed, humidity, temperature and runway visibility. This paper shows the usefulness of the proposed prediction model of runway visual range by comparing with the measured data.

• Advances in concrete construction
• /
• 제5권2호
• /
• pp.155-171
• /
• 2017

The phenomenon of concrete column shortening has been widely acknowledged since it first became apparent in the 1960s. Axial column shortening is due to the combined effect of elastic and inelastic deformations, shrinkage and creep. This study aims to investigate the effects of ambient temperature, relative humidity, cement hardening speed and aggregate type on concrete column shortening. The investigation was conducted using a column shortening prediction model which is underpinned by the Eurocode 2. Critical analysis and evaluation of the results showed that the concrete aggregate types used in the concrete have significant impact on column shortening. Generally, aggregates with higher moduli of elasticity hold the best results in terms of shortening. Cement type used is another significant factor, as using slow hardening cement gives better results compared to rapid hardening cement. This study also showed that environmental factors, namely, ambient temperature and relative humidity have less impact on column shortening.

• 한국연초학회지
• /
• 제12권2호
• /
• pp.59-65
• /
• 1990

저장온도, 상대습도 및 봉함도에 따른 유연필름 포장 담배의 저장수명을 컴퓨터를 이용하여 예측한 수치와 실험치를 Student's t test한 결과 $\alpha$=0.01로 높은 정확도를 나타냈다. 근본적으로 저장온도가 높을수록, 또 초기의 평형습도 차가 클수록 저장수명은 짧아졌으며 비교적 고온에서 water vapor의 이동이 더 높게 일어났다. 저장수명에 대한 온도, 봉함도, 상대습도등의 기여도가 SPSS를 이용한 통계분석에 의하면 각각 -0.49, -0.39, -0.28로 저장수명 요인중 봉함도(Sealing degree)도 수명을 단축시키는 주요 인자임을 밝혔으며 포장담배를 6개월 이상 유통시키기위해서는 film포장의 봉함도가 600ml/min, 20mmH2O이하가 되어야함을 알 수 있었다.

• 보존과학회지
• /
• 제32권4호
• /
• pp.491-500
• /
• 2016

안개는 매우 작은 물방울이 대기 중에 떠다니는 현상으로 주변의 온도, 습도, 바람, 지형 등에 따라 안개의 형태나 발생 여부 등이 달라진다. 특히 안개는 수분을 함유하는 수원(水源) 근처에서 온습도 차이로 인해 발생하기 쉬우며, 오늘날 기후 변화는 안개 발생에 더욱 영향을 미칠 수 있다. 본 연구에서는 다양한 기후요인 중 안개가 문화재에 미치는 영향을 알아보고자 각 재질에 대해 옥외폭로 및 인공열화를 실시하였으며 지속적인 안개 발생으로 인한 재질의 손상정도를 예측하기 위해 색변화를 중심으로 회귀분석을 실시하였다. 금속 표준시편에 대한 옥외폭로 및 인공열화 결과, 표면 변화가 크게 발생하였으며 화견 및 단청류의 경우 표면상 열화된 모습은 관찰되었으나 색차의 경우 일정한 경향성은 확인되지 않았다. 안개 발생빈도에 따라 유의적인 색변화가 발생한 금속 표준시편을 대상으로 안개 발생빈도에 따른 색 변화 예측식을 작성하였으며, 실제 옥외폭로를 통한 색차값의 비교를 통해 색 변화 예측식의 적용 가능성을 확인하였다. 이를 바탕으로 향후 보다 종합적인 손상 예측식을 수립한다면 실제 현장에서 보존환경에 따른 재질의 손상도 예측이 가능할 것으로 기대된다.

• 한국산림과학회지
• /
• 제99권2호
• /
• pp.163-171
• /
• 2010

Forest fires have potentials to change the structure and function of forest ecosystems and significantly influence on atmosphere and biogeochemical cycles. Forest fire also affects the quality of public benefits such as carbon sequestration, soil fertility, grazing value, biodiversity, or tourism. The prediction of fire occurrence and its spread is critical to the forest managers for allocating resources and developing the forest fire danger rating system. Most of fires were human-caused fires in Korea, but meteorological factors are also big contributors to fire behaviors and its spread. Thus, meteorological factors as well as social factors were considered in the fire danger rating systems. A total of 298 forest fires occurred during the fall season from 2002 to 2006 in South Korea were considered for developing a logistic model of forest fire occurrence. The results of statistical analysis show that only effective humidity and temperature significantly affected the logistic models (p<0.05). The results of ROC curve analysis showed that the probability of randomly selected fires ranges from 0.739 to 0.876, which represent a relatively high accuracy of the developed model. These findings would be necessary for the policy makers in South Korea for the prevention of forest fires.

• 한국태양에너지학회 논문집
• /
• 제36권1호
• /
• pp.1-9
• /
• 2016

To well design the solar energy system, the correlation to calculate and predict solar irradiation is basically needed. So, this study was performed to reveal the relationships between the solar irradiation and four meteorological observation data(dry-bulb temperature, relative humidity, duration of sunshine, and amount of cloud) that didn't show from previous any other researches. And then, we finally proposed the various order non-linear correlation from the measured solar irradiation and four meteorological measurement data using MINITAB. To show the deviation and accuracy of the solar irradiation between measured and calculated, this study compared for the daily total solar insolation. From those results, the calculation error could well predicted about maximum 97% for the daily total solar insolation. But, the coefficients of the proposed correlations didn't show any relationships. So, needs more studies to make the proper one correlation for the country.

• 한국환경과학회:학술대회논문집
• /
• 한국환경과학회 2003년도 International Symposium on Clean Environment
• /
• pp.89-94
• /
• 2003

The formation of a severe snow storm occurred in the mountainous coastal region near Mt. Taegualyang and Kangnung city in the eastern part of Korea was investigate from 0900LST, December 7 through 9, 2002, using MM5 model. As synoptic scale easterly wind induced a great amount of moisture from the East Sea into the inland coastal region and sea-breeze further induced more moisture from the basin toward the top of the mountain side. The lifted moisture toward the mountain top was cooled down along the eastern slope of the mountain and near the mid of the mountain the moisture was much cooled down with relative humidity of 100％ under the air temperature below $O^{\circ}C$, resulting in the formation of snow. Relative humidity of 100％ generally occurred at the 5km away from the coast toward the inland mountain and the band of 100％ RH was parallel to the coastal line. The 100％ band coincided with minimum air temperature band and line.

• 한국지구과학회지
• /
• 제24권5호
• /
• pp.449-455
• /
• 2003

포항일대의 대기환경을 이해하고 예측의 기초정보로 활용하기 위해 기후학적 요소들에 대한 조사를 수행하였다. 분석을 통해, 포항지역의 연평균 기온, 상대습도, 강수량은 각각 14.39$^{\circ}C$, 63.3%, 1,178mm로 나타났다. 포항지역에서의 연간 탁월풍은 남서풍으로 나타났으며 평균풍속은 2.7m/s였다. 운량은 장마기간이 포함되며 지형효과에 의한 대류운이 활발하게 나타나는 여름에 많았다. 또한 연평균 일조시간은 약 2,221시간으로 나타났다.

• Journal of Information Technology Applications and Management
• /
• 제29권5호
• /
• pp.27-37
• /
• 2022

This study intends to link agricultural machine history data with related organizations or collect them through IoT sensors, receive input from agricultural machine users and managers, and analyze them through AI algorithms. Through this, the goal is to track and manage the history data throughout all stages of production, purchase, operation, and disposal of agricultural machinery. First, LSTM (Long Short-Term Memory) is used to estimate oil consumption and recommend maintenance from historical data of agricultural machines such as tractors and combines, and C-LSTM (Convolution Long Short-Term Memory) is used to diagnose and determine failures. Memory) to build a deep learning algorithm. Second, in order to collect historical data of agricultural machinery, IoT sensors including GPS module, gyro sensor, acceleration sensor, and temperature and humidity sensor are attached to agricultural machinery to automatically collect data. Third, event-type data such as agricultural machine production, purchase, and disposal are automatically collected from related organizations to design an interface that can integrate the entire life cycle history data and collect data through this.