• Journal of the Korea Institute of Military Science and Technology
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• v.3 no.1
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• pp.13-25
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• 2000

A simulation model is developed to analyze the large-structure driving system and its integrated behavior in the whole weapon system. It models every component in the driving system such as mechanical and electrical characteristics, and it is programmed by simulation language in a way which strongly reflects the system's real time dynamics and reduces computation time as well. A useful parameter identification method is proposed, and it is tuned on the given physical system. The model is validated through comparing to real test, and it is applied to analysis and prediction of integrated system functions relating to the fire control system.

• Journal of the Korean Wood Science and Technology
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• v.23 no.4
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• pp.10-17
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• 1995

본 연구는 화염에 노출된 목재 부재의 거동에 관련된 자료를 얻기 위해 수행되었다. 목재 보에 대한 현행 내화 모델들은 외곽부 섬유의 MOE나 휨강도의 감소, 그리고 화염노출의 계속으로 인한 단면의 감소 등에 기초하고 있다. 하지만 이런 모델들은 정확한 거동 예측이 힘들다. 따라서 목재 보의 거동을 정확히 예측하기 위해 본 연구에서는 변형 단면을 이용한 내화거동 모델을 개발하고자 하였다. 이 변형 단면모델을 개발하기 위하여 온도분포 온도와 목재의 물리적 성질간의 상관관계를 이용하였다. 본 연구를 통해 온도와 목재의 휨성질간의 정확한 관계가 제공되기만 한다면 본 방법이 화염에 노출된 목재 보의 변형을 잘 예측할 수 있음을 알 수 있었다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2012.01a
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• pp.199-200
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• 2012

전 세계적으로 산불로 인한 산림 자원의 손실로 인한 피해는 막대하다. 산불로 인한 인명 및 재산 피해는 증가하는 추세이다. 또한 산불로 인한 산림 자원의 손실은 생태계에 회복되기 힘든 상처를 남긴다. 이런 산불을 분석하고 예방하기 위해 다양한 연구가 진행되고 있으나, 산불의 발생을 예측 할 수 있는 연구는 부족한 실정이다. 본 논문은 미래 예측 연구에 많이 사용되는 마코프 체인을 이용하여 산불을 예측 할 수 있는 산불 예측 모델링을 제안 하고 그 기대 효과에 대해 논의한다.

• Fire Science and Engineering
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• v.25 no.6
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• pp.184-189
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• 2011

This study measured the AITs of n-butanol + n-decane system from ignition delay time (time lag) by using ASTM E659 apparatus. The AITs of n-butanol and n-decane which constituted binary system were $340^{\circ}C$ and $212^{\circ}C$, respectively. The experimental AITs of n-butanol + n-decane system were a good agreement with the calculated AITs by the proposed equations with a few A.A.D. (average absolute deviation).

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 2010.10a
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• pp.380-384
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• 2010

본 연구는 가을철 산불조심기간 중 영동지방 활엽수림에 대하여 임분별로 강우 후 익일부터 6일간 임내의 지표연료를 직경별 0.6cm 이하, 0.6~3.0cm, 3.0~6.0cm, 6.0cm 이상에 대한 연료습도 변화를 실측하는 한편, 기상인자를 고려한 통계분석을 실시하여 경과일수별 연료습도추정 예측식을 개발하였다. 결정계수인 $R^2$ 값은 0.75~0.90의 적합성을 나타내었으며, 향후 강우 후 기상자료를 이용하여 임내 연료습도를 예측하여 산불위험예보로 활용하는데 매우 유용한 자료로 이용할 수 있을 것으로 판단된다.

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 2010.10a
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• pp.370-374
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• 2010

기후변화는 우리생활에서 많은 영향을 줄 것으로 예측되고 있다. 산불 또한, 발생 빈도와 강도 면에서 상당한 영향을 받을 것으로 예측된다. 본 연구에서는 기후변화모형(GCM)과 캐나다 산불 기상 지수의 미세연료 수분지수를 활용하여, 우리나라에서 기후변화 후 예측 되는 산불 발생의 가능성과 산불 계절의 변화를 예측하여, 향후 산불 방제 정책의 기본 자료로 삼고자 하였다. 밸런스형 사회가 유지될 경우의 미세 연료 수분 지수의 분석 결과, 산불 계절이 현재 보다 변화하는 것으로 나타나 이에 대한 사전 대비가 필요한 것으로 분석되었다.

• Wind and Structures
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• v.24 no.6
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• pp.529-564
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• 2017

The Santa Ana winds occur in Southern California during the September-May time frame, bringing low humidities across the area and strong winds at favored locations, which include some mountain gaps and on particular slopes. The exceptionally strong event of late October 2007, which sparked and/or spread numerous fires across the region, is compared to more recent events using a numerical model verified against a very dense, limited-area network (mesonet) that has been recently deployed in San Diego County. The focus is placed on the spatial and temporal structure of the winds within the lowest two kilometers above the ground within the mesonet, along with an attempt to gauge winds and gusts occurring during and after the onset of October 2007's Witch fire, which became one of the largest wildfires in California history.

• Journal of the Korean Wood Science and Technology
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• v.48 no.6
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• pp.917-935
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• 2020

In this study, an automated sensor to measure forest fire surface fuel moistures was developed to predict changes in the moisture content and risk of forest fire surface fuel, which was indicators of forest fire occurrence and spread risk. This measurement sensor was a method of automatically calculating the moisture content of forest fire surface fuel by electric resistance. The proxy of forest fire surface fuel used in this sensor is pine (50 cm long, 1.5 cm in diameter), and the relationship between moisture content and electrical resistance, R(R:Electrical resistance)=2E(E:Exponent of 10)+13X(X:Moisture content)-9.705(R²=0.947) was developed. In addition, using this, the software and case of the automated measurement sensor for forest fire surface fuel moisture were designed to produce a prototype, and the suitability (R²=0.824) was confirmed by performing field monitoring verification in the forest. The results of this study would contribute to develop technologies that can predict the occurrence, spread and intensity of forest fires, and are expected to be used as basic data for advanced forest fire risk forecasting technologies.

• Journal of the Korean Society of Marine Environment & Safety
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• v.22 no.7
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• pp.782-790
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• 2016

When a fire occurs on a ship that has mechanical ventilation facilities, the air supply and exhaust systems directly effect smoke diffusion. And there is a high possibility that occupant's visibility will be harmed because of smoke. In this study, the effects and risks of air supply and exhaust systems with regard to smoke diffusion given a shipboard fire analyzed with a Fire Dynamic Simulator(FDS). Suggested measures are also provided for using air supply and exhaust systems more efficiently. The results showed that, when air supply and exhaust systems were both working at the time of a fire, rather than stopping these systems as previously encouraged, continuing to operate both was an effective measure to gain evacuation time. When a fire occurred and the exhaust system was operating, also starting the air supply system near the origin of the fire was another effective approach to gain evacuation time. However, when only the air supply system was operating and a fire occurred, the air supply system accelerated smoke diffusion, so it was necessary to stop the air supply system to detect smoke diffusion as much as possible.

• Journal of Korea Water Resources Association
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• v.54 no.5
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• pp.301-309
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• 2021

In this study, we tried to improve the performance of the existing U-net-based deep learning rainfall prediction model, which can weaken the meaning of time series order. For this, ConvLSTM2D U-Net structure model considering temporal consistency of data was applied, and we evaluated accuracy of the ConvLSTM2D U-Net model using a RainNet model and an extrapolation-based advection model. In addition, we tried to improve the uncertainty in the model training process by performing learning not only with a single model but also with 10 ensemble models. The trained neural network rainfall prediction model was optimized to generate 10-minute advance prediction data using four consecutive data of the past 30 minutes from the present. The results of deep learning rainfall prediction models are difficult to identify schematically distinct differences, but with ConvLSTM2D U-Net, the magnitude of the prediction error is the smallest and the location of rainfall is relatively accurate. In particular, the ensemble ConvLSTM2D U-Net showed high CSI, low MAE, and a narrow error range, and predicted rainfall more accurately and stable prediction performance than other models. However, the prediction performance for a specific point was very low compared to the prediction performance for the entire area, and the deep learning rainfall prediction model also had limitations. Through this study, it was confirmed that the ConvLSTM2D U-Net neural network structure to account for the change of time could increase the prediction accuracy, but there is still a limitation of the convolution deep neural network model due to spatial smoothing in the strong rainfall region or detailed rainfall prediction.