• Journal of The Korean Association For Science Education
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• v.35 no.3
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• pp.509-521
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• 2015

Argumentation is a social and collaborative dialogic process. A large number of researchers have focused on analyzing the structure of students' argumentation occurring in the scientific inquiry context, using the Toulmin's model of argument. Since SSI dialogic argumentation often presents distinctive features (e.g. interdisciplinary, controversial, value-laden, etc.), Toulmin's model would not fit into the context. Therefore, we attempted to develop an analytical framework for SSI dialogic argumentation by addressing the concepts of 'discourse clusters' and 'discourse schemes.' Discourse clusters indicated a series of utterances created for a similar dialogical purpose in the SSI contexts. Discourse schemes denoted meaningful discourse units that well represented the features of SSI reasoning. In this study, we presented six types of discourse clusters and 19 discourse schemes. We applied the framework to the data of students' group discourse on SSIs (e.g. euthanasia, nuclear energy, etc.) in order to verify its validity and applicability. The results indicate that the framework well explained the overall flow, dynamics, and features of students' discourse on SSI.

• Fire Science and Engineering
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• v.30 no.3
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• pp.104-109
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• 2016

Candle fires do not occur frequently, but can easily result in death. In this study, the thermal characteristics of candles and conditions and debris of candle fires were investigated to determine the causes of candle fires. The rates of decrease in weight of 10 candles were measured and found to be between 2.6 g/h and 6.7 g/h. Most candle fires are caused by the ignitiong of combustible materials close to them. The temperature near a candle ranges from about $200^{\circ}C$ to $400^{\circ}C$ at a distance of 1 cm and low ignition temperature materials such as papers can easily catch fire. The melting temperature of candles ranges between $50^{\circ}C$ and $70^{\circ}C$ and their major chemical components are fatty acids and normal hydrocarbons (over C20). Using pretreatment conditions involving the use of activated charcoal strips at $150^{\circ}C$ for 16 hours, the fire debris including candle residues were analyzed using a Gas-chromatograph/Mass-spectrometer (GC/MS).

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.5
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• pp.7-15
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• 2022

Since the direct and indirect damage caused by the fire in the underground utility tunnel will cause great damage to society as a whole, it is necessary to make efforts to prevent and control it in advance. The most of the fires that occur in cables are caused by short circuits, earth leakage, ignition due to over-current, overheating of conductor connections, and ignition due to sparks caused by breakdown of insulators. In order to find the cause of fire at an early stage due to the characteristics of the underground utility tunnel and to prevent disasters and safety accidents, we are constantly managing it with a detection system using image analysis and making efforts. Among them, a case of developing a fire detection system using CCTV-based deep learning image analysis technology has been reported. However, CCTV needs to be supplemented because there are blind spots. Therefore, we would like to develop a high-performance acoustic-based deep learning model that can prevent fire by detecting the spark sound before spark occurs. In this study, we propose a method that can collect sound in underground utility tunnel environments using microphone sensor through development and experiment of prototype module. After arranging an acoustic sensor in the underground utility tunnel with a lot of condensation, it verifies whether data can be collected in real time without malfunction.

• Fire Science and Engineering
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• v.26 no.6
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• pp.7-14
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• 2012

This paper is to develop analytical techniques of flammable liquids which have been used for accelerating fire in accidental fires and arsons. We tested the temperature distribution of ceiling, fire patterns on the floor, and existence of flammable liquids and a check with GC/MS about flammable liquids comparing with papers, newspapers, and clothing. Research findings are as follows. The temperature of ceiling is influenced by flame. So gasoline and thinner was observed that combustible materials would be burned by flame. The fire patten on the floor was observed that flammable liquids had specialized pattern comparing combustible materials. When combustible materials on the PVC (Polyvinyl chloride) floor was burned, they didn't react to the gas detector. But flammable liquids had opposite results. After 7 days, we identified components of fire residues with the GC/MS (Gas Chromatography/Mass Spectrometry) about existence of flammable liquids and got components of flammable liquids. Fire investigation is a complicated processes. But we understand characteristics of materials, need detail investigations, and use the GC/MS to analyse flammable materials.

• Journal of Advanced Marine Engineering and Technology
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• v.13 no.4
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• pp.63-74
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• 1989

It is well-known that the fuel injection system if a diesel engine has taken a more important place in understanding of diesel combustion process with combustion chamber. But a diesel fuel injection system has an assembly of many complex and intricate problems such as the desired rate of injection, secondary injection and injection pump etc., in addition to the atomization for ignition and combustion, the penetration and diestribution for proper utilization of air. The analysis is carried out by simplifing and modeling the injection phenomena and dividing into three parts comprising of fuel injection pump, high pressure pipe and fuel injection nozzle. The purpose of this paper is to describe an analytical simulation of the injection system and to speed up the work of developing injection systems for new engines. The effects of important injection parameters as predicted by the present model are found to be in good agreement with experiment. It can be seen that there is an optimal pipe diameter for maximum quantity injected.

• Journal of the Korean Society of Hazard Mitigation
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• v.10 no.3
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• pp.85-90
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• 2010

In this study, we have examined the monthly combustion characteristics of Quercus variabilis, a representing Quercus Spp. in Korea, using its living leaves over the period of from June to October. As a result, we were able to identify that their moisture content was about 114%~155%. The leaves of Quercus variabilis collected in October showed the lowest moisture content and nonflaming ignition temperature. The leaves of July showed the fastest flaming ignition time of 27s while those from September showed the longest persistence of flame with 105s, and also showed the highest total heat release amount. There was a noticeable difference in each month of the above period regarding total heat release amount and total smoke release amount with a gradual increase from June to October. The maximum smoke density was a bit higher in October leaves but there was no significant monthly difference. In addition, July leaves were shown to reach the maximum value in the shortest time of 795s.

• Annual Conference on Human and Language Technology
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• 1996.10a
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• pp.158-162
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• 1996

본 논문에서는 문자출력이 가능한 무선호출기에 음성인식 기술을 접목한, 특성화된 한 음성인식 시스템에 대하여 설명하고자 한다. 시스템 동작 과정은, 일단 호출자가 음성인식 서버와 접속하게 되면 서버는 호출자의 자연스런 입력음성을 인식, 그 결과를 문장 형태로 피호출자의 호출기 단말기에 출력시키는 방식으로 되어 있다. 본 시스템에서는 통계적 음성인식 기법을 도입하여, 각 단어를 연속 HMM으로 모델링하였다. 가우시안 혼합 확률밀도함수를 사용하는 각 모델은 전통적인 HMM 학습법들 중의 하나인 Baum-Welch 알고리듬에 의해 학습되고 인식시에는 이들에 비터비 빔 탐색을 적용하여 최선의 결과를 얻도록 한다. MFCC와 파워를 혼용한 26 차원 특징벡터를 각 프레임으로부터 추출하여, 최종적으로, 83 개의 도메인 어휘들 및 무음과 같은 특수어휘들에 대한 모델링을 완성하게 된다. 여기에 구문론적 기능과 의미론적 기능을 함께 수행하는 FSN을 결합시켜 자연발화음성에 대한 연속음성인식 시스템을 구성한다. 본문에서는 이상의 사항들 외에도 음성 데이터베이스, 레이블링 등과 갈이 시스템 성능과 직결되는 시스템의 외적 요소들에 대해 고찰하고, 시스템에 구현되어 있는 다양한 특성들에 대해 밝히며, 실험 결과 및 앞으로의 개선 방향 등에 대해 논의하기로 한다.

• Fire Science and Engineering
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• v.30 no.3
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• pp.1-6
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• 2016

More than five thousands transportation fires occurannually in Korea and the resulting destruction of property and loss of life is huge and results in traffic and environmental pollution. The recent development of automobile technology such as the hybrid concept and use of bio fuels makes fire protection even more difficult due to a lack of understanding of the new adapted system including vehicle engines. In this study, a numerical simulation was performed on a PSR (perfectly Stirred Reactor) to simulate an automobile engine and to clarify the effect of gasoline/ethanol surrogates as a fuel. The temperature, NOx and soot emissions were predicted to decrease with increasing ethanol content, but that of unburned hydrocarbons was found to increase dramatically. The result will provide not only the basic thermal characteristics for engines and their after-treatment systems, but also make it possible to assess the potential for fire events in these systems when an ethanol mixed fuel is used in gasoline vehicles.

• Fire Science and Engineering
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• v.19 no.4 s.60
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• pp.1-6
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• 2005

To control forest fire effectively, it is necessary to understand forest fire behavior and relevance to forest fire environmental factors. In this paper, the behavior characteristics of the 2005 Yangyang forest fire were analyzed into the spread patterns and severity grades. The spread processes of the forest fire could be divided into two steps. At the first step, the fire ran fast to the east due to the strong west wind and then spreaded out in irregular direction. The maximum spread rate of the fire was 1.21km/hr and the mean was 0.65 km/hr. The result of the fire severity classification indicated that about $80\%$(1,110ha) of the whole study site was extremely burned and the remaining $15\%(211 ha)\;and\;5\%(61 ha)$ were damaged slightly and moderately respectively.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.5
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• pp.109-126
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• 2023

Early detection of forest fires is essential in preventing large-scale forest fires. Predicting forest fires serves as a vital early detection method, leading to various related studies. However, many previous studies focused solely on climate and geographic factors, overlooking human factors, which significantly contribute to forest fires. This study aims to develop forest fire prediction models that take into account human, weather and geographical factors. This study conducted a comparative analysis of four machine learning models alongside the logistic regression model, using forest fire data from Gangwon-do spanning 2003 to 2020. The results indicate that XG Boost models performed the best (AUC=0.925), closely followed by Random Forest (AUC=0.920), both of which are machine learning techniques. Lastly, the study analyzed the relative importance of various factors through permutation feature importance analysis to derive operational insights. While meteorological factors showed a greater impact compared to human factors, various human factors were also found to be significant.