• Korean Journal of Food Science and Technology
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• v.37 no.5
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• pp.695-701
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• 2005

Efficient method was established for analysis of sulfur-containing compounds, including dimethyl disulfide, dimethyl trisulfide, 3-methyl thiophene, allyl mercaptan, 2-methyl-3-furanthiol, and methional. Sulfur-containing compounds were extracted through solid phase microextraction (SPME) or static headspace extraction (SH), and quantified using gas chromatograph equipped with pulsed flame photometric detector. All sulfur compounds, except ally mercaptan, showed higher detection response when dissolved in hexane than in dichloromethane. Linear range was $10^2-10^4$. Dimethyl trisulfide showed lowest limit of detection (LOD) value of 15.2 ppt, and methional highest of 70.5 ppb. Highest extraction efficiency for sulfur-containing compounds, particularly polar and small molecular weight compounds, was observed in 75mm carboxen/polydimethylsiloxane fiber, followed by 65mm polydimethylsiloxane/divinylbenzene and 100mm polydimethylsiloxane. Compared to SPME, less sulfur-containing compounds could be analyzed by SH, mainly due to its low extraction efficiency, although lower amount of artifacts were formed during sample preparation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.4
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• pp.1555-1570
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• 2016

For monitoring forest fires, a real-time system to prevent fires in wider areas should be supported consistently. However, there has still been a lack of the support for real-time system related to forest fire monitoring. In addition, the 'real-time' processing in a forest fire detection system can lead to excessive consumption of energy. To solve these problems, the intelligent data acquisition of sensing nodes is required, and the maximum energy savings as well as rapid and accurate detection by flame sensors need to be done. In this regard, this paper proposes a node built-in filter algorithm for intelligent data collection of sensing nodes for the rapid detection of forest fires with focus on reducing the power consumption of the remote sensing nodes and providing efficient wireless sensor network-based forest environment monitoring in terms of data transmission, network stability and data acquisition. The experimental result showed that battery life can be extended through the intelligent sampling of remote sensing nodes, and the average accuracy of the measurement of flame detection based on the distance is 44%.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.1
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• pp.65-70
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• 2023

In this paper, we used a deep learning model to detect and segment flame and smoke in real time from fires. To this end, well known U-NET was used to separate and divide the flame and smoke of the fire using multi-class. As a result of learning using the proposed technique, the values of loss error and accuracy are very good at 0.0486 and 0.97996, respectively. The IOU value used in object detection is also very good at 0.849. As a result of predicting fire images that were not used for learning using the learned model, the flame and smoke of fire are well detected and segmented, and smoke color were well distinguished. Proposed method can be used to build fire prediction and detection system.

• Journal of the Korean Society of Hazard Mitigation
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• v.10 no.5
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• pp.49-54
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• 2010

The wooden cultural assets have the characteristics such as the fast spread of flame and leading to total destruction. Therefore, there is a need for a system for early countermeasure of recognized problem, along with the technological response for accurately recognizing the situation, for the prevention and early suppression of fire. To utilize such technology for detecting the situation through the latest ubiquitous technology and for a quick response to suppress fire, the ubiquitous sensor network (USN) technology, flame detector, image sensor, USN-based cultural asset disaster prevention management application case and malfunction identification system realization were examined in this study and the study result was presented focusing on the flame detector malfunction identification system for the ubiquitous-type cultural asset disaster prevention system.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.4
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• pp.516-521
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• 2010

In this paper, we propose a video-based intelligent unmanned fire surveillance system using fuzzy color models. In general, to detect heat or smoke, a separate device is required for a fire surveillance system, this system, however, can be implemented by using widely used CCTV, which does not need separate devices and extra cost. The systems called video-based fire surveillance systems use mainly a method extracting smoke or flame from an input image only. The smoke is difficult to extract at night because of its gray-scale color, and the flame color depends on the temperature, the inflammable, the size of flame, etc, which makes it hard to extract the flame region from the input image. This paper deals with a intelligent fire surveillance system which is robust against the variation of the flame color, especially at night. The proposed system extracts the moving object from the input image, makes a decision whether the object is the flame or not by means of the color obtained by fuzzy color model and the shape obtained by histogram, and issues a fire alarm when the flame is spread. Finally, we verify the efficiency of the proposed system through the experiment of the controlled real fire.

• Journal of Korean Society for Atmospheric Environment
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• v.13 no.5
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• pp.355-360
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• 1997

The atmospheric concentration of dimethylsulfide (DMS), known as the predominant volatile organic. sulfur compound, is determined at subnanogram level by a combined application of non-cryogenic preconcentration method and gas chromatography with flame photometric detection (GC/FPD). The volatile DMS in air is preconcentrated using a trapping tube containing adsorbent like Molecular Sieve 5A (or gold-coated sands). The tube is then connected to the GC/FPD system via a six-way rotary valve, thermally desorbed at 40$0^{\circ}C$, separated on OV101 column, and detected by a flame photometric detector. The DMS peak elutes at about 2.5 mins and is integrated electronically. The analytical precision, if expressed in terms of relative standard error, is around 5%. The detection limit of our GC/FPD system is ca 1 ng of DMS. Details of our analytical system are presented.

• Proceedings of the KIPE Conference
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• 2018.07a
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• pp.275-277
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• 2018

Recently, the importance of an early response has been emphasized due to the large fire. The most efficient method of extinguishing a large fire is early response to a small flame. To implement this solution, we propose a fire detection mechanism based on a deep learning artificial intelligence. In this study, a small amount of data sets is manipulated by an image augmentation technique using rotating, tilting, blurring, and distorting effects in order to increase the number of the data sets by 5 times, and we study the flame detection algorithm using faster R-CNN.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.573-574
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• 2023

화염 영역 검출을 위해 이전 기법에서는 화재 이미지에서 연기제거 및 색상보정을 통해 이미지를 전처리하였다. 그러나 이 기법은 임계값에 영향을 많이 받고, 밝기채널을 이용하여 검출하기 때문에 밤에 일어난 화재 이미지에서는 평균이상의 퍼포먼스를 수행하지만, 주변이 밝은 대낮의 화재 이미지에서는 퍼포먼스가 줄어드는 문제가 있다. 이를 보완하고자 본 논문에서는 CycleGAN을 이용하여 낮 이미지를 밤 이미지로 바꾸어 이미지 전처리를 진행하는 기법을 제안함으로써 화염 감지의 정확도가 개선되었음을 실험을 통해 보여준다.

• Mass Spectrometry Letters
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• v.14 no.4
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• pp.160-165
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• 2023

Citronella oil, an essential oil extracted through steam distillation from the leaves and stems of Cymbopogon, is a natural complex substance (NCS) regulated by the Korean government for its use in insect repellents. The component ratios of NCSs like citronella oil vary due to differences in manufacturing processes and origins, presenting a challenge in identifying and quantifying these substances in consumer chemical products. This study analysed ten commercially available products of the most commonly used types of citronella oil, specifically Java and Ceylon types, using gas chromatography (GC)-mass spectrometry (MS) and GC with flame ionization detection (FID). Through chromatographic data, we aimed to determine the components that can qualitatively identify citronella oil and the indicator ingredients that can be used for content analysis.

• Fire Science and Engineering
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• v.26 no.6
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• pp.24-30
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• 2012

This study is on the method of the detection for flame and series arc which can be happened at poor contact point added a vibration in part of contact point of low voltage line. In general, the causes of electric fire are over current, short circuit, poor contact, ect. The over-current or short circuit among those causes is detected by measuring a instant current value, but poor contact is difficult to detect by measuring a excessive value of the voltage and current and a distortion of waveforms. And therefore, in this paper, it is studied on the optimal technique of the arc judgement using fuzzy logic and MDET (Multi Dimension Estimation Technique). And it carries out the simulation for arc detection and the experiment for controller and load test. In result, the controller and detection algoristhm, is classified with normal wave and abnormal arc wave without relation with each loads and so the controller can detect a series arc successfully.