• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.45 no.4
• /
• pp.59-70
• /
• 2008

In this paper, we proposed image processing technique for automatic real-time fire and smoke detection in tunnel environment. To avoid the large scale of damage of fire occurred in variety environments, it is purposeful to propose many studies to minimize and to discover the incident as fast as possible. But we need new specific algorithm because tunnel environment is quite different and it is difficult to apply previous fire detection algorithm to tunnel environment. Therefore, in this paper, we proposed specific algorithm which can be applied in tunnel environment. To minimize false detection in tunnel we used color and motion information. And it is possible to detect exact position in early stage with detection, test, verification procedures. In addition, by comparing properties of each algorithm throughout experiment, we have proved the validity and efficiency of proposed algorithm.

• Journal of the korean Society of Automotive Engineers
• /
• v.15 no.6
• /
• pp.33-36
• /
• 1993

연소실 내의 화염 전파를 검출하는 대표적인 방법으로 High Schlieren Cinematography방식, 광섬유를 이용하는 방법, 이온화 현상을 이용하는 것 등이 있다. 광섬유를 이용하는 방법은 연소실 벽면에 광섬유를 적절히 배치하고 광섬유에서 나오는 신호를 전기적 신호로 바꾸어 해석하는 것이다. 연소시 화염에서 방출되는 빛을 연소실 벽에 적절하게 배치되어 있는 광섬유를 통하여 포토 다이오드로 전송된다. 포토 다이오드는 빛에너지를 전기 신호로 바꾸고, 이 아날로그 신호는 증폭된다. 전압 비교기는 증폭된 신호를 디지탈화하고, 이 신호가 PC에 저장된다. 이 방법은 연소실 벽이나 피스톤 등을 투명화하기 위한 구조변경이나 재질변화로 인한 엔진특성변화를 피할 수 있다. 본 연구에서는 광섬유를 이용하는 측정방법에 대해 연구하였다.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2023.01a
• /
• pp.391-394
• /
• 2023

일반적으로 화재 감지 시스템은 정확하고 빠르게 화재를 감지하는 것은 어려운 문제 중 하나이다. 본 논문에서는 U-net을 활용하여 기존의 화재(불) 영역 추출 기법으로 Segmentation으로 보다 정밀하게 탐지하는 기법을 제안한다. 이 기법은 화재 이미지에서 연기제거 및 색상보정을 통해 이미지를 전처리하여 화염 영역을 추출한 뒤 U-Net으로 학습시켜 이미지를 입력하면 불 영역의 Segmentation을 추출하도록 한다.

• Journal of the Korean Institute of Gas
• /
• v.14 no.3
• /
• pp.8-13
• /
• 2010

Hydrogen is expected to become a new, clean source of energy for the next generation. Therefore, many studies have investigated the characteristics of the hydrogen flame. However, because the hydrogen flame has high temperature, the flame does not emit visible light, and the flame propagates at a high velocity, investigating its characteristics is difficult. In the present study, in order to simultaneously examine the flame temperature and flame propagation velocity of hydrogen/air mixtures, ultra fine thermocouples with diameters of 12.7, 25.4, and 50.8 ${\mu}m$ are utilized. The results show that it is possible to detect the arrival time of the flame. Due to the temperature compensation with the time constants of thermocouples, it is also possible to estimate the flame temperature.

• Journal of Korea Multimedia Society
• /
• v.17 no.1
• /
• pp.8-14
• /
• 2014

In this paper, we propose a new fire detection method using moving features and colors of flames in video sequences. It uses YCbCr color space to separate the luminance from the chrominance components more effectively than RGB color space. In the proposed method, moving regions of flames are detected by cumulating the difference of luminance between two consecutive images and generate candidate flame regions by using the color of flames. Finally, it decides whether the candidate flame regions are flames or not by using their temporal changes of the areas. Experimental results show that the proposed method performs better in segmenting fire regions compared with the conventional fire detection method in video sequences.

• Journal of the Institute of Convergence Signal Processing
• /
• v.20 no.3
• /
• pp.159-164
• /
• 2019

Detecting wildfire using only color in image information is a very difficult issue. This paper proposes an algorithm to detect forest fire area by analyzing color and motion of the area in the video including forest fire. The proposed algorithm removes the background region using the Gaussian Mixture based background segmentation algorithm, which does not depend on the lighting conditions. In addition, the RGB channel is changed to an HSV channel to extract flame candidates based on color. The extracted flame candidates judge that it is not a flame if the area moves while labeling and tracking. If the flame candidate areas extracted in this way are in the same position for more than 2 minutes, it is regarded as flame. Experimental results using the implemented algorithm confirmed the validity.

• Journal of Advanced Navigation Technology
• /
• v.17 no.6
• /
• pp.792-797
• /
• 2013

4D film is just a film that made by adding some physical effects to 3D film or general film. In order to provide physical effects to the audience, the data that make the physical effect must be added to each frames. In this paper, we proposed a video detection system that can efficiently provide physical effects by assessing the present situation such as explosion scene, snowing scene. The proposed video detection system contains an algorithm for fire detection by using R color and $C_r$ value, and also an algorithm for snow detection by using RGB color model. The system constitutes in a MCU that from 8051 family. In the performance evaluations, the result shows that 91% of detection rate in case of fire and 25% of false detection rate in case of snow. Also the system is capable of providing physical effects automatically.

• Transactions of the Korean Society of Mechanical Engineers
• /
• v.17 no.6
• /
• pp.1633-1639
• /
• 1993

In spark ignition engine, EGR of lean mixture operation has advantage in emission, but disadvantages in power output and combustion flame propagation. Fast burn system is known to be a useful method to solve these disadvantages. This paper presents the characteristics of in-cylinder flow for different combustion chamber geometries, and the correlation between the in-cylinder flow and the combustion flame speed using an ion current method.

• Journal of the Korean Society of Food Science and Nutrition
• /
• v.45 no.2
• /
• pp.277-283
• /
• 2016

To improve the microbiological safety of wild vegetables after harvest, Aster scaber and Cirsium setidens Nakai were treated with combinations of 50 ppm aqueous chlorine dioxide ($ClO_2$)/0.5% citric acid or fumaric acid, and 50 ppm $ClO_2$/0.5% fumaric acid/blanching at $90^{\circ}C$ for 2 min. Combined treatment of 50 ppm $ClO_2$ and 0.5% citric acid reduced populations of total aerobic bacteria, yeast, and molds in Aster scaber and Cirsium setidens Nakai by 2.80~3.64 and 2.02~2.67 log CFU/g, respectively, compared to those of the control. Combined treatment of 50 ppm $ClO_2$ and 0.5% fumaric acid reduced total aerobic bacteria, yeast and molds populations by 3.62~3.82 and 2.47~3.02 log CFU/g, respectively. Based on the results, combined treatment of $ClO_2$ and fumaric acid was more effective in controlling microorganisms in the wild vegetables than either $ClO_2$ or citric acid. In addition, combined treatment of $ClO_2$/fumaric acid/blanching reduced the populations of total aerobic bacteria by 4.59~5.12 log CFU/g, and populations of yeast and molds were not detected by treatment. These results suggest that combined treatment of $ClO_2$/fumaric acid/blanching is the most effective method for improving microbiological safety of wild vegetables after harvest.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.20 no.9
• /
• pp.1649-1656
• /
• 2016

Performance of the existing sensor-based fire detection system is limited according to factors in the environment surrounding the sensor. A number of image-based fire detection systems were introduced in order to solve these problem. But such a system can generate a false alarm for objects similar in appearance to fire due to algorithm that directly defines the characteristics of a flame. Also fir detection systems using movement between video flames cannot operate correctly as intended in an environment in which the network is unstable. In this paper, we propose an image-based fire detection method using CNN (Convolutional Neural Network). In this method, firstly we extract fire candidate region using color information from video frame input and then detect fire using trained CNN. Also, we show that the performance is significantly improved compared to the detection rate and missing rate found in previous studies.