• Transactions of the Korean Society of Automotive Engineers
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• v.7 no.7
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• pp.42-53
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• 1999

Two dimensional flame temperature and KL value distribution from the luminous flame containing soot in a DI diesel engine were measured by the tow-color method using tow different wavelengths of the flame image. The combustion chamber of a DI diesel engine was visualized by elongating the piston and cylinder and the flame in the combustion chamber was photographed on a nega-color film using a high speed camera. In this study, color CCD camera was used to digitize the three color density of the film exposed to the flame and standard lamp . The accuracy of the measuring method depends on the calibration line of film made from a high temperature standard tungsten lamp. The formation and oxidization of soot in the diesel engine were studied by observing measured time history of KL factor and flame temperature . Also , effects of various shapes of combustion chamber and fuel injection on flame temperature. Also, effects of various shapes of combustion chamber and fuel injection on flame temperature and KL value distribution were examined.

• Journal of Satellite, Information and Communications
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• v.9 no.2
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• pp.33-37
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• 2014

This paper propose a method to sort flame regions and non-flame regions in a color image based on temperature Characteristics of flame. The traditional algorithms simply detect flame regions those are colored between yellow and red and there are lot of false detection in this method. But the colors of real flame are fallen between white and red and flame color variation over the flame. In this paper, it reduce false detection by separating colors according to temperature Characteristics of flame. The proposed method firstly finds a color model to express the temperature Characteristics of fire and then the color model is non-linearly quantized based on color values and analyzed using histogram and finally detect the candidate flame regions. The proposed method has 71.8% of matching rate and if it is compared with non-matching rate of traditional algorithms, the non-matching rate is improved by 27 times than others.

• Journal of Satellite, Information and Communications
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• v.8 no.3
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• pp.52-57
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• 2013

This paper describes the color comparison analysis of flame in each standard color model in order to propose the optimal color model for image processing based flame detection algorithm. Histogram intersection values were used to analyze the separation characteristics between color of flame and color of non-flame in each standard color model which are RGB, YCbCr, CIE Lab, HSV. Histogram intersection value in each color model and components is evaluated for objective comparison. The analyzed result shows that YCbCr color model is the most suitable for flame detection by average HI value of 0.0575. Among the 12 components of standard color models, each Cb, R, Cr component has respectively HI value of 0.0433, 0.0526, 0.0567 and they have shown the best flame separation characteristics.

• 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.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.25-27
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• 2009

This paper presents the stereoscopic conversion of flame images. The stereoscopic conversion is a technology that generates left and right images from a monoscopic image. Even though many conversion methods have been introduced and commercialized so far, the processing of flame images is relatively few. Such conventional methods are effectively used either real-time or off-line. However, the application of such schemes to special-effect images such as flame is hard to be applied. The proposed method is designed to convert a flame image into a stereoscopic image. Depth map of flame regions are produced based on the analysis of color models of flames. Experimental results tested on diverse flame image sets validates the effectiveness of the proposed method.

• 한국연소학회:학술대회논문집
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• 2000.12a
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• pp.175-182
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• 2000

Flame temperatures were measured and compared using a rapid insertion technique and a two-color pyrometry with Abel inversion process in co-flow ethylene diffusion flames. The measured line-of-sight temperature showed very limited usefulness in understanding the detailed soot formation/oxidation process in a co-flow diffusion flame. The flame temperatures could be measured with reasonable accuracy for the soot laden regions in ethylene diffusion flames using two-color pyrometry with an Abel inversion technique. Two-color-pyrometry with Abel inversion was demonstrated as a useful temperature measurement technique for co-flow diffusion flames, expecially under pressure conditions, where a thermocouple is not applicable. The soot volume fraction could be also obtained using tow-color pyrometry with Abel inversion, which provides important information for understanding the soot formation/oxidation mechanism in diffusion flames.

• Journal of Power System Engineering
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• v.16 no.4
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• pp.5-11
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• 2012

There were some papers for diesel engine performance tests using BDF, but few article deals with the temperature and soot concentration of Bio diesel flame. Since the flame temperature of diesel engines is so high and change rapidly, an optical method for measurement of flame temperature is known as the most effective one. The two-color method regarding the visible wavelength radiation for the soot particles in flame was applied on Bio diesel flame in order to measure flame temperature and soot concentration in a diesel engine. Photo detecting device was newly designed and employed TSL250R, photo-diode, to pick-up the light information emitted from the combustion flame. As a result, real flame temperature T, as a flame brightness temperature, through Ta1, Ta2, were obtained and finally the characteristics of KL value as a soot concentration reveal the difference of combustion information between diesel fuel, blending oil and Bio diesel fuel oil.

• Journal of IKEEE
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• v.23 no.1
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• pp.112-119
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• 2019

In this paper, we propose a deep learning structure suitable for embedded system. The flame detection process of the proposed deep learning structure consists of four steps : flame area detection using flame color model, flame image classification using deep learning structure for flame color specialization, $N{\times}N$ cell separation in detected flame area, flame image classification using deep learning structure for flame shape specialization. First, only the color of the flame is extracted from the input image and then labeled to detect the flame area. Second, area of flame detected is the input of a deep learning structure specialized in flame color and is classified as flame image only if the probability of flame class at the output is greater than 75%. Third, divide the detected flame region of the images classified as flame images less than 75% in the preceding section into $N{\times}N$ units. Fourthly, small cells divided into $N{\times}N$ units are inserted into the input of a deep learning structure specialized to the shape of the flame and each cell is judged to be flame proof and classified as flame images if more than 50% of cells are classified as flame images. To verify the effectiveness of the proposed deep learning structure, we experimented with a flame database of ImageNet. Experimental results show that the proposed deep learning structure has an average resource occupancy rate of 29.86% and an 8 second fast flame detection time. The flame detection rate averaged 0.95% lower compared to the existing deep learning structure, but this was the result of light construction of the deep learning structure for application to embedded systems. Therefore, the deep learning structure for flame detection proposed in this paper has been proved suitable for the application of embedded system.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.20 no.9
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• pp.3007-3014
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• 1996

The instantaneous flame temperature and soot formation and oxidation in a D.I. diesel engine are measured using a two-color method. The proposed method based on the continuous spectral radiation from the soot particles in the flame is applicable to industrial diesel engines without major modifications of their main characteristics. Measurements are performed at one location inside the combustion chamber of a D.I. diesel engine. Effects of different engine speeds and loads on flame temperature and KL factor which is an index of soot concentration were examined. Little temperature change were observed with increasing rpm, while increased with loads. The higher the flame temperature is, the lower the KL factor is.

• International Journal of Precision Engineering and Manufacturing
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• v.3 no.2
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• pp.45-51
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• 2002

Recently the interest for the environment is increasing. So the criterion for the evaluation of the burner has changed. For efficient driving problem, if the thermal efficiency is higher and the oxygen in exhaust gas is lower, then burner is evaluated better. For environmental problem. burner must satisfy NOx limit, soot limit and CO limit. Generally the experienced operator judge of the combustion status of the burner by the color of flame. we don't still have any satisfactory solution against it. the relation of the combustion status and the color of the flame hasn't still been established. This paper is the study about the relation of the combustion status and the color of the flame. This paper describes development of real time flame diagnosis technique that evaluate and diagnose combustion state such as consistency of components in exhaust gas, stability of flame in quantitative sense. In this paper, it was proposed on the flame diagnosis technique of burner using image processing algorithm, the parameter extracted from the image of the flame was used as the input variables of the flame diagnostic system. at first, linear regression algorithm and multiple regression algorithm was used to obtain linear multi-nominal expression. Using the constructed inference algorithm, the amount of NOx and CO of the combustion gas was successfully inferred. the combustion control system will be realized sooner or later.