• 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.

• The Korean Journal of Applied Statistics
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• v.31 no.1
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• pp.155-165
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• 2018

Outliers and influential observations often distort many numerical measures for data analysis. Jang and Anderson-Cook (Quality and Reliability Engineering International, 30, 1409-1425, 2014) proposed a graphical firework plot method for exploratory analysis purpose to provide a possible visualization of the trace of the impact of the possible outlying and influential observations on the univariate/bivariate data analysis and regression. They developed 3-D plot as well as pairwise plot for the appropriate measures of interest. We use firework plots as a graphical exploratory data analysis tool to detect outliers and evaluate the impact of outliers in statistical inference.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.6
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• pp.102-109
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• 2014

A particle system is used for modeling the physical phenomenon. There are many traditional ways for simulation modeling which can be well suited for application including the landscapes of branches, clouds, waves, fog, rain, snow and fireworks in the three-dimensional space. In this paper, we present a new fireworks modeling technique for modeling 3D firework based on Firework Particle Tracking (FPT) using the particle system. Our method can track and recognize the launched and exploded particle of fireworks, and extracts relatively accurate 3D positions of the particles using 3D depth values. It can realize 3D simulation by using tracking information such as position, speed, color and life time of the firework particle. We exploit Region of Interest (ROI) for fast particle extraction and the prevention of false particle extraction caused by noise. Moreover, Kalman filter is used to enhance the robustness in launch step. We propose a new fireworks particle tracking method for the efficient tracking of particles by considering maximum moving range and moving direction of particles, and shall show that the 3D speeds of particles can be obtained by finding the rotation angles of fireworks. Also, we carry out the performance evaluation of particle tracking: tracking speed and accuracy for tracking, classification, rotation angle respectively with respect to four types of fireworks: sphere, circle, chrysanthemum and heart.

• Fire Science and Engineering
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• v.14 no.2
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• pp.1-6
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• 2000

The radiant energy from a flaming fire of fuels containing carbonaceous material can be applied to fast growing fire. Raiant energy sensinsing technique applied detectors are ultimately effective when early detecting fire alarm system is required or the smoke and heat detectors can not be applied. This study investigated the characteristics of sun light, artificial light and flame radiation light and the foundation technique of flame detecting is established. Pyroelectric element proper for the characteristics of flame radiant energy developed and circuit stabilizing technique, electro-magnetic immunity technique, durable and reliable operating technique to circuits developed.

• Proceedings of the KSEE Conference
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• 2008.10a
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• pp.119-127
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• 2008

불꽃놀이는 한국명 연화(煙火), 중국명 폭죽(爆竹), 일본명 하나비(花火), 영어명 파이어웍스 디스플레이(Fireworks display) 표기되며 화약류를 연소 폭발시켜서 빛, 소리, 형태, 연기, 연막, 시간지연, 운동에너지 등을 조합하여 예술적인 감각을 연출시키는 공학으로써 공예술학으로 불랴는 일종의 예술작품이다. 우리나라의 연화기술은 세계적이다. 1980년대에 개발이 완료되었고, 1990년대에 완숙단계흘 거쳐 향후 2010년 이후에는 환경안전을 고려한 최첨단 나노 생명공학으로 발전하여야 할 것이다. 즐거운 불꽃축제 후에 환경오염방지 대책과 비상체제 운영능력, 저장소관리, 불발탄 및 폐기물처리, 시민의식 향상 등이 더욱 요망된다. 본 논문은 올해 제4회를 맞는 부산불꽃축제를 중심으로 현황 및 발전방향과 개선안을 제시하였다.

• The Korean Journal of Applied Statistics
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• v.29 no.2
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• pp.355-368
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• 2016

Outliers and influential data points distort many data analysis measures. Jang and Anderson-Cook (2014) proposed a graphical method called a rework plot for exploratory analysis purpose so that there could be a possible visualization of the trace of the impact of the possible outlying and/or influential data points on the univariate/bivariate data analysis and regression. They developed 3-D plot as well as pairwise plot for the appropriate measures of interest. This paper further extends their approach to identify its strength. We can use rework plots as a graphical exploratory data analysis tool to evaluate the impact of outliers in skewness and kurtosis of univariate data.

• 한국방재학회:학술대회논문집
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• 2010.02a
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• pp.70.2-70.2
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• 2010

본 연구는 최근 발생한 숭례문 화재와 같은 목조문화재의 화재를 초기에 인지하고 확인하는 차원에서 고안된 시스템으로써 불꽃감지기와 같은 초기 화재 감지시스템의 오작동 여부를 확인하여 화재감지기의 오작동으로 인한 경제적 시간적인 손실을 예방하고 목조문화재를 화재로부터 보호하기 위한 시스템이다. 초기에 화재를 감지하는 불꽃감지기는 현재 목조문화재뿐만 아니라 다양한 곳에서 활용되고 있지만 감지기의 오작동 및 오류를 확인하는 시스템은 실제로 실효성 등의 문제로 인해 활용이 미비한 실정이다. 본 연구에서는 유비쿼터스 센서 네트워크(USN) 기술, 불꽃감지기, 이미지 센서, USN 기반 문화재 방재 응용사례, 오작동 확인시스템 구현 등에 대해서 살펴보고 유비쿼터스형 문화재 방재시스템을 제시해 본다.

• Analytical Science and Technology
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• v.12 no.5
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• pp.355-359
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• 1999

Trace cadmium was determined in urine by flame and nameless AAS. Inflame method, cadmium was extracted into MIBK from Cd-DDTC complex. In flameless method, samples were pretreated with 1%, Triton X-100. 1% $HNO_3$ and matrix modifier $Pd(NO_3)_2$. Analysis of cadmium was, performed at $450-750^{\circ}C$ ashing temperature by use of pyrocoated tubes. Results of analysis, flameless method superior in sample treatment and reproducibility to flame method and highest absorbance was obtained at $550^{\circ}C$ ashing temperature. 100 mg/L $Pd(NO_3)_2$ concentration. Proposed method were applied to the determination of cadmium in standard urine Lononorm-Metalle 3.

• Journal of KIISE:Software and Applications
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• v.37 no.9
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• pp.712-721
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• 2010

This paper proposes a new fire-flame detection method using probabilistic membership function of visual features and Fuzzy Finite Automata (FFA). First, moving regions are detected by analyzing the background subtraction and candidate flame regions then identified by applying flame color models. Since flame regions generally have continuous and an irregular pattern continuously, membership functions of variance of intensity, wavelet energy and motion orientation are generated and applied to FFA. Since FFA combines the capabilities of automata with fuzzy logic, it not only provides a systemic approach to handle uncertainty in computational systems, but also can handle continuous spaces. The proposed algorithm is successfully applied to various fire videos and shows a better detection performance when compared with other methods.

• The Korean Journal of Applied Statistics
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• v.31 no.1
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• pp.97-108
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• 2018

It has been routine practice in regression analysis to check the validity of the assumed model by the use of regression diagnostics tools. Outliers and influential observations often distort the regression output in an undesired manner. Jang and Anderson-Cook (Quality and Reliability Engineering International, 30, 1409-1425, 2014) proposed a graphical method (called a firework plot) so that there could be an exploratory visualization of the trace of the impact of the possible outliers and influential observations on individual regression coefficients and the overall residual sum of the squares measure. This paper further extends a graphical approach to a multi-response surface methodology problem.