• Journal of IKEEE
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• v.26 no.3
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• pp.468-474
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• 2022

In this paper, we propose a method to implement a real-time fire alarm system using deep learning. The deep learning image dataset for fire alarms acquired 1,500 sheets through the Internet. If various images acquired in a daily environment are learned as they are, there is a disadvantage that the learning accuracy is not high. In this paper, we propose a fire image data expansion method to improve learning accuracy. The data augmentation method learned a total of 2,100 sheets by adding 600 pieces of learning data using brightness control, blurring, and flame photo synthesis. The expanded data using the flame image synthesis method had a great influence on the accuracy improvement. A real-time fire detection system is a system that detects fires by applying deep learning to image data and transmits notifications to users. An app was developed to detect fires by analyzing images in real time using a model custom-learned from the YOLO V4 TINY model suitable for the Edge AI system and to inform users of the results. Approximately 10% accuracy improvement can be obtained compared to conventional methods when using the proposed data.

• Proceedings of the KSME Conference
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• 2004.11a
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• pp.1140-1145
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• 2004

Laser-induced incandescence (LII) is introduced as a valuable tool for the characterization of nanoparticles in flame environments. This technique is based on the heating of the particles by a short laser pulse and the subsequent detection of the thermal radiation. It has been applied successfully for the investigation of soot in different fields of application. The evaluation of the temporal decay of the laser-induced incandescence (LII) signal from soot particles is introduced as a technique to obtain two-dimensional distributions of particle sizes and is applied to a laminar diffusion flame. This novel approach to soot sizing exhibits several theoretical and technical advantages compared with the established combination of elastic scattering and LII, especially as it yields absolute sizes of primary particles without requiring calibration. With this technique a spatially resolved 2-D measurement of soot primary particle sizes is feasible in a combination process form the ratio of emission signals obtained at two delay times after a laser pulse, as the cooling behavior is characteristic of particle size.

• Journal of Advanced Marine Engineering and Technology
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• v.39 no.9
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• pp.953-958
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• 2015

Raspberry Pi is a credit card-sized computer with support for a large number of input and output peripherals. This makes it the perfect platform for interaction with many different devices and for usage in a wide range of applications. When combined with Wi-Fi, it can communicate remotely, therefore increasing its suitability for the construction of wireless sensor nodes. In addition, data processing and decision-making can be based on artificial intelligence, what is performed in developed testbed on the example of monitoring and determining the confidence of fire. In this paper, we demonstrated the usage of Raspberry Pi as a sensor web node for fire-safety monitoring in a building. When the UV-flame sensors detect a flame as thin as that of a candle, the Raspberry Pi sends a push-message to notify the assigned smartphone of the on-site situation through the GCM server. A mobile app was developed to provide a real-time video streaming service in order to determine a false alarm. If an emergency occurs, one can immediately call for help.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.1027-1028
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• 2008

In this paper, we proposed image-processing technique for automatic real-time fire and smoke detection in tunnel fire environment. To minimize false detection of fire in tunnel we used motion information of video sequence. And this makes it possible to detect exact position of event in early stage with detection, test, and verification procedures. In addition, by comparing false detection elimination results of each step, we have proved the validity and efficiency of proposed algorithm.

• Fire Science and Engineering
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• v.27 no.5
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• pp.15-18
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• 2013

This paper is concerned with the Performance and Reliability Certification for fire detection system in outdoor area such small and middle sized cultural assets, natural monument and outdoor facilities. Especially, if a fire were to occur in vulnerable area, it is difficulty to detect a fire. therefore we propose a high efficiency and low cost unmanned fire detection system in capable of an early detection regardless spontaneously fire or firebug. for Adoption of Intelligent Fire Detection System with movable and unmanned function breaking from the existing Conventional Fire Detection System, this Range of R&D includes the Performance test, Function test, Field test, Flame Detection test and EMI/EMS Compliance test. the Result data of Performance test, Function test and Field test is generally good during 3 months. also we checked that thermal variation test and EMI/EMS compliance test are good result data within allowable range. As a result of general test, we verified improvement results that the measure distance of fire detection extend 75 m, the Power of waiting time increase 4 hours, the Power of operation time increase 3 days and the context awareness with video as well as sensors.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.6
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• pp.1506-1514
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• 2015

In this paper, we proposed a forest fires prediction and detection system. It could provide a situation of fire prediction and detection methods using context awareness sensor. A fire occurs wide range of sensing a fire in a single camera sensor, it is difficult to detect the occurrence of a fire. In this paper, we propose an algorithm for real-time by using a temperature sensor, humidity, Co2, the flame presence information acquired and comparing the data based on multiple conditions, analyze and determine the weighting according to fire in complex situations. In addition, it is possible to differential management of intensive fire detection and prediction for required dividing the state of fire zone. Therefore we propose an algorithm to determine the prediction and detection from the fire parameters as an temperature, humidity, Co2 and the flame in real-time by using a context awareness sensor and also suggest algorithm that provide the path of fire diffusion and service the secure safety zone prediction.

• Bulletin of the Korean Chemical Society
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• v.21 no.11
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• pp.1101-1105
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• 2000

A simultaneous determination method of BTEX (benzene, toluene, ethylbenzene, o,m.p-xylene) and TPH (kerosene, diesel, jet fuel and bunker C) in soil with gas chromatography/flame ionization detection (GC-FID) was described. The effects of extracti on method, extraction solvent, solvent volume and extraction time on the extraction performance were studied. A sonication method was simpler and more efficient than Soxhlet or shaking methods. Sonication with 10 mL of acetone/methylene chloride (1 : 1, v/v) for 10 min was found to be optimal extraction conditions for 20 g of soil. Peak shapes and quantification of BTEX and TPH were excellent, with linear calibration curves over a wide range of 1-500 mg/L for BTEX and 10-5000 mg/L for TPH. Good reproducibilities by sonication were obtained, with the RSD values below 10%. By using about 20 g of soil, detection limits were 0.8 mg/L for BTEX and 10 mg/L for TPH. The advantages of this procedure are the use of simple and common equipment, reduced volumes of organic solvents, rapid extraction periods of less than 20 min, and simultaneous analysis of volatile and semivolatile compounds.

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

• IE interfaces
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• v.19 no.1
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• pp.43-52
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• 2006

In order to make best use of NC machine tools with minimal labor costs, they need to be in operation 24 hours a day without being attended by human operators except for setup and tool changes. Thus, unattended machining is becoming a dream of every modern machine shop. However, without a proper mechanism for real-time monitoring of the machining processes, unattended machine could lead to a disaster. Investigated in this paper are ways to using PC camera as a real-time monitoring system for unattended NC milling operations. This study defined five machining states READY, NORMAL MACHINING, ABNORMAL MACHINING, COLLISION and END-OF-MACHINING and modeled them with DEVS (discrete event system) formalism. An image change detection algorithm has been developed to detect the table movements and a flame and smoke detection algorithm to detect unstable cutting process. Spindle on/off and cutting status could be successfully detected from the sound signals. Initial experimentation shows that the PC camera could be used as a reliable monitoring system for unattended NC machining.

• Bulletin of the Korean Chemical Society
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• v.23 no.6
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• pp.861-865
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• 2002

A sensitive method for the determination of trace copper(II) after the preconcentration by adsorbing its cupferron complex onto microcrystalline benzophenone was developed.Several experimental conditions such as the pH of sample solution,concentration of cupferron, amount of benzophenone and atirring time were optimized. Trace compper(II) in 100mL solution was chelated with $3.0\;{\times}\;10^3$ M cupferron at pH 5.0. After 0.20g benzophenone, The benzophenone adsorbing Cu-cupferron complex was filtered and then Cu-cupferron complex was desorbed in 10 mL ethanol. Copper was determined by a flame atomic absorption spectrophotomethry. The interfering effects of diverse concomitant ions were investigated. Fe(III) interfered seriously with, but the interference by Fe(III) was completely eliminated by adjusting the concentration of copferron to $5.0\;{\times}\;10^3$ M. The detection limit of this method was 8.6${\times}$10 M(5.5 ngmL$^1$). Recoveries of 97% and 96% were obtained for Cu(II) in a stream water and a brass sample, respectively. Based on the results from the experiment. this proposed technique could be applied to the determination of copper(II) in real samples.