• Journal of Satellite, Information and Communications
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• v.8 no.4
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• pp.100-104
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• 2013

In this paper, we present an optimal threshold setting method for flame detection of infrared thermal image. Conventional infrared flame detection methods used fixed intensity threshold to segment candidate flame regions and further processing is performed to decide correct flame detection. So flame region segmentation step using the threshold is important processing for fire detection algorithm. The threshold should be change in input image depends on camera types and operation conditions. We have analyzed the conventional thresholds composed of fixed-intensity, average, standard deviation, maximum value. Finally, we extracted that the optimal threshold value is more than summation of average and standard deviation, and less than maximum value. it will be enhance flame detection rate than conventional fixed-threshold method.

• Journal of the Korea institute for structural maintenance and inspection
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• v.20 no.4
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• pp.104-110
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• 2016

To get the empirical data of GPR detection and to develop the image prosessing program of GPR detection data, GPR detection were proceed by the underground pipes and cavities buried in the Chamber. In the case of non pavement and asphalt pavement, water filled cavity that was buried in 0.7m depth was able to detection. But in the case of 1.0 m and 1.3 m buring depth, water filled cavity was not able to detection. In the case of non-reinforced and reinforced concrete pavement, it was difficult to detect the cavity caused by signal interference. GPRiPP programs was developed for image processing of the GPR detection data. The major processing algorithm were background removal, stacking and gain function. With proper image processing of gain function and background removal in GPRiPP program, it was showed that similar results can be obtained with conventional image processing program.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.6
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• pp.605-610
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• 2016

The ELCB(: Earth Leakage Circuit Breaker) is the only way being used to prevent accidents from happening caused by electrical disaster. However, the existing ELCB has a limit to prevent damages to life and property due to a electric fire and a human body electric shock caused by the resistive leakage current, because of detecting the total leakage current in the block range of 15mA~30mA. It also has problems such as reduced productivity and reliability due to malfunctions by capacitive leakage currents. In this study, we have implemented the algorithm for the resistive leakage current detection technique and developed the resistive leakage current detection module based on a MSP430 processor, 16bit signal processor and this module can be operated in a desired block threshold within 0.03 seconds as specified in KS C 4613.

• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.366-368
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• 2000

When faults occur in transmission lines, the classification of faults is very important. If the fault is HIF(High Impedance Fault), it cannot be detected or removed by conventional overcurrent relays (OCRs), and results in fire hazards and causes damages in electrical equipment or personal threat. The fast discrimination of fault needs to effective protection and treatment and is important problem for power system protection. This paper proposes the fault detection and discrimination algorithm for LIFs(Low Impedance Faults) and HIFs(High Impedance Faults). This algorithm uses artificial neural networks and variation of 3-phase maximum currents per period while faults. A double lines-to-ground and line-to-line faults can be detected using Neural Network. Also, the other faults can be detected using the value of variation of maximum current. Test results show that the proposed algorithms discriminate LIFs and HIFs accurately within a half cycle.

• Journal of Korean Society of Water and Wastewater
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• v.26 no.1
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• pp.1-12
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• 2012

The purpose of water distribution system is supplying water to users by maintaining appropriate pressure and water quality. For efficient monitoring of the water distribution system, determination of optimal locations for pressure monitoring is essential. In this study, entropy theory was applied to determine the optimal locations for pressure monitoring. The entropy which is defined as the amount of information was calculated from the pressure change due to the variation of demand reflected the abnormal conditions at nodes, and the emitter function (fire hydrant) was used to reproduce actual pressure change pattern in EPANET. The optimal combination of monitoring points for pressure detection was determined by selecting the nodes receiving maximum information from other nodes using genetic algorithm. The Ozger's and a real network were evaluated using the proposed model. From the results, it was found that the entropy theory can provide general guideline to select the locations of pressure sensors installation for optimal design and monitoring of the water distribution systems. During decision-making phase, optimal combination of monitoring points can be selected by comparing total amount of information at each point especially when there are some constraints of installation such as limitation of available budget.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.9 no.1
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• pp.9-18
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• 2010

Our country has so large mountain area that the tunnel construction is inevitable and the need of incident detection that provides safe management of tunnels is increasing. In this paper, we suggest a tunnel incident detection system using computer vision techniques, which can detect the incidents in a tunnel and provides the information to the tunnel administrative office in order to help safe tunnel operation. The suggested system enhances the processing speed by using simple processing algorithm such as image subtraction, and ensures the accuracy of the system by focused on the incident detection itself rather than its classification. The system is also cost effective because the video data from 4 cameras can be simultaneously analyzed in a single PC-based system. Our system can be easily extended because the PC-based analyzer can be increased according to the number of cameras in a tunnel. Also our web-based structure is useful to connect the other remotely located tunnel incident systems to obtain interoperability between tunnels. Through the experiments the system has successfully detected the incidents in real time including dropped luggage, stoped car, traffic congestion, man walker or bicycle, smoke or fire, reverse driving, etc.

• Journal of Internet Computing and Services
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• v.23 no.4
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• pp.57-64
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• 2022

If the inside of a building collapses due to a disaster such as fire, collapse, or natural disaster, the physical security inside the building is likely to become ineffective. Here, physical security is needed to minimize the human casualties and physical damages in the collapsed building. Therefore, this paper proposes an algorithm to minimize the damage in a disaster situation by fusing existing research that detects obstacles and collapsed areas in the building and a deep learning-based object detection algorithm that minimizes human casualties. The existing research uses a single camera to determine whether the corridor environment in which the robot is currently located has collapsed and detects obstacles that interfere with the search and rescue operation. Here, objects inside the collapsed building have irregular shapes due to the debris or collapse of the building, and they are classified and detected as obstacles. We also propose a method to detect rescue requesters-the most important resource in the disaster situation-and minimize human casualties. To this end, we collected open-source disaster images and image data of disaster situations and calculated the accuracy of detecting rescue requesters in disaster situations through various deep learning-based object detection algorithms. In this study, as a result of analyzing the algorithms that detect rescue requesters in disaster situations, we have found that the YOLOv4 algorithm has an accuracy of 0.94, proving that it is most suitable for use in actual disaster situations. This paper will be helpful for performing efficient search and rescue in disaster situations and achieving a high level of physical security, even in collapsed buildings.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.66 no.2
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• pp.82-87
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• 2017

This paper presents performance improvement of CO gas sensor signal conditioner for early fire warning system. The warning system is based on the CO sensor and its advanced signal conditioning modules network that employ electochemical gas sensor. The electochemical has advantage of having a linear output and operating with a low consumption and fast response. This electrochemical gas sensor contains a gas membrane and three electrodes(working, counter, reference electrode) in contact with an electrolyte. To use a three-electrode sensor, a voltage has to be applied between the working and the reference electrode according to the specification of the sensor. In this paper, we designed these requirements that should be considered in temperature compensation algorithm and electrode measurement of CO sensor modules by using advanced signal conditioning method included 3-electrode. Simulation and experimental results show that signal conditioner of CO sensor module using 3-electrode have a advantage linearity, sensitivity and stability, fast response etc..

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.11
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• pp.2759-2764
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• 2014

Smoke detector is commonly used to reduce fire detection time. However, technical problems regarding its inaccuracy of laser beam-receiving point on the surface of the sensor associated with incoming interference are identified when the laser transmitter and receiver are installed at a distance of about 100m. In this paper, we propose the auto focus alignment algorithm with high precision to adjust tilting angle of lasers caused by environmental interference so that solve existing issues using multi-level worm gear set.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.6
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• pp.883-896
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• 2023

As human activities in Korea are spread throughout the mountains, forest fires often affect residential areas, infrastructure, and other facilities. Hence, it is necessary to detect fire-damaged areas quickly to enable support and recovery. Remote sensing is the most efficient tool for this purpose. Fire damage detection experiments were conducted on the east coast of Korea. Because this area comprises a mixture of forest and artificial land cover, data with low resolution are not suitable. We used Sentinel-2 multispectral instrument (MSI) data, which provide adequate temporal and spatial resolution, and the k-nearest neighbor (kNN) algorithm in this study. Six bands of Sentinel-2 MSI and two indices of normalized difference vegetation index (NDVI) and normalized burn ratio (NBR) were used as features for kNN classification. The kNN classifier was trained using 2,000 randomly selected samples in the fire-damaged and undamaged areas. Outliers were removed and a forest type map was used to improve classification performance. Numerous experiments for various neighbors for kNN and feature combinations have been conducted using bi-temporal and uni-temporal approaches. The bi-temporal classification performed better than the uni-temporal classification. However, the uni-temporal classification was able to detect severely damaged areas.