• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2001.10a
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• pp.371-374
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• 2001

This paper describes a technique that can predict electric fires by detecting a spark signal generated from operation of electric facilities. An electric fire lead a loss of life as well as huge property, therefore it is very Important to predict an electric fire and eliminate the causes of it. Electrical spark which is ranked as majority causes of electric fires has a characterized frequency bandwidthdistinguishedfrompowerfrequenry. In the experiment, various spark signals are simulated in a condition such as short circuit, flashover and surface discharge. The results showed that the monitoring of spark signals can predict electric fires.

• Proceedings of the Korean Institute of Industrial Safety Conference
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• 1998.11a
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• pp.335-340
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• 1998

This paper presents a study on the electrical fire hazards of leakage current in a distribution circuit simulating a domestic circuit for low voltage. In this simulated circuits, total charges and energy of leakage current are calculated by a theoretical approach and compared with the results obtained from experiments, The approach and results become a cornerstone for studying and analyzing causes of electrical fires.

• Journal of the Microelectronics and Packaging Society
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• v.15 no.1
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• pp.51-58
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• 2008

This paper is studied on a prevention system for electrical fire and electrical faults by using electrical temperature characteristic of PTC thermistor. The PTC thermistor has characteristic of positive resistivity temperature coefficient according to the temperature variation, which is construction of a regular square and cube demarcation with $BaTiO_3$ ceramics of positive temperature coefficient. Also PTC shows the phenomenon which is rapidly increased in the resistivity if the temperature is increased over Curie temperature point. This paper is proposed on a protective control system used with temperature sensor of PTC thermistor. The proposed prevention system will prevent electrical fires due to electric short circuit faults or overload faults. And the prevention system will solve the problems that circuit breakers to be commonly used at existing are happened frequently electrical fires and electrical disasters due to incapable operation, weak reliability and low speed response. Some experimental results of the proposed apparatus are confirmed to the validity of the analytical results.

• Smart Media Journal
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• v.9 no.1
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• pp.38-44
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• 2020

Due to the high dependence on electric energy, electric fires make up a significant portion of fires in Korea. Electric arcs by short circuits or poor contact cause three of four electrical fires. An electric arc is a discharge phenomenon of electrical current between the insulators, which instantaneously produces high temperature. In order to reduce the fire due to electric arc, this study aims to predict the electric arc risk. We collected arc data from the arc detectors and converted into graphs based on temporal arc data. We used machine learning for training converted graph with different number of temporal arc data. To measure the performance of the learning model, we use the test data. In the results, when the number of temporal arc data was 20, the prediction rate was high as 86%.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.2
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• pp.223-229
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• 2022

During the winter season, when the weather gets colder every year, electricity consumption increases rapidly. The occurrence of fires is increasing due to a short circuit in electrical facilities of buildings such as markets, bathrooms, and apartments with high population density while using a lot of electricity. The cause of these short circuit fires is mostly due to the aging of the wires, the usage increases, and the excessive load cannot be endured, and the wire sheath is melted and caused by nearby ignition materials. In this paper, the load and overheat generated in the electric wire are measured through a complex sensor composed of an overload sensor, a VoC sensor, and an overheat sensor. Based on this, big data analysis is carried out to develop a platform capable of predicting, alerting, and blocking electric fires in real time, and a simulator capable of simulated fire experiments.

• Proceedings of the KIPE Conference
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• 2017.07a
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• pp.493-494
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• 2017

• Proceedings of the KIEE Conference
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• 2003.07c
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• pp.1765-1767
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• 2003

The standard of living has been getting higher by reason of industrialization and economic growth. It makes people have more cultural desire(wants). Because of reasons mentioned above, a number of entertainment place and performance with high-tech is increased. This has been brought also increasing a number of electrical accident such as electrical shock, electrical fires and accidents of electrical facilities. This paper suggests technical, operational and special precautions of actual electric main line systems in entertainment places and is also written on the purpose of being applied with related provisions to designing electrical facilities, construction, check-up and inspection and maintenance for preventing further electrical disasters in entertainment places.

• Journal of Sensor Science and Technology
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• v.33 no.3
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• pp.119-124
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• 2024

Effective fire sensing is important to protect lives and property from the disaster. In this paper, we present an intelligent visual sensing method for detecting fires based on machine learning techniques. The proposed method involves a two-step process. In the first step, fire and non-fire images are used to train a convolutional neural network (CNN), and in the next step, feature vectors consisting of 256 values obtained from the CNN are used for the learning of a support vector machine (SVM). Linear and nonlinear SVMs with different parameters are intensively tested. We found that the proposed hybrid method using an SVM with a linear kernel effectively increased the recall rate of fire image detection without compromising detection accuracy when an imbalanced dataset was used for learning. This is a major contribution of this study because recall is important, particularly in the sensing of disaster situations such as fires. In our experiments, the proposed system exhibited an accuracy of 96.9% and a recall rate of 92.9% for test image data.

• Journal of the Korea Society of Computer and Information
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• v.24 no.10
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• pp.159-165
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• 2019

About 58% of fires are electric fires. In this study, we analyzed the factors of fire caused by electricity and studied the system that can prevent fire in advance. In order to prevent disasters, basically, various electrical IoT sensors are needed to detect fire signs. Each of these sensors continuously receives a lot of situation information and sends it to the main system. The collected big data continuously checks whether the disaster is over the threshold that can cause disaster through pattern analysis, and can check whether there is any problem by comparing the data. In the event of a threshold, alarms are signaled and problems are reported. This prevents fire by preventing electrical problems such as overcurrent and leakage current.

• Proceedings of the KIEE Conference
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• 2009.04a
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• pp.216-218
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• 2009

In this paper, a real time monitoring and management system being operated in the ubiquitous environment was developed to monitor leakage current, load current, and arc-fault, and an electrical safety network for reasonable management of electrical risk factor was proposed. For confirmation of usefulness and reliability of the proposed safety network and system, the developed intelligent panels were applied to 28 Korean traditional houses in Jeonjoo city, and the network including the panels was operated. If the National Electrical Safety Network is completely constructed in the houses of general electrical users, the network will have an effect on that a main manager transfers from general people to expert. As a result, the electrical fires caused by an over-load, an arc-fault, and an earth-fault will be prevented.