• Journal of the Korean Society of Safety
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• v.37 no.2
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• pp.69-75
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• 2022

Fire-alarm systems are safety equipment that facilitate rapid evacuation and early suppression in case of fire. It is highly desirable that fire-alarm systems have low false-alarm rates and are thus reliable. Until now, researchers have attempted to improve detector performance by applying new technologies such as IoT. To this end, IoT-based fire-detection systems have been developed. However, due to scarcity of large-scale operational data, researchers have barely studied malfunctioning in fire-alarm systems or attempted to reduce false-alarm rates in these systems. In this study, we analyzed false-alarm rates of smoke/temperature detectors and unwanted fire-alarm signal patterns at K institution, where Korea's largest IoT-based fire-detection system operates. After analyzing the fire alarm occurrences at the institution for five years, we inferred that the IoT-based fire-detection system showed lower false-alarm rates compared to the automatic fire-detection equipment. We analyzed the detection pattern by dividing it into two parts: normal operation and unwanted fire alarms. When a specific signal pattern was filtered out, the false-alarm rate was reduced to 66.9% in the smoke detector and to 46.9% in the temperature detector.

• Journal of the Korean Society of Safety
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• v.37 no.3
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• pp.16-23
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• 2022

This study aims to provide a fundamental material for identifying fire and no-fire signals using the detection signal characteristics of IoT-based fire-detection systems. Unlike analog automatic fire-detection equipment, IoT-based fire-detection systems employ wireless digital communication and are connected to a server. If a detection signal exceeds a threshold value, the measured values are saved to a server within seconds. This study was conducted with the detection data saved from seven fire accidents that took place in traditional markets from 2020 to 2021, in addition to 233 fire alarm data that have been saved in the K institute from 2016 to 2020. The saved values demonstrated variable and continuous VC-Signals. Additionally, we discovered that the detection signals of two fire accidents in the K institution had a VC-Signal. In the 233 fire alarms that took place over the span of 5 years, 31% of smoke alarms and 30% of temperature alarms demonstrated a VC-Signal. Therefore, if we selectively recognize VC-Signals as fire signals, we can reduce about 70% of false alarms.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.6
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• pp.229-236
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• 2022

Based on the Internet of Things, a web system that can check the condition around the fire extinguisher, whether a fire has occurred, and an application that can receive fire notifications in real time is implemented. Minimize errors that occur during development by using software engineering to clarify the goals of the system and define the structure in detail. In addition, for IoT-based fire extinguishers, a method of reducing defects by finding product defects in the demand analysis, design, and implementation stages and analyzing the cause thereof is proposed. Through the proposed research, it is possible to secure the reliability of defect management for IoT-based smart fire extinguisher.

• Journal of the Korea Society of Computer and Information
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• v.25 no.7
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• pp.9-16
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• 2020

In this paper, we propose a prototype design and implementation of a fire detection algorithm using multiple sensors. The proposed topic detection system determines fire by applying rules based on data from multiple sensors. The fire takes about 3 to 5 minutes, which is the optimal time for fire detection. This means that timely identification of potential fires is important for fire management. However, current fire detection devices are very vulnerable to false alarms because they rely on a single sensor to detect smoke or heat. Recently, with the development of IoT technology, it is possible to integrate multiple sensors into a fire detector. In addition, the fire detector has been developed with a smart technology that can communicate with other objects and perform programmed tasks. The prototype was produced with a success rate of 90% and a false alarm rate of 10% based on 10 actual experiments.

• Journal of Digital Convergence
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• v.17 no.1
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• pp.85-90
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• 2019

This paper is a study on IoT based map inside the building and fire perception system using microprocessor and LABVIEW program. The smart control system implemented in this paper is designed to identify the location of fire by using microprocessor, flame detection sensor, carbon monoxide sensor and temperature sensor, and to guide the optimal travel route through Zigbee communication. And the proposed system uses QR code to interoperate with smartphone. The coordinator control verified that the sensor value of the smart control system installed through the LABVIEW software was confirmed. The IoT based control system studied in this paper was implemented with Arduino mega board and LABVIEW software, and the operation status was confirmed by display device and coordination.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.6_3
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• pp.1251-1258
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• 2023

Traditional markets are infrastructure with facilities and characteristics of very high population density. Recently, arcades have been installed through traditional market modernization policies, and aging infrastructure has been repaired. However, gas and electrical facilities of traditional markets cannot be easily replaced because of its high density. And because regular inspections are not conducted, management of facilities is on very poor condition. In addition, when a fire occurs in a traditional market, the fire easily spreads to nearby stores and is likely to spread to a large fire because of a lot of highly flammable substances. Smoke detectors and heat detectors are installed in most traditional markets to monitor fires, but malfunctions are frequent due to the nature of smoke detectors and heat detectors, and network facilities are not properly maintained. Therefore, in this study, gas detection sensors and flame detectors are additionally installed in Gwangmyeong Traditional Market, and a digital twin-based traditional market fire monitoring system is implemented in conjunction with existing sensors in the market's 3D model. With this digital twin based fire monitoring system, we can reduce the malfunctions of fire detect sensors, and can easily guide the evacuation route.

• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.1
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• pp.147-154
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• 2017

This paper aims to build a home security system based on IoT to monitor a home on a mobile phone. The system consists of data gathering sensors, camera, gateway and Xively platform. The Raspberry Pi collects data from the three sensors and sends the data to Xively, and sends the video stream of home to a client in a smart phone through a internet. The servers are composed of Xively, socket server in Raspberry Pi and E-mail server in Google. The proposed system transmits e-mail, text message, and video stream when there are motion, fire, and gas leakage, and can control the gas valve through Raspberry Pi. The experimental results show that a user gets 'emergency E-mail' and text message and watches the video stream of the home through WIFI or LTE on a smart phone.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.4
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• pp.50-58
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• 2017

Recently, some methods of converging heterogeneous fire sensor data have been proposed for effective fire detection, but the rule-based methods have low adaptability and accuracy, and the fuzzy inference methods suffer from detection speed and accuracy by lack of consideration for images. In addition, a few image-based deep learning methods were researched, but it was too difficult to rapidly recognize the fire event in absence of cameras or out of scope of a camera in practical situations. In this paper, we propose a novel fire detection system combining a deep learning algorithm based on CNN and fuzzy inference engine based on heterogeneous fire sensor data including temperature, humidity, gas, and smoke density. we show it is possible for the proposed system to rapidly detect fire by utilizing images and to decide fire in a reliable way by utilizing multi-sensor data. Also, we apply distributed computing architecture to fire detection algorithm in order to avoid concentration of computing power on a server and to enhance scalability as a result. Finally, we prove the performance of the system through two experiments by means of NIST's fire dynamics simulator in both cases of an explosively spreading fire and a gradually growing fire.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.2
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• pp.67-72
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• 2022

Recently, various intelligent application services using artificial intelligence are being actively developed. In particular, research on artificial intelligence-based real-time prediction services is being actively conducted in the manufacturing industry, and the demand for artificial intelligence services that can detect and predict fire and odors is very high. However, most of the existing detection and prediction systems do not predict the occurrence of fires and odors, but rather provide detection services after occurrence. This is because AI-based prediction service technology is not applied in existing systems. In addition, fire prediction, odor detection and odor level prediction services are services with ultra-low delay characteristics. Therefore, in order to provide ultra-low-latency prediction service, edge computing technology is combined with artificial intelligence models, so that faster inference results can be applied to the field faster than the cloud is being developed. Therefore, in this paper, we propose an LSTM algorithm-based learning model that can be used for fire prediction and odor detection/prediction, which are most required in the manufacturing industry. In addition, the proposed learning model is designed to be implemented in edge devices, and it is proposed to receive real-time sensor data from the IoT terminal and apply this data to the inference model to predict fire and odor conditions in real time. The proposed model evaluated the prediction accuracy of the learning model through three performance indicators, and the evaluation result showed an average performance of over 90%.

• Journal of the Korea institute for structural maintenance and inspection
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• v.25 no.5
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• pp.1-7
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• 2021

This study attempted to detect possible collapse and fire accidents in facilities for disaster monitoring of large facilities, and to develop a customized beacon to recognize the internal situation of an IoT-based facility when a disaster occurs. In the case of data measurement using the existing strain gauge sensor, the strain gauge sensor was connected by wire to measure it, but this study changed it to wireless so that the presence and absence of structural deformation can be monitored in real time. In this process, in order to use the Wheatstone bridge, a strain sensor module that can be connected to a customized beacon was manufactured, and a system configuration was conducted to remotely check the measurement data. To verify measurement data, 10 customized beacons and 2 gateways were installed on the 15th floor of the Advanced Institue of Convergence Technology, and as a result of analysis of measurement data, it was confirmed that the strain data values were distributed between 7 and 8.