• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.246-248
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• 2021

This paper proposes a new general-purpose automatic fire detection system. And it deals with the contents of related research on how to design and develop for this system. The proposed system automatically notifies the user and the nearby fire department when a fire breaks out in a place where a fire alarm is installed. If this is the case, the nearby fire department can quickly confirm this fact and extinguish the fire at an early stage, thereby reducing human and property damage. The main targets of this system are houses and small buildings. The proposed fire alarm functions as a conventional fire alarm, and if a fire occurs, this fact is immediately transmitted to a nearby receiver through wireless data communication. The receiver in this paper communicates data using Korea's firefighting communication frequency band, and establishes one own network by installing it in various places to quickly receive fire alarm data at any time.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2018.11a
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• pp.362-363
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• 2018

해사안전정보는 항행경보, 기상특보, 기타 긴급정보로 구성되며, NAVTEX와 SafetyNET을 통해 육상에서 선박으로 제공되고 있다. 현행 해사안전정보 서비스를 개선하기 위해 세계 각국의 e-Navigation 프로젝트에서 해사안전정보 전자데이터 서비스를 개발하고 있으며, 국제기구 차원에서 S-124 항행경보 데이터 표준을 개발하고 있다. 본 연구에서는 한국형 e-Navigation 서비스 개발 사업에 추진 중인 해사안전정보 제공 서비스 개념과 기술을 바탕으로 해사안전정보 제공 서비스 운영 개념을 설계 하였다. 본 연구에서는 해사안전정보 중의 하나인 항행경보를 중심으로 해사안전정보 제공 서비스 운영 및 적용 방안을 기술 하였다.

• 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.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.1
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• pp.21-27
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• 2022

As society develops and becomes more complex, new and diverse types of disasters such as fine dust and infectious diseases are occurring. However, in the past, there was no PA(Public Address) system that provided accurate information to prepare for such a disaster. In this paper, we propose a public address system that automatically broadcasts an alarm by analyzing polluted air quality data collected from public data and IoT sensors. The warning level varies depending on the air quality, and the information provided by public data may show a significantly different result from the guide area due to various factors such as the distance from the measuring station or the wind direction. To compensate for this, we are going to propose a method for broadcasting by comparing and analyzing data obtained from public data and data from on-site IoT sensors.

• Journal of the Society of Disaster Information
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• v.16 no.4
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• pp.670-681
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• 2020

Purpose: This study aims to derive a PGV prediction equation and to enhance the application of the Onsite EEW technology which has developed through previous studies. Method: The prediction equation for the Onsite EEW derived from earthquake data M≥3.0 and MMI≥II over the past four years. Local seismic risk is estimated using M and PGV deduced from P wave properties. Result: The improved PGV prediction equation estimated the MMI with an average accuracy of 94.8% and the 𝜏_c : Pd method also showed valid performance for alerting local seismic risks. Conclusion: Onsite EEW technology is successfully applied to Korea, and becomes to reduce the blind zone to about 14km.

• Journal of KIISE:Databases
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• v.34 no.6
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• pp.473-482
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• 2007

Network-based IDS(Intrusion Detection System) gathers network packet data and analyzes them into attack or normal. They raise alarm when possible intrusion happens. But they often output a large amount of low-level of incomplete alert information. Consequently, a large amount of incomplete alert information that can be unmanageable and also be mixed with false alerts can prevent intrusion response systems and security administrator from adequately understanding and analyzing the state of network security, and initiating appropriate response in a timely fashion. So it is important for the security administrator to reduce the redundancy of alerts, integrate and correlate security alerts, construct attack scenarios and present high-level aggregated information. False alarm rate is the ratio between the number of normal connections that are incorrectly misclassified as attacks and the total number of normal connections. In this paper we propose a false alarm classification model to reduce the false alarm rate using classification analysis of data mining techniques. The proposed model can classify the alarms from the intrusion detection systems into false alert or true attack. Our approach is useful to reduce false alerts and to improve the detection rate of network-based intrusion detection systems.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.7
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• pp.217-224
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• 2022

Today, with the development of technology and industry, fire accidents in special buildings are increasing as special buildings increase. However, despite the rapid development of information and communication technology, human casualties are steadily occurring due to the underdeveloped and ineffective indoor fire alarm system. In this study, we confirmed that the existing indoor fire alarm system using acoustic alarm could not deliver a sufficiently large alarm to the in-room personnel. To improve this, we designed and implemented a fire alarm system using edge computing and beacons. The proposed improved fire alarm system consists of terminal sensor nodes, edge nodes, a user application, and a server. The terminal sensor nodes collect indoor environment data and send it to the edge node, and the edge node monitors whether a fire occurs through the transmitted sensor value. In addition, the edge node continuously generate beacon signals to collect information of smart devices with user applications installed within the signal range, store them in a server database, and send application push-type fire alarms to all in-room personnel based on the collected user information. As a result of conducting a signal valid range measurement experiment in a university building with dense lecture rooms, it was confirmed that device information was normally collected within the beacon signal range of the edge node and a fire alarm was quickly sent to specific users. Through this, it was confirmed that the "blind spot problem of the alarm" was solved by flexibly collecting information of visitors that changes time to time and sending the alarm to a smart device very adjacent to the people. In addition, through the analysis of the experimental results, a plan to effectively apply the proposed fire alarm system according to the characteristics of the indoor space was proposed.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.6
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• pp.1113-1122
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• 2020

Recently, as violent crimes of crime without reason (Korea : Do not ask), women and the elderly are getting serious. In the existing system, many CCTVs are installed, but it is difficult to prevent crime due to only follow-up measures after a crime occurs. This device prevents crime through this device for incidents in shaded areas and closed spaces such as apartments and buildings. To do this, we research this technology to develop products and software. It sends an alarm signal using communication technology to a specific place where you want to receive an event of an alarm or a CCTV device operated using image analysis big data technology and convergence sensor technology for a specific target of the behavior expected to be a crime or movement. Develop the device. This development device researches and develops this device and supplies low-cost devices to consumers, which is used as a device that predicts the occurrence of crime in advance, processes it as an alarm signal in real time, and transmits it, and constitutes a standalone device and a server. Will provide the device to be connected.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.2
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• pp.179-185
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• 2020

Recently, in order to improve the performance of the Earthquake Early Warning System (EEWS) and to supplement the effects of earthquake disaster prevention in epicenters or near epicenters, development of on-site EEWS has been attempted. Unlike the national EEWS, which is used for earthquake disaster prevention by using seismic observation networks for earthquake research and observation, on-site EEWS aims at earthquake disaster prevention and therefore requires efficient design and evaluation in terms of performance and cost. At present, Korea lacks the necessary core technologies and operational know-how, including the use of existing EEWS design criteria and evaluation methods for the development of On-Site EEWS as well as EEWS. This study proposes hardware and software design directions and performance evaluation items and methods for seismic data collection, data processing, and analysis for localization of On-Site EEWS based on the seismic accelerometer requirements of the Seismic and Volcanic Disaster Response Act.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.05a
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• pp.492-494
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• 2013

본 논문에서는 대형선박의 내부를 실시간으로 모니터링 하기 위해 무선센서네트워크(WSN)을 사용하였고 사용자의 접근성을 높이기 위해 안드로이드 단말기를 이용하였다. 수집되는 데이터의 평균과 표준편차를 구하고 가중치평균을 도입하여 현재 수집되는 데이터가 표준편차를 초과하게 되면 경보를 통해 사용자에게 알려준다. 경보는 알람과 현재 데이터가 급격한 변화를 보이는 장소의 이름과 정보가 나타난다.