• Structural Engineering and Mechanics
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• v.54 no.2
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• pp.221-237
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• 2015

An integrated method is proposed for structural nonlinear damage detection based on time series analysis and the higher statistical moments of structural responses in this study. It combines the time series analysis, the higher statistical moments of AR model residual errors and the fuzzy c-means (FCM) clustering techniques. A few comprehensive damage indexes are developed in the arithmetic and geometric mean of the higher statistical moments, and are classified by using the FCM clustering method to achieve nonlinear damage detection. A series of the measured response data, downloaded from the web site of the Los Alamos National Laboratory (LANL) USA, from a three-storey building structure considering the environmental variety as well as different nonlinear damage cases, are analyzed and used to assess the performance of the new nonlinear damage detection method. The effectiveness and robustness of the new proposed method are finally analyzed and concluded.

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 1997.11a
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• pp.573-581
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• 1997

Much activity is occurring throughout the world with respect to the implementation of new concepts and technology for fire detection and fire suppression applications. Obvious advances include tangible detection and suppression improvements, and also new methods and approaches such as performance based codes and standards. Examples of tangible advances include addressable detection systems, wireless detection technology, halon alternatives, water mist systems, advanced sprinkler technology, and so on. Examples of new approaches and methods include a revitalized focus on disaster planning and the need for a total fire protection plan. The concept of performance based codes and standards for the design and installation fire detection and suppression systems will be explored in detail.

• Journal of the Semiconductor & Display Technology
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• v.23 no.1
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• pp.19-24
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• 2024

It is not an exaggeration to say that a single fire causes a lot of damage, so fires are one of the disaster situations that must be alerted as soon as possible. Various technologies have been utilized so far because preventing and detecting fires can never be completely accomplished with individual human efforts. Recently, deep learning technology has been developed, and fire detection systems using object detection neural networks are being actively studied. In this paper, we propose a new fire detection system that improves the previously studied fire detection system. We train the YOLOv8 model using refined datasets through improved labeling methods, derive results, and demonstrate the superiority of the proposed system by comparing it with the results of previous studies.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.4
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• pp.97-108
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• 2016

In this paper, we consider a cooperative monitoring network, which consists of a large number of UAVs, in order to promptly detect event in a disaster area. A command center may not be able to control each UAV individually due to resource constraints. Therefore, UAVs need to autonomously construct a mobile monitoring network in order to maximize monitoring coverage and to adapt the network formation according to environment changes in the disaster area. To that end, we propose multiple UAVs-based cooperative monitoring schemes that uses virtual forces. In this monitoring scheme, an effective monitoring is enabled by extending monitoring coverage using each UAV's circle movements. The UAVs-based monitoring network can also be splitted or merged in order to increase the monitoring effectiveness. Through simulations, we show that the proposed scheme can effectively monitor a large area and achieve a high event detection ratio.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.4
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• pp.391-402
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• 2016

Among the various remote sensing sensors compared to the electro-optical sensors, SAR (Synthetic Aperture Radar) is very suitable for assessing damaged areas induced by disaster events owing to its all-weather day and night acquisition capability and sensitivity to geometric variables. The conventional CD (Change Detection) method that uses two-date data is typically used for mapping damage over extensive areas in a short time, but because data from only two dates are used, the information used in the conventional CD is limited. In this paper, we propose a novel CD method that is extended to use data consisting of two pre-disaster SAR data and one post-disaster SAR data. The proposed CD method detects changes by using a similarity weight image derived from the neighborhood information of a pixel in the data from the three dates. We conducted an experiment using three single polarization ALOS PALSAR (Advanced Land Observing Satellite/Phased Array Type L-Band) data collected over Miyagi, Japan which was seriously damaged by the 2011 east Japan tsunami. The results demonstrated that the mapping accuracy for damaged areas can be improved by about 26% with an increase of the g-mean compared to the conventional CD method. These improved results prove the performance of our proposed CD method and show that the proposed CD method is more suitable than the conventional CD method for detecting damaged areas induced by disaster.

• Journal of Digital Convergence
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• v.10 no.7
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• pp.141-152
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• 2012

The typical evacuation guidance system based on fire detectors, which is being widely used in theaters and large buildings, is often operated in an analog manner. In case of fire, it often causes the system to lose a wired line or wireless fire detection sensor, resulting in the difficulty of transmitting signals from a wired or wireless fire detection sensor to the main fire monitoring device. Accordingly, this paper has proposed the broadcasting system for disaster management, having an efficient evacuation guidance plan when a disaster occurs. The system reacts to an emergency situation along with fire alarm sirens in real time. We have implemented the above system by means of a simulation program that prints the evacuation guidance information (e.g., location and time of fire, and evacuation path) on an LCD located in a building through the fire sensor network in case of an emergency (e.g., actual fire). We have developed the simulation system by using mathematical algorithms, such as the optimal path search and the fire smoke diffusion algorithm. This simulation program considers the structure of a building and the location where the fire has initially occurred, applying it to the simulator.

• Spatial Information Research
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• v.19 no.5
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• pp.1-11
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• 2011

This study mainly focused on the method of accurately extracting damage information in the im agery change detection process using the constructed high resolution aerial im agery. Bongwha-gun in Gyungsangbuk-do which had been severely damaged from a localized torrential downpour at the end of July, 2008 was selected as study area. This study utilized aerial im agery having photographing scale of 30cm gray image of pre-disaster and 40cm color image of post-disaster. In order to correct errors from the differences of the image resolution of pre-/post-disaster and time series, the prelim inary phase of image processing techniques such as normalizing, contrast enhancement and equalizing were applied to reduce errors. The extent of the damage was calculated using one to one comparison of the intensity of each pixel of pre-/post-disaster im aged. In this step, threshold values which facilitate to extract the extent that damage investigator wants were applied by setting difference values of the intensity of pixel of pre-/post-disaster. The accuracy of optimal image processing and the result of threshold values were verified using the error matrix. The results of the study enabled the early exaction of the extents of the damages using the aerial imagery with identical characteristics. It was also possible to apply to various damage items for imagery change detection in case of utilizing multi-band im agery. Furthermore, more quantitative estimation of the dam ages would be possible with the use of numerous GIS layers such as land cover and cadastral maps.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.10
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• pp.678-684
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• 2016

The detection of an underwater work space boundary is very important when an underwater construction is carried out using seabed type underwater equipment, such as underwater machines for rubble mound leveling, because it can induce industrial disasters. Therefore, divers are needed to mark the underwater work space boundary. A nylon rope is used to improve the convenience during an underwater diver's work. The results showed that the work space boundary can be detected using a sonar system. Using these results, an efficient method to detect the underwater work space boundary can be obtained when an underwater construction is carried out using seabed type underwater equipment.

• Journal of Broadcast Engineering
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• v.19 no.4
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• pp.491-501
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• 2014

Recently, the need for disaster-preventing broadcast has increased gradually to cope with natural disaster like earthquake and tsunami causing enormous losses of both life and property. In disaster-preventing broadcast system, the wake-up signal is used to alert user terminal and switch the current state of channel to the emergency channel, which is for the fast and efficient delivery of emergency information. In this paper, we propose the detection method of wake-up signal for disaster-preventing broadcast systems. The wake-up signals for disaster-preventing broadcast should have a good auto-correlation property in low power and narrow-band conditions that does not affect the existing digital television (DTV) system. The suitability of the m-sequence and complementary code (CC) is analyzed for wake-up signals according to signal to noise ratio. A wake-up signal is proposed by combining the direct sequence spread spectrum (DSSS) technique and pseudo noise (PN) sequences such as Barker and Walsh-Hadamard codes. By using the proposed method, a higher detecting performance can be achieved by the spreading gain compared to the single long m-sequence and the Golay code.

• Korean Journal of Remote Sensing
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• v.38 no.6_3
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• pp.1761-1775
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• 2022

Due to unpredictable climate change, the frequency of occurrence of water-related disasters and the scale of damage are also continuously increasing. In terms of disaster management, it is essential to identify the damaged area in a wide area and monitor for mid-term and long-term forecasting. In the field of water disasters, research on remote sensing technology using Synthetic Aperture Radar (SAR) satellite images for wide-area monitoring is being actively conducted. Time-series analysis for monitoring requires a complex preprocessing process that collects a large amount of images and considers the noisy radar characteristics, and for this, a considerable amount of time is required. With the recent development of cloud computing technology, many platforms capable of performing spatiotemporal analysis using satellite big data have been proposed. Google Earth Engine (GEE)is a representative platform that provides about 600 satellite data for free and enables semi real time space time analysis based on the analysis preparation data of satellite images. Therefore, in this study, immediate water disaster damage detection and mid to long term time series observation studies were conducted using GEE. Through the Otsu technique, which is mainly used for change detection, changes in river width and flood area due to river flooding were confirmed, centered on the torrential rains that occurred in 2020. In addition, in terms of disaster management, the change trend of the time series waterbody from 2018 to 2022 was confirmed. The short processing time through javascript based coding, and the strength of spatiotemporal analysis and result expression, are expected to enable use in the field of water disasters. In addition, it is expected that the field of application will be expanded through connection with various satellite bigdata in the future.