• Journal of Korean Society of Steel Construction
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• v.22 no.1
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• pp.1-12
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• 2010

According to the capacity design concept which forms the basis of the current steel seismic codes, the braces in concentrically braced frames (CBFs) should dissipate seismic energy through cyclic tension yielding and cyclic compression buckling while the beams and the columns should remain elastic. Brace buckling in inverted V-braced frames induces unbalanced vertical forces which, in turn, impose the additional beam moments and column axial forces. However, due to difficulty in predicting the location of buckling stories, the most conservative approach implied in the design code is to estimate the column axial forces by adding all the unbalanced vertical forces in the upper stories. One alternative approach, less conservative and recommended by the current code, is to estimate the column axial forces based on the amplified seismic load expected at the mechanism-level response. Both are either too conservative or lacking technical foundation. In this paper, three combination rules for a rational estimation of the column axial forces were proposed. The idea central to the three methods is to detect the stories of high buckling potential based on pushover analysis and dynamic behavior. The unbalanced vertical forces in the stories detected as high buckling potential are summed in a linear manner while those in other stories are combined by following the SRSS(square root of sum of squares) rule. The accuracy and design advantage of the three methods were validated by comparing extensive inelastic dynamic analysis results. The mode-shape based method(MSBM), which is both simple and accurate, is recommended as the method of choice for practicing engineers among the three.

• Journal of the Korean Institute of Gas
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• v.16 no.6
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• pp.128-135
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• 2012

In this paper, we proposed a system and a scenario to raise efficiency of gas safety management by developing wireless ZigBee communication modules, smart-home gas safety appliances and the system suitable for gas safety. Our designed system consists of a micom gas meter, an automatic extinguisher, sensors, and a wall-pad. A micom-gas-meter monitors gas flow, gas pressure, and earthquake. An automatic fire extinguisher checks combustible gas leaks and temperature of $100^{\circ}C$(cut off) and $130^{\circ}C$(fire). Sensors measure smoke and CO gas. In our novel system, a micom-gas meter cut off inner valve with warnings, an automatic fire extinguisher cut off middle valve and spray extinguishing materials, and sensors generate signals when detecting smoke and CO and then take a next action. Gas safety appliances and sensors automatically takes measures, and transmit those information to a wall-pad. The wall-pad again transmits real time information to server. Users can check and manage gas safety situations by connecting BcN server through web or mobile application. We hereby devised scenarios for gas safety and risk management based on the smart, and demonstrated their efficiency through test applied to filed.

• Korean Journal of Remote Sensing
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• v.30 no.2
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• pp.259-274
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• 2014

On Aug 18, 2013, Sakurajima volcano in Japan erupted on a relatively large-scale. Geostationary Ocean Color Imager (GOCI) had used to detect volcanic ash in the surrounding area on the next day of this eruption. The geomagnetic variation has been analyzed using geomagnetic data from Cheongyang observatory in Korea and several geomagnetic observatories in Japan. First, we reconstruct geomagnetic data by principal component analysis and conduct semblance analysis by wavelet transform. Secondly, we minimize the error of solar effect by using wavelet based semblance filtering with Kp index. As a result of this study, we could confirm that the geomagnetic variation usually occur at the moment of Sakurajima volcano eruption. However, we cannot rule out the possibilities that it could have been impacted by other factors besides volcanic eruption in other variation's cases. This research is an exceptional study to analyze geomagnetic variation related with abroad volcanic eruption uncommonly in Korea. Moreover, we expect that it can help to develop further study of geomagnetic variation involved in earthquake and volcanic eruption.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.48 no.3
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• pp.13-20
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• 2011

In this paper, we proposed a system to raise gas safety management by using the wireless communication module and intelligent gas safety appliances. Our designed systems configure a micom-gas meter, an automatic extinguisher, sensors, and a wallpad. A micom-gas-meter monitors gas flow, gas pressure, and earthquake. An automatic fire extinguisher checks gas(combustible) leaks and temperature of $100^{\circ}C$ and $130^{\circ}C$. Sensors measure smoke and CO gas. In our novel system, a micom-gas meter cut off inner valve with warnings, an automatic fire extinguisher cut off middle valve and spray extinguishing materials, and sensors generate signals for smoke and CO when occurring gas risk. Gas safety appliances and sensors takes safety measures, and transmit those signal to a wallpad. The wallpad again transmit signal like events to a control server. Users can connect web pages for gas safety through B-ISDN and control and manage them. We hereby devised scenarios for gas safety and risk management, and demonstrated their effectiveness through experiments.

• Journal of the Society of Disaster Information
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• v.16 no.1
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• pp.96-110
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• 2020

Recently, due to global warming, it is easily exposed to various disasters such as fire, flood, and earthquake. In particular, large-scale disasters have continuously been occurring in crowded areas such as traditional markets, facilities for the elderly and children, and public facilities where various people stay. Purpose: This study aims to detect a fire occurred in crowded facilities early in the event to analyze and provide an optimal evacuation route using big data and advanced technology. Method: The researchers propose a new algorithm through context-aware 3D object model technology and A* algorithm optimization and propose a scenario-based optimal evacuation route selection technique. Result: Using the HPA* E algorithm, the evacuation simulation in the event of a fire was reproduced as a 3D model and the optimal evacuation route and evacuation time were calculated for each scenario. Conclusion: It is expected to reduce fatalities and injuries through the evacuation induction technique that enables evacuation of the building in the shortest path by analyzing in real-time via fire detection sensors that detects the temperature, flame, and smoke.

• Journal of Korea Multimedia Society
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• v.16 no.6
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• pp.667-677
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• 2013

With the huge success of Social Network Services, studies on social network analysis to extract the current issues or to track the symptoms of epidemic disease are being carried out actively. On Twitter, tweets reflect people's reaction to an event and users' individual status well, so it is possible to detect an event regarding a tweet as a sensory value. Recently, social signals are used to detect the spread of illness like the flu as well as the occurrence of disaster event like an earthquake in early stages. In this paper, we set up a cold as a target event and regarded tweets as Cold Signals. To evaluate the reliability of Cold Signals, we analyzed correlations between weather factors and the cold index provided by Korea Meteorological Administration.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.567-568
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• 2018

In recent years, there have been frequent occurrences of collapsing buildings and tilting accidents due to frequent earthquakes and aging of buildings. Various methods have been proposed to prevent disasters on these buildings. In this paper, we propose a system algorithm that provides an indication of anomalous phenomena such as collapse and tilting of buildings by real - time monitoring of IoT (Internet of Things) - based architectural anomalies. The multi-advanced sensor is based on the Inclinometer sensor and the Accelerometer sensor, transmits the detected data to the server in real time, accumulates the data, and provides the service to cope when the set threshold value is different. It is possible to evacuate and repair the collapse and tilting of the building by warning the occurrence of the upper threshold event event such as the collapse and tilting of the building.

• Journal of the Korean GEO-environmental Society
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• v.15 no.11
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• pp.5-12
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• 2014

A slope may fail after construction owing to external factors such as localized rainfall, earthquake, and weathering. Therefore, the grasp of failure probability for slope failures is necessary to maintain their stability. In particular, it is very difficult to detect the symptoms of rock slope failure in advance by using traditional methods, such as displacement due to the brittleness of rocks. However, Acoustic Emission (AE) techniques can predict slope failures earlier than the traditional methods. This study grasped failure probability of slope by applying AE techniques to a rock slope with a history of collapse. When applying AE techniques to a slope that has a high probability of failure, the grasp of failure probability of the specific location became possible.

• Journal of Korean Society of societal Security
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• v.2 no.4
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• pp.35-41
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• 2009

If any bridge has the monitoring system, the bridge manager can check the history of bridge behavior and the progress of the damage more exactly. When the unexpected event (ex: earthquake and flood) happens, the manager can check the safety condition of the bridge and make the pertinent action for bridge management which is reduction of vehicle speed or traffic control through the system. Additionary the manager can make the well-timed repair or reinforcement through the system, so he can save the management cost or the life cycle cost. This study presents the method of setting the criteria by FEM analysis in bridge monitoring system, and the standard progress for setting the criteria about measurement items of monitoring system for the steel box type bridge.

• Journal of Convergence for Information Technology
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• v.7 no.6
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• pp.201-207
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• 2017

Failure of the power apparatus causes many inconveniences and problems due to power outage in all places using power such as industry and home. The main causes of faults in the Power Apparatus are aging, natural disasters such as typhoons and earthquakes, and animals. At present, the long high temperature status is monitored only by the assumption that a fault occurs when the temperature of the power apparatus becomes higher. Therefore, it is difficult to cope with the failure of the power apparatus at the right time. In this paper, we propose a power apparatus monitoring system as an efficient countermeasure against general faults except for faults caused by sudden natural disasters. The proposed monitoring system monitors the power apparatus in real time by attaching a thermal sensor, collects the monitored data, and predicts the failure using the accumulated information through learning using the artificial neural network. Through the learning and experimentation of artificial neural network, it is shown that the proposed method is efficient.