• The Journal of the Petrological Society of Korea
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• v.23 no.4
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• pp.405-418
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• 2014

Ontake Volcano, Japan, began to erupt without any precursors on September 27, 2014, at 11:52 AM, and it caused many losses of life. Although Japan's preparation manual and prevention for volcanic eruptions and volcanic hazards has been well established, it could not prevent damage due to the sudden eruption of the volcano. Soon after the eruption, however, Japan Meteorological Agency (JMA) led many organizations and institutions, including JMA's Volcanic Eruption Prediction Liaison Council, Meteorological Research Institute (MRI) and National Agriculture and Food Research Organization and they understood the eruption situation quickly and shared the information based on their close cooperation and contact systems. Through these efforts, JMA published the unified result to the public, informing the public of the situation around the volcano and about the eruption and of how the residents and climbers around the volcano should react to the volcanic hazards caused by the eruption. The Korean Government can learn how to respond to a future eruption of a volcano, such as Mt. Baekdu which has the potential to erupt in the foreseeable future.

• Proceedings of the KSEEG Conference
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• 2007.04a
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• pp.130-132
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• 2007

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.6
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• pp.1129-1135
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• 2016

The NH-NongHyup network and servers were paralyzed in 2011, in the 2013 3.20 cyber attack happened and classified documents of Korea Hydro & Nuclear Power Co. Ltd were leaked on december in 2015. All of them were conducted by a foreign country. These attacks were planned for a long time compared to the script kids attacks and the techniques used were very complex and sophisticated. However, no successful solution has been implemented to defend an APT attacks(Advanced Persistent Threat Attacks) thus far. We will use big data analytics to analyze whether or not APT attacks has occurred. This research is based on the data collected through ISAC monitoring among 3 hierarchical Korean Defense System. First, we will introduce related research about big data analytics and machine learning. Then, we design two big data analytics models to detect an APT attacks. Lastly, we will present an effective response method to address a detected APT attacks.

• Journal of the Korean earth science society
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• v.30 no.7
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• pp.824-833
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• 2009

We analyzed a total of six months of geomagnetic data obtained at Cheonyang observatory, which is operated by Korean Meteorological Administration, to monitor earthquake precursors. Geomagnetic transfer functions (GTFs) and polarization values, which reflect the time-variations of the resistivity of subsurface, were estimated from 3-component geomagnetic data. The time-variant fluctuations were compared with the earthquake events occurred in the same period. Now that the daily GTFs show fairly irregular variations, we can not identify any correlation with the already occurred earthquakes and Kp index. On the other hand, we detect clear increases of the Ultra-Low-Frequency (ULF) band polarization values before the earthquakes, but the similar features are also observed even though the earthquake did not occur. This result may indicate that these time-variations are not just due to the earthquake precursor. For further understanding about these results, we need to investigate the relationship between the previous earthquake events and the geomagnetic data of other observatories.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.317-320
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• 2016

The NH-NongHyup network and servers were paralyzed in 2011, in the 2013 3.20 cyber attack happened and Classified documents of Korea Hydro & Nuclear Power Co. Ltd were leaked on December in 2015. All of them were conducted by a foreign country. These attacks were planned for a long time compared to the script kids attacks and the techniques used were very complex and sophisticated. However, no successful solution has been implemented to defend an APT attack thus far. Therefore, we will use big data analytics to analyze whether or not APT attack has occurred in order to defend against the manipulative attackers. This research is based on the data collected through ISAC monitoring among 3 hierarchical Korean defense system. First, we will introduce related research about big data analytics and machine learning. Then, we design two big data analytics models to detect an APT attack and evaluate the models' accuracy and other results. Lastly, we will present an effective response method to address a detected APT attack.

• Journal of the Society of Disaster Information
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• v.13 no.1
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• pp.89-95
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• 2017

The cases of animals unusual behavior have been reported occasionally before a major disaster occurs. Could animals unusual behavior associated with a major disaster occurrence, if so, could we foreknow a disaster occurrence? The purpose of study is to quest an answer through literature reviews on the relation between animals unusual behavior and premonitory symptoms of an earthquake. These empirical studies are classified three parts according as methodological framework; statistical analysis, experimental analysis, observation analysis. The results are simply divided as two, 'animals unusual behavior may be seen as a precursor of pre-disaster phenomena' and 'difficult to see.' A number of studies have been performed abroad, however there is no one in Korea. Most of the studies point out common limitations-difficult to verify the reliability of data, accidentally get and fewer samples of data, difficulty of ensuring appropriate data, etc. That is why more related research with animals unusual behavior and disaster occurrence is needed to validate cause-and-effect relation of animal unusual behavior and pre-disaster phenomena.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2008.05a
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• pp.186-188
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• 2008

자동차에 탑재된 부하 특히, 여름의 에어컨 사용과 같이 큰 부하가 걸릴 경우와 같이 차량내 전기적 부하의 변동이 발생하게 된다. 이러한 부하의 변동이 할로켄 램프를 사용하고 있는 전조등의 밝기를 변화시킨다. 이러한 밝기의 변동은 운전자가 인식할 수 있는 정도가 되며, 그로인해 운전자는 심리적으로 불쾌함을 느낄 것이다. 그러므로 차량 전조등의 밝기변화 패턴에 영향을 미치는 전조등의 전압과 시간을 변수로 하여, 순간적으로 밝기변동이 발생하였을 경우, 연령대별(20/60대)로 운전자가 느끼는 심리적인 반응(불쾌감)이 어떠한 경향을 갖는지를 파악하는 것이 중요하다. 따라서 본 논문에서는 밝기변동 요소가 되는 전압변동 범위 ${\Delta}V$와 전압변동 시간 ${\Delta}t$로 순간적으로 전조등의 밝기가 변동하였을 때, 이러한 현상이 운전자의 심리적인 반응에 어떠한 영향을 미치는가를 20대와 60대를 대상으로 평가하였다. 이 결과 각각의 전압패턴에 따라 불쾌감이 다르게 나타났으며, 또한 20대와 60대가 전압패턴에 대해 경향을 유사하였으나, 평가치에 대해서는 상당한 차이를 보였다.

• Geophysics and Geophysical Exploration
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• v.19 no.2
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• pp.91-96
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• 2016

From the Earth's polar motion time series (IERS 08 C04, since 1981), after removal of seasonal variation by band-pass filtering, we acquired Earth's free Eulerian motion (Chandler wobble) time series. By successive least square error fittings on it, we analyzed amplitude and phase variation of Chandler wobble. We attempted to identify any precursory behavior of the pole before large earthquakes but only to fail. Unlike Smylie's conjecture there was no appreciable motion of the Earth's pole detected at around the each times of recent six largest earthquakes of magnitude over 8.5.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.450-450
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• 2023

인공지능 기법의 급격한 발달에 따라 다양한 분야에서 인공지능 기법을 활용하기 위한 노력이 이루어지고 있다. 재난은 발생하기 전에 다양한 전조 현상을 나타내나 수많은 정보 속에서 전조 증상을 정확히 인지하는 것은 매우 어렵다. 따라서 인공지능은 방대한 사전 정보의 해석을 통해 재난 발생의 전조를 신속 정확하게 감지하는데 최적의 기술이다. 최근 OpenAI의 딥러닝 기반의 언어모델인 GPT(Generative Pre-trained Transformer)의 성능이 기대 이상을 나타내면서 많은 분야에서 GPT에 대한 관심과 실험이 시작되고 있다. 본 실험에서는 GPT를 이용하여 재난 검색 및 해석의 특징을 검토하여 보았다. 정확한 재난 기록은 정확한 재난 예측을 위해 반드시 필요한 자료이나 부정확한 재난 기록은 그 기록이 비록 방대하더라도 오히려 예측의 신뢰도를 크게 떨어뜨린 수 있다. 따라서 비지도학습 기반의 대화형 인공지능을 재난 검색에 활용하기 위해서는 인공지능 기법의 인지 특성을 반드시 가늠해 봐야 한다. 향후 보다 많은 연구자가 이에 관심을 가진다면 보다 정확한 인공지능 기반의 재난 탐지 기술의 개발이 가능할 것으로 기대된다.

• Tunnel and Underground Space
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• v.18 no.1
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• pp.1-9
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• 2008

Acoustic emission (AE) and Microseimsic (MS) activities are law-energy seismic events associated with a sudden inelastic deformation such as the sudden movement of existing fractures, the generation of new fractures or the propagation of fractures. These events rapidly increase before major failure and happen within a given rock volume and radiate detectable seismic waves. The main difference between AE and MS signals is that the seismic motion frequencies of AE signals are higher than those of MS signals. As the failure of geotechnical structures usually happens as a high velocity and small displacement, it is nat easy ta determine the precursor and initiation stress level of failure in displacement detection method. To overcame this problem, AE/MS techniques far detection of structure failure and damage have recently adapt in civil engineering. This study deal with the basic theory of AE/MS and state of arts in monitoring technique using AE/MS.