• 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.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.35 no.6
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• pp.1269-1276
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• 2015

The methodology of utilizing Intensity-Duration-flood Quantity (IDQ) curve for flood alert and warning was introduced and its performance was evaluated. For this purpose the lumped parameter model was calibrated and validated for gauged basin data set and the index precipitation equivalent to alert and warning flood was estimated. The index precipitation and IDQ curves associated by three different Antecedant Moisture Conditions (AMCs) are made provision for various possible flood scenarios. The test basin is Wonju-cheon basin ($94.4km^2$) located in Gangwon province, Korea. The IDQ curves corresponding to alert (50% of design flood level) and warning (70% of design flood level) level was estimated using the Clark unit hydrograph based lumped parameter model. The performance evaluation showed 0.704 of POD (Probability of Detection), 0.136 of FAR (False Alarm Ratio), and 0.633 of CSI (Critical Success Index), which is improved from the result of IDQ with single fixed AMC.

• Proceedings of the Korean Geotechical Society Conference
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• 2010.09a
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• pp.253-262
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• 2010

In this study, localized heavy rainfall occurred during the collapse of steep slopes adjacent to the construction site and to ensure the safety of residents to build an early warning system was performed. Forecast/Alert range was estimated based on vulnerability landslide map and past disaster history. And established a critical line in consideration of the characteristics of local rainfall and operating a snake line, the study calculated causing and non-causing points. Also, be measured in real-time analysis of rainfall data in conjunction with the system before the steep slope failure occurred forecast/Alert System is presented.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.303-304
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• 2022

본 논문에서는 재난위기경보관리시스템 구축을 위해 행정안전부가 구축중인 재난안전데이터 공유 플랫폼의 데이터를 연동하기 위한 기반 및 기술을 제안한다. 국가 R&D로 수행중인 재난위기경보관리시스템은 위기경보 수준을 판단하기 위해 필요한 데이터를 수집, 위기경보 수준 자동분석, 위기경보 수준 판단 및 표출하는 기능을 구현하였다. 그러나 실제 운영하기 위해서는 국가재난정보관리시스템(NDMS)내에서 운영할 수 있도록 재난안전데이터 공유 플랫폼의 데이터 연동을 위한 방안에 대해 연구하였다.

• Genomics & Informatics
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• v.5 no.4
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• pp.196-199
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• 2007

BOD is an FTP service management tool on the Internet. It was developed for biological researchers in South Korea. It enables easier and faster access of bioinformation without having to go through foreign FTP sites. BOD includes an automatic downloader with a management and email alert service from which the user can easily select and schedule any biological database. Once listed in BOD, the user can check and modify the download status and data from an additional email alert service.

• Journal of Broadcast Engineering
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• v.24 no.5
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• pp.726-734
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• 2019

This study investigates the number of news and correlated keywords concerning to Korean Wireless Emergency Alert(KWEA). The news was collected using BIGKinds, a news big data system provided by the Korea Press Foundation. When analyzing the annual published news articles, we investigated the frequency of the news grouped by disaster types, and the frequency of the news distinguishing between the earthquake and non-earthquake disasters, and finally the frequency of correlated keywords concerning to the disasters. We found that the KWEA news totaled 182 in 2016 due to the unprecedented powerful KyongJu earthquake, an increase of 20 times over the previous year. Ever since 2016, the news about the KWEA continued to hit high figures consistently. After the peak in KyongJu earthquake in 2016, the proportion of non-earthquakes had also increased in 2017 and 2018. Next, the keyword correlation analysis showed that the KWEA news gave major coverage to the following entities: The Ministry of the Interior and Safety which operates the KWEA, Korea Meteorological Administration, and the general public.

• Journal of Technology Innovation
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• v.22 no.2
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• pp.185-220
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• 2014

The purpose of this study is to get a whole picture of financial conditions of the new and renewable energy sector which have been growing rapidly and predict bankruptcy risk quantitatively. There have been many researches on the methodologies for company failure prediction, such as financial ratios as predictors of failure, analysis of corporate governance, risk factors and survival analysis, and others. The research method for this study is Altman Z-score which has been widely used in the world. Data Set was composed of 121 companies with financial statements from KIS-Value. Covering period for the analysis of the data set is from the year 2006 to 2011. As a result of this study, we found that 38 percent of the data set belongs to "Distress" Zone (on alert) while 38% (on watch), summed into 76%, whose level could be interpreted to doubt about the sustainability. The average of the SMEs in wind energy sector was worse than that of SMEs in solar energy sector. And the average of the SMEs in the "Distress" Zone (on alert) was worse than that of the companies of large group in the "Distress" Zone (on alert). In conclusion, Altman Z-score was well proved to be effective for New & Renewable Energy Industry in Korea as a result of this study. The importance of this study lies on the result to demonstrate empirically that the majority of solar and wind enterprises are facing the risk of bankruptcy. And it is also meaningful to have studied the relationship between SMEs and large companies in addition to advancing research on new start-up companies.

• Journal of the Korea Society of Computer and Information
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• v.17 no.10
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• pp.1-9
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• 2012

These days news about natural disaster caused by earthquake, typhoon is broadcasted frequently. At this moment, structure' healthy is threatened by natural disaster, only way to minimize the casualties and property damage is doing accurate alert. In this research, I designed Expert system to reduce wrong-alert and elevate the accuracy. The expert system put many sensors close to each other as a one group. We focus on elevating reliability of monitoring system by comparing each nearby sensor's state not one single sensor. Providing accurate data which can decide safe to structure manager can minimize current damage by early response, also has advantage that can prevent additional damage can be occur in the future.

• Journal of the Society of Disaster Information
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• v.19 no.1
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• pp.69-75
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• 2023

Purpose: The risk of disaster from extreme weather events is increasing due to the increase in occurrence and the strength of heavy rains and storms from continued climate change. To reduce these risks, emergency weather information customized for the characteristics of the information users and related circumstances should be provided. Method: A first-stage emergency weather information delivery system has been developed to provide weather information to the disaster-risk area residents and the disaster response personnel. Novel methods to apply artificial intelligence to identify emergencies have been studied. The relationship between special weather reports from meteorological administration and disaster-related news articles has been analyzed to identify the significance of a pilot study using text analytic artificial intelligence. Result: The basis to identify the significance of the relations between disaster-related articles and special weather reports has been established and the possibility of the development of a real-world applicable system based on a broader analysis of data has been suggested. Conclusion: Through direct alert delivery of weather emergency alerts, a weather emergency alert system is expected to reduce the risk of damage from extreme weather situations.

• Journal of the Society of Disaster Information
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• v.5 no.2
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• pp.76-85
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• 2009

SWE(Sensor Web Enablement) is the standard platform of OGC for the sensor data service. SWE is only focusing in the data transmission protocols, but supporting the semantic decision. Sensor data service is the decision service of the status whether is on normal or not. In this study, we study the semantic decision model of the sensor data. It can support the context-aware service based on the decision information.