• The Bulletin of The Korean Astronomical Society
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• v.39 no.1
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• pp.84.2-84.2
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• 2014

The intermediate polar V1323 Her = RXS J180340.0+401214 returned from its faint state 19.4-20.5 mag (mean brightness during the run, the instrumental system close to R or clear filter) (vsnet-alert 16958). On March 1, 2014, the brightness was 17.50 (clear filter) and next night 17.8 (R). During previous observations on January 24, the object was 19.6. We reported this findings to vsnet-alert 16958 and to The Astronomer's Telegramm (ATel #5944). The characteristics of the runs obtained before/after a switch between the high and low states will be presented.

• Proceedings of the KIPE Conference
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• 2017.07a
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• pp.497-498
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• 2017

Electrical fire is a bit part of the cause of a fire. To reduce fire, we study the system for efficient alert for ARC which is the most part of electrical fire. We use IoT to alert managers faster to prevent accident.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1753-1758
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• 2005

Human Adaptive Mechatronics (HAM), which is a system concept to adapt human characteristics, has been proposed. As the HAM application, this paper addresses an information emphasis scheme to alert some hazards which are undetectable by a human operator. The emphasis scheme employs cognitive psychological approach to human discrimination characteristics because excess or deficient emphasis may disturb the operation. One of advantages of teleoperation system is able to include human valuable abilities as global environment recognition, planning, prediction and so on. To implement these abilities to mechanical system is difficult because of not enough intelligence. Proposed teleoperation system is designed to progress the human abilities, and moreover, to not disturb the abilities. In this paper, we consider that the discrimination characteristics depend on window positions on GUI display and operator's individuality. Finally, the efficiency of the alert scheme is verified by some experiments.

• Journal of Auto-vehicle Safety Association
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• v.5 no.1
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• pp.5-10
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• 2013

This paper describes a Forward Collision Warning (FCW) system based on the radar driven fusion with camera. The objective of FCW system is to provide an appropriate alert with satisfying the evaluation scenarios of US-NCAP and a driver acceptance. For this purpose, this paper proposed a data fusion algorithm and a collision warning algorithm. The data fusion algorithm generates information of fusion target depending on the confidence of camera sensor. The collision warning algorithm calculates indexes and determines an appropriate alert-timing by using analysis results of manual driving data. The FCW system with the proposed data fusion and collision warning algorithm was investigated via scenarios of US-NCAP and a real-road driving. It is shown that the proposed FCW system can improve the accuracy of an alarm-timing and reduce the false alarm in real roads.

• The KIPS Transactions:PartC
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• v.10C no.6
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• pp.665-674
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• 2003

In this paper, we propose an approach to correlate alerts using a clustering analysis of data mining techniques in order to support intrusion detection system. Intrusion detection techniques are still far from perfect. Current intrusion detection systems cannot fully detect novel attacks. However, intrucsion detection techniques are still far from perfect. Current intrusion detection systems cannot fully detect novel attacks or variations of known attacks without generating a large amount of false alerts. In addition, all the current intrusion detection systems focus on low-level attacks or anomalies. Consequently, the intrusion detection systems to underatand the intrusion behind the alerts and take appropriate actions. The clustering analysis groups data objects into clusters such that objects belonging to the same cluster are similar, while those belonging to different ones are dissimilar. As using clustering technique, we can analyze alert data efficiently and extract high-level knowledgy about attacks. Namely, it is possible to classify new type of alert as well as existed. And it helps to understand logical steps and strategies behind series of attacks using sequences of clusters, and can potentially be applied to predict attacks in progress.

• 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.

• Magazine of the Korean Society of Hazard Mitigation
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• v.10 no.4
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• pp.78-84
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• 2010

• Proceedings of KOSOMES biannual meeting
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• 2003.11a
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• pp.109-122
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• 2003

• Proceedings of the Korea Water Resources Association Conference
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• 2006.05a
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• pp.28-35
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• 2006

Global Flood Alert System (GFAS) is an attempt to make the best use of satellite rainfall data in flood forecasting. The project of GFAS is promoted both by Ministry of Land, Infrastructure and Transport-Japan (MLIT) and Japan Aerospace Exploration Agency (JAXA), under which Infrastructure Development Institute-Japan (IDI) has been working on the development of Internet-based information system and just launched trial run of GFAS in April 2006 on International Flood Network (IFNet) website. The function of GFAS is to connect space agencies and hydrological services/river authorities in charge of flood forecasting and warning by providing global rainfall information in maps, text data e-mails and so on which is produced from binary global rainfall data downloaded from National Aeronautics and Space Administration (NASA) website. Although the effectiveness of satellite rainfall data in flood forecasting and warning has yet to be verified, satellite rainfall is expected to play an important role to strengthen existing flood forecasting systems by diversifying hydrological data source.