• Journal of KIISE:Information Networking
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• v.29 no.6
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• pp.674-684
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• 2002

Anomaly detection techniques have teen devised to address the limitations of misuse detection approach for intrusion detection. An HMM is a useful tool to model sequence information whose generation mechanism is not observable and is an optimal modeling technique to minimize false-positive error and to maximize detection rate, However, HMM has the short-coming of login training time. This paper proposes an effective HMM-based IDS that improves the modeling time and performance by only considering the events of privilege flows based on the domain knowledge of attacks. Experimental results show that training with the proposed method is significantly faster than the conventional method trained with all data, as well as no loss of recognition performance.

• Proceedings of the Korean Information Science Society Conference
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• 2001.04b
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• pp.460-462
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• 2001

인터넷 사용자가 급증하고, 인터넷을 통한 비즈니스에 수익 모델에 대한 관심이 높아지면서 방문자별로 맞춤 정보를 제공하는 퍼스널라이제이션이 인터넷 개발자 및 사용자들의 관심을 모으고 있다. 이러한 퍼스널라이제이션을 위해서 전처리과정인 사용자 프로파일 생성과정을 확장된 웹 로그 처리 시스템을 통해서 구현해본다. 웹사이트 서버의 확장된 이벤트 처리, 즉 사용자의 행위정보를 로그에 포함시켜 로그정보를 웹 로그 서버에 전송하도록 설계하였다. 그리고 이 웹 로그 정보를 쉽게 분석할 수 있다. 이때 데이터베이스 저장 기술로 OLE DB Provider상에서 수행되는 ADO 기술을 사용함으로써 확장된 웹 로그 처리 시스템을 설계하였다. 확장된 웹 로그 DB를 패턴분석, 군집분석 등의 마이닝(Mining) 기법을 통하여 맞춤 서비스에 대한 사용자 프로파일을 구축할 수 있다.

• The Bulletin of The Korean Astronomical Society
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• v.37 no.2
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• pp.131.2-131.2
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• 2012

태양전파폭발위치감지기(KSRBL)는 단일 안테나 전파분광기로써 미 뉴저지공과대학과의 협력으로 2009년 8월에 한국천문연구원에 개발 설치되었다. 1 MHz 스펙트럼 분해능과 1초의 시간 분해능을 가지고 있고 관측할 수 있는 주파수 대역은 245, 410 MHz 와 0.5-18 GHz에 이르는 광대역이다. 또한 태양 전면 태양 폭발 위치를 감지할 수 있다. 전파 관측은 LabVIEW와 IDL 프로그램에 의해 미리 짜여진 관측 스케줄에 따라 매일 자동으로 진행된다. 데이터 분석을 위해 필요한 플럭스, 안테나, 전파 이득에 대한 눈금조정 작업을 위한 소프트웨어를 개발하였다. 2009년 설치이후 지금까지 12개의 이벤트를 관측하였고 그 중 5개의 이벤트를 가지고 관측된 스펙트럼의 모듈레이션 패턴을 분석하여 태양면상에서 전파 폭발의 위치 값을 구했다. Solar Dynamics Observatory(SDO) AIA 이미지와 비교해 KSRBL의 위치감지 성능을 분석하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.04a
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• pp.678-680
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• 2009

본 논문에서는 성능 분석과 검증을 위한 방법으로 가상화 기법을 이용하여 성능을 분석할 수 있는 도구를 제안한다. 가상 머신의 성능 분석을 통해 원하는 이벤트들의 발생 양상에 따른 시스템의 성능을 호스트 머신에서 파악할 수 있다. 즉 가상 머신에서 사용하는 자원과 발생하는 이벤트에 대한 정보를 호스트 머신에서 확인할 수 있고, 가상 머신에서 발생한 사용자 입력을 호스트 머신이 임의로 재생시킬 수 있다. 이러한 기능을 통해 사용자 입력 패턴에 따른 시스템 자원의 상태 및 성능을 분석하여, 해당 시스템의 안정성을 시험할 수 있는 검증 환경을 제공한다.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.2
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• pp.137-144
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• 2013

A sequential pattern mining is finding out frequent patterns from the data set in time order. In this field, a dynamic weighted sequential pattern mining is applied to a computing environment that changes depending on the time and it can be utilized in a variety of environments applying changes of dynamic weight. In this paper, we propose a new sequence data mining method to explore the stream data by applying the dynamic weight. This method reduces the candidate patterns that must be navigated by using the dynamic weight according to the relative time sequence, and it can find out frequent sequence patterns quickly as the data input and output using a hash structure. Using this method reduces the memory usage and processing time more than applying the existing methods. We show the importance of dynamic weighted mining through the comparison of different weighting sequential pattern mining techniques.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.12
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• pp.2707-2715
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• 2010

This paper suggests a implements and a design of class diagram authorization tool based on the MVC pattern. It defines and descries the structure of ClassInformation, ScreenDisplay and ToolManager. ClassInformation is responsible for processing or handling information of a diagram. ScreenDisplay is responsible for GUI to configure the screen of the authorization tool. ToolManager is responsible for event handling to process I/O of the authorization tool. Based on MVC pattern, ClassInformation, ScreenDisplay and ToolManager of the authorization tool are assigned each role independently. It is flexible to new requirement, because of loose coupling.

• The Journal of the Korea Contents Association
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• v.10 no.11
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• pp.20-29
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• 2010

The typical Kinetic Family Drawing (KFD) Web database system, a form of prototype system, has been developed, relying on the suggestions from family art therapists, with an aim to handle large amounts of assessment data and to facilitate effective implement of assessment activities. However, unfortunately such a system has an intrinsic problem that it fails to collect clients' behaviors, attitudes, facial expressions, voices, and other critical information observed while they are drawing. Accordingly we propose the ontology based video event analysis and video retrieval system in this paper, in order to enhance the function of a KFD Web database system by using a web camera and drawing tool. More specifically, a newly proposed system is designed to deliver two kinds of services: the client video retrieval service and the sketch video retrieval service, accompanied by a summary report of occurred events and dynamic behaviors relative to each family member object, respectively. The proposed system can support the reinforced KFD assessments by providing quantitative and subjective information on clients' working attitudes and behaviors, and KFD preparation processes.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.6
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• pp.1245-1255
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• 2009

Recently, to analyze the cause of the railway accident objectively and quickly and prevent the accident, many countries are legislating for the installation of the black box what we call an event recorder, which records information about the operation of railway vehicle. Thus, the study of the event recorder has been in progress. Moreover, the analysis software that can analyze and express the stored data in the event recorder is required for the correct decision on the accident. Therefore, in this paper, we presented a design of analysis software which analyzes the data, plays the audio and video in the event recorder system. This software can quickly and accurately identify the cause of the accident and recognize the driving patterns and habits of the driver according to the operating section. In addition, by analyzing the audio and video data simultaneously in the previous accident, we expect that it is possible to prevent accidents in advance.

• Journal of Korea Multimedia Society
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• v.15 no.7
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• pp.870-878
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• 2012

Recently, very rapid development of computer and sensor technologies induces various kinds of user interface (UI) technologies based on user experience (UX). In this study, we investigate and develop a smart touch projector system technology on the basis of IR sensor and image processing. In the proposed system, a user can control computer by understanding the control events based on gesture of IR pen as an input device. In the IR image, we extract the movement (or gesture) of the devised pen and track it for recognizing gesture pattern. Also, to correct the error between the coordinate of input image sensor and display device (projector), we propose a coordinate correction algorithm to improve the accuracy of operation. Through this system technology as the next generation human-computer interaction, we can control the events of the equipped computer on the projected image screen without manipulating the computer directly.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.2
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• pp.80-92
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• 2023

As cities are becoming densely populated, previously unexpected events such as crimes, accidents, and infectious diseases are bound to affect user demands. With a time-series prediction of demand using information with uncertainty, it is impossible to derive reliable results. In particular, the COVID-19 outbreak in early 2020 caused changes in abnormal travel patterns and made it difficult to predict demand for time series. A methodology that accurately predicts demand by detecting and reflecting these changes is, therefore, required. The current study suggests a time series modeling pipeline that automatically detects and predicts abnormal events caused by COVID-19. We expect its wide application in various situations where there is a change in demand due to irregular and abnormal events.