• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2001.05a
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• pp.720-723
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• 2001

현재 인터넷 환경에서 크래킹은 보편화되어 있다. 이러한 크래킹을 탐지하거나 방어하기 위한 기법들은 대부분 기존의 불법 침입 유형을 분석하여 대응 알고리즘을 개발하는 것이 대부분이다. 현재 알려진 침입 탐지 기법은 비정상 탐지(Anomaly Detection)와 오용 탐지(Misuse Detection)로 분류할 수 있는데, 전자는 통계적 방법, 특징 추출 등을 이용하며, 후자는 조건부 화률, 전문가 시스템, 상태 전이 분석, 패턴 매칭 둥을 적용한다. 본 연구에서는 상태전이 기반의 연관 마이닝 기법을 이용한 침입 시나리오 탐지 알고리즘을 제안한다. 이를 위해 본 연구에서는 의사결정지원시스템에서 많이 적용한 연관 마이닝 기법을 여러 가지 불법 침입과 연관된 상태 정보를 분석할 수 있는 수정된 상태전이 알고리즘을 제시한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2003.11c
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• pp.1355-1358
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• 2003

본 연구는 공군에서 운영하고 있는 ASIS2000(공군보급정보체계)의 활용과 효과적인 창고관리를 위한 의사결정시스템의 설계 및 구현에 적용할 OLAP엔진을 설계 및 구현하고 창고운영자의 동선을 최소화 할 수 있는 알고리즘을 제안하는 것이다. 창고운영자의 다차원질의를 처리하여 창고운영전반에 대한 분석을 제공의 기반이 되는 OLAP엔진과 동선을 최소화하는 알고리즘의 목적 및 구성요소를 중심으로 동선최소 알고리즘을 제안한다.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2019.05a
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• pp.82-82
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• 2019

해상에서 긴급상황 발생 시 선박운항자는 짧은 시간에 신속 정확한 의사 결정을 할 필요가 있다. 이를 위해서 해양사고(충돌, 좌초, 화재, 엔진고장, 조타고장) 심각성에 따른 대피항로(해경선, 비상투묘, 표류, 임의좌주, 주변선박) 선정 알고리즘을 설계하였고, 선박운항자를 위한 긴급대피지원안내 시스템을 개발 중에 있다. 본 연구에서는 대피항로 선정 지원 기술 중 비상투묘와 충돌위험도를 중심으로 시스템 적용 모델의 타당성의 평가하고 알고리즘의 신뢰성을 검증하였다. 비상투묘 지원 기술의 검증을 위해 국내외 해양사고 보고서 및 재결서를 분석하고 알고리즘에 적용해 결과를 비교하였다. 충돌위험도를 검증하기 위해 재결서의 선박 충돌 사고 사례를 시뮬레이션으로 재현하였고, 시뮬레이션 기록 데이터를 기반으로 PARK model, IWRAP MK2 프로그램을 이용해 충돌위험도를 평가하였다. 본 연구의 결과를 통해 해양사고 발생 시 선박과 인명 피해를 최소화할 수 있을 것으로 예상된다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.07a
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• pp.375-378
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• 2020

본 연구는 토지 보상비의 추정 모델 개발을 위해서 건설 CALS (Continuous Acquisition & Life-cycle Support) 시스템의 내부데이터와 개별공시지가 및 표준지 공시지가 등의 외부데이터, 그리고 개발된 추정 모델의 고도화를 위한 개별공시가 데이터를 기반으로 생성된 데이터를 활용하였다. 이렇게 수집된 3가지 유형의 데이터를 분석하기 위해서 기존 선형 모델 또는 의사결정나무 (Tree) 기반의 모델상 과적합 오류를 제거할 경우 매우 유용한 알고리즘으로 Decision Tree 기반의 Xgboost 알고리즘을 데이터 분석 방법론으로 토지 보상비 추정 모델 개발에 활용하였다. Xgboost 알고리즘의 고도화를 위해 하이퍼파라미터 튜닝을 적용한 결과, 실제 보상비와 개발된 보상비 추정 모델의 MAPE(Mean Absolute Percentage Error) 범위는 19.5%로 확인하였다.

• Journal of Korea Port Economic Association
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• v.28 no.4
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• pp.193-207
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• 2012

The of purpose of this study is to predict export and import container volumes using a Decision Tree analysis. Factors which can influence the volume of container cargo are selected as independent variables; producer price index, consumer price index, index of export volume, index of import volume, index of industrial production, and exchange rate(won/dollar). The period of analysis is from january 2002 to December 2011 and monthly data are used. In this study, CRT(Classification and Regression Trees) algorithm is used. The main findings are summarized as followings. First, when index of export volume is larger than 152.35, monthly export volume is predicted with 858,19TEU. However, when index of export volume is between 115.90 and 152.35, monthly export volume is predicted with 716,582TEU. Second, when index of import volume is larger than 134.60, monthly import volume is predicted with 869,227TEU. However, when index of export volume is between 116.20 and 134.60, monthly import volume is predicted with 738,724TEU.

• Journal of Industrial Convergence
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• v.16 no.4
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• pp.33-46
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• 2018

Personal credit scoring is an effective tool for banks to properly guide decision profitably on granting loans. Recently, many classification algorithms and models are used in personal credit scoring. Personal credit scoring technology is usually divided into statistical method and non-statistical method. Statistical method includes linear regression, discriminate analysis, logistic regression, and decision tree, etc. Non-statistical method includes linear programming, neural network, genetic algorithm and support vector machine, etc. But for the development of the credit scoring model, there is no consistent conclusion to be drawn regarding which method is the best. In this paper, we will compare the performance of the most common scoring techniques such as logistic regression, neural network, and support vector machines using personal credit data of the financial institution in China. Specifically, we build three models respectively, classify the customers and compare analysis results. According to the results, support vector machine has better performance than logistic regression and neural networks.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.12
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• pp.2348-2354
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• 2016

In recent days, IoT (Internet of Things) technology has been widely used in the field of agriculture, which enables the collection of environmental data and biometric data into the database. The availability of big data on agriculture results in the increase of the machine learning based analysis. Through the analysis, it is possible to forecast agricultural production and the diseases of livestock, thus helping the efficient decision making in the management of smart farm. Herein, we use the environmental and biometric data of Smart Pig farm to derive the accurate relationship model between the environmental information and the daily weight increase of swine and verify the accuracy of the derived model. To this end, we applied the M5P tree algorithm of machine learning which reveals that the wind speed is the major factor which affects the daily weight increase of swine.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.4
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• pp.373-379
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• 2019

Smart-farm has been spreading across Korea to improve the labor efficiency and productivity of greenhouse crops. Although notable improvements have been made in the monitoring technologies and environmental-controlling systems in greenhouses, only a few simple decision-support systems are available for predicting the optimum environmental conditions for crop growth. In this study, a tomato growth model (GreenTom), which was developed by Seoul National University in 1997, was calibrated and validated to examine if the model can be used as a decision-supporting system. The original GreenTom model was not able to simulate artificial defoliation, which resulted in overestimation of the leaf area index in the late growth. Thus, an algorithm for simulating the artificial defoliation was developed and added to the original model. The node development, leaf growth, stem growth, fruit growth, and leaf area index were generally well simulated by the modified model indicating that the model could be used effectively in the decision-making of smart greenhouse.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.4D
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• pp.433-442
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• 2010

Recent decrease in the availability of experienced skilled labor and a corresponding lack of new entrants has required the need for automating many of the construction equipment used in the construction industry. In particular, excavators are widely used throughout earthwork operations and automating its tasks enables work to be performed with higher productivity and safety. This paper introduces an optimal path generation method which is one of the core technologies required to make "Intelligent" excavators a reality. The method divides a given earthwork area into unit cells, identifies networks created by linking these cells, and identifies the optimal path an excavator should follow to minimize its total transportation costs. In addition, the method also accounts for drainage direction and path continuity to ensure that the generated path considers site specific conditions.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.9
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• pp.1153-1158
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• 2018

Online customer evaluations and social media information among a variety of information sources are critical for businesses as it influences the customer's decision making. There are limitations on the time and money that the survey will ask to identify a variety of customers' needs and complaints. The customer review data at online shopping malls provide the ideal data sources for analyzing customer sentiment about their products. In this study, we collected product reviews data on the smartphone of Samsung and Apple from Amazon. We applied five classification algorithms which are used as representative sentiment analysis techniques in previous studies. The five algorithms are based on support vector machines, bagging, random forest, classification or regression tree and maximum entropy. In this study, we proposed entropy score which can comprehensively evaluate the performance of classification algorithm. As a result of evaluating five algorithms using an entropy score, the SVMs algorithm's entropy score was ranked highest.