• Proceedings of the Korea Information Processing Society Conference
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• 2017.04a
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• pp.407-409
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• 2017

지능형 튜터링 시스템(Intelligent Tutoring System, ITS)은 학습자에게 자동으로 학습코스를 제공하는 시스템으로, 현재 비전공자 대상 코딩 교육용 ITS 수요가 증가하고 있음에 따라 본 연구팀도 비전공자 대상 코딩 교육용 ITS "Everycoding"을 개발하였다. 더 나아가 학습자 별 맞춤형 코딩 교육 튜터링 시스템 개발 요구가 늘어나고 있다. 이에, 본 연구에서는 기계학습 기법을 적용하여 개발한 협업 능력 기반의 학습자 모델을 군집 분석하고, 그에 따른 학습자 별 맞춤 피드백을 제공하는 기준을 제안한다.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2017.11a
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• pp.177-179
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• 2017

한국해양대학교는 IALA WWA에서 인증 받은 Level 1 (AtoN Manager) 교육기관으로 개도국 훈련을 분담하고 있다. "항로표지 전문가 및 글로벌 리더 양성"이라는 교육목표를 가지고 개도국을 위한 교육프로그램을 구성하였으며, IALA WWA 모델코스를 바탕으로 초청연수 교재를 개발하였다. 2017년 7월 한 달 간 아시아 태평양 지역의 항로표지 관리자 공무원을 대상으로 초청연수(역량강화)을 시행하였으며 초청 연수 종료 후 연수생을 대상으로 설문조사를 시행하고 분석한 결과 항로표지에 관한 전문지식이 향상되었고 연수에 대한 만족도도 높게 나타났다. 한국해양대학교는 지속적인 항로표지교육을 실시하여 주변 개도국의 해양안전과 해양경쟁력을 발전시키는데 이바지할 계획이다.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2013.10a
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• pp.63-65
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• 2013

실시간 위치 기반 선박 충돌 위험도는 자선의 관점에서 선박충돌의 위험을 판단하는 것이 아니라 VTS(Vessel Traffic Service)의 관점에서 충돌 위험이 있는 선박을 식별하고 충돌 위험 지역을 전자 해도에서 실시간으로 확인하여 해당 해역 전체의 선박 교통흐름과 통항하는 선박간의 위험도를 평가하는 것이 목적이다. 항해사로써의 승선 경험과 관제사로써의 근무 경험, 그리고 다 년간 VTS 관제 업무를 수행하고 있는 관제사들로부터 충돌의 위험이 있는 선박을 식별하는 방법으로 주로 선박간의 벡터(코스와 속력)를 실시간으로 모니터링하여 충돌 위험이 있는 선박에게 피항 조치를 취하도록 정보를 제공하는 것으로 확인되었다. 따라서 DCPA(Distance to Closest Point of Approach)와 TCPA(Time to Closest Point of Approach), 그리고 최근접시간을 변수로 하는 충돌 위험 함수식(최대값=100)을 연구하여 최대 위험값을 가지는 지점과 주변의 위험값을 계산하여 해역 전체의 위험도를 실시간으로 표시하는 기초 모델을 연구하였다.

• Journal of the Korean Society of Marine Environment & Safety
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• v.22 no.6
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• pp.679-687
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• 2016

In developing countries, the establishment of maritime colleges and the training of excellent merchant marine officers can greatly impact on the growth of the marine industry in a given country. The project to establish the Kenya Maritime College ('KMC') has been driven by the Kenyan government, which has been challenged by the growth of the marine industry in Korea. This study aims to the development of curriculum for the navigation science department of the KMC. To do this, firstly the Kenyan environments was surveyed. Then, IMO maritime education requirements, IMO model courses and curriculums from the navigation science departments of major maritime universities around the world were reviewed. Lastly, based on these surveys and reviews, curriculum for the navigation science department of the KMC was developed. This curriculum is comprised of an educational course that requires a total of 5 years of study, including 1 year of on-board training and a total of 200 credit hours (20 credit hours per semester). Although this curriculum has been developed to fit the environments of Kenya, it is expected to offer a point of reference for similar studies in other countries with the goal of standardizing maritime education internationally.

• The Journal of Korean Association of Computer Education
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• v.8 no.5
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• pp.97-108
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• 2005

In this paper, we present our prototype of a web manual creator that is based on MSP technology embedded in webMathematica. This tool gives courseware authors more simple ways to make their own mathematical web contents. We first classified authoring models for creating mathematical content development and proposed an advanced model. The final application called phpMath can generate MSP-driven documents automatically using Mathematica commands typed by users. In other words, phpMath users can make interactive dynamic mathematical web contents even though they do not know anything about web server, HTML, and webMathematica. We illustrated the details of the prototype from the user's perspectives followed by comments on usefulness.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.5
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• pp.689-694
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• 2009

In this paper an approach to building fuzzy models for predicting daily and weekly stock prices is presented. Predicting stock prices with traditional time series analysis has proven to be difficult. Fuzzy logic based models have advantage of expressing the input-output relation linguistically, which facilitates the understanding of the system behavior. In building a stock prediction model we bear a burden of selecting most effective indicators for the stock prediction. In this paper information used in traditional candle stick-chart analysis is considered as input variables of our fuzzy models. The fuzzy rules have the premises and the consequents composed of trapezoidal membership functions and nonlinear equations, respectively. DE(Differential Evolution) identifies optimal fuzzy rules through an evolutionary process. The fuzzy models to predict daily and weekly open, high, low, and close prices of KOSPI(KOrea composite Stock Price Index) are built, and their performances are demonstrated.

• Journal of Digital Convergence
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• v.13 no.5
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• pp.237-244
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• 2015

Export-led policies, FTA signed and economics of scale through a variety of market-oriented policies, such as regulations to improve market grew constantly. Accordingly, the correct decision making accurately analyze the economics market for decision, a problem has been an important issue in predicting. For accurate analysis and decision-making of the most common indicators of the stock market by proposing a number of indicators of economic transformation techniques were applied to the convergence model combining estimation and forecasts problem confirmed its effectiveness. Experimental result, gave the model estimation method to apply a transform to show the valid combinations proposed model state estimation result was confirmed in a very similar exercise aspect of the physical problem and the KOSPI index prediction.

• Annual Conference on Human and Language Technology
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• 2021.10a
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• pp.66-71
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• 2021

본 연구는 한국어 자질 기반 감성분석(Feature-based Sentiment Analysis: FbSA)을 위한 대규모의 학습데이터 구축에 있어 반자동 언어데이터 증강 기법(SSP: Semi-automatic Symbolic Propagation)에 입각한 자질-감성 주석 데이터셋 FeSAD(Feature-Sentiment-Annotated Dataset)의 개발 과정과 성능 평가를 소개하는 것을 목표로 한다. FeSAD는 언어자원을 활용한 SSP 1단계 주석 이후, 작업자의 주석이 2단계에서 이루어지는 2-STEP 주석 과정을 통해 구축된다. SSP 주석을 위한 언어자원에는 부분 문법 그래프(Local Grammar Graph: LGG) 스키마와 한국어 기계가독형 전자사전 DECO(Dictionnaire Electronique du COréen)가 활용되며, 본 연구에서는 7개의 도메인(코스메틱, IT제품, 패션/의류, 푸드/배달음식, 가구/인테리어, 핀테크앱, KPOP)에 대해, 오피니언 트리플이 주석된 FeSAD 데이터셋을 구축하는 프로세싱을 소개하였다. 코스메틱(COS)과 푸드/배달음식(FOO) 두 도메인에 대해, 언어자원을 활용한 1단계 SSP 주석 성능을 평가한 결과, 각각 F1-score 0.93과 0.90의 성능을 보였으며, 이를 통해 FbSA용 학습데이터 주석을 위한 작업자의 작업이 기존 작업의 10% 이하의 비중으로 감소함으로써, 학습데이터 구축을 위한 프로세싱의 소요시간과 품질이 획기적으로 개선될 수 있음을 확인하였다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38A no.9
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• pp.765-772
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• 2013

In this paper, we studied the path loss model of Air Traffic Control(ATC) telecommunication radio channel at the Incheon International Airport(IIA) concourse area. We measured wave propagation characteristics on the two frequencies among VHF/UHF channel bands. The transmitting site radiated the Continuous Wave(CW). The propagation measurement was taken using the moving vehicle equipped with receiver and antenna. The transmitting power, frequency, and antenna height are the same as the current operating condition. The path loss exponent and intercept parameters were extracted by the basic path loss model and hata model. The path loss exponents at Concourse area were 3.1/3.13 and 3.01/3.38 respectively in 128.2MHz and 269.1MHz. The deviation of prediction error is 2.77/3.17 and 4.01/3.66. The new path loss equation at the Concourse area was also developed using the derived path loss parameters. The new path loss model was compared with other models. This result will be helpful for the ATC site selection and service quality evaluation.

• Journal of the Korea Society of Computer and Information
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• v.14 no.5
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• pp.19-28
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• 2009

Prediction problem of the time-series data has been a research issue for a long time among many researchers and a number of methods have been proposed in the literatures. In this paper, a method is proposed that similarities among time-series data are examined by use of Hidden Markov Model and Likelihood and future direction of the data movement is determined. Query sequence is modeled by Hidden Markov Modeling and then the model is examined over the pre-recorded time-series to find the subsequence which has the greatest similarity between the model and the extracted subsequence. The similarity is evaluated by likelihood. When the best subsequence is chosen, the next portion of the subsequence is used to predict the next phase of the data movement. A number of experiments with different parameters have been conducted to confirm the validity of the method. We used KOSPI to verify suggested method.