• Proceedings of the Safety Management and Science Conference
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• 2000.11a
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• pp.105-109
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• 2000

인천항은 우리나라 제 2의 수,출입항임에도 불구하고 지리적, 자연적 특성상 갑문이라는 조수간만의 차를 극복하기 위한 시설과 매우 다양한 화물의 취급으로 만성적인 체선·체화의 문제를 안고 있다. 본 연구에서는 이러한 인천항을 대상으로 항만운영의 기본인 선석배정문제를 기존 연구를 바탕으로 실제 인천항 운영에 있어서 행해지고 있는 선석의 연속관리측면과 전문가의 경험으로 축적된 비공식적인 규칙을 보다 면밀히 조사하여, 공식적인 인천항 배정규칙과 더불어 개선된 실시간 의사결정지원시스템을 구축하고자 한다. 특히 동일 하역사의 이웃한 선석을 하나의 선석군으로 묶어 연속으로 선박을 접안하는 사항과 사용자의 임의수정사항과 동적인 상황을 시스템에 반영함에 있어서 사용자 인터페이스를 강화하여 구축, 개발하였다.

• Journal of Intelligence and Information Systems
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• v.8 no.2
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• pp.71-89
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• 2002

In this paper, an integrated planning system is introduced for the efficient operation of container terminal. It consists of discharging and loading planning, yard planning, and berth scheduling subsystem. This interface of this system is considered for user's convenience, and the rule-based system is suggested and developed in order to make planning with automatic procedures, warning functions for errors.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.11a
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• pp.945-946
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• 2009

현재의 컴퓨터 설문 시스템은 설문의 규칙이나 처리절차들을 하드코딩하는 방식으로 구현하기 때문에 설문이 변경되면 처리엔진을 수정해야 한다. 이것은 설문 처리엔진이 설문에 종속되어 있음을 의미한다. 본 논문에서는 이러한 약점을 해결하고자 능동문서 모델을 이용하여 설문으로부터 처리엔진을 독립시키는 방법을 고안하였다. 능동문서 기반의 설문지는 컨텐츠, 로직, 지식베이스(사용자 응답), 질의로 구성된다. 이 중에서 로직은 다양한 질문유형에 대한 처리 방법을 기술하는 것으로서 어떤 설문 설계자도 정의할 수 있어야 한다. 또한 로직은 직관적으로 서술할 수 있고 실행가능 하도록 XML 형식의 규칙 마크업 언어인 ERML로 표현하였다. ERML로 작성된 로직은 Prolog로 변환된 후 추론기에 등록되며, 사용자 응답에 따라 설문을 제어하고 처리한다. 마지막으로 몇 가지 질문유형을 ERML로 구현하고 설문 시스템(WINAD: The Web Interview System with Active Documents)에 적용한 실험 결과 설문으로부터 처리엔진을 독립시킬 수 있음을 확인했다.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.11a
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• pp.811-812
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• 2009

프로젝트 개발에서 소프트웨어의 품질을 높이기 위한 방법 중 하나는 소스코드에 대한 잠재적인 결함을 초기에 발견하는 것이다. 이를 실현하기 위해 정형화된 기법으로 코드 인스펙션을 자동화하였으며, 개발자들이 ACI 규칙을 수립하였다. 논문에서는 실제 진행 중인 프로젝트를 기반으로 하여 결함 점검 수행에 따른 결함 발견 건수와 결함밀도가 감소되는 증명을 다룬다.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.2
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• pp.159-164
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• 2013

It is well known that a single moving camera video is capable of extracting the 3-dimensional position of an object. With this in mind, current research performed image-based monitoring to establish a floating structure model using a camcorder system. Following this, the present study extracted frame images from digital camcorder video clips and matched the interest points to obtain relative 3D coordinates for both regular and irregular wave conditions. Then, the researchers evaluated the transformation accuracy of the modified SURF-based matching and image-based displacement estimation of the floating structure model in regular wave condition. For the regular wave condition, the wave generator's setting value was 3.0 sec and the cycle of the image-based displacement result was 2.993 sec. Taking into account mechanical error, these values can be considered as very similar. In terms of visual inspection, the researchers observed the shape of a regular wave in the 3-dimensional and 1-dimensional figures through the projection on X Y Z axis. In conclusion, it was possible to calculate the displacement of a floating structure module in near real-time using an average digital camcorder with 30fps video.

• Proceedings of the Korea Contents Association Conference
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• 2009.05a
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• pp.105-111
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• 2009

Widely used color information recognition methods based on the RGB color model with static fuzzy inference rules have limitations due to the model itself - the detachment of human vision and applicability of limited environment. In this paper, we propose a method that is based on HSI model with new inference process that resembles human vision recognition process. Also, a user can add, delete, update the inference rules in this system. In our method, we design membership intervals with sine, cosine function in H channel and with functions in trigonometric style in S and I channel. The membership degree is computed via interval merging process. Then, the inference rules are applied to the result in order to infer the color information. Our method is proven to be more intuitive and efficient compared with RGB model in experiment.

• Journal of the Korean Data and Information Science Society
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• v.24 no.2
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• pp.267-275
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• 2013

Among data mining techniques, the association rule is the most recently developed technique, and it finds the relevance between two items in a large database. And it is directly applied in the field because it clearly quantifies the relationship between two or more items. When we determine whether an association rule is meaningful, we utilize interestingness measures such as support, confidence, and lift. Interestingness measures are meaningful in that it shows the causes for pruning uninteresting rules statistically or logically. But the criteria of these measures are chosen by experiences, and the number of useful rules is hard to estimate. If too many rules are generated, we cannot effectively extract the useful rules.In this paper, we designed a variety of non-linear regression equations considering all association thresholds between the number of rules and three interestingness measures. And then we diagnosed multi-collinearity and autocorrelation problems, and used analysis of variance results and adjusted coefficients of determination for the best model through numerical experiments.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.5
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• pp.506-512
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• 2000

In this paper, we propose a Fuzzy Classifier System(FCS) makes the classifier system be able to carry out the mapping from continuous inputs to outputs. The FCS is based on the fuzzy controller system combined with machine learning. Therefore the antecedent and consequent of a classifier in FCS are the same as those of a fuzzy rule. In this paper, the FCS modifies input message to fuzzified message and stores those in the message list. The FCS constructs rule-base through matching between messages of message list and classifiers of fuzzy classifier list. The FCS verifies the effectiveness of classifiers using Bucket Brigade algorithm. Also the FCS employs the Genetic Algorithms to generate new rules and modifY rules when performance of the system needs to be improved. Then the FCS finds the set of the effective rules. We will verifY the effectiveness of the poposed FCS by applying it to Autonomous Mobile Robot avoiding the obstacle and reaching the goal.

• Journal of the Korean Data and Information Science Society
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• v.27 no.3
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• pp.629-638
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• 2016

Among data mining techniques, the association rule is one of the most used in the real fields because it clearly displays the relationship between two or more items in large databases by quantifying the relationship between the items. There are three primary quality measures for association rule; support, confidence, and lift. We evaluate association rules using these measures. The approach taken in the previous literatures as to estimation of association rule number has been one of a determination function method or a regression modeling approach. In this paper, we proposed a few of non-linear regression equations useful in estimating the number of rules and also evaluated the estimated association rules using the quality measures. Furthermore we assessed their usefulness as compared to conventional regression models using the values of regression coefficients, F statistics, adjusted coefficients of determination and variation inflation factor.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.37 no.5
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• pp.1-10
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• 2000

In this paper, we propose a Fuzzy Classifier System(FCS) makes the classifier system be able to carry out the mapping from continuous inputs to outputs. The FCS is based on the fuzzy controller system combined with machine learning. Therefore the antecedent and consequent of a classifier in FCS are the same as those of a fuzzy rule. In this paper, the FCS modifies input message to fuzzified message and stores those in the message list. The FCS constructs rule-base through matching between messages of message list and classifiers of fuzzy classifier list. The FCS verifies the effectiveness of classifiers using Bucket Brigade algorithm. Also the FCS employs the Genetic Algorithms to generate new rules and modify rules when performance of the system needs to be improved. Then the FCS finds the set of the effective rules. We will verify the effectiveness of the poposed FCS by applying it to Autonomous Mobile Robot avoiding the obstacle and reaching the goal.