• The Journal of Korean Association of Computer Education
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• v.22 no.1
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• pp.87-98
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• 2019

It is easy to assume that contradictions are logically incorrect or empty sets that have no solvability. This dilemma, which can not be done, is difficult to solve because it has to solve the contradiction hidden in it. Paradoxically, therefore, contradiction resolution has been viewed as an innovative and creative problem-solving. TRIZ, which analyzes the solution of the problem from the perspective of resolving contradictions, has been used for people rather than computers. The Butterfly model, which analyzes the problem from the perspective of solving the contradiction like TRIZ, analyzed the type of contradiction problem using symbolic logic. In order to apply an appropriate concrete solution strategy for a given contradiction problems, we designed the Butterfly algorithm based on decision making tree. We also developed a visualization tool based on Python tkInter to find concrete solution strategies for given contradiction problems. In order to verify the developed tool, the third grade students of middle school learned the Butterfly algorithm, analyzed the contradiction of the wooden support, and won the grand prize at an invention contest in search of a new solution. The Butterfly algorithm developed in this paper systematically reduces the solution space of contradictory problems in the beginning of problem solving and can help solve contradiction problems without trial and errors.

• Proceedings of the ESK Conference
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• 1995.10a
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• pp.37-37
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• 1995

CNS의 인간공학적 실험 평가 항목중 중요 요소의 하나인 운전원의 인지적 작업부하(정보입력, 의사결정, 및 정서적 부하)를 평가하기 위해 인지적부하를 부과할 수 있는 사건 시나리오 구성방법, 피실험자에 대한 훈련 방법, 실험과 디브리핑을 통한 운전원 행위 및 사건 경위 기록 체계를 개발하였다. 운전원 행위자료 데이타베이스에는 특정 요소행위 시점, 관측행위 분류, 사용된 표시장치, 의사결정내용, 사용된 조종장치, 조종행위분류, 운전원간 의사소통 내용, 정서상태, 표시장치 및 조종장치의 사용편의성 여부 및 불편이유, 주요사건 발생시점에 관한 정보가 기록되었다. 운전중 CNS 운전원에 제시되는 자극의 변화에 수반되는 운전원의 생리적 변화를 측정하여 이로부터 운전원이 받는 정신부하를 측정하는 체계를 개발하였다. 이를 위해 ECG, EEG, 호흡패턴,피부온도, 수평EOG, 수직EOG, 심박률, 및 호흡률의 생리변수에 대한 측정이 이루 어졌으며 각각의 신호를 초당 100개씩 샘플링하여 A/D 변환하고 정신부하 추정 알고리즘을 거쳐 정신부하 여부를 판정하는 점수를 구하였다. 다차원의 정신부하 판정 점수는 OR gate에 의해 통합되어 정신부하 발생시점과 함께 정신부하데이터베이스에 기록되었다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.9
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• pp.3304-3310
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• 2010

This study solves the decision making problems for TFT-LCD manufacturing supply chain with demand and price uncertainties by establishing robust production and distribution strategies. In order to control the decisions regarding production graded by quality, inventory level and distribution, this study develop scenario model based stochastic mixed integer linear programs (SMILPs) that consider demand and price uncertainties as well as realistic constraints such as capacities etc. The performance of the solution obtained from the SMILPs using robust algorithms will be evaluated through various scenarios.

• Journal of Internet Computing and Services
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• v.14 no.6
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• pp.25-32
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• 2013

Due to increasing competition of globalization and fast technological improvements the appropriate method for evaluating and selecting IS-personnel is one of the key factors for an organization's success. Personnel selection is a multi-criteria decision-making (MCDM) problem which consists of both qualitative and quantitative metrics. Although many articles have discussed various knowledge and skills IS personnel should possess, no specific model for IS personnel selection and evaluation, to our knowledge, has been published up to now. After reviewing the IS personnel's important characteristics, we propose an approach for categorizing the IS personnel based on their skills, ability, and knowledge during evaluation and selection process. Our proposed approach is derived from a model of neural network algorithm. We have adapted and implemented the fuzzy ART algorithm with Jaccard choice function. The result of an illustrative numerical example is proposed to demonstrate the easiness and effectiveness of our approach.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.1
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• pp.370-381
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• 2012

Multi-criteria result extraction is crucial in many scientific applications that support real-time stream processing, such as habitat research and disaster monitoring. Skyline evaluation is computational intensive especially over continuous time-interval data streams where each object has its own customized expiration time. In this work, we propose TI-Sky - a continuous skyline evaluation framework. To ensure correctness, the result space needs to be continuously maintained as new objects arrive and older objects expire. TI-Sky strikes a perfect balance between the costs of continuously maintaining the result space and the costs of computing the final skyline result from this space whenever a pull-based user query is received. Our key principle is to incrementally maintain a partially precomputed skyline result space - however doing so efficiently by working at a higher level of abstraction. TI-Sky's algorithms for insertion, deletion, purging and result retrieval exploit both layers of granularity. Our experimental study demonstrates the superiority of TI-Sky over existing techniques to handle a wide variety of data sets.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.1
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• pp.47-56
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• 2023

Mezzanine products are financial investment products with both bond and stock characteristics, which are mainly issued by low-grade companies in the financial market to secure liquidity. Therefore, bondholders investing in mezzanine products must make decisions about when they want to convert to stocks, along with whether they invest in mezzanine products issued by the company. Therefore, in this paper, a total of 2,000 learning data and 200 predictive experimental data with stock conversion events completed by major industries are divided, and mezzanine event algorithms are designed and performance analyzed through artificial neural network models. This topic is meaningful in that it proposed a methodology to scientifically solve the difficulties of exercising mezzanine products, which are of high interest in the financial field, by applying artificial neural network technology.

• Journal of Digital Contents Society
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• v.5 no.3
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• pp.205-212
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• 2004

This paper presents a design and implementation example of intelligent society member management system that is constructed to induce various research activity. Based on members data and society activity record, the system executed data mining. In the process of data mining useful society activity rules was produced and in result members could effectively interact with the system. Decision Tree Algorithm was used in the process, which is one of the methods of data mining. We presemts a plan for personalization website to provide user oriented administration policy and dynamic interface by using analyzed information of society activity rules produced.

• Proceedings of the Korean Society of Computer Information Conference
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• 2018.01a
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• pp.143-146
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• 2018

서명기반 악성코드 탐지는 악성 파일의 고유 해싱 값을 사용하거나 패턴화된 공격 규칙을 이용하므로, 변형된 악성코드 탐지에 취약한 단점이 있다. 기계 학습을 적용한 악성코드 탐지는 이러한 취약점을 극복할 수 있는 방안으로 인식되고 있다. 본 논문은 정적 분석으로 n-gram과 API 특징점을 추출해 특징 벡터로 구성하여 XGBoost, k-최근접 이웃 알고리즘, 지지 벡터 기기, 신경망 알고리즘, 심층 학습 알고리즘의 일반화 성능을 비교한다. 실험 결과로 XGBoost가 일반화 성능이 99%로 가장 우수했으며 k-최근접 이웃 알고리즘이 학습 시간이 가장 적게 소요됐다. 일반화 성능과 시간 복잡도 측면에서 XGBoost가 비교 대상 알고리즘에 비해 우수한 성능을 보였다.

• Journal of Internet Computing and Services
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• v.12 no.3
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• pp.69-76
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• 2011

Matching customers becomes the key function in on-line mediate services. There are two matching methods: one-directional matching that requires requests from one side(e,g., information searching), bi-directional matching that considers requests from both sides. Previous bi-directional matching has difficulties of getting the interests explicitly and service collapse problems when the opposite side do not put responding interests. This paper attempts to automate the inputs of interests for bi-directional matching by calculating the interests with fuzzy matching algorithm for optimization. The results of the proposed Bi-directional Fuzzy Matching(BDFM) algorithm told that the job placement accuracy of employment information matching results is over 95%. And, BDFM gives statically significant positive effect for motivating the employment activities when analyzed the effect after completing the implementation.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2007.05a
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• pp.321-328
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• 2007

데이터마이닝은 대용량의 데이터에 숨겨진 의미있고 유용한 패턴과 상관관계를 추출하여 의사결정에 활용하는 작업이다. 그 중에서도 고객 트랜잭션의 데이터베이스에서 아이템 사이에 존재하는 연관규칙을 찾는 것은 중요한 일이 되었다. Apriori 알고리즘 이후 연관규칙을 찾기 위해 대용량 데이터베이스로부터 압축된 의미있는 정보를 저장하기 위한 데이터 구조와 알고리즘들이 제안되어 왔다. 본 논문에서는 정점으로 아이템을 표현하고, 간선으로 두 아이템집합을 표현하는 빈발 패턴 네트워크(FPN)이라 불리는 새 자료 구조를 제안한다. 빈발 패턴 네트워크에서 아이템 사이의 연관 관계를 발견하기 위해 이 구조를 어떻게 효율적으로 사용 하느냐에 초점을 두고 있다. 구조의 효율적인 사용을 위하여 한 아이템이 클러스터 내의 아이템과는 유사도가 높고, 다른 클러스터의 아이템과는 유사도가 낮도록 네트워크의 정점을 클러스터링하는 방법을 사용한다. 실험은 신뢰도, 상관관계 그리고 간선 가중치 유사도를 이용하여 네트워크에서 아이템 클러스터링의 정확도를 보여준다. 본 논문의 실험 결과를 통해 신뢰도 유사도가 네트워크의 정점을 클러스터링할 때 클러스터의 정확성에 가장 많은 영향을 미친다는 것을 알 수 있었다.