• 한국정밀공학회지
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• 제15권3호
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• pp.133-140
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• 1998

The neural network was applied to identify wear debris generated from the lubricated machine surface. The wear test was carried out under different experimental conditions. In order to describe characteristics of debris of various shapes and sizes, the four shape parameter(50％ volumetric diameter, aspect, roundness and reflectivity) of wear debris are used as inputs to the network and learned the friction condition of five values(material 3, applied load 1, sliding distance 1). It is shown that identification results depend on the ranges of these shape parameter learned. The three kinds of the wear debris had a different pattern characteristics and recognized the friction condition and materials very well by neural network.

• 한국공작기계학회논문집
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• 제12권5호
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• pp.53-58
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• 2003

Wear debris can be collected from the lubricants of operating machinery and its morphology is directly related to the fiction condition of the interacting materials from which the wear particles originated in lubricated machinery. But in order to predict and estimate working conditions, it is need to analyze the shape characteristics of wear debris and to identify. Therefore, if the shape characteristics of wear debris is identified by computer image analysis and the neural network, The four parameter (50% volumetric diameter, aspect, roundness and reflectivity) of wear debris are used as inputs to the network and learned the friction. It is shown that identification results depend on the ranges of these shape parameters learned. The three kinds of the wear debris had a different pattern characteristic and recognized the friction condition and materials very well by neural network. We resented how the neural network recognize wear debris on driving condition.

• 한국시뮬레이션학회:학술대회논문집
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• 한국시뮬레이션학회 2005년도 추계학술대회 및 정기총회
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• pp.102-107
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• 2005

PMS(Plant Monitoring System) for simulation is the program for training that users such as instructor, operator, and supervisor can operate the plant virtually. Presented in this paper, are some lessons learned from implementation of PMS for simulation. First, this PMS fer simulation is based on LINUX instead of UNIX. These two OS(Operating System) have different platform and compiler version on it, so debugging needs to be done about variable defining part. Second, In LINUX system, the size of shared memory and message queue is already designated as a default value that user has to set up and use the RTAP DB. Third, to decrease the dummy data size, it has to be changed into binary formation. Lastly, PMS software for simulation provides several environments according to the command, so considered should be status monitoring, alarm, system organization, point and group monitoring, history storage and searching function.

• 한국전자거래학회:학술대회논문집
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• 한국전자거래학회 2003년도 종합학술대회 논문집
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• pp.242-248
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• 2003

◎ Uncover the hidden pattern from massive data(data warehouse) -Builds a reasonable model to predict the future for business advantage -Decision Making based on the learned models(omitted)

• 대한교통학회:학술대회논문집
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• 대한교통학회 1998년도 Proceedings The 4th International Transport Symposium
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• pp.204-211
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• 1998

This paper reviewed the East Asia financial crisis, how China reacts to its impact, and what lessons should be learned from the crisis.

• Korean Journal of Acupuncture
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• 제39권3호
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• pp.97-99
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• 2022

• 한국수학교육학회지시리즈A:수학교육
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• 제45권1호
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• pp.75-96
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• 2006

The purpose of this study was to analyze how elementary pre-service teachers learned the pedagogical content knowledge of mathematics and to understand the challenges and difficulties that they experienced in the course of student teaching. A qualitative case study provided an in-depth description of the whole three weeks of student teaching process. Four pre-service teachers and two mentor teachers participated in this study. Multiple data collection techniques were used; classroom observations, in-depth interviews, document analysis, and researcher's field notes. The results of this study showed how pre-service teachers learn PCK of mathematics in designing mathematics lessons, understanding mathematics learners and delivering mathematics lessons and what are the difficulties and challenges they experienced. Finally this study discussed about some suggestions to pre-service program and future research.

• Asian Journal of Innovation and Policy
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• 제8권3호
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• pp.417-427
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• 2019

This article puts forward the opinion of a policy expert, who had been involved in Korea's science and technology policy for 30 years, about technology policy for SMEs. This article first explains why technology policies for SMEs are needed, and from what point of view. The next section looks at the current problems facing Korean SMEs, followed by the introduction of past and current polices to support SMEs. The comparison between current issues facing SMEs and past and current policies leads to lessons that can be learned. There are four lessons for the policy itself and three for the implementation of these policies. As for policy, the first four are about stages of development, concentration, R&D and diffusion, and policy mix. The latter three are policymakers, institutional building, and cooperation between ministries. This article makesfour suggestions for future policy: the importance of startups' culture, cooperation with others, infrastructure to reduce uncertainty, and policy monitoring and evaluation.

• 인터넷정보학회논문지
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• 제10권1호
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• pp.93-99
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• 2009

게임프로그램 제작이 단순히 3D 또는 온라인게임 등으로 분류하여 엔진과 게임프로그래밍을 하던 시기를 지나 이제는 게임프로그래밍의 종류를 세분화하여 인공지능 게임프로그래머의 역할이 게임을 좀 더 재미있게 할 수 있는 시점이라 하겠다. 본 논문에서는 강화학습 알고리즘을 이용하여 보상 값을 받아 줄고누 보드게임 말이 학습하게 하여 지능적으로 움직이게 하였다. 구현된 게임 말이 지능적으로 잘 움직이는지 확인하기위해, 보드게임을 제작하여 상대방 말과 승부를 하게 하였다. 실험결과 일정횟수 학습한 이후, 임의로 움직이는 말보다 성능이 월등히 향상됨을 알 수 있었다.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2011년도 한국컴퓨터종합학술대회논문집 Vol.38 No.1(A)
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• pp.278-281
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• 2011

Mobile devices equipped with various sensors have the potential of providing context-aware services. Location is one of the most common forms of context, which can be applied to diverse applications. In this paper, we present methods for learning and predicting users' routes between significant locations, e.g., home and workplaces, based on personal GPS data. A user's significant locations and routes between them are learned by a set of rules as well as clustering. When the user is moving, our methods can predict which of the learned routes is being taken now. After the route prediction, the user's next location can also be inferred. Our methods have been applied to the real GPS datasets from four subjects. For the next location prediction task, the achieved accuracy was 84.8%.