• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.421-426
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• 2005

Fuzzy logic, neural network, fuzzy-neural network play an important as the key technology of linguistic modeling for intelligent control and decision making in complex systems. The fuzzy-neural network (FNN) learning represents one of the most effective algorithms to build such linguistic models. This paper proposes particle swarm optimization algorithm based optimal learning fuzzy-neural network (PSOA-FNN). The proposed learning scheme is the fuzzy-neural network structure which can handle linguistic knowledge as tuning membership function of fuzzy logic by particle swarm optimization algorithm. The learning algorithm of the PSOA-FNN is composed of two phases. The first phase is to find the initial membership functions of the fuzzy neural network model. In the second phase, particle swarm optimization algorithm is used for tuning of membership functions of the proposed model.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 1996년도 추계학술대회발표논문집; 고려대학교, 서울; 26 Oct. 1996
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• pp.49-52
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• 1996

In this article, we develop a 'dynamic' approach to manufacturing improvement, based on perspectives of manufacturing learning and decision theory. First, we present an alternative definition of production system consistent with a decision-theoretic perspective: the system consists of structural, infra-structural, and decision making constructs. A primary proposition is that learning capability possessed by a manufacturing system be prerequisite for the system to improve its manufacturing performance through optimal controlling of the three constructs. To support the proposition, we elaborate on a mathematical representation of "learning" as defined in an applied setting. We show how the learning capability acts as an integrating force ameliorating the trade-off between two key manufacturing capabilities, i.e., process controllability and process flexibility.exibility.

• Journal of Information Processing Systems
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• 제13권1호
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• pp.57-67
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• 2017

In this paper, Atanassov's intuitionistic fuzzy set theory is used to handle the uncertainty of students' knowledgeon domain concepts in an E-learning system. Their knowledge on these domain concepts has been collected from tests that were conducted during their learning phase. Atanassov's intuitionistic fuzzy user model is proposed to deal with vagueness in the user's knowledge description in domain concepts. The user model uses Atanassov's intuitionistic fuzzy sets for knowledge representation and linguistic rules for updating the user model. The scores obtained by each student were collected in this model and the decision about the students' knowledge acquisition for each concept whether completely learned, completely known, partially known or completely unknown were placed into the information table. Finally, it has been found that the proposed scheme is more appropriate than the fuzzy scheme.

• 한국정밀공학회지
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• 제11권4호
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• pp.138-147
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• 1994

Performance management of computer networks is intended to improve a given network performance in order for more efficient information exchange between subsystems of an integrated large-scale system. Improtance of performance management is growing as many function of the large- scale system depend on the quality of communication services provided by the network. The role of performance management is to manipulate the adjustable protocol parameters on line so that the network can adapt itself to a dynamic environment. This can be divided into two subtasks : performance evaluation to find how changes in protocol parameters affect the network performance and decision making to detemine the magnitude and direction of parameter adjustment. This paper is the second part of the two papers focusing on conceptual design, development, and evaluation of performance management for token bus networks. This paper specifically deals with the task of decision making which utilizes the principles of stochastic optimization and learning automata. The developed algorithm can adjuxt four timer settings of a token bus protocol based on the result of performance evaluation. The overall performance management has been evaluated for its efficacy on a network testbed.

• 산업융합연구
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• 제21권12호
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• pp.141-151
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• 2023

본 연구의 목적은 간호대학생 전 학년을 대상으로 학습몰입, 진로결정 자기효능감, 전공만족도가 진로준비행동에 미치는 영향을 파악하고자 시도되었다. G광역시와 J도에 소재한 대학의 간호학과 전학년 208명에게 2022년 9월 15일부터 2022년 10월 14일까지 설문지를 통해 자료수집 하였다. 자료 분석은 SPSS 29.0 Program을 이용하여 서술적 통계, t-test, One-way ANOVA, Pearson's correlation, Stepwise Regression Analysis을 사용하였다. 본 연구 결과 진로준비행동은 학습몰입(r=.515, p<.001), 진로결정 자기효능감(r=.681, p<.001), 전공만족도(r=.621, p<.001)는 통계적으로 유의한 양적 상관관계를 나타내었다. 다중회귀분석 결과 진로준비행동에 미치는 영향요인은 진로결정 자기효능감(𝛽=.446, p<.001), 전공만족도(𝛽=.285, p<.001), 3학년(𝛽=.157, p=.001), 학습몰입(𝛽=.133, p=.018), 2학년(𝛽=.106, p=.038) 순이며, 진로준비행동의 설명력은 57.0%이었다. 따라서 간호대학생이 선택한 진로에 탐색을 고려한 진로 프로그램을 통해 맞춤 교육을 제공할 필요가 있다.

• 한국콘텐츠학회논문지
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• 제6권3호
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• pp.85-92
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• 2006

문제중심학습(Problem-Based Learning)은 학습자 중심으로 학습이 이루어지는 구성주의 모델중의 하나이다. 본 논문에서는 정보보호에 대한 중요성을 학습하기 위하여 캐릭터를 이용한 역할놀이 기반 PBL을 제안하였다. 역할놀이 기반 PBL은 학습자의 특성, 학습과제의 특성을 반영한다는 점에서 다른 PBL 모델과 차별화된다. 또한 인터넷과 모바일 디바이스를 사용하여 학습자들이 주체가 되어 학습하는 학습지원 시스템이다. 실험결과 제안 방법이 정보보호에 대한 전통적인 교사 중심의 수업 방식보다 자기 주도적 학습, 협동학습, 콘텐츠 메이킹, 몰입성에서 효율적임을 보였다.

• International Journal of Contents
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• 제12권4호
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• pp.62-68
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• 2016

This paper describes an attempt to integrate flip teaching into a language classroom by adopting wikispaces as an online learning platform. The purpose of this study is to examine student perceptions of the effectiveness of using video lectures and wikispaces to foster active participation and collaborative learning. Flipped learning was implemented in an English writing class over one semester. Participants were 27 low intermediate level Korean university students. Data collection methods included background questionnaires at the beginning of the semester, learning experience questionnaires at the end of the semester, and semi-structured interviews with 6 focal participants. Because of the significance of video lectures in flip teaching, oCam was used for making weekly online lectures as a way of pre-class activities. Every week, online lectures were posted on the school LMS system (moodle). Every week, participants met in a computer room to perform in-class activities. Both in-class activities and post-class activities were managed by wikispaces. The results indicate that the flipped classroom facilitated student learning in the writing class. More than 53% of the respondents felt that it was useful to develop writing skills in a flipped classroom. Particularly, students felt that the video lectures prior to the class helped them improve their grammar skills. However, with respect to their satisfaction with collaborative works, about 44% of the participants responded positively. Similarly, 44% of the participants felt that in-class group work helped them interact with the other group members. Considering these results, this paper concludes with pedagogical suggestions and implications for further research.

• 대한방사선기술학회지:방사선기술과학
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• 제42권1호
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• pp.9-17
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• 2019

Although digital mammography is a representative method for breast cancer detection. It has a limitation in detecting and classifying breast tumor due to superimposed structures. Machine learning, which is a part of artificial intelligence fields, is a method for analysing a large amount of data using complex algorithms, recognizing patterns and making prediction. In this study, we proposed a technique to improve the diagnostic accuracy of energy-selective mammography by training data using the machine learning algorithm and using dual-energy measurements. A dual-energy images obtained from a photon-counting detector were used for the input data of machine learning algorithms, and we analyzed the accuracy of predicted tumor thickness for verifying the machine learning algorithms. The results showed that the classification accuracy of tumor thickness was above 95% and was improved with an increase of imput data. Therefore, we expect that the diagnostic accuracy of energy-selective mammography can be improved by using machine learning.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권1호
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• pp.334-349
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• 2022

The effectiveness of Wi-Fi networks is greatly influenced by the optimization of contention window (CW) parameters. Unfortunately, the conventional approach employed by IEEE 802.11 wireless networks is not scalable enough to sustain consistent performance for the increasing number of stations. Yet, it is still the default when accessing channels for single-users of 802.11 transmissions. Recently, there has been a spike in attempts to enhance network performance using a machine learning (ML) technique known as reinforcement learning (RL). Its advantage is interacting with the surrounding environment and making decisions based on its own experience. Deep RL (DRL) uses deep neural networks (DNN) to deal with more complex environments (such as continuous state spaces or actions spaces) and to get optimum rewards. As a result, we present a new approach of CW control mechanism, which is termed as contention window threshold (CW_Threshold). It uses the DRL principle to define the threshold value and learn optimal settings under various network scenarios. We demonstrate our proposed method, known as a smart exponential-threshold-linear backoff algorithm with a deep Q-learning network (SETL-DQN). The simulation results show that our proposed SETL-DQN algorithm can effectively improve the throughput and reduce the collision rates.

• 비교문화연구
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• 제25권
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• pp.597-626
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• 2011

Civil college is a public educational institute for theoretical and practical learning. This study examines the social context behind France's civil college and how it is being operated. Many studies have been conducted in Korea to examine Germany in terms of lifelong learning or adult learning, but there is almost no study on France. Therefore, this study was conducted to analyze the history and operation of civil college, the "college outside college," in France and what Korea should learn from it. The civil college of France can be discussed in two contexts: first, it is AUPF, which stands for the French association of civil colleges, and it was mostly influenced by Northern Europe and Germany. Second, it is Caen Civil College, which was established by M. Onfray based his philosophical collaboration. The European civil college opened almost 1,000 courses in 2010-2011 for a variety of subjects, including Foreign Languages, Mother Tongue, the Dialects of Alsace, Philosophy, Cosmology, History, Art History, Psychology, Sociology, Astronomy, Botany, and Natural Science. Courses in Fine Arts include drawing, painting, sculpture, photography, music, and theater. For another form of civil college, Philosopher M. Onfray has been operating Caen Civil College since 2002 for general education and cultural education. It is not acknowledged by conventional philosophers, but it is contributing to the popularization of philosophy. In conclusion, the civil college in France has brought in-depth philosophical discussions out of the lecture rooms in an effort to popularize learning, making lifelong learning more accessible to the general public.