• 한국지능시스템학회논문지
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• 제13권3호
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• pp.316-321
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• 2003

최근들어, 서포트 벡터 학습은 패턴 분류, 함수 근사 및 비정상 상태 탐지 등의 분야에서 상당한 관심을 끌고 있다. 여러가지 서포트 벡터 학습 방법들 중 누-버전(nu-versions)으로 불리는 방법들은 서포트 벡터의 개수를 제어해야할 필요가 있는 경우에는 특히 유용한 것으로 알려져 있다. 본 논문에서는, $\nu-SVR$로 불리는 누-버전 서포트 벡터 학습 방법과 미리 정해진 기저함수를 모두 활용하는 함수 근사 문제를 고려한다. $\varepsilon-SVR$, $\nu-SVR$ 및 세미-파라메트릭 함수 근사 방법론등을 복습한 후에, 본 논문은 정해진 기저함수를 이용할 수 있는 방향으로 기존의 $\nu-SVR$ 방법을 확장하는 방안을 제시한다. 그리고, 제안된 방법의 적용가능성이 예제를 통하여 보여진다.

• 인간식물환경학회지
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• 제24권6호
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• pp.651-662
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• 2021

Background and objective: This study identifies whether children's planning-organizing executive function can be significantly classified and predicted by home environment quality and wealth factors. Methods: For empirical analysis, we used the data collected from the 10th Panel Study on Korean Children in 2017. Using machine learning tools such as support vector machine (SVM) and random forest (RF), we evaluated the accuracy of the model in which home environment factors classify and predict children's planning-organizing executive functions, and extract the relative importance of variables that determine these executive functions by income group. Results: First, SVM analysis shows that home environment quality and wealth factors show high accuracy in classification and prediction in all three groups. Second, RF analysis shows that estate had the highest predictive power in the high-income group, followed by income, asset, learning, reinforcement, and emotional environment. In the middle-income group, emotional environment showed the highest score, followed by estate, asset, reinforcement, and income. In the low-income group, estate showed the highest score, followed by income, asset, learning, reinforcement, and emotional environment. Conclusion: This study confirmed that home environment quality and wealth factors are significant factors in predicting children's planning-organizing executive functions.

• 디지털융복합연구
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• 제18권6호
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• pp.287-295
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• 2020

본 연구는 최근 이러닝 분야에서 모바일 러닝과 마이크로콘텐츠에 의해 부상하고 있는 마이크로러닝의 특성을 정의하고 이에 대한 적용 만족도를 분석하여, 향후 마이크로러닝이 새로운 학습 형태로 자리매김 할 수 있는지를 살펴보았다. 이를 위하여 사전 문헌분석을 통해 마이크로러닝의 특성을 정의하고 잘 갖추어진 마이크로러닝 사이트에 대하여 특성 만족도를 실증 검증하고, 이 특성 이외에 어떤 다른 기술적 기능이 필요한가에 대하여도 전문가의 의견수렴을 통하여 제시하였으며, 이러닝의 미래기술인 학습 분석이나 성과측정 등의 기술적 기능과 향후 연계되어야 한다는 것을 제시하였다. 본 연구의 결과에 따르면 마이크로러닝의 특성인 학습콘텐츠의 질적, 양적 수준, 학습콘텐츠에의 접근성, 모바일 기기 접근성, 동기부여 및 상호작용의 모든 면에서 특성 만족도를 보여주고 있었다. 따라서 마이크로러닝은 그 기능적 특성을 잘 반영한다면 이러닝 분야에서 효과적인 학습 형태로 자리 잡을 것이며 밀레니얼 세대를 위한 교육과 학습 및 훈련에 크게 기여할 수 있을 것이다.

• 대한수학교육학회지:학교수학
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• 제5권2호
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• pp.191-208
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• 2003

본 연구는 수학 및 과학 영재 아동과 일반 아동의 성격유형과 학습양식의 차이를 알아보는 것을 목적으로 하였다. 이를 위해 수학 및 과학 영재교육을 받고 있는 부산광역시 소재의 초등학교 5, 6학년 135명과 일반아동 66명을 대상으로 하여 MMTIC과 학습양식검사를 실시하였다. 성격유형의 분석은 선호지표와 기능별, 기질별 분포를 중심으로 하였고, 학습양식은 독립형, 의존형, 협동형, 경쟁형, 참여형, 회피형의 유형으로 분류되었다. 연구결과에 의하면, 수학 및 과학 영재 아동은 성격유형, 학습양식 그리고 성격유형에 따른 학습양식에서 큰 차이가 없었으나, 일반 아동과는 유의한 차이를 나타냈다. 또한 연구대상의 성격유형에 따라 선호하는 학습양식에는 차이가 있는 것으로 나타났다.

• The Journal of the Acoustical Society of Korea
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• 제21권4E호
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• pp.146-155
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• 2002

We present a new technique for achieving source separation when given only a single charmel recording. The main idea is based on exploiting the inherent time structure of sound sources by learning a priori sets of time-domain basis functions that encode the sources in a statistically efficient manner. We derive a learning algorithm using a maximum likelihood approach given the observed single charmel data and sets of basis functions. For each time point we infer the source parameters and their contribution factors. This inference is possible due to the prior knowledge of the basis functions and the associated coefficient densities. A flexible model for density estimation allows accurate modeling of the observation, and our experimental results exhibit a high level of separation performance for simulated mixtures as well as real environment recordings employing mixtures of two different sources. We show separation results of two music signals as well as the separation of two voice signals.

• 한국음향학회지
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• 제21권4호
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• pp.146-146
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• 2002

We present a new technique for achieving source separation when given only a single channel recording. The main idea is based on exploiting the inherent time structure of sound sources by learning a priori sets of time-domain basis functions that encode the sources in a statistically efficient manner. We derive a learning algorithm using a maximum likelihood approach given the observed single channel data and sets of basis functions. For each time point we infer the source parameters and their contribution factors. This inference is possible due to the prior knowledge of the basis functions and the associated coefficient densities. A flexible model for density estimation allows accurate modeling of the observation, and our experimental results exhibit a high level of separation performance for simulated mixtures as well as real environment recordings employing mixtures of two different sources. We show separation results of two music signals as well as the separation of two voice signals.

• 한국수학사학회지
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• 제32권3호
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• pp.135-155
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• 2019

One of the reasons students have difficulty in studying probability is that they do not understand the meaning of mathematical terms precisely. One such term is a continuous random variable. Students tend not to think of the accurate definition of continuous random variables but to understand the definition of continuity of functions and the meaning of continuity in probability as equal. In this study, we try to explore the degree of pre-service teachers' understanding on the concept of continuation of functions and continuous random variables. To do this, the questionnaire items related to continuous random variables and continuity of functions were developed by experts and examined by pre-service teachers. Based on this, we make suggestions on implications for teaching and learning about continuous random variables.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.1296-1301
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• 2004

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 learning approach of fuzzy-neural network by immune algorithm. The proposed learning model is presented in an immune based fuzzy-neural network (FNN) form which can handle linguistic knowledge by immune algorithm. The learning algorithm of an immune based FNN is composed of two phases. The first phase used to find the initial membership functions of the fuzzy neural network model. In the second phase, a new immune algorithm based optimization is proposed for tuning of membership functions and structure of the proposed model.

• 대한전기학회논문지
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• 제43권6호
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• pp.991-1000
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• 1994

To control nonlinear systems adaptively, we improve learning speed of neural networks and present a novel control algorithm characterized by compensation of control inputs. In an error-backpropagation algorithm for tranining multilayer neural networks(MLNN's) the effect of the slope of activation functions on learning performance is investigated and the learning speed of neural networks is improved by auto-adjusting the slope of activation functions. The control system is composed of two MLNN's, one for control and the other for identification, with the weights initialized by off-line training. The control algoritm is modified by a control strategy which compensates the control error induced by the indentification error. Computer simulations show that the proposed control algorithm is efficient in controlling a nonlinear system with abruptly changing parameters.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2004년도 추계학술대회 학술발표 논문집 제14권 제2호
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• pp.303-308
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• 2004

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 learning approach of fuzzy-neural network by immune algorithm. The proposed learning model is presented in an immune based fuzzy-neural network (FNN) form which can handle linguistic knowledge by immune algorithm. The learning algorithm of an immune based FNN is composed of two phases. The first phase used to find the initial membership functions of the fuzzy neural network model. In the second phase, a new immune algorithm based optimization is proposed for tuning of membership functions and structure of the proposed model.