• 전기학회논문지
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• 제67권1호
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• pp.114-123
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• 2018

This paper concerns a design and learning method of softmax function neural networks based on K-means clustering. The partial discharge data Information is preliminarily processed through simulation using an Epoxy Mica Coupling sensor and an internal Phase Resolved Partial Discharge Analysis algorithm. The obtained information is processed according to the characteristics of the pattern using a Motor Insulation Monitoring System program. At this time, the processed data are total 4 types that void discharge, corona discharge, surface discharge and slot discharge. The partial discharge data with high dimensional input variables are secondarily processed by principal component analysis method and reduced with keeping the characteristics of pattern as low dimensional input variables. And therefore, the pattern classifier processing speed exhibits improved effects. In addition, in the process of extracting the partial discharge data through the MIMS program, the magnitude of amplitude is divided into the maximum value and the average value, and two pattern characteristics are set and compared and analyzed. In the first half of the proposed partial discharge pattern classifier, the input and hidden layers are classified by using the K-means clustering method and the output of the hidden layer is obtained. In the latter part, the cross entropy error function is used for parameter learning between the hidden layer and the output layer. The final output layer is output as a normalized probability value between 0 and 1 using the softmax function. The advantage of using the softmax function is that it allows access and application of multiple class problems and stochastic interpretation. First of all, there is an advantage that one output value affects the remaining output value and its accompanying learning is accelerated. Also, to solve the overfitting problem, L2-normalization is applied. To prove the superiority of the proposed pattern classifier, we compare and analyze the classification rate with conventional radial basis function neural networks.

• 대한수학교육학회지:학교수학
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• 제9권2호
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• pp.313-326
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• 2007

최근 대수 교육과정에서 패턴들을 표현하면서 일반적인 규칙을 인식하고 설명하는 것이 하나의 대안으로 제시되고 강조되고 있다. 우리나라 역시 제 7 차 교육과정에서 '규칙성과 함수' 영역과 관련하여 초등학교 과정에서 다양한 형태의 패턴활동을 지도하고 있다. 그러나 최근 패턴활동을 통한 학습에 대한 연구에서 학생들의 어려움과 문제점이 지적되고 있다. 이 글에서는 우리나라 초등학교 교육과정에 많이 도입되고 있는 시각적 패턴의 탐구 활동을 통한 일반화 과정을 중심으로 하여, 시각적 패턴의 일반화 과정에서의 다양한 접근과 학생들의 사고전략, 기호화 상태를 고찰한다. 그리고 시각적 패턴의 일반화, 기호화의 어려움을 논의하고 시각적 패턴의 탐구 활동 학습을 위한 몇 가지 제안을 하였다.

• 대한수학교육학회지:학교수학
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• 제15권1호
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• pp.15-35
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• 2013

본 연구의 목적은 중학생용 수학학습양식 판별도구를 개발하여 선호유형을 범주화하는 것이다. 개발된 수학학습양식 판별도구로 976명의 중학생을 대상으로 설문조사하여 16가지의 수학학습양식 유형이 존재하는지를 확인하였고 이를 선행 연구들과 비교 분석하였다. 또한 수학학습양식의 각 요인에 따른 양식별 남녀 학습자, 학년별 학습자의 분포에 어떠한 차이가 있는지 분석하였다. 수학학습양식 판별도구를 통해서 학습자의 인지적 정의적 학습양식을 파악함으로써 수학학습에 대한 학습자 특성을 전체적으로 파악하여 획일화된 수업형태에서 벗어나 개별화 수업으로 나아갈 수 있는 방향을 제시하고자 한다.

• 컴퓨터교육학회논문지
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• 제6권2호
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• pp.107-120
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• 2003

웹기반 교육에서 학습과정을 평가한다는 것은 개별 학습자들의 학습 활동을 평가하는 것을 의미하기 때문에 학습자의 특정 수업내용에 대한 학습 시간, 학습 패턴, 학습 참여도(의견 교환, 질문), 학습 환경 등의 정보가 요구된다. 본 연구의 목적은 웹 기반 교육에서 쟁점이 되고 있는 학습과정 평가문제를 해결하기 위해 최적의 웹 로그 마이닝을 이용하여 학습자 개인별 학습현황에 관한 정보를 얻어 이를 수행 평가에 반영하고자 함이다. 연구 내용 및 결과로는 먼저, 학습현황 분석을 위한 항목을 선정하고 웹 로그 마이닝을 위한 로그 데이터 전처리 과정을 실행하였다. 다음으로는, 위의 웹 로그 데이터를 기초로 학습자별 데이터베이스를 구축하고 질의어를 사용하여 학습현황을 분석하였다.

• Journal of Information Technology Applications and Management
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• 제26권5호
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• pp.27-39
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• 2019

The Internet of Things (IoT) is a new paradigm that is revolutionizing computing. It is intended that all objects around us will be connected to the network, providing "anytime, anywhere" access to information. This study introduces IoT with Kolb's learning style in order to enhance the learning experience especially for inclusive education for primary and secondary schools where delivery of knowledge is not limited to physical, cognitive disabilities, human diversity with respect to ability, language, culture, gender, age and of other forms of human differences. The article also emphasizes the role of learning style as a discovery process that incorporates the characteristics of problem solving and learning. Kolb's Learning Style was chosen as it is widely used in research and in practical information systems applications. A consistent pattern of finding emerges by using a combination of Kolb's learning style and internet of things where specific individual differences, learning approach differences and IoT application differences are taken as a main research framework. Further several suggestions were made by using this combination to IoT architecture and smart environment of internet of things. Based on these suggestions, future research directions are proposed.

• 복식문화연구
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• 제29권3호
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• pp.346-360
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• 2021

Universities have recently introduced problem-based learning (PBL) to various subjects to enhance problem-solving skills (including self-directed learning and small-group learning) required in industry. The PBL module was applied to the personal production process in a draping class. A study was based on a questionnaire after conducting two PBL modules with a group of students. Each PBL module included 'design analysis', 'presentation of flat sketch and draping plan', 'discussion of the plan', 'evaluation of the draping result and correcting the problem', and 'final evaluation of the completed project'. Results showed that satisfaction with the PBL method and its activities was higher than satisfaction with existing teaching methods. In particular, among the various components, the 'design analysis' and 'the presentation step of flat sketch and draping plan' stages were more helpful to students compared to small-group discussion. Moreover, the effects of PBL were observed through student reflection essays, in which students suggested that PBL was very effective in enhancing problem-solving through self-directed and small-group learning. Despite the overall satisfaction with PBL, students expressed some minor difficulties associated with awkwardness with a novel learning method, lack of diverse perspectives among each group, and poor communication skills. Therefore, the study shows that PBL is highly likely to be useful to students when they are solving pattern drafting problems and making samples through self-directed learning and small-group learning.

• 산업경영시스템학회지
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• 제20권42호
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• pp.161-169
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• 1997

A variety of learning methods are used for neural networks. Among them, the backpropagation algorithm is most widely used in such image processing, speech recognition, and pattern recognition. Despite its popularity for these application, its main problem is associated with the running time, namely, too much time is spent for the learning. This paper suggests a method which maximize the convergence speed of the learning. Such reduction in e learning time of the backpropagation algorithm is possible through an adaptive adjusting of the slope of the activation function depending on total errors, which is named as the variable slope algorithm. Moreover experimental results using this variable slope algorithm is compared against conventional backpropagation algorithm and other variations; which shows an improvement in the performance over pervious algorithms.

• 한국건설관리학회:학술대회논문집
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• 한국건설관리학회 2003년도 학술대회지
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• pp.610-613
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• 2003

건설산업의 국제경쟁력 강화를 위해서는 기술력의 확보가 무엇보다 중요하다. 기술력은 유능한 엔지니어의 양성과정에서부터 시작됨의 인식으로 대학교육의 중요성이 대두되고 있는 현실이다. 본 연구에서는 수동적 교육의 한계를 극복하기 위하여 능동적 학습방식의 의의 및 중요성을 제시하고자 한다. 사례로서 중량의 양중작업용 타인크레인을 대상으로 능동적 학습 절차와 주요 수행 내용을 설명하고 학습효과를 분석한다.

• 한국군사과학기술학회지
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• 제23권2호
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• pp.139-146
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• 2020

The camouflage pattern was difficult to distinguish from the surrounding background, so it was difficult to classify the object and the background image when the color image is used as the training data of deep-learning. In this paper, we proposed a red-green color blindness image transformation method using the principle that people of red-green blindness distinguish green color better than ordinary people. Experimental results show that the camouflage soldier's recognition performance improved by proposed a deep learning model of the ensemble technique using the imitated red-green-blind image data and the original color image data.

• Journal of Multimedia Information System
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• 제3권4호
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• pp.155-160
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• 2016

The Pulse Coupled Neural Network (PCNN) is a kind of neural network models that consists of spiking neurons and local connections. The PCNN was originally proposed as a model that can reproduce the synchronous phenomena of the neurons in the cat visual cortex. Recently, the PCNN has been applied to the various image processing applications, e.g., image segmentation, edge detection, pattern recognition, and so on. The method for the image matching using the PCNN had been proposed as one of the valuable applications of the PCNN. In this method, the Genetic Algorithm is applied to the PCNN parameter learning for the image matching. In this study, we propose the method of the similar image rating using the PCNN. In our method, the Genetic Algorithm based method is applied to the parameter learning of the PCNN. We show the performance of our method by simulations. From the simulation results, we evaluate the efficiency and the general versatility of our parameter learning method.