• Journal of Information Technology Applications and Management
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• 제9권2호
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• pp.1-16
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• 2002

Recently, neural network methods have been studied to find out more valuable information in data bases. But the supervised learning methods of neural networks have an overfitting problem, which leads to errors of target patterns. And the unsupervised learning methods can distort important information in the process of regularizing data. Thus they can't efficiently classify data, To solve the problems, this paper introduces a hybrid neural networks HACAB(Hybrid Algorithm combining a Competition learning model And BP Algorithm) combining a competition learning model and 8P algorithm. HACAB is designed for cases which there is no target patterns. HACAB makes target patterns by adopting a competition learning model and classifies input patterns using the target patterns by BP algorithm. HACAB is evaluated with random input patterns and Iris data In cases of no target patterns, HACAB can classify data more effectively than BP algorithm does.

• 영어어문교육
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• 제16권3호
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• pp.59-80
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• 2010

The present study investigated what kind of form-focused instruction would yield better results for incidental learning of English verb patterns in two experiments. Experiment 1 compared the effectiveness of focus on form (reading + translation) and focus on forms (verb pattern list + translation) tasks in learning new English verb patterns incidentally in Korean EFL college classrooms. The results of Experiment 1 showed significantly higher results for the focus on forms group. Since it was revealed by Experiment 1 that the learners did not notice unknown target verb patterns, Experiment 2 was undertaken to examine whether the difference between the focus on form and focus on forms conditions found in Experiment 1 would be retained even after the isolated form-focused instruction or focus on forms aiming at teaching students how to recognize verb patterns was provided for the learners before the focus on form and focus on forms tasks were carried out. The results showed that the focus on form group yielded significantly higher incidental learning scores than the focus on forms group. The effectiveness rates of the focus on form in Experiment 2 were statistically higher than those of the focus on forms in Experiment 1. The results of the two experiments indicated that the combination of the isolated form-focused instruction and focus on form was significantly more effective in learning English verb patterns incidentally. In conclusion, form-focused instruction including both isolated form-focused instruction and focus on form is an effective way to incidental learning of English verb patterns.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1990년도 추계학술대회 논문집 학회본부
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• pp.465-469
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• 1990

This thesis proposes a new scheme for the recognition of presegmented Hangul characters. The proposed approach is rather insensitive to noise and variation by applying 2 dimensional convolution to learning patterns. In this thesis, the hangul recognition neural network is implemented in the basis of this scheme and recognition rate is analyzed in boo cases of learning which are learning by binary patterns and learning by binary patterns and convoluted patterns together.

• 전자공학회논문지B
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• 제33B권3호
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• pp.169-175
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• 1996

In this paper, a new parallel neural network system that performs dynamic competitive learning is proposed. Conventional learning mehtods utilize the full dimension of the original input patterns. However, a particular attribute or dimension of the input patterns does not necessarily contribute to classification. The proposed system consists of parallel neural networks with the reduced input dimension in order to take advantage of the information in each dimension of the input patterns. Consensus schemes were developed to decide the netowrks performs a competitive learning that dynamically generates output neurons as learning proceeds. Each output neuron has it sown class threshold in the proposed dynamic competitive learning. Because the class threshold in the proposed dynamic learning phase, the proposed neural netowrk adapts properly to the input patterns distribution. Experimental results with remote sensing and speech data indicate the improved performance of the proposed method compared to the conventional learning methods.

• 대한감각통합치료학회지
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• 제16권3호
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• pp.11-21
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• 2018

목적 : 본 연구는 대학생의 감각처리 유형과 학습유형, 학습전략 간에 상관관계를 조사하고자 실시되었다. 연구방법 : 부산소재 K대학교의 학생 115명을 대상으로 실시하였다. 측정도구는 청소년/성인 감각프로파일(Adolescent/Adult Sensory Profile; AASP)과 학습과정 설문지(Study Process Questionnaire; SPQ)와 학습동기전략 설문지(Motivated Strategies for Learning Questionnarie; MSLQ)를 사용하여 감각처리 유형, 학습유형, 학습전략에 대한 설문을 실시하였다. 수집된 자료는 SPSS/WIN 20.0을 이용하여 카이 제곱 검정(chi square test), 피어슨 상관계수(Pearson correlation coefficient)로 분석하였다. 결과 : 감각처리 유형과 학습유형의 상관관계에서 감각등록저하 유형은 표층형 학습유형(p=0.03), 감각추구 유형은 심층형 학습유형(p=0.02)과 상관관계가 있었다. 감각처리 유형과 학습전략의 상관관계에서 감각추구 유형은 조직화 학습전략(p=0.00), 감각민감 유형은 조직화 학습전략(p=0.03)및 초인지 학습전략(p=0.00)과 상관관계가 있었다. 감각추구 성향의 차이에 따라 group A와 B로 나누었을 때 학습유형(p=0.00) 및 학습전략(p=0.03)에서 유의한 차이가 나타났다. 결론 : 감각처리 유형과 학습유형, 학습전략 간에 상관관계가 나타났으며 감각처리 유형에 따라 학습유형, 학습전략이 달라짐으로 개인에게 맞는 학습 유형과 학습전략을 선택함에 있어 기초자료로써 활용되어지는데 의의가 있다.

• 한국간호교육학회지
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• 제20권4호
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• pp.577-586
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• 2014

Purpose: This study investigates patterns of small group interaction and examines the influence among graduate nursing students of online collaborative learning strategies on small group interaction patterns, task performance and learning attitude in web-based team learning environments. Method: To analyze patterns of small group interaction, group discussion dialogues were reviewed by two instructors. Groups were divided into two categories depending on the type of feedback given (passive or active). For task performance, evaluation of learning processes and numbers of postings were examined. Learning attitude toward group study and coursework were measured via scales. Results: Explorative interactions were still low among graduate nursing students. Among the students given active feedback, considerable individual variability in interaction frequency was revealed and some students did not show any specific type of interaction pattern. Whether given active or passive feedback, groups exhibited no significant differences in terms of task performance and learning attitude. Also, frequent group interaction was significantly related to greater task performance. Conclusion: Active feedback strategies should be modified to improve task performance and learning attitude among graduate nursing students.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1990년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 26-27 Oct. 1990
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• pp.929-932
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• 1990

This paper considers the problem of patterns recognition using the artificial neural network systems. The artificial neural network systems provide an effective tool for classifying patterns and/or characters by learning them in a certain repeated hashion. The mechanism of the learning process and the structure of neural network systems used are main concerns in the accurate and fast classification of the patterns which are slightly different each other. The neural network system employed in this study has three layers structure which is composed of input, intermidiate, and output layers. Our main concern is to develope an effective learning mechanism how to learn the patterns fastly and accurately. The experimental study performed shows that there exists an effective learning method to get higher recognition ratio in classifying the several different patterns by artificial neural network system constructed.

• Educational Technology International
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• 제10권2호
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• pp.127-143
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• 2009

The purpose of this study was to identify the interaction patterns in distance only mode e-Learning. In order to investigate this study, messages shown in the electronic notice board were analyzed to see how interaction occurs between teacher and learner or learner and learner under the e-learning of cyber university. To analyze messages was applied according to the framework by Henri's contents analysis model. As a result of contents analysis on electronic board, the participative dimension was 399 messages. A learner put on 7~8 messages a day. The number of messages was low compared to the number of learners, but the number of inquiries was about 140. That means that each learner contacts and checks messages at least once a day. The meaning dimension was 600 units. The main interaction patterns were Interactive-social-cognitive-metacognitive. This means that e-Learning in distance only mode leads a positive attitude of learners as a self-directed learning, and needs teacher's well-structured instructional strategies for increasing interaction. In conclusion, social dimension and interactive dimension of messages support learners psychologically in the process of learning though they directly guide learning under the circumstances of e-learning lacking face-to-face element. It can be interpreted that the teacher's role is significantly important in order to attract learners' positive participation and cognitive and meta-cognitive dimension of messages and activities

• 한국경영과학회:학술대회논문집
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• 한국경영과학회/대한산업공학회 2005년도 춘계공동학술대회 발표논문
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• pp.599-606
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• 2005

Novelty detection involves identifying novel patterns. They are not usually available during training. Even if they are, the data quantity imbalance leads to a low classification accuracy when a supervised learning scheme is employed. Thus, an unsupervised learning scheme is often employed ignoring those few novel patterns. In this paper, we propose two ways to make use of the few available novel patterns. First, a scheme to determine local thresholds for the Self Organizing Map boundary is proposed. Second, a modification of the Learning Vector Quantization learning rule is proposed so that allows one to keep codebook vectors as far from novel patterns as possible. Experimental results are quite promising.

• 정보학연구
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• 제5권2호
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• pp.17-26
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• 2002

본 연구에서는 자기조직화 교사학습 신경망인 SOSL(Self-Organized Superised Learning)과 이 신경망의 구조를 제안한다. SOSL신경망은 하이브리드 형태의 신경망으로써 다수 개의 컴포넌트 에러 역전파 신경망들과 수정된 PCA신경망으로 구성된다. CBP신경망은 군집화되고 복잡한 입력패턴에 대하여 교사학습을 병렬적으로 수행한다. 수정된 PCA신경망은 군집화 및 지역투영에 의하여 원 입력패턴을 보다 작은 차원으로 변환시키기 위하여 사용된다. 제안된 SOSL은 많은 입력패턴을 가짐으로써 큰 네트워크 크기를 가지게 되는 신경망에 효과적으로 적용이 가능하다.