• Journal of the Korean Society of Food Culture
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• v.26 no.3
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• pp.249-260
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• 2011

The current research aims to verify whether distance learning can be adopted in practical cooking class for Korean foods in a two-year college. The distance learning education can be a supplementary method to the traditional cooking class. The face-to-face teaching method and the distance learning method were compared in order to determine which of the one is more effective teaching method in the practical cooking class. The results of the present experimental study were analyzed based on the participant's learning expectation and satisfaction, the evaluation of the experimental process, and the academic performance. The results of this study showed that the participants in the face-to-face class evaluated their class experience higher than those in the distance learning class with respect to the participant's learning expectation and satisfaction, and the evaluation of the experimental process. On the contrary, regarding the academic performance, the participants in the distance learning class showed higher scores than those in the face-to-face class. The end result supports the claim that the distance learning method is more effective in the participants for gaining cooking knowledge.

• Journal of Korean Elementary Science Education
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• v.33 no.1
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• pp.129-139
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• 2014

This study was conducted to examine the effect of elementary science class using name card method on scientific learning motivation and academic achievement of elementary students. Two sixth grade classes were divided into experimental group and comparison group to treat the experimental group with elementary science class using name card method. General class according to teacher manual was implemented for the comparison group. Elementary science class applying name card method was conducted for 10 sessions throughout the experimental period of 8 weeks. The results of this study were as follows. First, elementary science class with name card method was effective in improving scientific learning motivation. Second, elementary science class with name card method had significant effect on improvement of scientific learning academic achievement. The study results showed that elementary science class with name card method was effective for scientific learning motivation and academic achievement of elementary students.

• Journal of Technologic Dentistry
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• v.42 no.4
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• pp.394-401
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• 2020

Purpose: The present study is an analysis of the extents of class satisfaction of college students who had applied the design thinking technique to capstone design class. Methods: The experimental method involved an analysis of 122 cases of data where advance and post replies were performed for the subject by students who had applied the design thinking technique and students who had not. The students involved had attended the capstone design class as a junior in the Department of Dental Technology at D University. Results: In the satisfaction with performance process of the capstone design class, five questions among nine had a high positive rating, while all eight questions on the satisfaction with performance methods had a high negative rating. Among ten questions on subjective learning outcomes, six showed a high positive rating. After the application of the design thinking class method, all mean values of the group with the application were higher than the group with no application in satisfaction with performance process, satisfaction with performance method, and subjective class outcomes. Hence, the design thinking class did have positive effects on the students' improvement of class. Conclusion: Based on the results of the study, it is implied that the considerations about diverse class composition methods and operation methods capable of improving the students' satisfaction are needed for efficient operation of the capstone design class.

• Communications for Statistical Applications and Methods
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• v.14 no.2
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• pp.377-388
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• 2007

As one of multi-class classification methods, ECOC (Error Correcting Output Coding) method is known to have low classification error rate. This paper aims at suggesting effective multi-class classification method (1) by comparing various encoding methods and decoding methods in ECOC method and (2) by comparing ECOC method and direct classification method. Both SVM (Support Vector Machine) and logistic regression model were used as binary classifiers in comparison.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.2
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• pp.215-224
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• 1997

This paper addresses the vertical class fragmentation in distributed object-oriented databases. In the proposed vertical fragmentation, after producing the attribute fragment by partitioning attributes, then the method fragment is produced by gathering methods referring the attribute in each fragment. For partitioning attributes, we define query access matrix(QAM) and method access matrix(MAM) to express attributes that method refers, and extend QAM, MAM and attribute usage matrix(AUM) to universal class environment for representing relationship among other classes through class hierarchy and class composite hierarchy.

• The Transactions of the Korea Information Processing Society
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• v.5 no.5
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• pp.1185-1203
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• 1998

When the class is added of deleted in object-oriented system, restructuring of class hierarchy is needed which enables new relationship with classes. But existing system requires much additional analysis costs because it is difficult to know the meaning between parent class and child class. This paper presents the updates method based on semantic modification through new relationship classification method. This method measures the similarity of classes and based on it's relationship, this method restructures class hierarchy by classifying not-equality, part-of, equality, inclusion, subset relation. This method can minimize the probability of meaning error for classes when the class hierarchy is changed. Also this enhances the reusability and understandability through various graphic and text processing.

• Journal of Korea Society of Digital Industry and Information Management
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• v.14 no.2
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• pp.69-78
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• 2018

When it comes to the value of modern education, teachers are required to perform the role of a helper to promote interaction between learners, the role of a manager to facilitate smooth learning, and the role of a guide who has expert knowledge in the learning contents. Therefore, this study investigated what kind of learner-centered teaching methods there are, which require teachers to perform the roles of helper, manager, and guide, and conducted a pedagogical research on coding education to explore class models for self-directed learning. Subsequently, a class model was proposed by applying the flipped learning and havruta learning to a coding class. In this study, the learner-centered education methods of flipped learning and havruta method were applied to constructing a coding class as a university general education course. The feature of this class is that it enables dynamic interaction between teachers and learners as well as active interaction between leaners in a classroom instruction. After applying the proposed method to the actual class and analyzing it, the students taught using suggested method were more positively assessed in learning interest than those taught using a traditional method. And that in academic achievement as well, suggested method was more effective.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.32A no.12
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• pp.9-24
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• 1995

In this paper, we proposed and analysed two kinds of priority control mechanism to archive the cell loss rate requirement and the delay requirement of each class. The service classes of our concern are the high time priority class(class 1) and the high loss priority class(class 2). Two kinds of priority control mechanism is divided by the method of storing the arriving class 2 cell in buffer on case of buffer full. The first one is the method which discarding the arriving class 2 cell, the second one is the mothod which storing the arriving class 2 cell on behalf of pushing out the class 1 cell in buffer. In the proposed priority schemes, one cell of the class 1 is transmitted whenever the maximum K cells of the class 2 is transmitted on case of transmitting the class 1 cell and the class 2 cell sequentially. In this paper, we analysed the cell loss rate and the mean cell delay for each class of the proposed priority scheme by using the Markov chain. The analytical results show that the characteristic of the mean cell delay becomes better for the class 1 cell and that of the cell loss rate becomes better for the class 2 cell by selecting properly the cell transfer ratio according to the condition of input traffic.

• Journal of Information Processing Systems
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• v.13 no.5
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• pp.1345-1357
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• 2017

A prototype selection method chooses a small set of training points from a whole set of class data. As the data size increases, the selected prototypes play a significant role in covering class regions and learning a discriminate rule. This paper discusses the methods for selecting prototypes in a classification framework. We formulate a prototype selection problem into a set covering optimization problem in which the sets are composed with distance metric and predefined classes. The formulation of our problem makes us draw attention only to prototypes per class, not considering the other class points. A training point becomes a prototype by checking the number of neighbors and whether it is preselected. In this setting, we propose a greedy algorithm which chooses the most relevant points for preserving the class dominant regions. The proposed method is simple to implement, does not have parameters to adapt, and achieves better or comparable results on both artificial and real-world problems.

• Journal of Korea Multimedia Society
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• v.24 no.7
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• pp.880-889
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• 2021

In everyday life, recognizing people's emotions from their frames is essential and is a popular research domain in the area of computer vision. Visual emotion has a severe class imbalance in which most of the data are distributed in specific categories. The existing methods do not consider class imbalance and used accuracy as the performance metric, which is not suitable for evaluating the performance of the imbalanced dataset. Therefore, we proposed a method for recognizing visual emotion using balanced data augmentation to address the class imbalance. The proposed method generates a balanced dataset by adopting the random over-sampling and image transformation methods. Also, the proposed method uses the Focal loss as a loss function, which can mitigate the class imbalance by down weighting the well-classified samples. EfficientNet, which is the state-of-the-art method for image classification is used to recognize visual emotion. We compare the performance of the proposed method with that of conventional methods by using a public dataset. The experimental results show that the proposed method increases the F1 score by 40% compared with the method without data augmentation, mitigating class imbalance without loss of classification accuracy.