• Journal of Fisheries and Marine Sciences Education
• /
• v.17 no.2
• /
• pp.252-259
• /
• 2005

In this research, as an educational tool in the Refrigeration instruction, a project learning method was applied which enables active hand on practice and encourages a more active participation of students with an increased level of interest in learning. The purpose of this study is to present project learning method as an educational tool that can arouse interest and motivation of students by an active hands-on learning process. This will aid in the enhancement of understanding of educators and students in the project learning method, and assist in development of project learning method that can increase intuitiveness and originality of students. For the purpose of the study, the following questions were asked. First, what is the level of existing knowledge pertaining to Refrigeration Training Program of students? Second, what effect does Refrigeration Training Program conducted via the project approach have on the level of motivation and interest of students? Third, what effect does Refrigeration Training Program conducted via the project approach have on the study habits of students?

• Journal of Practical Engineering Education
• /
• v.6 no.2
• /
• pp.77-84
• /
• 2014

As the interests in the flipped learning has recently been increased in the educational field, the number of engineering professors has tended to apply the flipped learning method to their classes. However there has been few research which guide to design engineering courses applying the flipped learning method. This study aims to suggest an instructional design model for the flipped learning according to the case of applying the flipped learning method to an engineering course, "Mechanical Vibrations". This study provides practical guidelines for engineering professors to design their theory oriented courses applying the flipped learning method.

• The Journal of the Korea Contents Association
• /
• v.9 no.5
• /
• pp.346-354
• /
• 2009

This paper proposes a new learning recommendation system for learning patterns that educators are applying to learners using item-based method. The proposed method in this paper first collects personal learning methods based on learning information that learners are performing through the internet contents site. Then this system recommends a learning method which is estimated most properly to learners after classifying learning elements based on these information. The students of a middle school took part in the experiment in order to evaluate the proposed system, and the students were divided into three groups according to their grades. We gave inter-attribute and intra-attribute weights to learning elements applying to each group for recommending the most efficient method to improve learning achievement. The experiment showed that the learning achievement of learners in the proposed method is improved considerably compared to the previous grades.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.13 no.5
• /
• pp.2023-2029
• /
• 2012

The purpose of this study were to identify the effect of Learning Together(LT) on underachiever's academic achievement and learning attitudes. To solve above study purpose, the subject of this study were 20 second-grade underachiever selected from two classes of middle school. The subject classified into two group. In this study, the experimental group were participated in LT method and comparison group were participated lecture-centered instruction. Experimental period were 8 weeks. After the experimental treatment, underachiever's academic achievement and learning attitudes carried out post-test. And the results of the data were analyzed by t-test and MANOVA. The results were as follows: A academic achievement and learning attitude of underachiever's through LT method was significantly higher than those learning through lecture-centered instruction. Especially, a self-confidence, success-expect, attention, affection as subordinate variables of learning attitude was significantly higher than those learning through lecture-centered instruction. In conclusion, LT method effect on underachiever's academic achievement and learning attitudes. As observed by the above results, a LT method should be an applicable to underachiever's education program plan as an effective means of enhancing their academic achievement and learning attitudes.

• The Journal of the Korea Contents Association
• /
• v.9 no.7
• /
• pp.371-378
• /
• 2009

A lot of learning systems are applying and verify evaluating the item difficulty to increase learner's learning effect. But most of this methods ware calculated the item difficulty when it analyze the learning result before or after learning. So, it is hard to support learning contents with changing item difficulty during learning to learner. In this research, we proposed the method that system can support learning contents to next learning to fit leaner's level immediately as apply to calculate item difficulty during the proceed learning. Through this method, learner could supported learning contents by calculated difficulty through pre-test and it caused this method was helped learner to increase learning effect.

• Journal of Multimedia Information System
• /
• v.3 no.4
• /
• pp.155-160
• /
• 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.

• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.29B no.1
• /
• pp.94-101
• /
• 1992

The multilayer perceptron, trained by the error back-propagation learning rule, has been known as a mapping network which can represent arbitrary functions. However depending on the complexity of a function and the initial weights of the multilayer perceptron, the error back-propagation learning may fall into a local minimum or a flat area which may require a long learning time or lead to unsuccessful learning. To solve such difficulties in training the multilayer perceptron by standard error back-propagation learning rule, the paper proposes a learning method which progressively enlarges the learning domain from a small area to the entire region. The proposed method is devised from the investigation on the roles of hidden nodes and connection weights in the multilayer perceptron which approximates a function of one variable. The validity of the proposed method was illustrated through simulations for a function of one variable and a function of two variable with many extremal points.

• Journal of Information Processing Systems
• /
• v.17 no.3
• /
• pp.453-461
• /
• 2021

Many deep learning approaches have been studied for image classification in computer vision. However, there are not enough data to generate accurate models in medical fields, and many datasets are not annotated. This study presents a new method that can use both unlabeled and labeled data. The proposed method is applied to classify cervix images into normal versus cancerous, and we demonstrate the results. First, we use a patch self-supervised learning for training the global context of the image using an unlabeled image dataset. Second, we generate a classifier model by using the transferred knowledge from self-supervised learning. We also apply attention learning to capture the local features of the image. The combined method provides better performance than state-of-the-art approaches in accuracy and sensitivity.

• Journal of information and communication convergence engineering
• /
• v.1 no.3
• /
• pp.157-162
• /
• 2003

We propose a learning method for improving the performance of the backpropagation algorithm. The proposed method is using a fuzzy logic system for automatic tuning of the learning rate of each weight. Instead of choosing a fixed learning rate, the fuzzy logic system is used to dynamically adjust the learning rate. The inputs of fuzzy logic system are delta and delta bar, and the output of fuzzy logic system is the learning rate. In order to verify the effectiveness of the proposed method, we performed simulations on the XOR problem, character classification, and function approximation. The results show that the proposed method considerably improves the performance compared to the general backpropagation, the backpropagation with momentum, and the Jacobs'delta-bar-delta algorithm.

• The Transactions of the Korean Institute of Electrical Engineers
• /
• v.39 no.8
• /
• pp.856-867
• /
• 1990

In this paper, the convergence and the learning accuracy of the back-propagation (BP) method in neural network are investigated by 1) analyzing the reason for decelerating the convergence of BP method and examining the rapid deceleration of the convergence when the learning is executed on the part of sigmoid activation function with the very small first derivative and 2) proposing the modified logistic activation function by defining, the convergence factor based on the analysis. Learning on the output patterns of binary as well as analog forms are tested by the proposed method. In binary output patter, the test results show that the convergence is accelerated and the learning accuracy is improved, and the weights and thresholds are converged so that the stability of neural network can be enhanced. In analog output patter, the results show that with extensive initial transient phenomena the learning error is decreased according to the convergence factor, subsequently the learning accuracy is enhanced.