• International Journal of Costume and Fashion
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• v.9 no.2
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• pp.1-18
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• 2009

Since the 1990s, the domestic fashion industry has been changing rapidly and has become more competitive. Due to these circumstances, the roles of Shop masters were intensified and a training course to acquire a certificate of qualification as a Shop master was in great demand. The 1st Shop master certification exam took place in the year 2001. The purpose of this study was to research the formality of Shop master certificate training courses via e-learning, which is a hot topic in 21st century education, and to provide a development example. First, an analysis was made of the definition and basic characteristics needed of a Shop-master. Next, we noted the problems of former Shop master training facilities and their training process. Thirdly, we did a research on the definition of e-learning and the elements to embody the system. Based on the information obtained through this research, we provided a development example on Shop-master certificate training courses via e-learning that overcame the problems of courses that are currently provided.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.422-425
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• 2004

Many believe that electronic distance teaming education transform higher education, saving money and improving learning qualify So, the open University, which teaches around 280,000 students at a distance, is examining the adaption of its distance teaching methods for the internet. But, there are only one type of distance learning education of one way direction. To understand all of a student which selected some of e teaming course, teacher must check that how many student to understand and what is the difficult problems. Without checking this condition, It will be a very difficult and boring distance learning course. In this paper, we introduce of intelligent learning contents of full duplex direction that teach understanding student and not understanding student. The computer simulation results confirms that full duplex e learning system has been proven to be much more efficient than one way direction which not considering about understanding problems.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.35.3-35
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• 2002

Reinforcement Learning (RL) is one of machine learning methods and an RL agent autonomously learns the action selection policy by interactions with its environment. At the beginning of RL research, it was limited to problems in environments assumed to be Markovian Decision Process (MDP). However in practical problems, the agent suffers from the incomplete perception, i.e., the agent observes the state of the environments, but these observations include incomplete information of the state. This problem is formally modeled by Partially Observable MDP (POMDP). One of the possible approaches to POMDPS is to use historical nformation to estimate states. The problem of these approaches is how t..

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1998.04a
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• pp.216-225
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• 1998

Although artificial neural network based on backpropagation algorithm is an excellent system simulator, it has still unsolved problems of its structure-decision and learning method. That is, we cannot find a general approach to decide the structure of the neural network and cannot train it satisfactorily because of the local optimum point which it frequently falls into. In addition, although there are many successful applications using backpropagation learning algorithm, there are few efforts to improve the learning algorithm itself. In this study, we suggest a general way to construct the hidden layer of the neural network using binary genetic algorithm and also propose the various learning methods by which the global minimum value of the teaming error can be obtained. A XOR problem and line heating problems are investigated as examples.

• Journal for History of Mathematics
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• v.33 no.1
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• pp.1-19
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• 2020

Under 2009 Revised Mathematics Curriculum and 2015 Revised Mathematics Curriculum, mathematics teachers can help students inductively express real life problems related to sequences but have difficulties in dealing with problems asking the general terms of the sequences defined inductively due to 'Guidelines for Teaching and Learning'. Because most of textbooks mainly deal with the simple calculation for the sums of sequences, students tend to follow them rather than developing their inductive and deductive reasoning through finding patterns in the sequences. In this study, we reconstruct 8 problems to find the sums of sequences in MukSaJipSanBup which is known as one of the oldest mathematics book of Chosun Dynasty, using the terminology and symbols of the current curriculum. Such kind of problems can be given in textbooks and used for teaching and learning. Using problems in mathematical books of Chosun Dynasty with suitable modifications for teaching and learning is a good method which not only help students feel the usefulness of mathematics but also learn the cultural value of our traditional mathematics and have the pride for it.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.1
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• pp.44-51
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• 2016

Recently, artificial intelligence has reached the level of top information technologies that will have significant influence over many aspects of our future lifestyles. In particular, in the fields of machine learning technologies for classification and decision-making, there have been a lot of research efforts for solving estimation and control problems that appear in the various kinds of portfolio management problems via data-driven approaches. Note that these modern data-driven approaches, which try to find solutions to the problems based on relevant empirical data rather than mathematical analyses, are useful particularly in practical application domains. In this paper, we consider some applications of modern data-driven machine learning methods for portfolio management problems. More precisely, we apply a simplified version of the sparse Gaussian process (GP) classification method for classifying users' sensitivity with respect to financial risk, and then present two portfolio management issues in which the GP application results can be useful. Experimental results show that the GP applications work well in handling simulated data sets.

• Nuclear Engineering and Technology
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• v.53 no.2
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• pp.522-531
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• 2021

We describe a new deep learning model - Convolutional eXtreme Gradient Boosting (ConvXGB) for classification problems based on convolutional neural nets and Chen et al.'s XGBoost. As well as image data, ConvXGB also supports the general classification problems, with a data preprocessing module. ConvXGB consists of several stacked convolutional layers to learn the features of the input and is able to learn features automatically, followed by XGBoost in the last layer for predicting the class labels. The ConvXGB model is simplified by reducing the number of parameters under appropriate conditions, since it is not necessary re-adjust the weight values in a back propagation cycle. Experiments on several data sets from UCL Repository, including images and general data sets, showed that our model handled the classification problems, for all the tested data sets, slightly better than CNN and XGBoost alone and was sometimes significantly better.

• The Journal of the Korea Contents Association
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• v.22 no.10
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• pp.535-545
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• 2022

The purpose of this study is to classify the various problems in surface road according to their severity and to propose a priority decision making process for road policy makers. For this purpose, the road problems reported by Cheok-cheok app were classified, and the EPDO was adopted and calculated as an index of their severity. To test applicability of the proposed process, some images of road problems reported by the app were classified and annotated, and the Deep Learning was used for machine learning of the curated images, and then the other images of road problems were used for verification. The detecting success rate of the road problems with high severity such as road kills, obstacles in a lane, road surface cracks was over 90%, which shows the applicability of the proposed process. It is expected that the proposed process will make the app possible to be used in the filed to make a priority decision making by classifying the level of severity of the reported road problems automatically.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.8
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• pp.5242-5247
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• 2014

The aim of this study was to specify the procedures of problem design for employing problem based learning (PBL) and designing problems for the learning content of the subject. The procedures for designing good problems are composed of the selection of educational contents, determining the learner's characteristics, identifying problems, setting roles and situations, and writing down problems. Using the procedures, four integration problems covering the contents of a creative engineering design subject were designed. The talent needed in a current industrial site can be fostered with PBL, not whole-class learning. A creative engineering design subject based on PBL was planned, which focused on the process of design problems. To make an effect of this PBL, studies applying this instructional design to many lectures should be implemented.

• Journal of The Korean Association For Science Education
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• v.17 no.1
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• pp.75-83
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• 1997

The purpose of this study was to determine the strategies that middle school students used in solving problems concerning density and solubility. These were compared in the aspects of problem contexts for 42 students of varying logical reasoning ability, spatial ability, and learning approach. A coding scheme used consists of five categories: reading & organization, production, errors, evaluation, and strategy. Students' protocols were analyzed after intercoder agreement had been established to be .95. The results were as follows: 1. Students had more difficulties in reading and organizing the problems in everyday contexts than in scientific contexts. Students at the concrete-operational stage and / or surface approach were more likely to have difficulties in reading and organizing the problems than those at the formal-operational stage and / or deep approach. 2. Students tended to split up the solubility problems into sub-problems and to solve the density problem in everyday contexts in random manner. These were significantly correlated with the test scores concerning logical reasoning ability, spatial ability, and learning approach at the .1 level of significance. 3. Major errors in solving the density problems were to disregard the given information or generated and to use inappropriate information. Many errors in solving the solubility problems were found to be executive errors. The strategy to use the information given appropriately was positively related to students' logical reasoning ability, spatial ability, and learning approach. 4. More evaluation strategies were found in everyday contexts. Their strategies to grasp the meaning of answers and to check the math were significantly related to students' logical reasoning ability. 5. Students used the random trial-and-error strategy more than the systematic strategy and the systematic trial-and-error strategy, especially in everyday contexts. The strategies used by the students were significantly related to students' logical reasoning ability, spatial ability, and learning approach.