• International Journal of Costume and Fashion
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• 제9권2호
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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.

• International Journal of Computer Science & Network Security
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• 제21권11호
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• pp.23-30
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• 2021

Sentiment Analysis has become very important field of research because posting of reviews is becoming a trend. Supervised, unsupervised and semi supervised machine learning methods done lot of work to mine this data. Feature engineering is complex and technical part of machine learning. Deep learning is a new trend, where this laborious work can be done automatically. Many researchers have done many works on Deep learning Convolutional Neural Network (CNN) and Long Shor Term Memory (LSTM) Neural Network. These requires high processing speed and memory. Here author suggested two models simple & bidirectional deep leaning, which can work on text data with normal processing speed. At end both models are compared and found bidirectional model is best, because simple model achieve 50% accuracy and bidirectional deep learning model achieve 99% accuracy on trained data while 78% accuracy on test data. But this is based on 10-epochs and 40-batch size. This accuracy can also be increased by making different attempts on epochs and batch size.

• 제어로봇시스템학회논문지
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• 제21권6호
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• pp.565-570
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• 2015

Supervised machine learning has become popular in discovering context descriptions from sensor data. However, collecting a large amount of labeled training data in order to guarantee good performance requires a great deal of expense and time. For this reason, semi-supervised learning has recently been developed due to its superior performance despite using only a small number of labeled data. In the existing semi-supervised learning algorithms, unlabeled data are used to build a graph Laplacian in order to represent an intrinsic data geometry. In this paper, we represent the unlabeled data as the spatial-temporal dataset by considering smoothly moving objects over time and space. The developed algorithm is evaluated for position estimation of a smartphone-based robot. In comparison with other state-of-art semi-supervised learning, our algorithm performs more accurate location estimates.

• 전기학회논문지P
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• 제60권1호
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• pp.1-5
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• 2011

The main purpose of this study was to examine the learning abilities of college electrical students, especially self-regulated learning abilities and problem solving ability. In addition, this study was to explore the effects of self-regulated learning abilities and problem solving ability of the college students on academic achievement. For this purpose, a total of 58 college freshmen was chosen to conduct a survey. The results of this study showed that self-regulated learning abilities and problem solving ability significantly influenced on the college engineering students' academic achievement. Based on these study results, the above variables investigated in this study should sufficiently considered in the design and development of the college engineering courses that enable students to become self-regulated learners and improve their academic achievement.

• 한국멀티미디어학회논문지
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• 제21권4호
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• pp.464-470
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• 2018

With the introduction of the e-learning since 2000, the place of the education has not been limited to off-line, but the range of it has become broader in online. The e-learning market has evolved steadily over time. With the advent of the term "Edu-tech", which means a combination of education and technology, various IT technologies have incorporated education. Particularly, the Korean education market collects patterns by computerizing the learning history in classes taught according to curriculums. Because of that environment, various personalized learning services have been developed which maximize the effect of the learning. These services have qualitative differences depending on how many data is accumulated and algorithms are developed for the precise analysis. The purpose of this study is to recognize and data-ize OMR marking by the most suitable method to convert analog data into digital data without harming the Korean education system.

• 한국산업융합학회 논문집
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• 제25권3호
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• pp.467-476
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• 2022

The Bank of Korea raised the benchmark interest rate by a quarter percentage point to 1.75 percent per year, and analysts predict that South Korea's policy rate will reach 2.00 percent by the end of calendar year 2022. Furthermore, because market volatility has been significantly increased by a variety of factors, including rising rates, inflation, and market volatility, many investors have struggled to meet their financial objectives or deliver returns. Banks and financial institutions are attempting to provide Robo-Advisors to manage client portfolios without human intervention in this situation. In this regard, determining the best hyper-parameter combination is becoming increasingly important. This study compares some activation functions of the Deep Deterministic Policy Gradient(DDPG) and Twin-delayed Deep Deterministic Policy Gradient (TD3) Algorithms to choose a sequence of actions that maximizes long-term reward. The DDPG and TD3 outperformed its benchmark index, according to the results. One reason for this is that we need to understand the action probabilities in order to choose an action and receive a reward, which we then compare to the state value to determine an advantage. As interest in machine learning has grown and research into deep reinforcement learning has become more active, finding an optimal hyper-parameter combination for DDPG and TD3 has become increasingly important.

• 대한안전경영과학회지
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• 제9권3호
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• pp.153-163
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• 2007

Over the fast few years, web-based e-Learning have made remarkable progress. According to advance of e-Learning, the evaluation of e-Learning effectiveness and success model become more important. This study had a focus on the effect of system characteristic of e-Learning systems and self-efficacy on learning performance. Data has been collected from 192 person experienced in e-Learning. The questionnaire method was adopted to collect the data for this study. The research was conducted by using SPSS 12.0 and AMOS 4.0. The research results and suggestions of the study are as follow. First of all, system quality and information quality of e-Learning system had positive relationship with perceived usefulness. Second, information quality was related positively to user satisfaction. Third, perceived usefulness was positively connected with user satisfaction. Fourth, user satisfaction and self-efficacy had relation to learning performance.

• East Asian mathematical journal
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• 제38권2호
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• pp.187-214
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• 2022

This study compares the functions of the Learning Management Systems (LMS) in three widely used Edu-Tech platforms, that support students' individualized learning by using the learning characteristics of the students. The rapid advances in artificial intelligence (AI) are broadening their impacts in the education industry, and play a broad role in supporting student learning. In many countries, online classes have become a norm due to the COVID-19 crisis, and the demand for Edu-Tech in classes has increased rapidly. As a result, many countries, including South Korea, are now preparing and implementing various policy measures to adopt Edu-Tech in the class setting. Therefore, in this study, we analyze and compare the structures and characteristics of the three widely used Edu-Tech platforms that support individualized mathematics learning. In particular, we compare the LMSs of the three platforms by considering the elements such as learning design, learning management, learner analysis, learning result analysis, and student management functions. The results of this study give implications in the future directions to take on how to build Edu-Tech platform models that promote students' individualized mathematics learning in public education.

• 한국간호교육학회지
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• 제11권2호
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• pp.184-190
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• 2005

Purpose: The purpose of this study was to examine the effect of PBL on self-directed learning and critical thinking disposition. Method: The research design was a nonequivalent control group pretest-posttest design using two groups of first-year students from two nursing schools in Gwangju, South Korea. PBL was conducted for one semester. Result: Significant difference was found in self-directed learning between the two groups after PBL (p<0.05). but, not in critical thinking disposition. Conclusion: The findings indicate that there is a need for paradigm shift in nursing education from the traditional teacher-centered methods to a more learner-centered approach. PBL program will facilitate the development of abilities to become self-directed in learning.

• 로봇학회논문지
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• 제18권2호
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• pp.225-232
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• 2023

With the recent increase of multiple robots cooperating in smart manufacturing logistics environments, it has become very important how to predict the safety and efficiency of the individual tasks and dynamically assign them to the best one of available robots. In this paper, we propose a novel task policy learner based on deep relational reinforcement learning for predicting the safety and efficiency of tasks in a multi-robot manufacturing logistics environment. To reduce learning complexity, the proposed system divides the entire safety/efficiency prediction process into two distinct steps: the policy parameter estimation and the rule-based policy inference. It also makes full use of domain-specific knowledge for policy rule learning. Through experiments conducted with virtual dynamic manufacturing logistics environments using NVIDIA's Isaac simulator, we show the effectiveness and superiority of the proposed system.