• 한국디지털정책학회:학술대회논문집
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• 2005.06a
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• pp.571-589
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• 2005

Nowadays, demand for human resource for the e-learning industry is rapidly increasing along with the expansion of e-learning market capacity. However, there arc numerous difficulties in expansion and industrialization of e-learning due to insufficient supply of human resources to meet the demand. Therefore, the goal of this study is to present various policy measures that can supplement the supply of e-learn ing manpower. Overall contents of this study focus on presenting the long-term directions for fostering of human resources for e-learning industry. Among these, role of government policies for fostering of human resources for e-learning industry is being particularly emphasized because e-learning industry is still at its infant stage.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.11
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• pp.795-803
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• 2020

The purpose of this study is to verify the differences in learning capability according to the demand of learning programs. The participants of this study included 1,738 S University students. The results of the study are as follows. First, in learning capability according to participation in learning programs, we found statistically significant differences in the ability to organize information, resource utilization, self-awareness, self-improvement and sociality. Second, there were no statistically significant differences in the ability to organize information, resource utilization, self-awareness, self-improvement and sociality in participating in learning programs. Third, there were statistically significant differences in the ability to organize information, resource utilization, self-awareness, self-improvement and sociality in not participating in learning programs. Fourth, there were statistically significant differences in the ability to organize information, resource utilization, self-awareness, self-improvement and sociality in experiencing difficulties in learning. Fifth, learning capabilities in specific areas that students wanted to improve had no statistically significant differences in the ability to organize information, resource utilization, self-awareness. However, there were statistically significant differences in self-improvement and sociality.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.7
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• pp.307-314
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• 2022

Recently, researches using deep learning-based models are being actively conducted to replace statistical-based time series forecast techniques to predict electric power demand. The result of analyzing the researches shows that the performance of the LSTM-based prediction model is acceptable, but it is not sufficient for long-term regional-wide power demand prediction. In this paper, we propose a WaveNet deep learning model to predict electric power demand 24-hour-ahead with temperature data in order to achieve the prediction accuracy better than MAPE value of 2% which statistical-based time series forecast techniques can present. First of all, we illustrate a delated causal one-dimensional convolutional neural network architecture of WaveNet and the preprocessing mechanism of the input data of electric power demand and temperature. Second, we present the training process and walk forward validation with the modified WaveNet. The performance comparison results show that the prediction model with temperature data achieves MAPE value of 1.33%, which is better than MAPE Value (2.33%) of the same model without temperature data.

• Journal of The Korean Association of Information Education
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• v.12 no.3
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• pp.313-322
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• 2008

As cyber education using e-Learning system is expanded, various Learning Contents has been created to satisfy the demand of various students. But since it takes long time and spends high cost to create e-Learning contents, it is hard to satisfy the demand of various students. To solve this problem standardizations of the Learning Contents and researches of Learning Contents Metadata are focused in reusability of Learning Contents and information retrieval of Learning Contents. In this paper to improve manageability and retrievability, legacy version control and document management system are introduced. Based on existing version control and document management system, we developed automated history control system. To conveniently provide retrieval, inquiry and integrating of Learning Contents, we researched Learning Contents Management System based on SOA to easily approach with the Learning Contents Management Server which is dispersed on wide area

• International Journal of Advanced Culture Technology
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• v.6 no.4
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• pp.303-308
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• 2018

Demand is increasing rapidly in recent years than supply to machine learning professionals. To alleviate this gap, user-friendly machine learning software that can be used by non-specialists has emerged, which is Machine Learning-as-a-Service(MLaaS). MLaaS provides services that enable businesses to easily leverage ML capabilities without expertise. In this paper, we will compare and analyze features, interfaces, supporting programming language, ML framework, and Machine Learning services of MLaaS, to help companies easily use ML service.

• The Korean Journal of Applied Statistics
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• v.32 no.6
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• pp.795-835
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• 2019

Big data has been generated in various fields. Many companies have now tried to make profits by building a system capable of analyzing big data based on artificial intelligence (AI) techniques. Integrating AI technology has made analyzing and utilizing vast amounts of data increasingly valuable. In particular, demand forecasting with maximum accuracy is critical to government and business management in various fields such as finance, procurement, production and marketing. In this case, it is important to apply an appropriate model that considers the demand pattern for each field. It is possible to analyze complex patterns of real data that can also be enlarged by a traditional time series model or regression model. However, choosing the right model among the various models is difficult without prior knowledge. Many studies based on AI techniques such as machine learning and deep learning have been proven to overcome these problems. In addition, demand forecasting through the analysis of stereotyped data and unstructured data of images or texts has also shown high accuracy. This paper introduces important areas where demand forecasts are relatively active as well as introduces machine learning and deep learning techniques that consider the characteristics of each field.

• Korean Journal of Artificial Intelligence
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• v.10 no.1
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• pp.9-14
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• 2022

The demand for public bicycles operated by the Seoul Metropolitan Government is increasing every year. The size of the Seoul public bicycle project, which first started with about 5,600 units, increased to 3,7500 units as of September 2021, and the number of members is also increasing every year. However, as the size of the project grows, excessive budget spending and deficit problems are emerging for public bicycle projects, and new bicycles, rental office costs, and bicycle maintenance costs are blamed for the deficit. In this paper, the Azure Machine Learning Studio program and the Boosted Decision Tree Regression technique are used to predict the number of public bicycle rental over environmental factors and time. Predicted results it was confirmed that the demand for public bicycles was high in the season except for winter, and the demand for public bicycles was the highest at 6 p.m. In addition, in this paper compare four additional regression algorithms in addition to the Boosted Decision Tree Regression algorithm to measure algorithm performance. The results showed high accuracy in the order of the First Boosted Decision Tree Regression Algorithm (0.878802), second Decision Forest Regression (0.838232), third Poison Regression (0.62699), and fourth Linear Regression (0.618773). Based on these predictions, it is expected that more public bicycles will be placed at rental stations near public transportation to meet the growing demand for commuting hours and that more bicycles will be placed in rental stations in summer than winter and the life of bicycles can be extended in winter.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.47 no.2
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• pp.155-167
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• 2024

Distribution and logistics industries contribute some of the biggest GDP(gross domestic product) in South Korea and the number of related companies are quarter of the total number of industries in the country. The number of retail tech companies are quickly increased due to the acceleration of the online and untact shopping trend. Furthermore, major distribution and logistics companies try to achieve integrated data management with the fulfillment process. In contrast, small and medium distribution companies still lack of the capacity and ability to develop digital innovation and smartization. Therefore, in this paper, a deep learning-based demand forecasting & recommendation model is proposed to improve business competitiveness. The proposed model is developed based on real sales transaction data to predict future demand for each product. The proposed model consists of six deep learning models, which are MLP(multi-layers perception), CNN(convolution neural network), RNN(recurrent neural network), LSTM(long short term memory), Conv1D-BiLSTM(convolution-long short term memory) for demand forecasting and collaborative filtering for the recommendation. Each model provides the best prediction result for each product and recommendation model can recommend best sales product among companies own sales list as well as competitor's item list. The proposed demand forecasting model is expected to improve the competitiveness of the small and medium-sized distribution and logistics industry.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.3
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• pp.147-152
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• 2023

This paper is a study of a linear algebra course taught in a high school on-demand course. Compared to the regular course, flipped learning was added to the course, and applications to signal processing related problems were covered in consideration of students' career aspirations. Overall, the class was a mixture of traditional lectures and flipped learning. Flipped learning was implemented twice. The flipped class consisted of pre-class, in-class and post-class. To verify the effectiveness of the course, a survey was conducted and most of the evaluation items were above 4. The topics of the flipped learning were Markov chains and least squares problem, which are very important in the field of signal processing.

• The Journal of Bigdata
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• v.3 no.2
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• pp.59-70
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• 2018

In order to provide stable district heat supplying service to the certain limited residential area, it is the most important to forecast the short-term future demand more accurately and produce and supply heat in efficient way. However, it is very difficult to develop a universal heat demand forecasting model that can be applied to general situations because the factors affecting the heat consumption are very diverse and the consumption patterns are changed according to individual consumers and regional characteristics. In particular, considering all of the various variables that can affect heat demand does not help improve performance in terms of accuracy and versatility. Therefore, this study aims to develop a demand forecasting model using deep learning based on only limited information that can be acquired in real time. A demand forecasting model was developed by learning the artificial neural network of the Tensorflow using past data consisting only of the outdoor temperature of the area and date as input variables. The performance of the proposed model was evaluated by comparing the accuracy of demand predicted with the previous regression model. The proposed heat demand forecasting model in this research showed that it is possible to enhance the accuracy using only limited variables which can be secured in real time. For the demand forecasting in a certain region, the proposed model can be customized by adding some features which can reflect the regional characteristics.