• The Journal of Bigdata
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• v.4 no.1
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• pp.63-72
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• 2019

For power energy, optimal generation and distribution plans based on accurate demand forecasts are necessary because it is not recoverable after they have been delivered to users through power generation and transmission processes. Failure to predict power demand can cause various social and economic problems, such as a massive power outage in September 2011. In previous studies on forecasting power demand, ARIMA, neural network models, and other methods were developed. However, limitations such as the use of the national average ambient air temperature and the application of uniform criteria to distinguish seasonality are causing distortion of data or performance degradation of the predictive model. In order to improve the performance of the power demand prediction model, we divided Korea into five major regions, and the power demand prediction model of the linear regression model and the neural network model were developed, reflecting seasonal characteristics through regional characteristics and migration period learning techniques. With the proposed approach, it seems possible to forecast the future demand in short term as well as in long term. Also, it is possible to consider various events and exceptional cases during a certain period.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.3
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• pp.231-238
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• 2023

As the demand for distance education increases, interest in the management of learners' rights is increasing. Blockchain technology is a technology that guarantees the integrity of the learner's learning history, and enables learner-led learning control, data security, and sharing of learning resources. In this paper, we proposed a blockchain technology-based learning management system based on Hyperledger Fabric that can be verified through permission between nodes among blockchain platforms. Learning resources can be shared differentially according to the learning progress. Also the percentage of individual learners that can be managed. As a result of the study, the superiority of the platform in terms of convenience compared to the existing platform was demonstrated. As a result of the performance evaluation for the research in this paper, it was confirmed that the convenience was improved by more than 5%, and the performance was 4-5% superior to the existing platform in terms of learner satisfaction.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.6
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• pp.70-86
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• 2022

An autonomous demand-responsive bus with mobility-on-demand service is an innovative transport compensating for the disadvantages of an autonomous bus and a demand-responsive bus with mobility-on-demand service. However, less attention has been paid to the quantitative impact assessment of the autonomous demand-responsive bus due to the technological complexity of the autonomous demand-responsive bus. This study simulates autonomous demand-responsive bus trips by reinforcement learning on a microscopic traffic simulation to quantify the impact of the autonomous demand-responsive bus. The Chungju campus of the Korea National University of Transportation is selected as a testbed. Simulation results show that the introduction of the autonomous demand-responsive bus can reduce the wait time of passengers, average control delay, and increase the traffic speed compared to the results with fixed route bus service. This study contributes to the quantitative evaluation of the autonomous demand-responsive bus.

• ETRI Journal
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• v.41 no.6
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• pp.760-770
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• 2019

Based on the growing demand for neural network technologies, various neural network inference engines are being developed. However, each inference engine has its own neural network storage format. There is a growing demand for standardization to solve this problem. This study presents interworking techniques for ensuring the compatibility of neural networks and data among the various deep learning frameworks. The proposed technique standardizes the graphic expression grammar and learning data storage format using the Neural Network Exchange Format (NNEF) of Khronos. The proposed converter includes a lexical, syntax, and parser. This NNEF parser converts neural network information into a parsing tree and quantizes data. To validate the proposed system, we verified that MNIST is immediately executed by importing AlexNet's neural network and learned data. Therefore, this study contributes an efficient design technique for a converter that can execute a neural network and learned data in various frameworks regardless of the storage format of each framework.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.29 no.4
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• pp.1-20
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• 2021

Due to the recent rapid increase in passenger and cargo air transport demand, the capacity of Jeju International Airport has been approaching its limit. Even though in COVID-19 crisis which has started from Nov 2019, Jeju International Airport still suffers from strong demand in terms of air passenger and cargo transportation. However, it is an undeniable fact that the delay has also increased in Jeju International Airport. In this study, we analyze the correlation between weather and delayed departure operation based on both datum collected from the historical airline operation information and aviation weather statistics of Jeju International Airport. Adopting machine learning techniques, we then analyze weather condition Jeju International Airport and construct a delay prediction model. The model presented in this study is expected to play a useful role to predict aircraft departure delay and contribute to enhance aircraft operation efficiency and punctuality in the Jeju International Airport.

• English Language & Literature Teaching
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• v.17 no.4
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• pp.75-95
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• 2011

The purpose of this study was to evaluate a group activity, reciprocal peer tutoring (RPT), in order to investigate advantages and challenges of RPT in promoting cooperative and collaborative learning environment for EFL University reading instruction. The participants in this study were 89 students taking an English reading course at a Korean university. RPT is a learning strategy whereby learners help each other and learn by teaching. This program was supported by a Vygotskyan perspective which assumes that learners gain mastery and develop cognitive skills through social interaction with more proficient others and their environment. This study relied particularly on participant perceptions through questionnaire survey and Anonymous Online class Report of the course. This study showed various advantages for tutors such as learning through teaching and becoming more autonomous and responsible for their own learning. Non-threatening and highly motivating learning atmosphere are parts of benefits for tutees. Other advantages for tutees included improved level of academic self-confidence, and motivation. This study also revealed several drawbacks associated with the problem of inaccuracy in students' production and students' demand for more direct teacher role. (182 words).

• The Journal of the Korea Contents Association
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• v.5 no.2
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• pp.19-28
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• 2005

A web-based instructor-learner system has changed the form into a learner-centered environment. Especially a self-regulated learning which is a self-leading and a positive learning, is an ideal learning, and the interest on it is more increasing. In this research, learners can organize the individualized course based on the learner's demand and learning level after making a contract process with the system, The self-regulated learning system which can recognize a learning status and result by analyzed data, and which can lead to a learning goal effectively by establishing a learning strategy, is designed and implemented. The proposed system provides the learner-centered learning environment which can process the differentiated and flexible individualized-teaming service considering an individual characteristic.

• International journal of advanced smart convergence
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• v.9 no.2
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• pp.1-7
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• 2020

As the demand for health screening increases, there is a need for efficient design of screening items. We build machine learning models for health screening and recommend screening items to provide personalized health care service. When offline, a synthetic data set is generated based on guidelines and clinical results from institutions, and a machine learning model for each screening item is generated. When online, the recommendation server provides a recommendation list of screening items in real time using the customer's health condition and machine learning models. As a result of the performance analysis, the accuracy of the learning model was close to 100%, and server response time was less than 1 second to serve 1,000 users simultaneously. This paper provides an adaptive and automatic recommendation in response to changes in the new screening environment.

• Information Systems Review
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• v.6 no.2
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• pp.161-179
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• 2004

The purpose of this study is to suggest practical experience to develop small sized e-Learning system. The system is designed to help lecturers can arrange interface, organize contents, submit examinations and assess learner's score with no professional computing skills. The system has three advantages. First, it reduced implementation period through the use of GUI. Second, it is ordered to be personalized to construct format of the whole interface. Third, it has operational convenience which can be implemented on PC based system. These personalized features are enabling Learning on Demand. Also, there is comparatively low cost and high effectiveness on e-Learning implementation which facilitating quick adoption of e-Learning in its lectures.

• Korean Journal of Artificial Intelligence
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• v.2 no.1
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• pp.1-3
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• 2014

These days, interest and demand on smart learning has rapidly increased. Video English and mobile system based English speaking service have become popular. This study gave prototype of application to give and take voice message with world people and to give new concept of voice pen-pal beyond exchange of text messages. In modern society having rapidly increasing demand on smart learning, you can study foreign language by smart phone and communicate with foreigners by voice anytime and anywhere. The app allows global exchange to learn conversation. Recruitment of initial users and profit model have problems. We shall develop to improve problems and to solve difficulty.