• Journal of Hydrogen and New Energy
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• v.33 no.5
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• pp.591-597
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• 2022

Recently, technologies for efficient power grid operation have become important due to climate change. For this reason, predicting power demand using deep learning is being considered, and it is necessary to understand the influence of characteristics of each region, industrial structure, and climate. This study analyzed the power demand of New Jersey in US, with a high urbanization rate and a large service industry, and West Virginia in US, a low urbanization rate and a large coal, energy, and chemical industries. Using recurrent neural network algorithm, the power demand from January 2020 to August 2022 was learned, and the daily and weekly power demand was predicted. In addition, the power grid operation based on the power demand forecast was discussed. Unlike previous studies that have focused on the deep learning algorithm itself, this study analyzes the regional power demand characteristics and deep learning algorithm application, and power grid operation strategy.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.1
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• pp.1-5
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• 2005

The purpose of reinforcement learning is to maximize rewards from environment, and reinforcement learning agents learn by interacting with external environment through trial and error. Q-Learning, a representative reinforcement learning algorithm, is a type of TD-learning that exploits difference in suitability according to the change of time in learning. The method obtains the optimal policy through repeated experience of evaluation of all state-action pairs in the state space. This study chose n-Queen problem as an example, to which we apply reinforcement learning, and used Q-Learning as a problem solving algorithm. This study compared the proposed method using reinforcement learning with existing methods for solving n-Queen problem and found that the proposed method improves the convergence rate to the optimal solution by reducing the number of state transitions to reach the goal.

• IE interfaces
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• v.25 no.4
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• pp.441-449
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• 2012

Solar PV(photovoltaic) is paid great attention to as a possible renewable energy source to overcome recent global energy crisis. However to be a viable alternative energy source compared with fossil fuel, its market competitiveness should be attained. Grid parity is one of effective measure of market competitiveness of renewable energy. In this paper, we forecast the grid parity timing of solar PV energy in Korea using two factor learning curve model. Two factors considered in the present model are production capacity and technological improvement. As a result, it is forecasted that the grid parity will be achieved in 2019 in Korea.

• Journal of the Semiconductor & Display Technology
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• v.19 no.2
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• pp.100-104
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• 2020

Over the past several years, deep learning has been widely used for feature extraction in image and video for various applications such as object classification and facial recognition. This paper introduces an implantation of embedded Linux system for embossed digits recognition using CNN based deep learning methods. For this purpose, we implemented a coin recognition system based on deep learning with the Keras open source library on Raspberry PI. The performance evaluation has been made with the success rate of coin classification using the images captured with ultra-wide angle camera on Raspberry PI. The simulation result shows 98% of the success rate on average.

• Journal of Sensor Science and Technology
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• v.30 no.2
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• pp.76-81
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• 2021

In this study, we propose a machine vision system with a high object recognition rate. By utilizing a multiple-exposure image sensing technique, the proposed deep learning-based machine vision system can cover a wide light intensity range without further learning processes on the various light intensity range. If the proposed machine vision system fails to recognize object features, the system operates in a multiple-exposure sensing mode and detects the target object that is blocked in the near dark or bright region. Furthermore, short- and long-exposure images from the multiple-exposure sensing mode are synthesized to obtain accurate object feature information. That results in the generation of a wide dynamic range of image information. Even with the object recognition resources for the deep learning process with a light intensity range of only 23 dB, the prototype machine vision system with the multiple-exposure imaging method demonstrated an object recognition performance with a light intensity range of up to 96 dB.

• Korean Journal of Comparative Education
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• v.28 no.3
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• pp.291-307
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• 2018

As the value of lifelong learning becomes important, states are making efforts to build a system of lifelong learning. According to this tendency, this paper intends to compare the participation rate of lifelong learning, learning outcomes, learning support infrastructure, support of learning expenses, and recognition of lifelong learning. For the comparative pedagogical approach, Bray and Thomas' cubes such as geographical / regional level, non - geographical demographic statistics, social and educational aspects were utilized. The participation rate of lifelong learning in Korea is 34.4% in 2017, which is lower than the OECD average of 46%. The competency scores of Korean adults were lower than the OECD national averages of the PIAAC survey which measured adult competence, language ability, numeracy, and computer-based problem solving ability. In order to recognize prior learning, EU countries have developed EQFs to evaluate all non-formal and informal learning outcomes, while Korea recognizes qualification as a credit banking credit under the academic credit banking system. International comparisons of lifelong learning can be used as an important tool for diagnosing the actual conditions of lifelong learning in a country and establishing future lifelong learning policies. Therefore, it is necessary to maintain that the comparative pedagogical approach of lifelong learning differs according to the historical context, socioeconomic characteristics, and population dynamics, including the formation process and characteristics of modern countries.

• Korea Journal of Hospital Management
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• v.18 no.2
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• pp.127-152
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• 2013

The purpose of this study was to investigate the effect of balancing, coordinating and learning strategy on performance of private university hospitals. We think that the study will contribute to establish effective management strategy of private university hospitals. Data were collected from 69 private university hospitals. We measured balancing, coordinating and learning strategy, and perceived performance of the hospital by using 5-point Likert scale. Upper-grade general hospitals were significantly higher rate of growth and profitability than others. However, general hospitals were higher level in perceived performance than upper-grade general hospitals. Hospitals located in Seoul were significantly higher growth rate than those in other regions. Large-scale hospitals were significantly higher rate of growth and profitability than small hospitals. Qualitative performance did not different in any hospital characteristics. Growth of hospitals were significantly influenced from business strategies: selective strategy, formal coordinating strategy, and external learning strategy. Profitability of hospitals were also significantly influenced from business strategies: selective strategy, adaptive strategy, and external learning strategy. Subjective performance of hospitals were significantly influenced from external learning strategy. There were no factors that are significantly influencing on qualitative performance of hospital. To have successful performance in the competitive environment, it is recommended that private university hospitals should have to establish management strategy such as balancing, coordinating, and learning strategy.

• Journal of the Korea Society of Computer and Information
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• v.19 no.9
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• pp.33-38
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• 2014

The FCM based hybrid RBF network is a heterogeneous learning network model that applies FCM algorithm between input and middle layer and applies Max_Min algorithm between middle layer and output. The Max-Min neural network uses winner nodes of the middle layer as input but shows inefficient learning in performance when the input vector consists of too many patterns. To overcome this problem, we propose a dynamic learning rate control based on fuzzy logic. The proposed method first classifies accurate/inaccurate class with respect to the difference between target value and output value with threshold and then fuzzy membership function and fuzzy decision logic is designed to control the learning rate dynamically. We apply this proposed RBF network to the character recognition problem and the efficacy of the proposed method is verified in the experiment.

• Journal of Korean Public Health Nursing
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• v.27 no.3
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• pp.513-526
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• 2013

Purpose: The purpose of this study was to determine the effects of the Traditional Play-centered Obesity Control Cooperative Learning Program based on the cooperative learning theory on obesity rate, physical fitness, self-esteem, and body image specifically in obese elementary school children. Methods: The research design for this study was based on a non- equivalent control group pretest-posttest design. The study was conducted from September, 5 to November 30, 2012. The subjects included 74 obese children ($Exp.=25^{(a)}$, $Com.=24^{(b)}$, $Cont.=25^{(c)}$) with an obesity rate above 20% at an elementary school in G City. Data analysis was performed using SPSS/WIN 18.0, using Chi-square test, one-way ANOVA, and Scheffe test. Results: The obesity rate (F=4.033, p<.022) in the experimental group was significantly lower than that in the group (Com, Cont), in which the Traditional Play-Centered Obesity Control Cooperative Learning Program was not implemented. Self-esteem (F=4.310, p<.017) also caused significant differences. However, physical fitness (Muscular endurance F=1.545, p=.220; Flexibility F=.671, p=.514; Agility F=1.594, p=.210; Speed F=5.386, p<.007, scheffe (a,b

• Journal of the Korean Data and Information Science Society
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• v.27 no.4
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• pp.993-1000
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• 2016

Semi-supervised learning makes it easy to use an unlabeled data in the supervised learning such as classification. Applying the semi-supervised learning on the regression analysis, we propose two methods for a better regression function estimation. The proposed methods have been assumed different marginal densities of independent variables and different smoothing parameters in unlabeled and labeled data. We shows that the overfitted pilot estimator should be used to achieve the fastest convergence rate and unlabeled data may help to improve the convergence rate with well estimated smoothing parameters. We also find the conditions of smoothing parameters to achieve optimal convergence rate.