• Journal of the Korea Concrete Institute
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• v.22 no.1
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• pp.69-76
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• 2010

The majority of research that has been performed on cracking potential of concrete by shrinkage has assumed that concrete acts as a homogeneous material. However, with this approach, it is not able to evaluate the micro-cracking behavior in concrete due to autogenous shrinkage under unrestrained boundary condition (free boundary condition) nor to understand the cracking behavior properly because of the heterogeneous nature of concrete. To better understand the micro-cracking behavior of concrete induced by autogenous shrinkage, series of experiments were performed measuring the length change and acoustic emission energy. As an analytical approach, this research uses an object oriented finite element analysis code (OOF code) to simulate the behavior of the concrete on a meso-scale. The concrete images used in the simulations were directly obtained from mortar samples. From the experiments and simulation results, it was able to better understand the micro-cracking behaviour of concrete due to shrinking of paste phase and internal restraint by aggregates.

• Journal of the Korean Data and Information Science Society
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• v.28 no.2
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• pp.395-406
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• 2017

The purpose of this study is to identify the pattern of daily electricity demand through clustering and classification. The hourly data was collected by KPS (Korea Power Exchange) between 2008 and 2012. The time trend was eliminated for conducting the pattern of daily electricity demand because electricity demand data is times series data. We have considered k-means clustering, Gaussian mixture model clustering, and functional clustering in order to find the optimal clustering method. The classification analysis was conducted to understand the relationship between external factors, day of the week, holiday, and weather. Data was divided into training data and test data. Training data consisted of external factors and clustered number between 2008 and 2011. Test data was daily data of external factors in 2012. Decision tree, random forest, Support vector machine, and Naive Bayes were used. As a result, Gaussian model based clustering and random forest showed the best prediction performance when the number of cluster was 8.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.12
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• pp.1768-1775
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• 2015

Recent major domestic facilities, such as nuclear power plants, many control cables are installed and are degraded by long-term use, but research on deterioration diagnosis is lacking. In the event of a fault in the cable due to deterioration can be developed into a major accident such as the main plant is stopped, so the deterioration diagnostic techniques of high reliability for the cable is required. In this paper, proposes a methodology using a cable resonance that can effectively diagnose the deterioration of the cable. Prior to the test, we developed a setup for stable measuring the characteristics of the cable and it verified the suitable of the measurement set-up in terms of interactivity and reliability, also measured S-parameters applying verified measurement set-up to the cables that deterioration degree is different. Then, we had amplified the difference in resonance frequency between the healthy state and the deteriorated state using connection in a series of measured S-parameters. In a result from the method, we have verified that the more deteriorate the cables is, the more decrease the resonance frequency is. Measured results are justified by inducing the resonance frequency calculation of the cable from the S- parameters represented by the hyperbolic function formula. VNA(Vector Network Analyzer) for S-parameter measurements used in this study is Agilent E5061B and shielded twisted-pair cables was used for deterioration diagnostic test.

• Communications of Mathematical Education
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• v.31 no.2
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• pp.139-152
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• 2017

In this paper, we examine how secondary school mathematics textbooks on calculus introduce the history of calculus. In order to identify the problem, we consider the Babylonian integration by trapezoidal rule, which was made to calculate the location of Jupiter in 350-50 B.C., and the integration by the method of the rotating plate of ibn al-Haytham in Egypt, about 1000 years. In conclusion, our secondary school mathematics textbooks describe Newton and Leibniz as inventing calculus and place their roots in ancient Greece. The origin of the calculus is in Babylonia and the Faṭimah Dynasty (909-1171) (Egypt) and it is desirable that the calculus is developed in Europe after the development of the power series in India, and that the value of Asia Africa is introduced in the textbooks.

• Journal of the Korean Society for Railway
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• v.19 no.4
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• pp.445-455
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• 2016

A goal of the Real-time railway safety supervision system is to improve the safety oversight efficiency and to prevent accidents by integrating existing distributed monitoring systems, train, signal, power and facilities. So, the system require better performance regarding real-time processing based on big data. The disk-based database that is used in existing railway control systems has a problem with real-time processing; memory-based databases haves a limitation in terms of big-data processing; and time series databases haves a limitation in terms of real-time processing. So, we need a new database architecture for simultaneous real-time processing based on big data. In this study, we review the existing railway monitoring systems and propose a new database architecture for a real-time railway safety supervision system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.7
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• pp.884-889
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• 2021

The number of people infected with Covid-19 in Korea seemed to be gradually decreasing thanks to various efforts such as social distancing and vaccines. However, just as the number of infected people increased after a particular incident on February 20, 2020, the number of infected people has been increasing rapidly since December 2020 by approximately 500 per day. Therefore, the future Covid-19 is predicted through the Prophet algorithm using Kaggle's dataset, and the explanatory power for this prediction is added through the coefficient of determination, mean absolute error, mean percent error, mean square difference, and mean square deviation through Scikit-learn. Moreover, in the absence of a specific incident rapidly increasing the cases of Covid-19, the proposed method predicts the number of infected people in Korea and emphasizes the importance of implementing epidemic prevention and quarantine rules for future diseases.

• The Korean Journal of Applied Statistics
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• v.35 no.5
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• pp.631-644
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• 2022

This paper considers robust estimation of the sparse vector autoregressive model (sVAR) useful in high-dimensional time series analysis. First, we generalize the result of Xu et al. (2008) that the adaptive lasso indeed has robustness in sVAR as well. However, adaptive lasso method in sVAR performs poorly as the number and sizes of outliers increases. Therefore, we propose new robust estimation methods for sVAR based on least absolute deviation (LAD) and Huber estimation. Our simulation results show that our proposed methods provide more accurate estimation in turn showed better forecasting performance when outliers exist. In addition, we applied our proposed methods to power usage data and confirmed that there are unignorable outliers and robust estimation taking such outliers into account improves forecasting.

• Nuclear Engineering and Technology
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• v.53 no.1
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• pp.148-163
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• 2021

Timely fault identification is important for safe and reliable operation of the electric valve system. Many research works have utilized different data-driven approach for fault diagnosis in complex systems. However, they do not consider specific characteristics of critical control components such as electric valves. This work presents an integrated shallow-deep fault diagnostic model, developed based on signals extracted from DN50 electric valve. First, the local optimal issue of particle swarm optimization algorithm is solved by optimizing the weight search capability, the particle speed, and position update strategy. Then, to develop a shallow diagnostic model, the modified particle swarm algorithm is combined with support vector machine to form a hybrid improved particle swarm-support vector machine (IPs-SVM). To decouple the influence of the background noise, the wavelet packet transform method is used to reconstruct the vibration signal. Thereafter, the IPs-SVM is used to classify phase imbalance and damaged valve faults, and the performance was evaluated against other models developed using the conventional SVM and particle swarm optimized SVM. Secondly, three different deep belief network (DBN) models are developed, using different acoustic signal structures: raw signal, wavelet transformed signal and time-series (sequential) signal. The models are developed to estimate internal leakage sizes in the electric valve. The predictive performance of the DBN and the evaluation results of the proposed IPs-SVM are also presented in this paper.

• Journal of Korean Academy of Nursing
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• v.52 no.1
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• pp.36-51
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• 2022

Purpose: The purpose of this study was to examine the effects of electroencephalogram (EEG) biofeedback training for emotion regulation and brain homeostasis on anxiety about COVID-19 infection, impulsivity, anger rumination, meta-mood, and self-regulation ability of late adolescents in the prolonged COVID-19 pandemic situation. Methods: A non-equivalent control group pretest-posttest design was used. The participants included 55 late adolescents in the experimental and control groups. The variables were evaluated using quantitative EEG at pre-post time points in the experimental group. The experimental groups received 10 sessions using the three-band protocol for five weeks. The collected data were analyzed using the Shapiro-Wilk test, Wilcoxon rank sum test, Wilcoxon signed-rank test, t-test and paired t-test using the SAS 9.3 program. The collected EEG data used a frequency series power spectrum analysis method through fast Fourier transform. Results: Significant differences in emotion regulation between the two groups were observed in the anxiety about COVID-19 infection (W = 585.50, p = .002), mood repair of meta-mood (W = 889.50, p = .024), self-regulation ability (t = - 5.02, p < .001), self-regulation mode (t = - 4.74, p < .001), and volitional inhibition mode (t = - 2.61, p = .012). Neurofeedback training for brain homeostasis was effected on enhanced sensory-motor rhythm (S = 177.00, p < .001) and inhibited theta (S = - 166.00, p < .001). Conclusion: The results demonstrate the potential of EEG biofeedback training as an independent nursing intervention that can markedly improve anxiety, mood-repair, and self-regulation ability for emotional distress during the COVID-19 pandemic.

• Journal of Korea Water Resources Association
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• v.37 no.12
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• pp.1019-1032
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• 2004

Flood frequency estimate is an essential index for determining the scale of small and middle hydraulic structure. However, this flood quantity could not be estimated directly for practical design purpose due to the lack of available flood data, and indirect method like design rainfall-runoff method have been used for the estimation of design flood. To give the good explain for design flood estimates, regional flood frequency analysis was performed by flood index method in this study. First, annual maximum series were constructed by using the collected data which covers from Japanese imperialism period to 1999. Wakeby distribution recommended by WMO(1989) was used for regional flood frequency analysis and L-moment method by Hosking (1990) was used for parameter estimation. For the homogeneity of region, the discordance and heterogeneity test by Hosking and Wallis(1993) was carried for 4 major watersheds in Korea. Physical independent variable correlated with index flood was watershed area. The relationship between specific discharge and watershed area showed a type of power function, i.e. the specific discharge decreases as watershed area increases. So flood quantity according to watershed area and return period was presented for each watershed(Han rivet, Nakdong river, Geum river and Youngsan/Seomjin river) by using this relation type. This result was also compared with the result of point frequency analysis and its regionalization. It was shown that the dam construction couldn't largely affect the variation of peak flood. The property of this study was also examined by comparison with previous studies.