• The Transactions of the Korean Institute of Electrical Engineers A
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• v.55 no.7
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• pp.273-280
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• 2006

This paper suggests the method that forecasts Dynamic Line Rating (DLR). Thermal Overload Risk Probability (TORP) of the next time is forecasted based on the present weather conditions and DLR value by Monte Carlo Simulation (MCS). To model weather elements of transmission line for MCS process, this paper will propose the use of statistical weather models that time series is applied. Also, through the case study, it is confirmed that the forecasted TORP can be utilized as a criterion that decides DLR of next time. In short, proposed method may be used usefully to keep security and reliability of transmission line by forecasting transmission capacity of the next time.

• Journal of the Korean Data and Information Science Society
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• v.18 no.3
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• pp.609-617
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• 2007

The goal of this study is to forecast the trend of stream quality and to suggest some policy alternatives in Gumbo river. It used the five different monthly time series data such as BOD, COD, T-N and EC of the nine of Gumbo River measurement points from Jan. 1998 to Dec. 2006. Water pollution is serious at Gumbo2 and Palgeo stream measurement points. BOD, COD, T-N and EC data are analyzed with the exponential smoothing model and the trend is forecasted until Dec. 2009.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.835-837
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• 1996

In this paper, a method for the daily maximum load forecasting which uses a chaotic time series in power system and artificial neural network(Back-propagation) is proposed. We find the characteristics of chaos in power load curve and then determine a optimal embedding dimension and delay time. For the load forecast of one day ahead daily maximum power, we use the time series load data obtained in previous year. By using of embedding dimension and delay time, we construct a strange attractor in pseudo phase plane and the artificial neural network model trained with the attractor mentioned above. The one day ahead forecast errors are about 1.4% for absolute percentage average error.

• Proceedings of the KIEE Conference
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• 1994.11a
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• pp.9-11
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• 1994

FACTS concept is the control of power flow and increase of the loading on existing lines to the thermal limuts. This paper focuses on the ability of the thyristor controlled series compensator (TCSC) to stabilize the disturbed power systems. The result shows the potential benefit of the TCSC in addition to the role of controlling the steady state power flow. In order to show the effectiveness of controlled series capacitor, power system dynamic model is augmented and the effect of the SC into the power system dynamics is included. As a control algorithm, Linear Optimal Control theory is applied.

• Journal of the Korea Society of Computer and Information
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• v.24 no.9
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• pp.21-27
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• 2019

In this paper, we propose a deep auto-encoder-based pipe leak detection (PLD) technique from time-series acoustic data collected by microphone sensor nodes. The key idea of the proposed technique is to learn representative features of the leak-free state using leak-free time-series acoustic data and the deep auto-encoder. The proposed technique can be used to create a PLD model that detects leaks in the pipeline in an unsupervised learning manner. This means that we only use leak-free data without labeling while training the deep auto-encoder. In addition, when compared to the previous supervised learning-based PLD method that uses image features, this technique does not require complex preprocessing of time-series acoustic data owing to the unsupervised feature extraction scheme. The experimental results show that the proposed PLD method using the deep auto-encoder can provide reliable PLD accuracy even considering unsupervised learning-based feature extraction.

• Quantitative Bio-Science
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• v.37 no.2
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• pp.73-79
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• 2018

As high frequency (HF, for short) time series is now prevalent in the presence of real time big data, volatility computations based on traditional ARCH/GARCH models need to be further developed to suit the high frequency characteristics. This article reviews realized volatilities (RV) and multivariate GARCH (MGARCH) to deal with high frequency volatility computations. As a (functional) infinite dimensional models, the fARCH and fGARCH are introduced to accommodate ultra high frequency (UHF) volatilities. The fARCH and fGARCH models are developed in the recent literature by Hormann et al. [1] and Aue et al. [2], respectively, and our discussions are mainly based on these two key articles. Real data applications to domestic UHF financial time series are illustrated.

• Journal of Web Engineering
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• v.20 no.3
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• pp.795-816
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• 2021

The existing industry evaluation method utilizes the method of collecting the structured information such as the financial information of the companies included in the relevant industry and deriving the industrial evaluation index through the statistical analysis model. This method takes a long time to calculate the structured data and cause the time delay problem. In this paper, to solve this time delay problem, we derive monthly industry-specific interest and likability as a time series data type, which is a new industry evaluation indicator based on unstructured data. In addition, we propose a method to predict the industrial risk index, which is used as an important factor in industrial evaluation, based on derived industry-specific interest and likability time series data.

• Proceedings of the Korea Water Resources Association Conference
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• 2002.05a
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• pp.134-139
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• 2002

• Journal of The Korean Astronomical Society
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• v.2 no.1
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• pp.23-32
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• 1969

The photometric orbital elements of the eclipsing variable, RZ Pyxidis, are derived by means of Fourier series from photoelectric observations which were made by Michel Breger (1964). Physical dimensions and a model of the system are suggested here.

• CRM연구
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• v.1 no.1
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• pp.49-69
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• 2006

Previous studies, Technology Acceptance Model (TAM) and Post Acceptance Model (PAM) have a little limitation in time series analysis. To solve this limitation, we used system dynamics as research methodology and designed simulation model based on TAM and PAM. Moreover, we designed new simulation model which can analyize time series data in customers' demand change from initial acceptance to post acceptance. This study targeted domestic mobile phone market. The simulation results showed that diffusion graph was similar to real data. That means we validated our simulation model. Since the simulation model offers the graph of customer's demand change by time, so it can be useful as a leaning tool. Therefore, we think this study helps IT companies use the model for forecasting of market demand.