• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.2
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• pp.155-160
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• 2015

We say that human have an animal of emotion. There are various kind in the emotion of human. One of among them, love has been studied in sociology and psychology as a matter of great concern. In this paper, we propose a novel love model with the delay time as response time for love. We also consider it in the Romeo and Juliet of love model to analyze their romantic behaviors. First we consider the Juliet only have a time delay, Romeo only have a time delay, and both Romeo and Juliet have a time delay. We represent their behaviors as time series and phase portrait, and we analyze their difference.

• Journal of Electrical Engineering and Technology
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• v.8 no.4
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• pp.760-765
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• 2013

An Interline Power Flow Controller (IPFC) is a converter based controller which compensates and balance the power flow among multi-lines within the same corridor of the multi-line subsystem. The Interline Power Flow Controller consists of a voltage source converter based Flexible AC Transmission System (FACTS) controller for series compensation. The reactive voltage injected by individual Voltage Source Converter (VSC) can be controlled to regulate active power flow in the respective line in which one VSC regulates the DC voltage, the other one controls the reactive power flows in the lines by injecting series active voltage. In this paper, a circuit model for IPFC is developed and simulation of interline power flow controller is done using the proposed circuit model. Simulation is done using MATLAB Simulink and PSPICE. The results obtained by MATLAB are compared with the results obtained by PSPICE and compared with theoretical values.

• The Korean Journal of Applied Statistics
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• v.28 no.5
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• pp.1025-1034
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• 2015

Traditional Shewhart control charts assume that the observations are independent over time. Current progress in measurement and data collection technology lead to the presence of autocorrelated process data that may affect poor performance in statistical process control. One of the most popular charts for autocorrelated data is to model a correlative structure with an appropriate time series model and apply control chart to the sequence of residuals. Model parameters are estimated by an in-control Phase I reference sample since they are usually unknown in practice. This paper deals with the effects of parameter estimation on Phase II control limits to monitor autocorrelated data.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.499-499
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• 2018

Hydro-meteorological extremes are trivial in these days. Therefore, it is important to identify extreme hydrological events in advance to mitigate the damage due to the extreme events. In this context, exploring temporal distribution of sub-daily extreme rainfall at multiple rain gauges would informative to identify different states to describe severity of the disaster. This study proposehidden Markov chain model (HMM) based rainfall analysis tool to understand the temporal sub-daily rainfall patterns over South Korea. Hourly and daily rainfall data between 1961 and 2017 for 92 stations were used for the study. HMM was applied to daily rainfall series to identify an observed hidden state associated with rainfall frequency and intensity, and further utilized the estimated hidden states to derive a temporal distribution of daily extreme rainfall. Transition between states over time was clearly identified, because HMM obviously identifies the temporal dependence in the daily rainfall states. The proposed HMM was very useful tool to derive the temporal attributes of the daily rainfall in South Korea. Further, daily rainfall series were disaggregated into sub-daily rainfall sequences based on the temporal distribution of hourly rainfall data.

• Asia-Pacific Journal of Business
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• v.9 no.4
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• pp.137-155
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• 2018

It has been reported that there is a significant positive relationship between the unexpected earnings on the earnings announcement date and the cumulative abnormal returns following the earnings announcement date. This study investigates whether the results of prior studies are because the public announcement of shareholders' meeting date was selected as the event date instead of either the preliminary earnings disclosure date or the profit/loss change announcement date. The results of this study are as follows. First, post-earnings-announcement drift(PEAD) occurs when unexpected earnings were computed based on the prior period earnings and the public announcement of the shareholders' meeting date as the profit disclosure date. Second, when analyzing the PEAD with the unexpected earnings calculated using the financial analysts' forecasts, no PEAD has been found both on the date of the shareholders' meeting and the earlier date of the preliminary earnings disclosure, profit/loss change announcement, or the public announcement of the shareholders' meeting. Foster et al. (1984) analyze the PEAD using time series model and earnings forecasting model and suggest that the PEAD appears only in the time series model. In this study, too, in the case of using analysts' profit forecasts, the lack of the PEAD shows that the PEAD can be changed according to the method of measuring the unexpected earnings.

• The Korean Journal of Applied Statistics
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• v.31 no.6
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• pp.785-799
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• 2018

With the development of renewable energy sector, the importance of solar energy is continuously increasing. Solar radiation forecasting is essential to accurately solar power generation forecasting. In this paper, we used time series models (ARIMA, ARIMAX, seasonal ARIMA, seasonal ARIMAX, ARIMA GARCH, ARIMAX-GARCH, seasonal ARIMA-GARCH, seasonal ARIMAX-GARCH). We compared the performance of the models using mean absolute error and root mean square error. According to the performance of the models without exogenous variables, the Seasonal ARIMA-GARCH model showed better performance model considering the problem of heteroscedasticity. However, when the exogenous variables were considered, the ARIMAX model showed the best forecasting accuracy.

• The Journal of Economics, Marketing and Management
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• v.9 no.2
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• pp.1-10
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• 2021

Purpose: In this work, we examine several time series models to predict internet and mobile transaction amount in South Korea, whereas Jeong (2020) has obtained the optimal forecasts for online shopping transaction amount by using time series models. Additionally, optimal forecasts based on the model considered can be calculated and applied to the Corona 19 situation. Research design, data, and methodology: The data are extracted from the online shopping trend survey of the National Statistical Office, and homogeneous and comparable in size based on 46 realizations sampled from January 2007 to October 2020. To achieve the goal of this work, both multiplicative ARIMA model and Holt-Winters Multiplicative seasonality method are taken into account. In addition, goodness-of-fit measures are used as crucial tools of the appropriate construction of forecasting model. Results: All of the optimal forecasts for the next 12 months for two online shopping transactions maintain a pattern in which the slope increases linearly and steadily with a fixed seasonal change that has been subjected to seasonal fluctuations. Conclusions: It can be confirmed that the mobile shopping transactions is much larger than the internet shopping transactions for the increase in trend and seasonality in the future.

• Journal of Korea Multimedia Society
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• v.25 no.1
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• pp.82-91
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• 2022

In this paper, time series prediction method of photovoltaic power is introduced using seasonal autoregressive integrated moving average (SARIMA). In order to obtain the best fitting model by a time series method in the absence of an environmental sensor, this research was used data below 50% of cloud cover. Three samples were extracted by time intervals from the raw data. After that, the best fitting models were derived from mean absolute percentage error (MAPE) with the minimum akaike information criterion (AIC) or beysian information criterion (BIC). They are SARIMA (1,0,0)(0,2,2)14, SARIMA (1,0,0)(0,2,2)28, SARIMA (2,0,3)(1,2,2)55. Generally parameter of model derived from BIC was lower than AIC. SARIMA (2,0,3)(1,2,2)55, unlike other models, was drawn by AIC. And the performance of models obtained by SARIMA was compared. MAPE value was affected by the seasonal period of the sample. It is estimated that long seasonal period samples include atmosphere irregularity. Consequently using 1 hour or 30 minutes interval sample is able to be helpful for prediction accuracy improvement.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.12
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• pp.151-157
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• 1996

The tool condition monitoring is one of the most important aspects to improve productivity and quality of workpiece. In this study, the wear of ceramic tool (A1$_{2}$0$_{3}$-TiC Series) cutting the hardened die material(SKD11) was investigated. Flank wear was more dominant than crater wear. Therefore the modeling of cutting force related to flank wear has been performed. The cutting force model was construct- ed by an assumption that the stress distribution on the tool face is affected by tool wear. The relationship between characteristics as cutting force and tool wear can be suggested by machining parameters depending on cutting conditions. Experiments were performed under the various cutting conditions to ensure the validity of force models. The theoretical predictions on the flank wear are approximately in good agreement with experimental results.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.150-152
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• 2022

In this work, we propose a model that considers the behavior and synaptic plasticity of sensory neurons based on Liquid Time-constant Network (LTC). The neuron connection structure was experimented with four types: the increasing number of neurons, the decreasing number, the decreasing number, and the decreasing number. In this study, we experimented using a time series prediction dataset to see if the performance of the changed model improved compared to LTC. Experimental results show that the application of modeling of sensory neurons does not always bring about performance improvements, but improves performance through proper selection of learning rules depending on the type of dataset. In addition, the connective structure of neurons showed improved performance when it was less than four layers.