• Journal of Digital Contents Society
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• v.19 no.3
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• pp.513-519
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• 2018

The objective of this study was to examine the trends on social network services. The abstracts of 308 articles were extracted from web of science database published between 1994 and 2016. Time series analysis and topic modeling of text mining were implemented. The topic modeling results showed that the research topics were mainly 20 topics: trust, support, satisfaction model, organization governance, mobile system, internet marketing, college student effect, opinion diffusion, customer, information privacy, health care, web collaboration, method, learning effectiveness, knowledge, individual theory, child support, algorithm, media participation, and context system. The time series regression results indicated that trust, support satisfaction model, and remains of the topics were hot topics. This study also provided suggestions for future research.

• Journal of Ocean Engineering and Technology
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• v.32 no.3
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• pp.151-157
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• 2018

Offshore structures are exposed to low- and high-frequency responses due to environmental loads, and fatigue damage models are used to calculate the fatigue damage from these. In this study, we tried to optimize the main parameters used in fatigue damage calculation to derive a new fatigue damage model. A total of 162 bi-modal spectra using the elliptic equation were defined to describe the response of offshore structures. To calculate the fatigue damage from the spectra, time series were generated from the spectra using the inverse Fourier transform, and the rain-flow counting method was applied. The considered optimization variables were the size of the frequency increments, ratio of the time increment, and number of repetitions of the time series. In order to obtain optimized values, the fatigue damage was calculated using the parameter values proposed in previous work, and the fatigue damage was calculated by increasing or decreasing the proposed values. The results were compared, and the error rate was checked. Based on the test results, new values were found for the size of the frequency increment and number of time series iterations. As a validation, the fatigue damage of an actual tension spectrum found using the new proposed values and fatigue damage found using the previously proposed method were compared. In conclusion, we propose a new optimized calculation process that is faster and more accurate than the existed method.

• Journal of the Society of Naval Architects of Korea
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• v.33 no.4
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• pp.32-47
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• 1996

Systematic theoretical arm experimental studies have been performed to establish the methods of the hull form design, the optimum dimension selection and the resistance estimation for the displacement type super high-speed ships. In this study, theoretical hull form design method of the displacement type super high-speed ships has been developed first by the minimum resistance theory and the sectionally-varying hull form equation. Utilizing the established hull form design method, sixty(60) series hull forms have been prepared according to the systematic variations of the important design variables, and model tests were conducted for the sixty(60) series ship models. Finally, regression analyses have been performed for the results of model tests. It is considered that this is the first systematic and multi-purpose study in the world for the super high-speed ships. The study has been completed very successfully. The prepared computer program is now being actively utilized as an efficient tool for the design of the displacement type super high-speed ships.

• The Journal of the Korea Contents Association
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• v.16 no.12
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• pp.735-748
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• 2016

Since the number of student is regarded as the fundamental basis to calculate the future allocation of employed teachers, it needs to be systematically estimated based on statistical data. In order to achieve this purpose, the number of high school students is projected following the assumption that the teacher-student ratio of Korea should be adjusted to the level of OECD to improve the quality of education. Hence, this paper introduced the projection methods by time series model. To predict the number of high school students and error estimation, various models were adopted.

• The Korean Journal of Applied Statistics
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• v.20 no.3
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• pp.487-497
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• 2007

As is pointed out by Gourieroux (1997), the volatility effects in financial time series vary according to the signs of the return rates and therefore asymmetric Threshold-GARCH (TGARCH, henceforth) processes are natural extensions of the standard GARCH toward asymmetric volatility modeling. For preliminary detection of asymmetry in volatility, we suggest graphs of squared-log-returns for various financial time series including KOSPI, KOSDAQ and won-Euro exchange rate. Next, asymmetric TGARCH(1,1) model fits are provided in comparisons with standard GARCH(1.1) models.

• Journal of Navigation and Port Research
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• v.32 no.6
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• pp.425-431
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• 2008

The future cargo transportation and traffic volume on container in Gwangyang port was forecasted by using univariate time series models in this research. And the container ship traffic was produced. The constructed models all were most adapted to Winters' additive models with a trend and seasonal change. The cargo transportation on container in Gwangyang port was estimated each about 2,756 thousand TEU and 4,470 thousand TEU in 2011 and 2015 by increasing each 7.4%, 16.2% compared with 2007. The volume per ship on container was estimated each about 675TEU and 801TEU in 2011 and 2015 by increasing each 30.3%, 54.6% compared with 2007. Also, traffic volume on container incoming in Gwangyang Port was prospected each about 4,078ships and 5,921ships in 2011 and 2015.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.5
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• pp.689-694
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• 2009

In this paper an approach to building fuzzy models for predicting daily and weekly stock prices is presented. Predicting stock prices with traditional time series analysis has proven to be difficult. Fuzzy logic based models have advantage of expressing the input-output relation linguistically, which facilitates the understanding of the system behavior. In building a stock prediction model we bear a burden of selecting most effective indicators for the stock prediction. In this paper information used in traditional candle stick-chart analysis is considered as input variables of our fuzzy models. The fuzzy rules have the premises and the consequents composed of trapezoidal membership functions and nonlinear equations, respectively. DE(Differential Evolution) identifies optimal fuzzy rules through an evolutionary process. The fuzzy models to predict daily and weekly open, high, low, and close prices of KOSPI(KOrea composite Stock Price Index) are built, and their performances are demonstrated.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.5
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• pp.655-660
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• 2009

The context prediction algorithms are not suitable to provide real-time personalized service for users in context-awareness environment. The algorithms have problems like time delay in training data processing and the difficulties of implementation in real-time environment. In this paper, we propose a prediction algorithm with user modeling to shorten of processing time and to improve the prediction accuracy in the context prediction algorithm. The algorithm uses moving path of user contexts for context prediction and generates user model by time-series analysis of user's moving path. And that predicts the user context with the user model by sequence matching method. We compared our algorithms with the prediction algorithms by processing time and prediction accuracy. As the result, the prediction accuracy of our algorithm is similar to the prediction algorithms, and processing time is reduced by 40% in real time service environment.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.3
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• pp.234-241
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• 2021

By recognizing the importance of demand forecasting, the military is conducting many studies to improve the prediction accuracy for repair parts. Demand forecasting for repair parts is becoming a very important factor in budgeting and equipment availability. On the other hand, the demand for intermittent repair parts that have not constant sizes and intervals with the time series model currently used in the military is difficult to predict. This paper proposes a method to improve the prediction accuracy for intermittent repair parts of the Patriot. The authors collected intermittent repair parts data by classifying the demand types of 701 repair parts from 2013 to 2019. The temperature and operating time identified as external factors that can affect the failure were selected as input variables. The prediction accuracy was measured using both time series models and data mining models. As a result, the prediction accuracy of the data mining models was higher than that of the time series models, and the multilayer perceptron model showed the best performance.

• Korea Real Estate Review
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• v.27 no.3
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• pp.41-50
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• 2017

Previous studies, especially that by Lee (2014), showed how time series volatility models can be applied to the house price series. As the regional housing market trends, however, have shown significant differences of late, analysis with national data may have limited practical implications. This study applied volatility models in analyzing and forecasting regional house prices. The estimation of the AR(1)-ARCH(1), AR(1)-GARCH(1,1), and AR(1)-EGARCH(1,1,1) models confirmed the ARCH and/or GARCH effects in the regional house price series. The RMSEs of out-of-sample forecasts were then compared to identify the best-fitting model for each region. The monthly rates of house price changes in the second half of 2017 were then presented as an example of how the results of this study can be applied in practice.