• Transactions of the Korean Society of Automotive Engineers
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• v.11 no.4
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• pp.135-143
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• 2003

In this study, a real-time simulation model was developed for tracked vehicles with in-arm type semi-active hydro-pneumatic suspension unit using MATLAB S-functions. Since the vehicle model uses relative coordinates and massless link elements, the developed model has an enhanced analytic time performance. Through the comparison of simulation results with multi-body software(DADS), the vehicle model is verified. A controller using on-off skyhook control algorithm is designed with the pilot-centre]led proportional valve based on conventional damper characteristics. Exploiting the developed tracked vehicle model with other subsystem model such as a controller model, a suspension unit model, and a test road model, computer simulations are carried out. Control simulation results with the developed tracked vehicle model show that the semi-active suspension control system has a better performance than the conventional suspension system.

• Journal of Korean Society of Forest Science
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• v.95 no.2
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• pp.203-208
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• 2006

This study compared the roundwood demand prediction accuracy of econometric and time-series models using Korean data. The roundwood was divided into softwood and hardwood by species. The econometric model of roundwood demand was specified with four explanatory variables; own price, substitute price, gross domestic product, dummy. The time-series model was specified with lagged endogenous variable. The dummy variable reflected the abrupt decrease in roundwood demand in the late 1990's in the case of softwood roundwood, and the boom of plywood export in the late 1970's in the case of hardwood roundwood. On the other hand, the prediction accuracy was estimated on the basis of Residual Mean Square Errors(RMSE). The results showed that the softwood roundwood demand prediction can be performed more accurately by econometric model than by time-series model. However, the hardwood roundwood demand prediction accuracy was similar in the case of using econometric and time-series model.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.3
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• pp.493-499
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• 2017

This paper proposes a combined very-short-term and short-term multi-variate time-series model for 24 hour load forecasting. First, the best model for very-short-term and short-term load forecasting is selected by considering the least error value, and then they are combined by the optimal forecasting time. The actual load data of industry complex is used to show the effectiveness of the proposed model. As a result the load forecasting accuracy of the combined model has increased more than a single model for 24 hour load forecasting.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.9
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• pp.95-104
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• 2020

In financial economics studies, the autoregressive model has been a workhorse for a long time. However, the model has a fixed value on every parameter and requires the stationarity assumptions. Time-varying coefficient autoregressive model that we use in this paper offers some desirable benefits over the traditional model such as the parameters are allowed to be varied over-time and can be applies to non-stationary financial data. This paper provides the Monte Carlo simulation studies which show that the model can capture the dynamic movement of parameters very well, even though, there are some sudden changes or jumps. For the daily data from January 1, 2015 to February 12, 2020, our paper provides the empirical studies that Thailand, Taiwan and Tokyo Stock market Index can be explained very well by the time-varying coefficient autoregressive model with lag order one while South Korea's stock index can be explained by the model with lag order three. We show that the model can unveil the non-linear shape of the estimated mean. We employ GJR-GARCH in the condition variance equation and found the evidences that the negative shocks have more impact on market's volatility than the positive shock in the case of South Korea and Tokyo.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.89-89
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• 2000

The Current CORBA has many drawbacks to be deployed successfully in real-time system applications. Recently, OMG adopted Real-Time CORBA specification. In this paper, we report our efforts on an implementation of Priority Model of Real-Time CORBA spec., which is one of the most important components in Real-Time CORBA spec. The improvement of real-time performances of our implementation is verified experiments.

• Journal of Environmental Science International
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• v.8 no.3
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• pp.349-354
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• 1999

The transfer function was introduced to establish the prediction method for the DO concentration at the intaking point of Kongju Water Works System. In the mose cases we analyze a single time series without explicitly using information contained in the related time series. In many forecasting situations, other events will systematically influence the series to be forecasted(the dependent variables), and therefore, there is need to go beyond a univariate forecasting model. Thus, we must bulid a forecasting model that incorporates more than one time series and introduces explicitly the dynamic characteristics of the system. Such a model is called a multiple time series model or transfer function model. The purpose of this study is to develop the stochastic stream water quality model for the intaking station of Kongju city waterworks in Keum river system. The performance of the multiplicative ARIMA model and the transfer function noise model were examined through comparisons between the historical and generated monthly dissolved oxygen series. The result reveal that the transfer function noise model lead to the improved accuracy.

• Journal of the Korean Data and Information Science Society
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• v.20 no.6
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• pp.991-998
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• 2009

This paper deals with directivity forecasting of time series which is useful for futures trading in stock market. Directivity forecasting of time series is to forecast whether a given time series will rise or fall at next observation time point. For directional forecasting, we consider time regression model and ARIMA model. In particular, we study two statistics, intra-model and extra-model deviation and then show usefulness of intra-model deviation.

• Proceedings of the KSRS Conference
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• v.2
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• pp.635-638
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• 2006

Regression of conventional prediction techniques in data mining uses the model which is generated from the training step. This model is applied to new input data without any change. If this model is applied directly to time series, the rate of prediction accuracy will be decreased. This paper proposes an incremental regression for time series prediction like typhoon track prediction. This technique considers the characteristic of time series which may be changed over time. It is composed of two steps. The first step executes a fractional process for applying input data to the regression model. The second step updates the model by using its information as new data. Additionally, the model is maintained by only recent data in a queue. This approach has the following two advantages. It maintains the minimum information of the model by using a matrix, so space complexity is reduced. Moreover, it prevents the increment of error rate by updating the model over time. Accuracy rate of the proposed method is measured by RME(Relative Mean Error) and RMSE(Root Mean Square Error). The results of typhoon track prediction experiment are performed by the proposed technique IMLR(Incremental Multiple Linear Regression) is more efficient than those of MLR(Multiple Linear Regression) and SVR(Support Vector Regression).

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.1
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• pp.64-71
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• 2016

This paper proposes nonlinear behavior in a love model for Romeo and Juliet with an external force of discontinuous time. We investigated the periodic motion and chaotic behavior in the love model by using time series and phase portraits with respect to some variable and fixed parameters. The computer simulation results confirmed that the proposed love model with an external force of discontinuous time shows periodic motion and chaotic behavior with respect to parameter variation.

• Journal of the Ergonomics Society of Korea
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• v.35 no.4
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• pp.277-291
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• 2016

Objective: The aim of this study is to propose a unit touch gesture model, which would be useful to predict the performance time on mobile devices. Background: When estimating usability based on Model-based Evaluation (MBE) in interfaces, the GOMS model measured 'operators' to predict the execution time in the desktop environment. Therefore, this study used the concept of operator in GOMS for touch gestures. Since the touch gestures are comprised of possible unit touch gestures, these unit touch gestures can predict to performance time with unit touch gestures on mobile devices. Method: In order to extract unit touch gestures, manual movements of subjects were recorded in the 120 fps with pixel coordinates. Touch gestures are classified with 'out of range', 'registration', 'continuation' and 'termination' of gesture. Results: As a results, six unit touch gestures were extracted, which are hold down (H), Release (R), Slip (S), Curved-stroke (Cs), Path-stroke (Ps) and Out of range (Or). The movement time predicted by the unit touch gesture model is not significantly different from the participants' execution time. The measured six unit touch gestures can predict movement time of undefined touch gestures like user-defined gestures. Conclusion: In conclusion, touch gestures could be subdivided into six unit touch gestures. Six unit touch gestures can explain almost all the current touch gestures including user-defined gestures. So, this model provided in this study has a high predictive power. The model presented in the study could be utilized to predict the performance time of touch gestures. Application: The unit touch gestures could be simply added up to predict the performance time without measuring the performance time of a new gesture.