• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 1988년도 한국자동제어학술회의논문집(국내학술편); 한국전력공사연수원, 서울; 21-22 Oct. 1988
• /
• pp.332-337
• /
• 1988

A digital series-feedback compensator algorithm for tracking time-varying signal is presented. The series-feedback compensator is composed of one closed loop pole / zero cancellation compensator and one desired-input generator. This algorithm is applied to nonlinear hydraulic position control system. The hydraulic servo system is modelled as a second order linear model and cancellation compensator is modelled from it. The desired input generator is inserted to reduce modelling error. Digital computer simulation output using this control method is present and the usefulness of this control algorithm for nonlinear hydraulic system is verified.

• Journal of Distribution Science
• /
• 제12권2호
• /
• pp.59-71
• /
• 2014

Purpose - In recent years, many firms have attempted various approaches to cope with the continual increase of aviation transportation. The previous research into freight charge forecasting models has focused on regression analyses using a few influence factors to calculate the future price. However, these approaches have limitations that make them difficult to apply into practice: They cannot respond promptly to small price changes and their predictive power is relatively low. Therefore, the current study proposes a freight charge-forecasting model using time series data instead a regression approach. The main purposes of this study can thus be summarized as follows. First, a proper model for freight charge using the autoregressive integrated moving average (ARIMA) model, which is mainly used for time series forecast, is presented. Second, a modified ARIMA model for freight charge prediction and the standard process of determining freight charge based on the model is presented. Third, a straightforward freight charge prediction model for practitioners to apply and utilize is presented. Research design, data, and methodology - To develop a new freight charge model, this study proposes the ARIMAC(p,q) model, which applies time difference constantly to address the correlation coefficient (autocorrelation function and partial autocorrelation function) problem as it appears in the ARIMA(p,q) model and materialize an error-adjusted ARIMAC(p,q). Cargo Account Settlement Systems (CASS) data from the International Air Transport Association (IATA) are used to predict the air freight charge. In the modeling, freight charge data for 72 months (from January 2006 to December 2011) are used for the training set, and a prediction interval of 23 months (from January 2012 to November 2013) is used for the validation set. The freight charge from November 2012 to November 2013 is predicted for three routes - Los Angeles, Miami, and Vienna - and the accuracy of the prediction interval is analyzed using mean absolute percentage error (MAPE). Results - The result of the proposed model shows better accuracy of prediction because the MAPE of the error-adjusted ARIMAC model is 10% and the MAPE of ARIMAC is 11.2% for the L.A. route. For the Miami route, the proposed model also shows slightly better accuracy in that the MAPE of the error-adjusted ARIMAC model is 3.5%, while that of ARIMAC is 3.7%. However, for the Vienna route, the accuracy of ARIMAC is better because the MAPE of ARIMAC is 14.5% and the MAPE of the error-adjusted ARIMAC model is 15.7%. Conclusions - The accuracy of the error-adjusted ARIMAC model appears better when a route's freight charge variance is large, and the accuracy of ARIMA is better when the freight charge variance is small or has a trend of ascent or descent. From the results, it can be concluded that the ARIMAC model, which uses moving averages, has less predictive power for small price changes, while the error-adjusted ARIMAC model, which uses error correction, has the advantage of being able to respond to price changes quickly.

• Bulletin of the Korean Mathematical Society
• /
• 제52권4호
• /
• pp.1113-1122
• /
• 2015

This paper is concerned with the pattern formation of a diffusive predator-prey model with two time delays. Based upon an analysis of Hopf bifurcation, we demonstrate that time delays can induce spatial patterns under some conditions. Moreover, by use of a series of numerical simulations, we show that the type of spatial patterns is the spiral wave. Finally, we demonstrate that the spiral wave is asymptotically stable.

• Journal of the Korean Data and Information Science Society
• /
• 제27권6호
• /
• pp.1547-1555
• /
• 2016

The variability of exchange rate influences on the various aspect, especially economics, social phenomenon, industry, and culture of the country. In this article, time series model that won/yen exchange rate can be explained by won/dollar exchange rate has been studied. Daily exchange rate data have been used from January 1, 1999 to December 31, 2015. The daily data divided into two period based on the world financial crisis, September 13, 2008. The first period was January 1, 1999 through September 12, 2008 and the second period was October 1, 2008 through December 31, 2015. The AR+IGARCH (1, 1) model has been used for analyzing the variability of exchange rate. In both first period and second period, the estimation of won/yen exchange rate are somewhat underestimated compared with the actual value.

• Journal of Korea Water Resources Association
• /
• 제42권10호
• /
• pp.809-824
• /
• 2009

Hydrological system forecasting, which is the short term runoff historical data during the limited period in dam site, is a conditional precedent of hydrological persistence by stochastic analysis. We have forecasted the monthly hydrological system from Andong dam basin data that is the rainfall, evaporation, and runoff, using the seasonal ARIMA (autoregressive integrated moving average) model. Also we have conducted long term runoff simulations through the forecasted results of TANK model and ARIMA+TANK model. The results of analysis have been concurred to the observation data, and it has been considered for application to possibility on the stochastic model for dam inflow forecasting. Thus, the method presented in this study suggests a help to water resource mid- and long-term strategy establishment to application for runoff simulations through the forecasting variables of hydrological time series on the relatively short holding runoff data in an object basins.

• Journal of the Korean Institute of Intelligent Systems
• /
• 제19권5호
• /
• pp.655-660
• /
• 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.

• Structural Monitoring and Maintenance
• /
• 제7권2호
• /
• pp.149-166
• /
• 2020

This paper presents the results of an experimental investigation on a vibration-based damage identification framework for a steel girder type and a truss bridge based on acceleration responses to operational loading. The method relies on sensor clustering-based time-series analysis of the operational acceleration response of the bridge to the passage of a moving vehicle. The results are presented in terms of Damage Features from each sensor, which are obtained by comparing the actual acceleration response from the sensors to the predicted response from the time-series model. The damage in the bridge is detected by observing the change in damage features of the bridge as structural changes occur in the bridge. The relative severity of the damage can also be quantitatively assessed by observing the magnitude of the changes in the damage features. The experimental results show the potential usefulness of the proposed method for future applications on condition assessment of real-life bridge infrastructures.

• Journal of the Korean Institute of Intelligent Systems
• /
• 제17권6호
• /
• pp.799-803
• /
• 2007

In this paper, we propose a new fuzzy membership function for FSVM(Fuzzy Support Vector Machines). We apply a fuzzy membership to each input point of SVM and reformulate SVM into fuzzy SVM (FSVM) such that different input points can make different contributions to the learning of decision surface. The proposed method enhances the SVM in reducing the effect of outliers and noises in data points. This paper compares classification and estimated performance of SVM, FSVM(1), and FSVM(2) model that are getting into the spotlight in time series prediction.

• The Journal of Fisheries Business Administration
• /
• 제29권2호
• /
• pp.97-110
• /
• 1998

This paper tries to estimate Korea's import demand function for fisheries using cointegration analysis. The estimation function consists of one dependent variable-import quantity of fisheries(FTIW) and two independent variables-relative price(RP) between importable and domestic products and real income(GDP). As it has been empirically found out that almost all of time series of macro-variables such as GDP, price index are nonstationary, existing studies which ignore this fact need to be reexamined. Conventional econometric method can not analyze nonstationary time series in level. To perform the analysis, time series should be differenciated until stationarity is guaranteed. Unfortunately, the difference method removes the long run element of data, and so leads to difficulties of interpretation. But according to new developed econometric theory, cointegration approach could solve these problems. Therefore this paper proceeds the estimation on the basis of cointegration analysis, because the quartly variables from 1988 to 1997 used in the model is found out to be nonstationary. The estimation results show that all of the variables are statistically significant. Therefore Korea's import demand for fisheries has been strongly affected by the variation of real income and the relative price.

• Journal of Korean Institute of Industrial Engineers
• /
• 제28권2호
• /
• pp.171-177
• /
• 2002

In this paper, we propose a new data fusion method to improve the performance of individual prediction models for time series data. Individual models used are ARIMA and neural network and their results are combined based on the weight reflecting the inverse of EWMA of squared prediction error of each individual model. Monte Carlo simulation is used to identify the situation where the proposed approach can take a vintage point over typical fusion methods which utilize MSE for weight. Study results indicate the following: EWMA performs better than MSE fusion when the data size is large with a relatively big amplitude, which is often observed in intra-cranial pressure data. Additionally, EWMA turns out to be a best choice among MSE fusion and the two individual prediction models when the data size is large with relatively small random noises, often appearing in tax revenue data.