• Journal of Environmental Science International
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• v.3 no.4
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• pp.349-356
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• 1994

The purpose of this study is to develop a linear reservoir model with Kalman filter using Kalman filter theory which removes a physical uncertainty of :ainfall-runoff process. A linear reservoir model, which is the basic model of Kalman filter, is used to calculate runoff from rainfall in river basin. A linear reservoir model with Kalman filter is composed of a state-space model using a system model and a observation model. The state-vector of system model in linear. The average value of the ordinate of IUH for a linear reservoir model with Kalman filter is used as the initial value of state-vector. A .linear reservoir model with Kalman filter shows better results than those by linear reserevoir model, and decreases a physical uncertainty of rainfall-runoff process in river basin.

• IE interfaces
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• v.24 no.4
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• pp.351-361
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• 2011

Forecasting of electricity demand have difficulty in adapting to abrupt weather changes along with a radical shift in major regional and global climates. This has lead to increasing attention to research on the immediate and accurate forecasting model. Technically, this implies that a model requires only a few input variables all of which are easily obtainable, and its predictive performance is comparable with other competing models. To meet the ends, this paper presents an energy demand forecasting model that uses the variable selection or extraction methods of data mining to select only relevant input variables, and employs support vector regression method for accurate prediction. Also, it proposes a novel performance measure for time-series prediction, shift index, followed by description on preprocessing procedure. A comparative evaluation of the proposed method with other representative data mining models such as an auto-regression model, an artificial neural network model, an ordinary support vector regression model was carried out for obtaining the forecast of monthly electricity demand from 2000 to 2008 based on data provided by Korea Energy Economics Institute. Among the models tested, the proposed method was shown promising results than others.

• Journal of the Korean Data and Information Science Society
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• v.25 no.4
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• pp.807-817
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• 2014

This paper considers multivariate time series modelling of PM10 data in Korea collected from 2008 to 2011. We consider both temporal and spatial dependencies of PM10 by applying the sparse vector autoregressive (sVAR) modelling proposed by Davis et al. (2013). It utilizes the partial spectral coherence to measure cross correlation between different regions, in turn provides the sparsity in the model while balancing the parsimony of model and the goodness of fit. It is also shown that sVAR performs better than usual vector autoregressive model (VAR) in forecasting.

• The Journal of Fisheries Business Administration
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• v.46 no.1
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• pp.93-107
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• 2015

This study aims to analyze causalities among Hairtail prices by distribution channel using a vector autoregressive model. This study applies unit-root test for stability of data, uses Granger causality test to know interaction among Hairtail Prices by distribution channel, and employes the vector autoregressive model to estimate statistical impacts among t-2 period variables used in model. Analyzing results of this study are as follows. First, ADF, PP, and KPSS tests show that the change rate of Hairtail price by distribution channel differentiated by logarithm is stable. Second, a Granger causality test presents that the producer price of Hairtail leads the wholesale price and then the wholesale price leads the consumer price. Third, the vector autoregressive model suggests that the change rate of Hairtail producer price of t-2 period variables statistically, significantly impacts change rates of own, wholesale, and consumer prices at current period. Fourth, the impulse response analysis indicates that impulse responses of the structural shocks with a respectively distribution channel of the Hairtail prices are relatively more powerful in own distribution channel than in other distribution channels. Fifth, a forecast error variance decomposition of the Hairtail prices points out that the own price has relatively more powerful influence than other prices.

• Journal of Information Technology Services
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• v.10 no.2
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• pp.235-245
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• 2011

Early criticality prediction models that determine whether a design entity is fault-prone or not are becoming more and more important as software development projects are getting larger. Effective predictions can reduce the system development cost and improve software quality by identifying trouble-spots at early phases and proper allocation of effort and resources. Many prediction models have been proposed using statistical and machine learning methods. This paper builds a prediction model using Support Vector Machine(SVM) which is one of the most popular modern classification methods and compares its prediction performance with a well-known prediction model, BackPropagation neural network Model(BPM). SVM is known to generalize well even in high dimensional spaces under small training data conditions. In prediction performance evaluation experiments, dimensionality reduction techniques for data set are not used because the dimension of input data is too small. Experimental results show that the prediction performance of SVM model is slightly better than that of BPM and polynomial kernel function achieves better performance than other SVM kernel functions.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.27 no.8
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• pp.98-104
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• 2013

AC machines are in wide use in industry and d-q transformation from 3 phase of a, b, c is commonly used to analyze these kinds of machines. The equivalent circuits of d and q axis are, however, generally cross coupled and difficult to analyze. In this study, a modeling technique of AC machine including induction and PM synchronous motors using matrix vector is proposed. With that model, it can not only explain the AC machines physically but also make it simple to analyze them. The separating process of d and q components is not needed in this model and this model can be applied to analyze asymmetric motors like IPMSM machine. With this technique, the model becomes simple, easy to understand physically, and yields results that are the same as those from other models. These simulation results of the proposed model for induction motor are compared with those of other models to verify the method proposed.

• Structural Engineering and Mechanics
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• v.81 no.4
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• pp.411-428
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• 2022

This paper presents the Campbell diagram analysis of the rotordynamic system using the full order model (FOM) and the reduced order model (ROM) techniques to determine the critical speeds, identify the stability and reduce the computational time. Due to the spin-speed-dependent matrices (e.g., centrifugal stiffening matrix), several model order reduction (MOR) techniques may be considered, such as the modal superposition (MS) method and the Krylov subspace-based MOR techniques (e.g., Ritz vector (RV), quasi-static Ritz vector (QSRV), multifrequency quasi-static Ritz vector (MQSRV), multifrequency/ multi-spin-speed quasi-static Ritz vector (MMQSRV) and the combined Ritz vector & modal superposition (RV+MS) methods). The proposed MMQSRV method in this study is extended from the MQSRV method by incorporating the rotational-speed-dependent stiffness matrices into the Krylov subspace during the MOR process. Thus, the objective of this note is to respond to the question of whether to use the MS method or the Krylov subspace-based MOR technique in establishing the Campbell diagram of the shaft-disc-blade assembly systems in three-dimensional (3D) finite element analysis (FEA). The Campbell diagrams produced by the FOM and various MOR methods are presented and discussed thoroughly by computing the norm of relative errors (ER). It is found that the RV and the MS methods are dominant at low and high rotating speeds, respectively. More precisely, as the spinning velocity becomes large, the calculated ER produced by the RV method is significantly increased; in contrast, the ER produced by the MS method is smaller and more consistent. From a computational point of view, the MORs have substantially reduced the time computing considerably compared to the FOM. Additionally, the verification of the 3D FE rotordynamic model is also provided and found to be in close agreement with the existing solutions.

• Communications for Statistical Applications and Methods
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• v.22 no.3
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• pp.209-222
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• 2015

Time series are used in many studies for model building and analysis. We must be very careful to understand the kind of time series data used in the analysis. In this review article, we will begin with some issues related to the use of aggregate and systematic sampling time series. Since several time series are often used in a study of the relationship of variables, we will also consider vector time series modeling and analysis. Although the basic procedures of model building between univariate time series and vector time series are the same, there are some important phenomena which are unique to vector time series. Therefore, we will also discuss some issues related to vector time models. Understanding these issues is important when we use time series data in modeling and analysis, regardless of whether it is a univariate or multivariate time series.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.20 no.9
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• pp.84-89
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• 2006

This paper presents the thickness effect of hysteresis ring of hysteresis motor using finite element method combined with a vector hysteresis model. From the magnitude and direction of the magnetic field intensity, the magnetization of each ring element is calculated by a vector hysteresis model. The developed torque can be obtained with the vector sum of individual torque of each element on the hysteresis ring. From these calculations, it can be found that the motor torque is not in proportion to the thickness of the ring. As a result, there exists a proper point of thickness and that can be determined using the proposed methoㅇ in this paper.

• Journal of international Conference on Electrical Machines and Systems
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• v.2 no.2
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• pp.165-170
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• 2013

This paper presents the iron loss prediction of rotating electric machines taking account of the vector hysteretic properties of electrical steel sheet. The E&S vector hysteresis model is adopted to describe the vector hysteretic properties of a non-oriented electrical steel sheet, and incorporated into finite element analysis (FEA) for magnetic field analysis and iron loss prediction. A permanent magnet synchronous generator is taken as a numerical model, and the analyzed magnetic field distribution and predicted iron loss by using the proposed method is compared with those from a conventional method which employs an empirical iron loss formula with FEA based on a non-linear B-H curve. Through the comparison the effectiveness of the presented method for the iron loss prediction of the rotating machine is verified.