• The Transactions of the Korean Institute of Electrical Engineers
• /
• v.39 no.3
• /
• pp.312-320
• /
• 1990

Analysis and lattice implementation of Extended Instrumental Variable (EIV) methods for high resolution spectral analysis are presented. The performance of EIV is improved by using prefilters and the unbiasness of EIV is proved by using the fact that residual processes are white. We derive the order and time update formulas for the covariance lattice algorithm which is particularly useful in case of short data or nonstationary processes. The ARMA model can be modeled as two channel AR processes. Using this model, the lattice algorithms of EIV are derived. Computer simulations are performed to show the usefulness of the proposed algorithms.

• Journal of the Korean Data and Information Science Society
• /
• v.28 no.4
• /
• pp.927-936
• /
• 2017

To analyze longitudinal count data, Poisson linear mixed models are commonly used. In the models the random effects covariance matrix explains both within-subject variation and serial correlation of repeated count outcomes. When the random effects covariance matrix is assumed to be misspecified, the estimates of covariates effects can be biased. Therefore, we propose reasonable and flexible structures of the covariance matrix using autoregressive and moving average Cholesky decomposition (ARMACD). The ARMACD factors the covariance matrix into generalized autoregressive parameters (GARPs), generalized moving average parameters (GMAPs) and innovation variances (IVs). Positive IVs guarantee the positive-definiteness of the covariance matrix. In this paper, we use the ARMACD to model the random effects covariance matrix in Poisson loglinear mixed models. We analyze epileptic seizure data using our proposed model.

• Magazine of the Korean Society of Agricultural Engineers
• /
• v.36 no.3
• /
• pp.60-66
• /
• 1994

This paper is to validate the proposed models for the real-time forecasting for the Keum river estuary dam such as tidal-level forecasting model, one-dimensional unsteady flood routing model, and Kalman filter models. The tidal-level forecasting model was based on semi-range and phase lag of four tidal constituents. The dynamic wave routing model was based on an implicit finite difference solution of the complete one-dimensional St. Venant equations of unsteady flow. The Kalman filter model was composed of a processing equation and adaptive filtering algorithm. The processng equations are second ordpr autoregressive model and autoregressive moving average model. Simulated results of the models were compared with field data and were reviewed.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.29 no.2
• /
• pp.104-112
• /
• 2015

This paper deals with an methodology for acquiring optimal order of rational function model in FDNE(frequency dependent network equivalents) with GA(genetic Algorithm). In order to analyze the modern power system with huge complexity, an practical and efficient equivalent model is needed which represents the system's characteristics of transient phenomenon. this paper shows developing a z domain rational function model which have the resultant coefficient from proposed GA simulation. To demonstrate this methodology, some simulations are performed with practical power system of NZ which applied with fault condition and nonlinear converter load.

• Communications for Statistical Applications and Methods
• /
• v.23 no.6
• /
• pp.575-585
• /
• 2016

Longitudinal studies repeatedly measure outcomes over time. Therefore, repeated measurements are serially correlated from same subject (within-subject variation) and there is also variation between subjects (between-subject variation). The serial correlation and the between-subject variation must be taken into account to make proper inference on covariate effects (Diggle et al., 2002). However, estimation of the covariance matrix is challenging because of many parameters and positive definiteness of the matrix. To overcome these limitations, we propose autoregressive moving average Cholesky decomposition (ARMACD) for the linear mixed models. The ARMACD allows a class of flexible, nonstationary, and heteroscedastic models that exploits the structure allowed by combining the AR and MA modeling of the random effects covariance matrix. We analyze a real dataset to illustrate our proposed methods.

• Communications for Statistical Applications and Methods
• /
• v.25 no.1
• /
• pp.61-70
• /
• 2018

Modeling of the random effects covariance matrix in generalized linear mixed models (GLMMs) is an issue in analysis of longitudinal categorical data because the covariance matrix can be high-dimensional and its estimate must satisfy positive-definiteness. To satisfy these constraints, we consider the autoregressive and moving average Cholesky decomposition (ARMACD) to model the covariance matrix. The ARMACD creates a more flexible decomposition of the covariance matrix that provides generalized autoregressive parameters, generalized moving average parameters, and innovation variances. In this paper, we analyze longitudinal count data with overdispersion using GLMMs. We propose negative binomial loglinear mixed models to analyze longitudinal count data and we also present modeling of the random effects covariance matrix using the ARMACD. Epilepsy data are analyzed using our proposed model.

• Journal of Power System Engineering
• /
• v.15 no.5
• /
• pp.54-60
• /
• 2011

In this study, an analytical realization of end-milling system was introduced using recursive parametric modeling analysis. Also, the numerical mode analysis of end-milling system with different conditions was performed systematically. In this regard, a recursive least square(RLS) modeling algorithm and the natural mode for real part and imaginary one was discussed. This recursive approach (RLSM) can be adopted for the on-line system identification and monitoring of an end-milling for this purpose. After experimental practice of the end-milling, the end-milling force was obtained and it was used for the calculation of FRF(Frequency response function) and mode analysis. Also the FRF was analysed for the prediction of a end-milling system using recursive algorithm.

• Journal of the Korean Society of Mechanical Technology
• /
• v.13 no.3
• /
• pp.73-79
• /
• 2011

In this study, an analytical realization of end-milling system was introduced using recursive parametric modeling analysis. Also, the numerical mode analysis of end-milling system with different conditions was performed systematically. In this regard, a recursive least square modelling algorithm and the natural mode for real part and imaginary one was discussed. This recursive approach (RLSM) can be adopted for on-line end-milling identification. After experimental practice of the end-milling, the end-milling force was obtained and it was used for the calculation of FRF (Frequency response function) and mode analysis. Also the FRF was analysed for the prediction of a end-milling system using recursive algorithm.

• Smart Structures and Systems
• /
• v.30 no.3
• /
• pp.263-271
• /
• 2022

In this paper we present an overview of damage diagnosis algorithms that have been developed over the past two decades using vibration signals obtained from structures. Then, the paper focuses primarily on algorithms that can be used following an extreme event such as a large earthquake to identify structural damage for responding in a timely manner. The algorithms presented in the paper use measurements obtained from accelerometers and gyroscope to identify the occurrence of damage and classify the damage. Example algorithms are presented include those based on autoregressive moving average (ARMA), wavelet energies from wavelet transform and rotation models. The algorithms are illustrated through application of data from test structures such as the ASCE Benchmark structure and laboratory tests of scaled bridge columns and steel frames. The paper concludes by identifying needs for research and development in order for such algorithms to become viable in practice.

• The Transactions of the Korean Institute of Power Electronics
• /
• v.27 no.4
• /
• pp.316-324
• /
• 2022

This paper uses seasonal auto-regressive integrated moving average (S-ARIMA), which is efficient in seasonality between time-series models, to predict the degradation tendency for lithium-ion batteries and study a method for improving the predictive performance. The proposed method analyzes the degradation tendency and extracted factors through an electrical characteristic experiment of lithium-ion batteries, and verifies whether time-series data are suitable for the S-ARIMA model through several statistical analysis techniques. Finally, prediction of battery aging is performed through S-ARIMA, and performance of the model is verified through error comparison of predictions through mean absolute error.