• Journal of the Computational Structural Engineering Institute of Korea
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• v.13 no.4
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• pp.405-415
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• 2000

Time series of wind-induced pressure on a structure are modeled using autoregressive moving average (ARMA) model. In an AR process, the current value of the time series is expressed in terms of a finite, linear combination of the previous values and a white noise. In a MA process, the value of the time series is linearly dependent on a finite number of the previous white noises. The ARMA process is a combination of the AR and MA processes. In this paper, the ARMA models with several different combinations of the AR and MA orders are fitted to the wind-induced pressure time series, and the procedure to select the most appropriate ARMA model to represent the data is described. The maximum likelihood method is used to estimate the model parameters, and the AICC model selection criterion is employed in the optimization of the model order, which is assumed to be a measure of the temporal complexity of the pressure time series. The goodness of fit of the model is examined using the LBP test. It is shown that AR processes adequately fit wind pressure time series.

• Communications for Statistical Applications and Methods
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• v.17 no.1
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• pp.27-37
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• 2010

This article concerns the forecasting in binomial AR(p) models which is proposed by Wei$\ss$ (2009b) for time series of binomial counts. Our method extends to binomial AR(p) models a recent result by Jung and Tremayne (2006) for integer-valued autoregressive model of second order, INAR(2), with simple Poisson innovations. Forecasts are produced by conditional median which gives 'coherent' forecasts, and we estimate the forecast distributions of future values of binomial AR(p) models by means of a Monte Carlo method allowing for parameter uncertainty. Model parameters are estimated by the method of moments and estimated standard errors are calculated by means of block of block bootstrap. The method is fitted to log data set used in Wei$\ss$ (2009b).

• Proceedings of the KIEE Conference
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• 1998.07g
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• pp.2486-2488
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• 1998

In this paper, Prameters esimated t using AR model in order to approach linearly the heart sound which include the nonlinear characteristic from the characteristics based on a statistical theory. The parameters which is figured out using AR model is a very important information which show the characteristic heart sound In this paper parameters estimated using autocorrelation method and order selected by proposed Akaike[6] method. Compared the similirities of the spectrums between estimated by using AR model and estimated by using FFT method.

• Bulletin of the Korean Mathematical Society
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• v.43 no.4
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• pp.813-820
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• 2006

We consider a MAR model with ARCH type conditional heteroscedasticity. MAR-ARCH model can be derived as a smoothed version of the double threshold AR-ARCH model by adding a random error to the threshold parameters. Easy to check sufficient conditions for strict stationarity, ${\beta}-mixing$ property and existence of moments of the model are given via Markovian representation technique.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.59 no.4
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• pp.417-422
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• 2010

Atmosphere Plasma of Gas Discharge (APGD) has been used in plasma sources for material processing such as etching, deposition, surface modification and so on due to having no thermal damages. The APGD researches on AC source with high frequency have been mainly processed. However, DC APGD studies have been not. In order to understand APGD further, it is necessary to study on fundamental properties of DC APGD. In this paper, we developed a one-dimensional fluid simulation model with capacitively coupled plasma chamber at the atmosphere pressure (760 [Torr]). Nine kinds of Ar discharge particles such as electron (e), positive ions ($Ar^+$, $Ar_2^+$) and neutral particles ($Ar_m^*$, $Ar_r^*$, $Ar_h^*$, $Ar_2^*$(1), $Ar_2^*$(3) and Ar gas) are considered in the computation. The simulation was worked at the current range of 1~15 [mA]. The characteristics of voltage-current were calculated and the structure of Joule heating were discussed. The spatial distributions of Ar DC APGD and the mechanism of power consumption were also investigated.

• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.1-17
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• 2013

In this article, several types of hybrid forecasting models are suggested. In particular, hybrid models using the generalized additive model (GAM) are newly suggested as an alternative to those using neural networks (NN). The prediction performances of various hybrid and non-hybrid models are evaluated using simulated time series data. Five different types of seasonal time series data related to an additive or multiplicative trend are generated over different levels of noise, and applied to the forecasting evaluation. For the simulated data with only seasonality, the autoregressive (AR) model and the hybrid AR-AR model performed equivalently very well. On the other hand, if the time series data employed a trend, the SARIMA model and some hybrid SARIMA models equivalently outperformed the others. In the comparison of GAMs and NNs, regarding the seasonal additive trend data, the SARIMA-GAM evenly performed well across the full range of noise variation, whereas the SARIMA-NN showed good performance only when the noise level was trivial.

• Journal of the Korean Operations Research and Management Science Society
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• v.26 no.1
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• pp.109-116
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• 2001

The detection of corporate failures is a subject that has been particularly amenable to cross-sectional financial ratio analysis. In most of firms, however, the financial data are available over past years. Because of this, a model utilizing these longitudinal data could provide useful information on the prediction of bankruptcy. To correctly reflect the longitudinal and firm-specific data, the generalized linear model with assuming the first order AR(autoregressive) process is proposed. The method is motivated by the clinical research that several characteristics are measured repeatedly from individual over the time. The model is compared with several other predictive models to evaluate the performance. By using the financial data from manufacturing corporations in the Korea Stock Exchange (KSE) list, we will discuss some experiences learned from the procedure of sampling scheme, variable transformation, imputation, variable selection, and model evaluation. Finally, implications of the model with repeated measurement and future direction of research will be discussed.

• International Journal of Contents
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• v.18 no.2
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• pp.32-46
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• 2022

In recent years, large-scale and high-density use of LED on facades has exposed some disadvantages, such as light pollution, high energy consumption, unsustainability, and poor interactivity. Because of the development of smartphones and augmented reality (AR), AR has emerged as a new technology available to users to interact with the media façade. As an augmented reality app for public space, the AR map app can superimpose virtual images on the surface of a building to form an AR media façade, which can be applied in the fields of navigation, advertising, interactive public art, smart retail, etc. This study establishes the variables influencing usage intention and the consequent outcomes of Huawei AR map app and uses the technology acceptance model (TAM) to discuss their relationship. Results show that consumer innovativeness, information quality, and design quality have a strong influence on perceived ease of use. Information quality has a positive impact on perceived usefulness, but design quality has a weak influence. Also, the design quality of Huawei AR map app and consumer innovativeness have a higher effect on perceived enjoyment than information quality. Users' usage attitude and perceived usefulness when using Huawei AR map app are key factors determining their usage intention. This study inspires city planners, architects, developers, and designers of AR apps that augmented reality can partly replace media façade, and that investment in augmented reality will achieve significant sustainable economic and social benefits.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.11a
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• pp.187-190
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• 2006

We proposed a new real estate price appra isal model that can appreciate the efficiencies of each criteria that would affect the price. The proposed Real Estate Price Appraisal Model is developed by the DEA-AR model which enhances the DEA-CCR model. We used the unit-cost per criteria method to set the assurance region of each weights of the DEA-AR model. In order to estimate the unit cost of major criteria effecting the price of real estate, we used the Goal Programming so that the price of real estate reaches the actual price being traded in. We expect that this approach could be helpful to make an objective real estate price appraisal.

• Wind and Structures
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• v.11 no.3
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• pp.241-256
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• 2008

This paper adopts autoregressive (AR) model to simulate the wind velocity of spatial three-dimensional fields in accordance with the time and space dependent characteristics of the 3-D fields. Based on the built MATLAB programming, this paper discusses in detail the issues of the AR model deduced by matrix form in the simulation and proposes the corresponding solving methods: the over-relaxation iteration to solve the large sparse matrix equations produced by large number of degrees of freedom of structures; the improved Gauss formula to calculate the numerical integral equations which integral functions contain oscillating functions; the mixed congruence and central limit theorem of Lindberg-Levy to generate random numbers. This paper also develops a method of ascertaining the rank of the AR model. The numerical examples show that all those methods are stable and reliable, which can be used to simulate the wind velocity of all large span structures in civil engineering.