• Smart Structures and Systems
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• v.11 no.1
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• pp.1-17
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• 2013

This paper intended to investigate the feasibility of bridge health monitoring using a linear system parameter of a time series model identified from traffic-induced vibrations of bridges through a laboratory moving vehicle experiment on scaled model bridges. This study considered the system parameter of the bridge-vehicle interactive system rather than modal ones because signals obtained under a moving vehicle are not the responses of the bridge itself but those of the interactive system. To overcome the shortcomings of modal parameter-based bridge diagnosis using a time series model, this study considered coefficients of Autoregressive model (AR coefficients) as an early indicator of anomaly of bridges. This study also investigated sensitivity of AR coefficients in detecting anomaly of bridges. Observations demonstrated effectiveness of using AR coefficients as an early indicator for anomaly of bridges.

• Architectural research
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• v.19 no.1
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• pp.1-6
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• 2017

Over the past decade, building information modeling (BIM) has gained a foothold in the construction sector. However, as digital data generated in the virtual space of a computer environment, BIM-based data have an inherent limitation in their application efficacy under field conditions. To overcome this, the present study employs augmented reality (AR) to reduce the discrepancy between the digital data generated in virtual spaces and real-world conditions. We assessed the potential applicability of an AR-based interface by analyzing existing examples of Apple, Google, and Facebook, which reflect the recent developments of technologies focusing on user experience. We then proceeded with an AR study, restricting the scope of application to a mobile environment in which an efficient information transmission between a digital model and real life can take place. Object-oriented software engineering was employed to ensure an efficient implementation of a BIM-based AR system for campus space management (CSM) in a mobile environment. Finally, we conducted a module test to check the reliability of the CSM method by using an AR-based mobile system with a prototype of the model used in university campuses, and extracted and itemized the supplementary requirements for CSM by using BIM tools for running AR applications.

• Journal of the Korean Society of Safety
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• v.16 no.1
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• pp.88-93
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• 2001

This study discusses frequency analysis based on autoregressive (AR) time series model, and process characterization in orthogonal cutting of a fiber-matrix composite materials. A sparsely distributed idealized composite material, namely a glass reinforced polyester (GFRP) was used as workpiece. Analysis method employs a force sensor and the signals from the sensor are processed using AR time series model. The resulting pattern vectors of AR coefficients are then passed to the feature extraction block. Inside the feature extraction block, only those features that are most sensitive to different types of cutting mechanisms are selected. The experimental correlations between the different chip formation mechanisms and AR model coefficients are established.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.5
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• pp.713-719
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• 2009

In this paper, we develope a water demand forecasting algorithm using AR(Auto-regressive) and MLP(Multi-layer perceptron). To show effectiveness of the proposed method, we analyzed characteristics of time-series data collected in "A" purification plant at Jeon-Buk province during 2007-2008, and then performed the proposed method with various input factors selected through various analyses. As noted in experimental results, the performance of three types model such as multi-regressive, AR(Auto-regressive), and AR+MLP(Auto-regressive + Multi-layer perceptron) show 5.1%, 3.8%, and 3.6% with respect to MAPE(Mean Absolute Percentage Error), respectively. Thus, it is noted that the proposed method can be used to predict short-term water demand for the efficient operation of a water purification plant.

• Journal of Internet Computing and Services
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• v.19 no.1
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• pp.49-66
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• 2018

This research explores a value chain structure of VR-AR media including user's perception, uses, and evaluation. The purpose of this research focused on factor analysis and the relationship among user's VR-AR adoption motivations and utilities. This research explores correlation between personal value and using motivation. This study was to identify the value structure of respondent on VR-AR usages based on means-end chain theory. The research used structured APT laddering questions and 251 data was analysed. Through such analysis, category difference by stage and relationship difference were identified and hierarchical value map was compared. There are four different value ladders: first is attributes, functional consequences, psychological consequences, and final value. This study is based on the analysis of the value chain structure factors that affect VR and AR use behavior (attributes, functional benefits, psychological benefits, use value), 'Hierarchical Value Map' between users' The purpose of the model is to construct a model. For this, 'means-end chain theory' was applied to measure the causal relationship between personal value and VR related use behavior. In order to solve this research problem, 135 people were analyzed through the structured questionnaire using the AR and VR content fitness measure and the second APT laddering, and the use of VR-AR : 1) Functional benefits; 2) Psychological benefits; 3) Means to reach value, 4) Objective value chain structure was identified. The results show that VR users tried to smooth the social life through the new virtual reality audiovisual element, the newness of experience, fun, and pleasure through the departure of reality, vividness of experience, and leading fashion. The AR fitness was a game and a new program, and the value of interacting with other people and the value of 'periwinkle' played an important role through the vividness and peripheral interaction of AR, It was an important choice. The important basic values of users' VR and AR selection were correlated with psychological attributes of interaction with others, achievement, happiness and favorable values.

• Communications for Statistical Applications and Methods
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• v.30 no.2
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• pp.163-178
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• 2023

GARCH-X(1, 1) model specifies that conditional variance follows an AR(1) process and includes a past exogenous variable. This study proposes a new class from that model by allowing a more general (non-linear) variance function to follow an AR(1) process. The functions applied to the variance equation include exponential, Tukey's ladder, and Yeo-Johnson transformations. In the framework of normal and student-t distributions for return errors, the empirical analysis focuses on two stock indices data in developed countries (FTSE100 and SP500) over the daily period from January 2000 to December 2020. This study uses 10-minute realized volatility as the exogenous component. The parameters of considered models are estimated using the adaptive random walk metropolis method in the Monte Carlo Markov chain algorithm and implemented in the Matlab program. The 95% highest posterior density intervals show that the three transformations are significant for the GARCHX(1, 1) model. In general, based on the Akaike information criterion, the GARCH-X(1, 1) model that has return errors with student-t distribution and variance transformed by Tukey's ladder function provides the best data fit. In forecasting value-at-risk with the 95% confidence level, the Christoffersen's independence test suggest that non-linear models is the most suitable for modeling return data, especially model with the Tukey's ladder transformation.

• Communications for Statistical Applications and Methods
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• v.7 no.1
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• pp.97-106
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• 2000

In time series analysis we generally use Box-Cox power transformation for variance stabilization. In this paper we show that order estimator and one step ahead forecast of transformed AR(1) model are approximately invariant to those of the original model under some assumptions. A small Monte-Carlo simulation is performed to support the results.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.1
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• pp.56-62
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• 1993

OWT is a tool for block transform coding with wavelet basis functions that overlap adjacent blocks. The OWT can reduce the block effect. In this paper performances of OWT are evaluated for AR model. Some simulation results show that performances are nearly same to DCT, but block effect is reduced to very low level.

• Communications for Statistical Applications and Methods
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• v.12 no.1
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• pp.169-179
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• 2005

In this study, we investigate the effects of the weight function in the bounded influence regression quantile (BIRQ) estimator for the AR(l) model with additive outliers. In order to down-weight the outliers of X -axis, the Mallows' (1973) weight function has been commonly used in the BIRQ estimator. However, in our Monte Carlo study, the BIRQ estimator using the Tukey's bisquare weight function shows less MSE and bias than that of using the Mallows' weight function or Huber's weight function. Thus, the use of the Tukey's weight function is recommended in the BIRQ estimator for our model.

• Journal of the Korean Statistical Society
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• v.17 no.1
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• pp.24-29
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• 1988

The asymptotic distribution of the sample partial autocorrelation terms after lag p of an AR(p) model has been shown by Dixon(1944). The derivation is based on multivariate analysis and looks tedious. In this paper we present an interesting contour-integral derivation.