• Communications for Statistical Applications and Methods
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• v.17 no.3
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• pp.413-421
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• 2010

In this study, we considered bivariate conditional auto-regressive model taking into account spatial association as well as correlation between the two dependent variables, which are the counts of murder and burglary. We conducted likelihood ratio test for checking over-dispersion issues prior to applying spatial poisson models. For the real application, we used the annual counts of violent crimes at 25 districts of Seoul in 2007. The statistical results are visually illustrated by geographical information system.

• The Journal of the Acoustical Society of Korea
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• v.14 no.6
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• pp.62-67
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• 1995

In this paper, the features extraction of reflected ultrasonic signals from various type of defects existing in Al metal has been studied by digital signal processing. Since the reflected signals from various type of the defects are ambiguous in features distinction from effects of noise, Wiener filtering using AR (auto-regressive) technique and least-absolute-values norm method has been used in features extraction and comparison of signals. In this experiment, three types of the defect in aluminum specimen have been considered: a flat cut, an angular cut, a circular hole. And the reflected signal have been measured by pulse-echo methods. In the result of digital signal processing of the reflected signal, it has been found that the features extraction method have been effective for classification of the reflected signals from various defects.

• The Korean Journal of Applied Statistics
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• v.16 no.2
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• pp.203-215
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• 2003

In order to provide the drivers with more reliable transportation information in NHTMS(National Highway Transportation Management System), it is important to estimate the expected passage time by using the traffic volume and speed. In this study, we analyze the characteristics of the traffic volume in the national highway and we investigate two real-time adjustment methods: the average adjustment method and the auto-regressive adjustment method. In addition, we compare them using the real data collected at the National Highway Route 3 in 2000.

• Smart Structures and Systems
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• v.17 no.6
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• pp.1031-1053
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• 2016

The health conditions of in-service civil infrastructures can be evaluated by employing structural health monitoring technology. A reliable health evaluation result depends heavily on the quality of the data collected from the structural monitoring sensor network. Hence, the problem of sensor fault diagnosis has gained considerable attention in recent years. In this paper, an innovative sensor fault diagnosis method that focuses on fault detection and isolation stages has been proposed. The dynamic or auto-regressive characteristic is firstly utilized to build a multivariable statistical model that measures the correlations of the currently collected structural responses and the future possible ones in combination with the canonical correlation analysis. Two different fault detection statistics are then defined based on the above multivariable statistical model for deciding whether a fault or failure occurred in the sensor network. After that, two corresponding fault isolation indices are deduced through the contribution analysis methodology to identify the faulty sensor. Case studies, using a benchmark structure developed for bridge health monitoring, are considered in the research and demonstrate the superiority of the new proposed sensor fault diagnosis method over the traditional principal component analysis-based and the dynamic principal component analysis-based methods.

• Communications for Statistical Applications and Methods
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• v.26 no.3
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• pp.261-272
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• 2019

In this paper, we propose a procedure to build a prediction interval of the sum of dependent binary random variables over a graph to account for the dependence among binary variables. Our main interest is to find a prediction interval of the weighted sum of dependent binary random variables indexed by a graph. This problem is motivated by the prediction problem of various elections including Korean National Assembly and US presidential election. Traditional and popular approaches to construct the prediction interval of the seats won by major parties are normal approximation by the CLT and Monte Carlo method by generating many independent Bernoulli random variables assuming that those binary random variables are independent and the success probabilities are known constants. However, in practice, the survey results (also the exit polls) on the election are random and hardly independent to each other. They are more often spatially correlated random variables. To take this into account, we suggest a spatial auto-regressive (AR) model for the surveyed success probabilities, and propose a residual based bootstrap procedure to construct the prediction interval of the sum of the binary outcomes. Finally, we apply the procedure to building the prediction intervals of the number of legislative seats won by each party from the exit poll data in the $19^{th}$ and $20^{th}$ Korea National Assembly elections.

• Communications for Statistical Applications and Methods
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• v.14 no.1
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• pp.205-213
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• 2007

In this paper, the normal mixture model subjected to general linear restriction for component-means based on linear regression is proposed, and its fitting method by EM algorithm and Lagrange multiplier is provided. This model is applied to gene clustering of microarray expression data, which demonstrates it has very good performances for real data set. This model also allows to obtain the clusters that an analyst wants to find out in the fashion that the hypothesis for component-means is represented by the design matrices and the linear restriction matrices.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1998.03a
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• pp.40-52
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• 1998

Among the non-traditional machining methods, Abrasive waterjet machining process shows big promise in drilling difficult-to-machine materials due to its numerous advantages such as absence of heat affect zone and thermal distortion. Acoustic emission signal technique is used to understand about material removal mechanisms during abrasive waterjet drilling process. More information about the drilling process is derived through frequency decomposition of auto regressive moving average modeling representing acoustic emission signals.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.8
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• pp.1551-1559
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• 2015

Recently, the performance of smart devices is almost similar to that of the existing PCs, thus the users of smart devices can perform similar works such as messengers, SNSs(Social Network Services), smart banking, etc. originally performed in PC environment using smart devices. Although the development of smart devices has led to positive impacts, it has caused negative changes such as an increase in security threat aimed at mobile environment. Specifically, the threats of mobile devices, such as leaking private information, generating unfair billing and performing DDoS(Distributed Denial of Service) attacks has continuously increased. Over 80% of the mobile devices use android platform, thus, the number of damage caused by mobile malware in android platform is also increasing. In this paper, we propose android based malware detection mechanism using time-series analysis, which is one of statistical-based detection methods.We use auto-regressive moving-average model which is extracting accurate predictive values based on existing data among time-series model. We also use fast and exact malware detection method by extracting possible malware data through Z-Score. We validate the proposed methods through the experiment results.

• Animal Bioscience
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• v.34 no.4
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• pp.770-782
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• 2021

Objective: The aim of the present study was to compare the effectiveness of three approaches (the seasonal auto-regressive integrated moving average [SARIMA] model, the nonlinear autoregressive exogenous [NARX] artificial neural networks and Wood's model) to the prediction of milk yield during lactation. Methods: The dataset comprised monthly test-day records from 965 Polish Holstein-Friesian Black-and-White primiparous cows. The milk yields from cows in their first lactation (from 5 to 305 days in milk) were used. Each lactation was divided into ten lactation stages of approximately 30 days. Two age groups and four calving seasons were distinguished. The records collected between 2009 and 2015 were used for model fitting and those from 2016 for the verification of predictive performance. Results: No significant differences between the predicted and the real values were found. The predictions generated by SARIMA were slightly more accurate, although they did not differ significantly from those produced by the NARX and Wood's models. SARIMA had a slightly better performance, especially in the initial periods, whereas the NARX and Wood's models in the later ones. Conclusion: The use of SARIMA was more time-consuming than that of NARX and Wood's model. The application of the SARIMA, NARX and Wood's models (after their implementation in a user-friendly software) may allow farmers to estimate milk yield of cows that begin production for the first time.

• Computational Structural Engineering
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• v.2 no.4
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• pp.111-116
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• 1989

Methods for the estimation of the coefficient matrices in the equation of motion for a linear multi-degree-of-freedom structure are studied. For this purpose, the equation of motion is transformed into an auto-regressive and moving average with auxiliary input(ARMAX) model. The ARMAX parameters are evaluated using several methods of parameter estimation : such as the least squares, the instrumental variable, the maximum likelihood and the limited information maximum likelihood methods. Then the parameters of the equation of motion are recovered therefrom. Numerical example is given for a 3-story building model subjected to an earthquake exitation.