• Journal of the Korean Data and Information Science Society
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• v.28 no.4
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• pp.911-925
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• 2017

We investigate the properties of multivariate Shewart control charts with VSI procedure for monitoring simultaneous monitoring mean vector and covariance matrix in term of ANSW (average number of switches), probability of switch and ASI (average sampling interval), ATS (average time to signal). From examining the ANSW values, we know that it does not switch frequently. The VSI control charts are superior to the corresponding FSI control charts in terms of ATS. And, it can be also seen that the VSI procedures have substantially fewer switches for small or moderate shifts of the mean vector and variances.

• Journal of the Korean Data and Information Science Society
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• v.21 no.4
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• pp.819-829
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• 2010

Properties of multivariate variable sampling interval (VSI) Shewhart and CUSUM charts for monitoring mean vector of related quality variables are investigated. To evaluate average time to signal (ATS) and average number of switches (ANSW) of the proposed charts, Markov chain approaches and simulations are applied. Performances of the proposed charts are also investigated both when the process is in-control and when it is out-of-control.

• Journal of the Korean Data and Information Science Society
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• v.23 no.4
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• pp.859-866
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• 2012

Some switching properties of multivariate control charts are investigated when the interval between two consecutive sample selections is not fixed but changes according to the result of the previous sample observation. Many articles showed that the performances of variable sampling interval control charts are more efficient than those of fixed sampling interval control charts in terms of average run length (ARL) and average time to signal (ATS). Unfortunately, the ARL and the ATS do not provide any information on how frequent a switch is being made. We evaluate several switching properties of two sampling interval Shewhart and CUSUM procedures for controlling mean vector of correlated quality variables.

• The Korean Journal of Applied Statistics
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• v.32 no.5
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• pp.671-681
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• 2019

Studies on predicting arrhythmia using machine learning have been actively conducted with increasing number of arrhythmia patients. Existing studies have predicted arrhythmia based on multivariate data of feature variables extracted from RR interval data at a specific time point. In this study, we consider that the pattern of the heart state changes with time can be important information for the arrhythmia prediction. Therefore, we investigate the usefulness of predicting the arrhythmia with multivariate time series data obtained by extracting and accumulating the multivariate vectors of the feature variables at various time points. When considering 1-nearest neighbor classification method and its ensemble for comparison, it is confirmed that the multivariate time series data based method can have better classification performance than the multivariate data based method if we select an appropriate time series distance function.

• The Korean Journal of Applied Statistics
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• v.33 no.3
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• pp.297-308
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• 2020

We focus on the functional autoregressive conditional heteroscedasticity (fARCH) modelling to analyze intraday volatilities based on high frequency financial time series. Multivariate volatility models are investigated to approximate fARCH(1). A formula of multi-step ahead volatilities for fARCH(1) model is derived. As an application, in implementing fARCH(1), a choice of appropriate time interval for the intraday return is discussed. High frequency KOSPI data analysis is conducted to illustrate the main contributions of the article.

• Wind and Structures
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• v.32 no.3
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• pp.193-204
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• 2021

The estimation of the extreme wind load (effect) under a mean recurrence interval (MRI) is an important task in the wind-resistant design for the structure. It can be predicted by either first-order method or full-order method, depending on the accuracy and complexity requirement. Although the first-order method with the consideration of wind directionality has been proposed, less work has been done on the full-order method, especially with the wind directionality. In this study, the full-order method considering the wind directionality is proposed based on multivariate joint probability distribution. Meanwhile, considering two wind directions, the difference of the corresponding results based on the first-order method and full-order method is analyzed. Finally, based on the measured wind speed data, the discrepancy between these two methods is investigated. Results show that the difference between two approaches is not obvious under larger MRIs while the underestimation caused by the first-order method can be larger than 15% under smaller MRIs. Overall, the first-order method is sufficient to estimate the extreme wind load (effect).

• Journal of the Korean Data and Information Science Society
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• v.14 no.4
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• pp.1045-1053
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• 2003

Markovian EWMA control chart for simultaneously monitoring mean vector of the several correlated quality characteristics is investigated. Properties of multivariate Shewhart chart and EWMA chart are evaluated for matched FSI (fixed sampling interval) and VSI(variable sampling interval) scheme. We obtained VSI EWMA chart is more efficient than Shewhart chart for small or moderate shifts. And, we obtained stablized numerical results with Markov chain method when the number of transient state is greater than 100.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.1
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• pp.314-320
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• 2024

Objectives: This study aimed to identify the prevalence of falls in community-dwelling older adults and to identify associated factors using the 2019 Community Health Survey. Methods: The original data was from the 2019 Community Health Survey, and the study sample comprised 1,642 older adults aged 65 years and older in Jeju province. Data collection was conducted from August 16 to November 20, 2019, through an interview done by a trained investigator. Respondents were queried about demographic characteristics, riding bicycles, hospital treatment due to an accident or poisoning in the previous year, fall experiences in the past year, fear of falling, self-management status, and pain and discomfort. Multivariate logistic regression analysis was used to evaluate for associations between potential risk factors and falls. Results: The prevalence of falls in this community-dwelling older adults was 13.1%. Falls were associated with riding bicycles (odds ratio = 4.7; 95% confidence interval: 2.26-9.81), fear of falling (odds ratio = 0.3; 95% confidence interval: 0.24-0.49), hospital treatment due to an accident or poisoning in the previous year (odds ratio = 7.8; 95% confidence interval: 5.02-12.19), self-management status (odds ratio = 0.6; 95% confidence interval: 0.34-0.89), and pain and discomfort (odds ratio = 0.6; 95% confidence interval: 0.40-0.87). Conclusions: We found that the prevalence of approximately about 13% of older adults living in a community has experienced falls. Based on the results of the study, we provided primary data to develop the care management intervention program to prevent falls and avoid risk factors that cause falls in community-dwelling older adults.

• Journal of Wetlands Research
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• v.21 no.4
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• pp.281-289
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• 2019

In this study, the typhoon damage forecasting model was developed for southern inland district. The typhoon damage in the inland district is caused by heavy rain and strong winds, variables are many and varied, but the damage data of the inland district are not enough to develop the model. The hydrological data related to the typhoon damage were hour maximum rainfall amount which is accumulated 3 hour interval, the total rainfall amount, the 1-5 day anticipated rainfall amount, the maximum wind speed and the typhoon center pressure at latitude 33° near the Jeju island. The Multivariate Analysis such as cluster Analysis considering the lack of damage data and principal component analysis removing multi-collinearity of rainfall data are adopted for the damage forecasting model. As a result of applying the developed model, typhoon damage estimated and observed values were up to 2.2 times. this is caused it is difficult to estimate the damage caused by strong winds and it is assumed that the local rainfall characteristics are not considered properly measured by 69 ASOS.

• Structural Engineering and Mechanics
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• v.46 no.4
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• pp.447-458
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• 2013

Splitting tensile strength (STS) is a respectable mechanical property reflecting ability of the concrete. The STS of concrete is mainly related to compressive strength (CS), water/binder (W/B) ratio and concrete age. In this study, the assessment of STS is made by a novel uncertainty-oriented method which uses least square optimization and then predicts STS of concrete by uncertain (fuzzy) numbers. The approximation method addresses a novel integration of fuzzy set theory and multivariate statistics. The numerical examples showed that the method is applicable with relatively limited data. In addition, the prediction of uncertainty at various levels of possibility can be described. In conclusion, the uncertainty-oriented interval analysis can be suggested an effective tool for appraising the uncertainties in concrete technology.