• Journal of Korean Society on Water Environment
• /
• v.37 no.6
• /
• pp.520-530
• /
• 2021

Multivariate statistical analysis and an environmental hydrological model were applied for investigating the causes of water pollution and providing best management practices for water quality improvement in urban and agricultural watersheds. Principal component analysis (PCA) and cluster analysis (CA) for water quality time series data show that chemical oxygen demand (COD), total organic carbon (TOC), suspended solids (SS) and total phosphorus (T-P) are classified as non-point source pollutants that are highly correlated with river discharge. Total nitrogen (T-N), which has no correlation with river discharge and inverse relationship with water temperature, behaves like a point source with slow and consistent release. Biochemical oxygen demand (BOD) shows intermediate characteristics between point and non-point source pollutants. The results of the PCA and CA for the spatial water quality data indicate that the cluster 1 of the watersheds was characterized as upstream watersheds with good water quality and high proportion of forest. The cluster 3 shows however indicates the most polluted watersheds with substantial discharge of BOD and nutrients from urban sewage, agricultural and industrial activities. The cluster 2 shows intermediate characteristics between the clusters 1 and 3. The results of hydrological simulation program-Fortran (HSPF) model simulation indicated that the seasonal patterns of BOD, T-N and T-P are affected substantially by agricultural and livestock farming activities, untreated wastewater, and environmental flow. The spatial analysis on the model results indicates that the highly-populated watersheds are the prior contributors to the water quality degradation of the river.

• Educational Technology International
• /
• v.24 no.1
• /
• pp.1-27
• /
• 2023

This study aimed to examine an augmented reality-based teaching simulation in a mobile application. We examined how AR-enabled interactions affect users' perceived scenario usefulness and avatar realism. The participants were forty-six undergraduate students. We randomly grouped them into two conditions: AR and Non-interactive video groups with equal sample sizes. This study employed an experimental design approach with a one-way multivariate analysis of variance with repeated measures. The independent variable is the presence/absence of AR interaction with a mobile application. The dependent variables were avatar realism and scenario usefulness. The measures explored how the student avatar's emotional intensity in a scenario influences user perception. The results showed that participants in the AR-interaction group perceived avatar realism significantly higher than those in the non-interactive video group. Also, participants perceived the high emotional intensity scenario (aggression toward peers) to be significantly higher usefulness than the low emotional intensity scenario (classroom disruption).

• Proceedings of the Korea Water Resources Association Conference
• /
• 2016.05a
• /
• pp.164-164
• /
• 2016

Radar rainfall estimates have been widely used in calculating rainfall amount approximately and predicting flood risks. The radar rainfall estimates have a number of error sources such as beam blockage and ground clutter hinder their applications to hydrological flood forecasting. Moreover, it has been reported in paper that those errors are inter-correlated spatially and temporally. Therefore, in the current study, we tested influence about spatio-temporal errors in radar rainfall estimates. Spatio-temporal errors were simulated through a stochastic simulation model, called Multivariate Autoregressive (MAR). For runoff simulation, the Nam River basin in South Korea was used with the distributed rainfall-runoff model, Vflo. The results indicated that spatio-temporal dependent errors caused much higher variations in peak discharge than spatial dependent errors. To further investigate the effect of the magnitude of time correlation among radar errors, different magnitudes of temporal correlations were employed during the rainfall-runoff simulation. The results indicated that strong correlation caused a higher variation in peak discharge. This concluded that the effects on reducing temporal and spatial correlation must be taken in addition to correcting the biases in radar rainfall estimates. Acknowledgements This research was supported by a grant from a Strategic Research Project (Development of Flood Warning and Snowfall Estimation Platform Using Hydrological Radars), which was funded by the Korea Institute of Construction Technology.

• Communications for Statistical Applications and Methods
• /
• v.20 no.2
• /
• pp.137-145
• /
• 2013

The use of pooled standard deviation can reduce the efficiency loss in optimum allocation when strata standard deviations are estimated and several of them are equal. Also shown is that the pooled standard deviation is useful in optimum allocation under a multivariate setting. In addition to theoretical development, we provide the result of simulation study to support the theory.

• Proceedings of the KIEE Conference
• /
• 2006.10c
• /
• pp.229-231
• /
• 2006

NLPCA(Nonlinear Principal Component Analysis) is a novel technique for multivariate data analysis, similar to the well-known method of principal component analysis. NLPCA operates by a feedforward neural network called AANN(Auto Associative Neural Network) which performs the identity mapping. In this work, a sensor fault detection system based on NLPCA is presented. To verify its applicability, simulation study on the data supplied from sensor network is executed.

• Journal of Korean Society for Quality Management
• /
• v.26 no.2
• /
• pp.51-60
• /
• 1998

Exponetially weighted moving average(EWMA) control chart to simultaneously monitor correlation coefficients of several correlated quality variables under multivariate normal process are proposed. Performances of the proposed control charts are measured in terms of average run length(ARL) by simulation. Numerical results show that smaller values of smoothing constant are more efficient in terms of ARL.

• Journal of the Korean Data and Information Science Society
• /
• v.5 no.1
• /
• pp.1-10
• /
• 1994

Projection pursuit(PP) is a computer-intensive method which seeks out interesting linear projections of multivariate data onto a lower dimension space by machine. By working with lower dimensional projections, projection pursuit avoids the sparseness of high dimensional data. We show through simulation that two projection pursuit discriminant mothods proposed by Chen(1989) and Huber(1985) do not improve very much the error rate than the existing methods and compare several classification procedures.

• 한국전산유체공학회:학술대회논문집
• /
• 1999.05a
• /
• pp.1-10
• /
• 1999

This paper briefly introduces the work done up to 1998 during the past twenty years for numerical modeling of ocean process focussing on the neighbouring seas of Korean Peninsula. Modeling of global ocean dynamics has also been performed as a pathway to understand the regional ocean dynamics. The ocean simulation produces a vast amount of multidimensional multivariate dataset therefore adoption of scientific and technical visualization techniques were essential to properly understand the physics involved.

• Research in Mathematical Education
• /
• v.10 no.3 s.27
• /
• pp.229-238
• /
• 2006

During World War II there was a statistical analysis conducted by the Allied analysts to estimate the German war productions, including their tank productions. This article revisits the analysis of the tank productions as a classroom activity format. Various reformed ideas are proposed in order to enhance students' perspectives of the point estimation. Comprehensive simulation works and actual classroom discussions will be provided along with the theoretical investigations.

• Journal of the Korean Data and Information Science Society
• /
• v.17 no.3
• /
• pp.999-1007
• /
• 2006

The semiparametric gamma frailty models have been often used for multivariate survival analysis because they give an explicit marginal likelihood. The commonly used estimation procedure is the profile likelihood method based on marginal likelihood, which provides the same parameter estimates as the EM algorithm. In this paper we show in finite samples the standard profile-likelihood method can lead to an underestimation of parameters, particularly for the frailty parameter. To overcome this problem, we propose an adjusted profile-likelihood method. For the illustration a numerical example and a small-sample simulation study are presented.