• Honam Mathematical Journal
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• v.27 no.2
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• pp.301-315
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• 2005

Let $\{A_u,\;u=0,\;1,\;2,\;{\cdots}\}$ be a sequence of coefficient matrices such that ${\sum}_{u=0}^{\infty}{\parallel}A_u{\parallel}<{\infty}$ and ${\sum}_{u=0}^{\infty}\;A_u{\neq}O_{m{\times}m}$, where for any $m{\times}m(m{\geq}1)$, matrix $A=(a_{ij})$, ${\parallel}A{\parallel}={\sum}_{i=1}^m{\sum}_{j=1}^m{\mid}a_{ij}{\mid}$ and $O_{m{\times}m}$ denotes the $m{\times}m$ zero matrix. In this paper, a functional central limit theorem is derived for a stationary m-dimensional linear process ${\mathbb{X}}_t$ of the form ${\mathbb{X}_t}={\sum}_{u=0}^{\infty}A_u{\mathbb{Z}_{t-u}}$, where $\{\mathbb{Z}_t,\;t=0,\;{\pm}1,\;{\pm}2,\;{\cdots}\}$ is a stationary sequence of linearly positive quadrant dependent m-dimensional random vectors with $E({\mathbb{Z}_t})={{\mathbb{O}}$ and $E{\parallel}{\mathbb{Z}_t}{\parallel}^2<{\infty}$.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.6
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• pp.577-582
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• 2002

Neural network can be trained to approximate an arbitrary nonlinear function of multivariate data like the mini-mill crown values in Automatic Shape Control. The trained weights of neural network can evaluate or generalize the process data outside the training vectors. Sometimes, the blind modeling of the process data is necessary to compare with the scattered analytical model of mini-mill process in isolated electro-mechanical forms. To come up with a viable model, we propose the blind neural-based range-division domain-clustering piecewise-linear modeling scheme. The basic ideas are: 1) dividing the range of target data, 2) clustering the corresponding input space vectors, 3)training the neural network with clustered prototypes to smooth out the convergence and 4) solving the resulting matrix equations with a pseudo-inverse to alleviate the ill-conditioning problem. The simulation results support the effectiveness of the proposed scheme and it opens a new way to the data analysis technique. By the comparison with the statistical regression, it is evident that the proposed scheme obtains better modeling error uniformity and reduces the magnitudes of errors considerably. Approximatly 10-fold better performance results.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.7
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• pp.706-712
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• 2006

Recursive Partial Least Squares(RPLS) method has been used for processing the on-line available multivariate chemical process data and modeling adaptive prediction model for process changes. However, RPLS method is unstable in PLS model updating because RPLS method updates PLS model by merging past PLS model and new data. In this study, Adaptive Predictive Partial Least Squres(APPLS) method is suggested for more sensitive adaptation to process changes. By expanding APPLS method, block-wise Adaptive Predictive Partial Least Squares(block-wise APPLS) method is suggested for a lager scale data of chemical processes. APPLS method has been applied to predict the reactor properties and the product quality of a direct esterification reactor for polyethylene terephthalate(PTT), and block-wise APPLS method has been applied to predict the cetane number using NIR Diesel Spectra data. APPLS and block-wise APPLS methods show better prediction and updating performance than RPLS method.

• Nuclear Engineering and Technology
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• v.48 no.3
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• pp.684-701
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• 2016

The Gaussian process model (GPM) is a flexible surrogate model that can be used for nonparametric regression for multivariate problems. A unique feature of the GPM is that a prediction variance is automatically provided with the regression function. In this paper, we estimate the safety margin of a nuclear power plant by performing regression on the output of best-estimate simulations of a large-break loss-of-coolant accident with sampling of safety system configuration, sequence timing, technical specifications, and thermal hydraulic parameter uncertainties. The key aspect of our approach is that the GPM regression is only performed on the dominant input variables, the safety injection flow rate and the delay time for AC powered pumps to start representing sequence timing uncertainty, providing a predictive model for the peak clad temperature during a reflood phase. Other uncertainties are interpreted as contributors to the measurement noise of the code output and are implicitly treated in the GPM in the noise variance term, providing local uncertainty bounds for the peak clad temperature. We discuss the applicability of the foregoing method to reduce the use of conservative assumptions in best estimate plus uncertainty (BEPU) and Level 1 probabilistic safety assessment (PSA) success criteria definitions while dealing with a large number of uncertainties.

• Journal of the Korean Society for Precision Engineering
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• v.34 no.2
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• pp.125-131
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• 2017

2-Axis Pan and Tilt Motion Platform, a complex multivariate non-linear system, may incur any disturbance, thus requiring system controller with robustness against various disturbances. In this study, we designed an adaptive backstepping compensated controller by estimating the disturbance and error using the Radial Basis Function Neural Network (RBF NN). In this process, Uniformly Ultimately Bounded (UUB) was demonstrated via Lyapunov and stability was confirmed. By generating progressive disturbance to the irregular frequency and amplitude changes, it was verified for various environmental disturbances. In addition, by setting the RBF NN input vector to the minimum, the estimated disturbance compensation process was analyzed. Only two input vectors facilitated compensatory function of RBF NN via estimating the modeling and control error values as well as irregular disturbance; the application of the process resulted in improved backstepping controller performance that was confirmed through simulation.

• KIPS Transactions on Computer and Communication Systems
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• v.13 no.1
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• pp.1-9
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• 2024

In the development and mass production of polymers, there are many uncontrollable variables. Even small changes in chemical composition, structure, and processing conditions can lead to large variations in properties. Therefore, Traditional linear modeling techniques that assume a general environment often produce significant errors when applied to field data. In this study, we propose a new modeling method (GPR-SEM) that combines Structural Equation Modeling (SEM) and Gaussian Process Regression (GPR) to study the Friction-Coefficient and Flexural-Strength properties of Polyacetal resin, an engineering plastic, in order to meet the recent trend of using plastics in industrial drive components. And we also consider the possibility of using it for materials modeling with nonlinearity.

• Archives of design research
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• v.19 no.5 s.67
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• pp.255-268
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• 2006

This study raised a question about 'how user's psychological value consciousness influences design evaluation and evaluated 14 product samples. Three main categories were chosen to be analyzed; product evaluation by step, value consciousness toward products, and creation of shape image and design evaluation. In order to obtain results, four research hypotheses were established a and case study was conducted as part of 'conclusive research' for verification. 120 university students in their 20s majoring in product design were chosen as testees in a bid to increase the accuracy of data. With data collected, the whole flow was analyzed by simple tabulation and further analysis was carried out in three evaluation items. An analysis method was mainly quantitative, focusing on multivariate analysis like factor analysis, cluster analysis, etc. The empirical analysis of this study was verified at P<.05 significance level and SPSSWIN 12.0 program was used for statistic process. As a result, four conclusions were gained regarding 'user's value consciousness toward products and pattern of design evaluation'.

• Quality Improvement in Health Care
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• v.25 no.1
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• pp.52-65
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• 2019

Purpose: Outbreaks resulting from medication injections have recently been on the rise in Korea despite various established guidelines. The objective of this study was to assess the degree to which healthcare professionals are aware of safe injection practice guidelines and to account for the adherence to and the deviation from safe injection guidelines formulated by healthcare providers. Methods: In November 2016, a cross-sectional anonymous questionnaire covering general characteristics of injections, patient safety culture, awareness of safe injection practices, and adherence to and barriers to safe injection guidelines was issued to healthcare providers who administer medication injections or manage and supervise these injections (N=550). Multivariate logistic regression analysis via enter method was performed to define the influencing factors of adherence of safe injection practices. Results: On average, respondents adhere to 17 of the 24 guidelines. Multivariate logistic regression found that those who were more likely to adhere to safe injection guidelines either underwent a patient safety training experience within the last year, provided care in a setting characterized by a highly developed patient safety culture, or were employed as physicians or nurses, as opposed to some other type of care provider. Barriers to safe injection guidelines were attributable to; thoughts of waste to discard leftover medicine, provisions that made adherence cumbersome, a weak culture of compliance, and insufficient amounts of injectable medicine, products, and education. Conclusions: The results of this study indicate that controllable factors like training experience of healthcare providers and patient safety culture were positively associated with adherence to safe injection practices. It was suggested that the training of healthcare providers on safe injection practices be a continuous process to promote patient safety. Additionally, there should be an increased focus on developing and implementing policies to improve patient safety culture from a prevention rather than post-management perspective.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.12 no.4
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• pp.275-286
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• 2014

This study evaluated the evolution stage and origin of chemical components of 12 boreholes at crystalline bedrock using multivariate statistical and groundwater quality analyses. Groundwater types are mostly belonged to Na(Ca)-$HCO_3$ and Ca-$HCO_3$ types, indicating that directly reaction of cation exchange ($Ca^{2+}{\rightarrow}Na^+$) prevailed. The degree of groundwater evolution is included the range from low to intermediate stage based on field and laboratory analytical conditions. As a result of multivariate statistical analysis, a typical indicator of groundwater contamination, $NO_3$-, has the positive correlation with $Na^+$ and $Cl^-$. The origin of sea spary ($Cl^-$) has the positive correlation with $Na^+$, $SO{_4}^{2-}$, $Mg^{2+}$, and $K^+$, while not correlation with $Ca^{2+}$, $Fe^{2+}$, $HCO_3{^-}$, $F^-$, and $SiO_2$. The concentration of $Cl^-$ and $NO_3{^-}$ belongs to general quality of groundwater and not exceeds over the Korean standard for drinking water. And the negative values of saturation index of minerals are calculated with chemical components in groundwater. Therefore, most of chemical components of groundwater in the study area are originated from natural process between rock and groundwater, whereas some of components are derived from sea spary and anthropogenic sources related to agricultural activities.

• Resources Recycling
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• v.27 no.1
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• pp.31-37
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• 2018

To enhance the recycling rate of used metal resources, it is strongly desired to develop a metal sorting system that can automatically identify metal type from mixed metal scraps and sort them separately. Laser-induced breakdown spectroscopy(LIBS) is a technique that enables real time classification of different metals based on multi-elemental and in-air analysis. In this work, we report the results of LIBS elemental analysis of field scrap samples acquired from a recycling company. By applying multivariate analysis, it was found that the LIBS signals of five different metals could be perfectly classified if surface contamination was removed. The classification accuracy degraded for LIBS signals including contaminant emission, which however could be overcome by performing the multivariate analysis using properly selected emission lines of higher correlation only. The significant improvement in classification accuracy and process speed by the selection of proper emission lines demonstrated the feasibility of LIBS technique as an industrial tool for metal scrap sorting.