• Korean Journal of Adult Nursing
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• v.27 no.5
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• pp.505-514
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• 2015

Purpose: The purpose of this study was to examine their levels of knowledge of the kidney, self-efficacy, and kidney function in pre-dialysis patients with chronic renal insufficiency. Methods: A total of 142 pre-dialysis patients with chronic renal insufficiency were recruited from a nephrology clinic of a hospital in Korea. Participants' knowledge of the kidney, self-efficacy, and kidney function were measured, and the correlations between these factors were computed. Results: The levels of knowledge of the kidney were moderate, with a mean score of $12.30{\pm}5.35$. Knowledge level was significantly correlated with age, education level, occupation, income, physical symptoms, and information resources (p<.05). The mean score for self-efficacy was $6.06{\pm}2.00$. Self-efficacy was significantly associated with patients' age, education level, occupation, income, cigarette use, and information resources (p<.05). The mean score for kidney function was $35.66{\pm}18.68mL/min/1.73m^2$. Kidney function was significantly correlated with use of medications and drinking behavior (p<.05). Knowledge of the kidney was significantly correlated with self-efficacy (r=.31, p<.001), but not with kidney function. There was a significant correlation between self-efficacy and kidney function (r=.30, p<.001). Multiple regression analysis revealed that self-efficacy and drinking behavior accounted for 11% of the variance in kidney function of pre-dialysis patients with chronic renal insufficiency. Conclusion: Nursing interventions are necessary to increase self-efficacy among pre-dialysis patients with chronic renal insufficiency in order to maintain their kidney function.

• Proceedings of the KIEE Conference
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• 2006.10b
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• pp.159-162
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• 2006

In this paper energy distribution function in $SiH_4$ has been analysed over the E/N range 0.5${\sim}$300Td and Pressure value 0.5, 1.0, 2.5 Torr by a two-term approximation Boltzmann equation method and by a Monte Carlo simulation. The motion has been calculated to give swarm parameters for the electron drift velocity, diffusion coefficient, electron ionization, mean energy and the electron energy distribution function. The electron energy distribution function has been analysed in $SiH_4$ at E/N=30, 50Td for a case of the equilibrium region in the mean electron energy and respective set of electron collision cross sections. The results show that the deduced electron drift velocities, the electron ionization or attachment coefficients, longitudinal and transverse diffusion coefficients and mean energy agree reasonably well with theoretical for a rang of E/N values.

• Bulletin of the Korean Mathematical Society
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• v.54 no.6
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• pp.1981-1990
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• 2017

Let R be a root system in $\mathbb{R}^d$ with Coxeter-Weyl group W and let k be a nonnegative multiplicity function on R. The generalized volume mean of a function $f{\in}L^1_{loc}(\mathbb{R}^d,m_k)$, with $m_k$ the measure given by $dmk(x):={\omega}_k(x)dx:=\prod_{{\alpha}{\in}R}{\mid}{\langle}{\alpha},x{\rangle}{\mid}^{k({\alpha})}dx$, is defined by: ${\forall}x{\in}\mathbb{R}^d$, ${\forall}r$ > 0, $M^r_B(f)(x):=\frac{1}{m_k[B(0,r)]}\int_{\mathbb{R}^d}f(y)h_k(r,x,y){\omega}_k(y)dy$, where $h_k(r,x,{\cdot})$ is a compactly supported nonnegative explicit measurable function depending on R and k. In this paper, we prove that for almost every $x{\in}\mathbb{R}^d$, $lim_{r{\rightarrow}0}M^r_B(f)(x)= f(x)$.

• Communications for Statistical Applications and Methods
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• v.30 no.3
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• pp.245-258
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• 2023

This study proposes a modification to the objective function of the support vector machine for the linearly non-separable case of a binary classifier y_i ∈ {-1, 1}. The modification takes into account the position of each data item x_i from its corresponding class centroid. The resulting optimization function involves the centroid mean vector, and the spread of data besides the support vectors, which should be minimized by the choice of hyper-plane β. Theoretical assumptions have been tested to derive an optimal separable hyperplane that yields the minimal misclassification rate. The proposed method has been evaluated using simulation studies and real-life COVID-19 patient outcome hospitalization data. Results show that the proposed method performs better than the classical linear SVM classifier as the sample size increases and is preferred in the presence of correlations among predictors as well as among extreme values.

• Management & Information Systems Review
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• v.9
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• pp.25-39
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• 2002

We consider the Fuzzy clustering which is devised for partitioning a set of objects into a certain number of groups by assigning the membership probabilities to each object. The researches carried out in this field before show that the Fuzzy clustering concept is involved so much that for a certain set of data, the main purpose of the clustering cannot be attained as desired. Thus we propose a new objective function, named as Fuzzy-Entroppy Function in order to satisfy the main motivation of the clustering which is classifying the data clearly. Also we suggest Mean Field Annealing Algorithm as an optimization algorithm rather than the. ISODATA used traditionally in this field since the objective function is changed. We show the Mean Field Annealing Algorithm works pretty well not only for the new objective function but also for the classical Fuzzy objective function by indicating that the local minimum problem resulted from the ISODATA can be improved.

• Communications for Statistical Applications and Methods
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• v.19 no.6
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• pp.885-898
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• 2012

In this paper, we consider Bayesian approaches to partially linear models, in which a regression function is represented by a semiparametric additive form of a parametric linear regression function and a nonparametric regression function. We make a comparative study on the performance of widely used Bayesian partially linear models in terms of empirical analysis. Specifically, we deal with three Bayesian methods to estimate the nonparametric regression function, one method using Fourier series representation, the other method based on Gaussian process regression approach, and the third method based on the smoothness of the function and differencing. We compare the numerical performance of three methods by the root mean squared error(RMSE). For empirical analysis, we consider synthetic data with simulation studies and real data application by fitting each of them with three Bayesian methods and comparing the RMSEs.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.25 no.6
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• pp.36-41
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• 2002

We consider the Fuzzy clustering which is devised for partitioning a set of objects into a certain number of groups by assigning the membership probabilities to each object. The researches carried out in this field before show that the Fuzzy clustering concept is involved so much that for a certain set of data, the main purpose of the clustering cannot be attained as desired. Thus we propose a new objective function, named as Fuzzy-Entroppy Function in order to satisfy the main motivation of the clustering which is classifying the data clearly. Also we suggest Mean Field Annealing Algorithm as an optimization algorithm rather than the ISODATA used traditionally in this field since the objective function is changed. we show the Mean Field Annealing Algorithm works pretty well not only for the new objective function but also for the classical Fuzzy objective function by indicating that the local minimum problem resulted from the ISODATA can be improved.

• Management & Information Systems Review
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• v.14
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• pp.51-65
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• 2004

We consider the Fuzzy clustering which is devised for partitioning a set of objects into a certain number of groups by assigning the membership probabilities to each object. The researches carried out in this field before show that the Fuzzy clustering concept is involved so much that for a certain set of data, the main purpose of the clustering cannot be attained as desired. Thus we Propose a new objective function, named as Fuzzy-Entroppy Function in order to satisfy the main motivation of the clustering which is classifying the data clearly. Also we suggest Mean Field Annealing Algorithm as an optimization algorithm rather than the ISODATA used traditionally in this field since the objective function is changed. We show the Mean Field Annealing Algorithm works pretty well not only for the new objective function but also for the classical Fuzzy objective function by indicating that the local minimum problem resulted from the ISODATA can be improved.

• Journal of muscle and joint health
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• v.13 no.2
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• pp.130-139
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• 2006

Purpose: This study was designed to present preliminary data for the development of appropriate nursing care system for the patients with ALS by analyzing their physical function. Method: The clinical data of 36 ALS patients, who visited ALS Clinic of H University Hospital in Seoul, were collected from January, 2006 to August, 2006. To determine the physical function, Norris ALS scale and Appel ALS Rating Scale were used. The data were analyzed by frequency, percentage, mean, standard deviation, range, t-test, ANOVA, using SPSS PC program. Results: The mean score of physical activity, muscle strength, upper extremity function, lower extremity function was 18.08, 27.72, 25.94, 25.19 respectively. There were significant differences in physical activity, muscle strength, and upper extremity function according to sender and comorbid disease(diabetes). Although sites of symptom onset were not statistically significant with all physical function, patients with bulbar onset showed relatively severe physical disabilities. Conclusion: The preliminary data on physical function of patients with ALS would be helpful for the development of ALS nursing guideline system.

• Communications for Statistical Applications and Methods
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• v.27 no.3
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• pp.349-363
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• 2020

Penalized spline is a suitable nonparametric approach in estimating mean model in small area. However, application of the approach in informative sampling in a published article is uncommon. We propose a semiparametric mixed-model using penalized spline under informative sampling to estimate mean of small area. The response variable is explained in terms of mean model, informative sample effect, area random effect and unit error. We approach the mean model by penalized spline and utilize a penalized spline function of the inclusion probability to account for the informative sample effect. We determine the best and unbiased estimators for coefficient model and derive the restricted maximum likelihood estimators for the variance components. A simulation study shows a decrease in the average absolute bias produced by the proposed model. A decrease in the root mean square error also occurred except in some quadratic cases. The use of linear and quadratic penalized spline to approach the function of the inclusion probability provides no significant difference distribution of root mean square error, except for few smaller samples.