• Communications of the Korean Mathematical Society
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• v.32 no.3
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• pp.601-617
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• 2017

We give a function associated with generalized Ostrowski type inequality and its integral representation for local fractional calculus. Then, using this function and its integral representation, we establish several inequalities of generalized Ostrowski type for twice local fractional differentiable functions. We also consider some special cases of the main results which are further applied to a concrete function to yield two interesting inequalities associated with two generalized means.

• Nuclear Engineering and Technology
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• v.54 no.10
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• pp.3803-3810
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• 2022

OpenMC is a community-driven open-source Monte Carlo neutron and photon transport simulation code. The Weight Window Mesh (WWM) function and an automatic Global Variance Reduction (GVR) method was recently developed and implemented in a developmental branch of OpenMC. This WWM function and GVR method broaden OpenMC's usage in general purposes deep penetration shielding calculations. However, the Local Variance Reduction (LVR) method, which suits the source-detector problem, is still missing in OpenMC. In this work, the Weight Window Generator (WWG) function has been developed and benchmarked for the same branch. This WWG function allows OpenMC to generate the WWM for the source-detector problem on its own. Single-material cases with varying shielding and sources were used to benchmark the WWG function and investigate how to set up the particle histories utilized in WWG-run and WWM-run. Results show that there is a maximum improvement of WWM generated by WWG. Based on the above results, instructions on determining the particle histories utilized in WWG-run and WWM-run for optimal computation efficiency are given and tested with a few multi-material cases. These benchmarks demonstrate the ability of the OpenMC WWG function and the above instructions for the source-detector problem. This developmental branch will be released and merged into the main distribution in the future.

• Journal of IKEEE
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• v.20 no.2
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• pp.136-142
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• 2016

This paper presents a non-local means (NLM) denoising algorithm based on a new weighting function using a mixed norm. The fidelity of the difference between an anchor patch and the reference patch in the NLM denoising depends on noise level and local activity. This paper introduces a new weighting function based on a mixed norm type of which the order is determined by noise level and local activity of an anchor patch, so that the performance of the NLM denoising can be enhanced. Experimental results demonstrate the objective and subjective capability of the proposed algorithm. In addition, it was verified that the proposed algorithm can be used to improve the performance of the other $l_2$ norm based non-local means denoising algorithms

• Journal of Information Technology Services
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• v.12 no.2
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• pp.103-118
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• 2013

Harmful contents on a mobile platform are becoming serious problems to young people due to the prevalence of smart phones with the fast development of mobile technology. Mobile applications and contents are so much optimized on the mobile environment that young men are exposed to many harmful contents. A system for blocking harmful contents is suggested in this study. The system includes a local proxy function, filtering module, and local database in order to increase the blocking efficiency. The local proxy function and the filtering module are implemented on an Android platform, and the local database are running on a PC-based server. The suggested system perfectly blocks harmful contents, and shows relatively high speed.

• Communications of the Korean Mathematical Society
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• v.16 no.3
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• pp.509-518
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• 2001

The purpose of this paper is to propose a numerical algorithm for the problem of coefficient identification of the scalar wave equation based on a local observation on the boundary: Determine the unknown coefficient function with the knowledge of simultaneous Dirichlet and Neumann boundary values on a part of boundary. To find the unknown coefficient function, the unknown Neumann boundary value is also identified. We recast our inverse problem to variational problem. The gradient method is applied to find the minimizing functions. We confirm the effectiveness of our algorithm by numerical experiments.

• Women's Health Nursing
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• v.20 no.2
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• pp.126-136
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• 2014

Purpose: The purpose of this study was to examine the effects of aroma hand massage on anxiety and immune function in patients who had gynecology surgery under local anesthesia. Methods: The research design was a nonequivalent control group with pre-and posttest design. Data were collected from June 5 to October 6, 2010. Participants included 20 patients in the aroma hand massage group, 20 patients in a hand massage group, and 20 in a control group. As an experimental treatment, hand massage was carried out following the hand massage protocol. Measures consisted of the State Trait Anxiety, Numeric Rating Scale (NRS) for anxiety, vital signs (systolic and diastolic blood pressure, pulse rate), and salivary cortisol for anxiety, and immunoglobulin A for immune function. Results: Aroma hand massage and hand massage group showed lower levels in NRS for anxiety, systolic and diastolic blood pressure, and pulse rate (p<.001) compared to controls. No group differences were found for state anxiety, salivary cortisol and immunoglobulin A. Conclusion: The results indicate that aroma hand massage and hand massage are effective in reducing anxiety and can be complementary alternative interventions for women having gynecology surgery under local anesthesia.

• Industrial Engineering and Management Systems
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• v.10 no.1
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• pp.84-94
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• 2011

In this paper, we propose a method to provide the distribution of option price under local volatility model when market-provided implied volatility data are given. The local volatility model is one of the most widely used smile-consistent models. In local volatility model, the volatility is a deterministic function of the random stock price. Before estimating local volatility surface (LVS), we need to estimate implied volatility surfaces (IVS) from market data. To do this we use local polynomial smoothing method. Then we apply the Dupire formula to estimate the resulting LVS. However, the result is dependent on the bandwidth of kernel function employed in local polynomial smoothing method and to solve this problem, the proposed method in this paper makes use of model averaging approach by means of bandwidth priors, and then produces a robust local volatility surface estimation with a confidence interval. After constructing LVS, we price barrier option with the LVS estimation through Monte Carlo simulation. To show the merits of our proposed method, we have conducted experiments on simulated and market data which are relevant to KOSPI200 call equity linked warrants (ELWs.) We could show by these experiments that the results of the proposed method are quite reasonable and acceptable when compared to the previous works.

• Journal of Electrical Engineering and Technology
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• v.13 no.6
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• pp.2511-2520
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• 2018

In this study, Multi-Radial Basis Function Support Vector Machine (Multi-RBF SVM) classifier is introduced based on a composite kernel function. In the proposed multi-RBF support vector machine classifier, the input space is divided into several local subsets considered for extremely nonlinear classification tasks. Each local subset is expressed as nonlinear classification subspace and mapped into feature space by using kernel function. The composite kernel function employs the dual RBF structure. By capturing the nonlinear distribution knowledge of local subsets, the training data is mapped into higher feature space, then Multi-SVM classifier is realized by using the composite kernel function through optimization procedure similar to conventional SVM classifier. The original training data set is partitioned by using some unsupervised learning methods such as clustering methods. In this study, three types of clustering method are considered such as Affinity propagation (AP), Hard C-Mean (HCM) and Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA). Experimental results on benchmark machine learning datasets show that the proposed method improves the classification performance efficiently.

• Journal of The Korean Society of Integrative Medicine
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• v.8 no.4
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• pp.49-58
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• 2020

Purpose : This study examined the effect of a home visit cognitive training program that uses a tablet-based digital recognition rehabilitation application, Brain Doctor, on local elderly people's cognitive function and depression. Methods : This study featured 20 elderly people living in Busan Metropolitan City, South Korea, who received a voucher for a home visit service to prevent dementia. The subjects were evenly divided into an intervention group provided with Brain Doctor and a control group provided with a conventional cognitive training program. Korean version of Mini Mental State Examination (MMSE-K) and Korean version of Montreal Cognitive Assessment (K-MoCA) were used to assess cognitive function in each group. Patient Health Questionnaire-9 (PHQ-9) was used to evaluate the depression levels. Results : The intervention group showed a significant change in cognitive function and depression after the intervention (p<.05). There was a statistically significant change in cognitive function and depression between the intervention and control groups (p<.05). Conclusion : This study confirmed that Brain Doctor had a positive effect on the cognitive function and depression of elderly people in the local community. It is expected to become a useful home visit program for dementia prevention in the future.

• Communications for Statistical Applications and Methods
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• v.19 no.5
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• pp.655-662
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• 2012

Classification is an important research field in pattern recognition with high-dimensional predictors. The support vector machine(SVM) is a penalized feature selector and classifier. It is based on the hinge loss function, the non-convex penalty function, and the smoothly clipped absolute deviation(SCAD) suggested by Fan and Li (2001). We developed the algorithm for the multiclass SVM with the SCAD penalty function using the local quadratic approximation. For multiclass problems we compared the performance of the SVM with the $L_1$, $L_2$ penalty functions and the developed method.