• Journal of the Korean Mathematical Society
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• v.39 no.5
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• pp.783-800
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• 2002

Let B denote the unit ball in $C^n$, and ν the normalized Lebesgue measure on B. For $\alpha$ > -1, define $dv_\alpha$(z) ＝ $c_\alpha$$(1-\midz\mid^2)^{\alpha}$dν(z), z $\in$ B. Here $c_\alpha$ is a positive constant such that $v_\alpha$(B) ＝ 1. Let H(B) denote the space of all holomorphic functions in B. For $p\geq1$, define the Bergman-Privalov space $(AN)^{p}(v_\alpha)$ by $(AN)^{p}(v_\alpha)$ = ${f\inH(B)$ : $\int_B{log(1+\midf\mid)}^pdv_\alpha\;<\;\infty}$ In this paper we prove that a function $f\inH(B)$ is in $(AN)^{p}$$(v_\alpha)$ if and only if $(1+\midf\mid)^{-2}{log(1+\midf\mid)}^{p-2}\mid\nablaf\mid^2\;\epsilon\;L^1(v_\alpha)$ in the case 1＜p＜$\infty$, or $(1+\midf\mid)^{-2}\midf\mid^{-1}\mid{\nabla}f\mid^2\;\epsilon\;L^1(v_\alpha)$ in the case p ＝ 1, where $nabla$f is the gradient of f with respect to the Bergman metric on B. This is an analogous result to the characterization of the Hardy spaces by M. Stoll [18] and that of the Bergman spaces by C. Ouyang-W. Yang-R. Zhao [13].

• Genomics & Informatics
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• v.18 no.2
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• pp.20.1-20.4
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• 2020

Accessible negative results are relevant for researchers and clinicians not only to limit their search space but also to prevent the costly re-exploration of research hypotheses. However, most biomedical relation extraction datasets do not seek to distinguish between a false and a negative relation among two biomedical entities. Furthermore, datasets created using distant supervision techniques also have some false negative relations that constitute undocumented/ unknown relations (missing from a knowledge base). We propose to improve the distinction between these concepts, by revising a subset of the relations marked as false on the phenotype-gene relations corpus and give the first steps to automatically distinguish between the false (F), negative (N), and unknown (U) results. Our work resulted in a sample of 127 manually annotated FNU relations and a weighted-F1 of 0.5609 for their automatic distinction. This work was developed during the 6th Biomedical Linked Annotation Hackathon (BLAH6).

• Investigative Magnetic Resonance Imaging
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• v.14 no.2
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• pp.115-120
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• 2010

Purpose : To separate and evaluate the low frequency spontaneous fluctuation BOLD signals from the functional magnetic resonance imaging data using sensorimotor active task. Materials and Methods : Twenty female archery players and twenty three control subjects were included in this study. Finger-tapping task consisted of three cycles of right finger tapping, with a subsequent 30 second rest. Blood oxygenation level-dependent (BOLD) data were collected using $T2^*$-weighted echo planar imaging at a 3.0 T scanner. A 3-D FSPGR T1-weighted images were used for structural reference. Image processing and statistical analyses were performed using SPM5 for active finger-tapping task and GIFT program was used for statistical analyses of low frequency spontaneous fluctuation BOLD signal. Results : Both groups showed the activation in the left primary motor cortex and supplemental motor area and in the right cerebellum for right finger-tapping task. ICA analysis using GIFT revealed independent components corresponding to contralateral and ipsilateral sensorimotor network and cognitive-related neural network. Conclusion : The current study demonstrated that the low frequency spontaneous fluctuation BOLD signals can be separated from the fMRI data using finger tapping paradigm. Also, it was found that these independent components correspond to spontaneous and coherent neural activity in the primary sensorimotor network and in the motor-cognitive network.

• Smart Media Journal
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• v.13 no.4
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• pp.57-64
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• 2024

Attributes such as material, color, and fit in fashion images are important factors for consumers to purchase clothing. However, the process of classifying clothing attributes requires a large amount of manpower and is inconsistent because it relies on the subjective judgment of human operators. To alleviate this problem, there is a need for research that utilizes artificial intelligence to classify clothing attributes in fashion images. Previous studies have mainly focused on classifying clothing attributes for either tops or bottoms, so there is a limitation that the attributes of both tops and bottoms cannot be identified simultaneously in the case of full-body fashion images. In this study, we propose a deep learning model that can distinguish between tops and bottoms in fashion images and classify the category of each item and the attributes of the clothing material. The deep learning models ResNet and EfficientNet were used in this study, and the dataset used for training was 1,002,718 fashion images and 125 labels including clothing categories and material properties. Based on the weighted F1-Score, ResNet is 0.800 and EfficientNet is 0.781, with ResNet showing better performance.

• East Asian mathematical journal
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• v.33 no.1
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• pp.37-44
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• 2017

We will prove size estimates of the Bergman kernel for the generalized Fock space ${\mathcal{F}}^2_{\varphi}$, where ${\varphi}$ belongs to the class $\mathcal{W} $. The main tool for the proof is to use the estimate on the canonical solution to the ${\bar{\partial}}$-equation. We use Delin's weighted $L^2$-estimate ([3], [6]) for it.

• Journal of the military operations research society of Korea
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• v.5 no.1
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• pp.123-135
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• 1979

Weapon System effectiveness indices are a key factor to the cost-effectiveness analysis in the process of allocating national resources in an optimal fashion. First part of this paper reviews the need of weapon -effectiveness indices, historical development of methodologies on military strength evaluation with various models of indices, and the interrelation-ship between weapon effectiveness indices and war-game. Second part of this paper analyzes the concepts and usage of the already developed methodologies, i .e., index of power, weapon lethality index (W.L.I.), index of fire power potential (I.F.P), fire power potential (F. P. P), and weapon effectiveness indices/weighted unit value (W. E. I/W. U. V.). Finally, weaknesses and limits of these methodologies are compared and evaluated. Result of this research shows that the W. L. I, I. F. P, and F. P. P seem to possess many non-scientific or ambiguous facets, but the W. E. I/W. U. V method produces more detailed, inclusive, and reasonable indices. The paper concludes with the emphasis on the importance of the provision of the theoretical bases to develop effectiveness indices which can be used to evaluate Korean weapon systems, and the early establishment of a specialized research group to manage and develop methodologies on the weapon effectiveness indices.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.2
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• pp.73.2-73.2
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• 2019

The physical origin of low escape fractions of ionizing radiation derived from Lyman-break galaxies (LBGs) at z ~ 3 - 4 is a puzzle in the theory of reionization. We perform idealized disk galaxy simulations to investigate how galactic properties, such as metallicity and gas mass, affect the escape of Lyman continuum (LyC) photons using radiation-hydrodynamic code RAMSES-RT, with strong stellar feedback. We find that the luminosity-weighted escape fraction from a metal-poor (Z=0.002) galaxy embedded in a halo of mass M_h ~ 10¹¹ M_⊙ is 〈f^3D_esc〉 ~ 8%. However, when the gas metallicity is increased to Z=0.02, the escape fraction is significantly reduced to 〈f^3D_esc〉 ~ 1%, as young stars are enshrouded by their birth clouds for a longer period of time. On the other hand, increasing the gas mass by a factor of 5 leads to 〈f^3D_esc〉 ~ 4%, as LyC photons are only moderately absorbed by the thicker disk. Our experiments seem to suggest that high metallicity is primarily responsible for the low escape fractions observed from LBGs, supporting the scenario in which the escape fraction has a negative correlation with halo mass. Indeed, our simulated galaxy with the typical metallicity of LBGs (Z=0.006) shows the relative escape fraction of 8%, consistent with recent observations of galaxies with M₁₅₀₀ = -20.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.626-634
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• 2018

This retrospective study investigates the accuracy of triage procedures for pediatric patients in emergency departments (EDs) using the Korean Triage and Acuity Scale (KTAS). The study includes 250 randomly selected initial nursing records and clinical outcomes of pediatric patients who visited one regional ED or a local ED from October 2016 to September 2017. The collected data were analyzed by a qualified expert to determine the true triage score. The accuracy of triage was defined as the agreement between the triage score of the emergency nurses (ENs) and the true triage score as determined by the expert. Based on expert comments, the cause of the triage error was analyzed and the KTAS score was compared with the discharge, length of stay (LOS), and medical cost. The results showed that the degree of agreement in the triage score between the experts and the ENs was excellent (weighted kappa=0.77). Among the causes of triage discordance, the most frequent was the incorrect application of vital signs to the KTAS algorithm criteria (n=13). Patients with high severity KTAS levels 1 and 2 were discharged less often (${\chi}=43.25$, p<0.001). There were significant differences in the length of stay (F=12.39, p<0.001) and cost (F=11.78, p<0.001) between KTAS scores when adjusting for age. The results of this study indicate that KTAS is highly accurate in EDs. Hence, the newly developed triage tool is becoming well established in Korea.

• Journal of the Korea Society of Computer and Information
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• v.27 no.9
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• pp.59-68
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• 2022

A recommender system covers users, searches the items or services which users will like, and let users purchase them. Because recommendations from a recommender system are predictions of users' preferences for the items which they do not purchase yet, it is rarely possible to be drawn a perfect answer. An evaluation has been conducted to determine whether a prediction is right or not. However, it can be lower user's satisfaction if a recommender system focuses on only the preferences, that is caused by a 'filter bubble effect'. The filter bubble effect is an algorithmic bias that skews or limits the information an individual user sees on the recommended list. It is the reason why multiple metrics are required to evaluate recommender systems, and a diversity metrics is mainly used for it. In this paper, we compare three different methods for enhancing diversity for personalized recommendation - bin packing, weighted random choice, greedy re-ranking - with a practical e-commerce data acquired from a fashion shopping mall. Besides, we present the difference between experimental results and F1 scores.

• Bulletin of the Korean Mathematical Society
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• v.47 no.3
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• pp.467-482
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• 2010

For a double array of random elements {$V_{mn};m{\geq}1,\;n{\geq}1$} in a real separable Banach space, some mean convergence theorems and weak laws of large numbers are established. For the mean convergence results, conditions are provided under which $k_{mn}^{-\frac{1}{r}}\sum{{u_m}\atop{i=1}}\sum{{u_n}\atop{i=1}}(V_{ij}-E(V_{ij}|F_{ij})){\rightarrow}0$ in $L_r$ (0 < r < 2). The weak law results provide conditions for $k_{mn}^{-\frac{1}{r}}\sum{{T_m}\atop{i=1}}\sum{{\tau}_n\atop{j=1}}(V_{ij}-E(V_{ij}|F_{ij})){\rightarrow}0$ in probability where {$T_m;m\;{\geq}1$} and {${\tau}_n;n\;{\geq}1$} are sequences of positive integer-valued random variables, {$k_{mn};m{{\geq}}1,\;n{\geq}1$} is an array of positive integers. The sharpness of the results is illustrated by examples.