• Journal of the Korean Mathematical Society
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• v.59 no.3
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• pp.495-517
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• 2022

Let M_α be a bilinear fractional maximal operator and BM_α be a fractional maximal operator associated with the bilinear Hilbert transform. In this paper, the compactness on weighted Lebesgue spaces are considered for commutators of bilinear fractional maximal operators; these commutators include the fractional maximal linear commutators M^j_α,β and BM^j_α,β (j = 1, 2), the fractional maximal iterated commutator ${\mathcal{M}}_{{\alpha},{\vec{b}}}$, and $BM_{{\alpha},{\vec{b}}}$, where b ∈ BMO(ℝ^d) and ${\vec{b}}\;=\;(b_1,b_2)\;{\in}\;BMO({\mathbb{R}}^d)\;{\times}\;BMO({\mathbb{R}}^d)$. In particular, we improve the well-known results to a larger scale for 1/2 < q < ∞ and give positive answers to the questions in [2].

• IEMEK Journal of Embedded Systems and Applications
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• v.15 no.4
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• pp.197-204
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• 2020

The ImageNet dataset is a large scale dataset and contains various natural scene images. In this paper, we propose a convolutional neural network (CNN)-based weighted ensemble technique for the ImageNet classification task. First, in order to fuse several models, our technique uses weights for each model, unlike the existing average-based ensemble technique. Then we propose an algorithm that automatically finds the coefficients used in later ensemble process. Our algorithm sequentially selects the model with the best performance of the validation set, and then obtains a weight that improves performance when combined with existing selected models. We applied the proposed algorithm to a total of 13 heterogeneous models, and as a result, 5 models were selected. These selected models were combined with weights, and we achieved 3.297% Top-5 error rate on the ImageNet test dataset.

• Smart Structures and Systems
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• v.15 no.1
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• pp.169-189
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• 2015

Convergence difficulty and available complete measurement information have been considered as two primary challenges for the identification of large-scale engineering structures. In this paper, a time domain substructural identification approach by combining a weighted adaptive iteration (WAI) algorithm and an extended Kalman filter method with a weighted global iteration (EFK-WGI) algorithm was proposed for simultaneous identification of physical parameters of concerned substructures and unknown external excitations applied on it with limited response measurements. In the proposed approach, according to the location of the unknown dynamic loadings and the partially available structural response measurements, part of structural parameters of the concerned substructure and the unknown loadings were first identified with the WAI approach. The remaining physical parameters of the concerned substructure were then determined by EFK-WGI basing on the previously identified loadings and substructural parameters. The efficiency and accuracy of the proposed approach was demonstrated via a 20-story shear building structure and 23 degrees of freedom (DOFs) planar truss model with unknown external excitation and limited observations. Results show that the proposed approach is capable of satisfactorily identifying both the substructural parameters and unknown loading within limited iterations when both the excitation and dynamic response are partially unknown.

• 한국정보디스플레이학회:학술대회논문집
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• 2005.07b
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• pp.1347-1350
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• 2005

A modified Time-Ratio Gray Scale AMOLED drive technique is described in which the frame period is split into two half-frames, each of which is divided into binary weighted sub-frames and driven in the conventional time-ratio manner. The proposed technique improves aperture ratio by reducing TFT sizes in pixel circuits.

• The Journal of Korean Medicine Ophthalmology and Otolaryngology and Dermatology
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• v.20 no.3
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• pp.147-160
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• 2007

Background and Objective : The facial nerve grading system proposed by House and Brackmann is most widely accepted for the clinical assessment of facial nerve injury. Because of the limitations and subjectivity of the House-Brackmann scale, several new scales of varying degrees of objectivity and ease of use have been introduced. To assess methods of evaluating the function of the facial nerve that have been introduced over the past 20 years, We compared with the House-Brackmann scale. Method : We referred to the information through Entrez Pubmed and Korean studies information(KSI) from 1985 to 2006 about methods of evaluating facial nerve function. We choose 7 scales that focused on objective and easy of use. Result and conclusion : Sunnybrook scale is a weighted, subjective scale with incorporation of secondary defects into a single composite score. Sunnybrook scale can be recommended over House-Brackmann scale.

• Journal of the Korean Association of Geographic Information Studies
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• v.26 no.4
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• pp.218-236
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• 2023

This study attempted to analyze the environmental equity of fine dust(PM₁₀) in Daegu using MGWR(Multi-scale Geographically Weighted Regression) and KT(Korea Telecom Corporation) sensor data. Existing national monitoring network data for measuring fine dust are collected at a small number of ground-based stations that are sparsely distributed in a large area. To complement these drawbacks, KT sensor data with a large number of IoT(Internet of Things) stations densely distributed were used in this study. The MGWR model was used to deal with spatial heterogeneity and multi-scale contextual effects in the spatial relationships between fine dust concentration and socioeconomic variables. Results indicate that there existed an environmental inequity by land value and foreigner ratio in the spatial distribution of fine dust in Daegu metropolitan city. Also, the MGWR model showed better the explanatory power than Ordinary Least Square(OLS) and Geographically Weighted Regression(GWR) models in explaining the spatial relationships between the concentration of fine dust and socioeconomic variables. This study demonstrated the potential of KT sensor data as a supplement to the existing national monitoring network data for measuring fine dust.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.3
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• pp.1454-1466
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• 2016

This paper implements an estimated ranking algorithm of closeness centrality measures in large-scale workflow-supported social networks. The traditional ranking algorithms for large-scale networks have suffered from the time complexity problem. The larger the network size is, the bigger dramatically the computation time becomes. To solve the problem on calculating ranks of closeness centrality measures in a large-scale workflow-supported social network, this paper takes an estimation-driven ranking approach, in which the ranking algorithm calculates the estimated closeness centrality measures by applying the approximation method, and then pick out a candidate set of top k actors based on their ranks of the estimated closeness centrality measures. Ultimately, the exact ranking result of the candidate set is obtained by the pure closeness centrality algorithm [1] computing the exact closeness centrality measures. The ranking algorithm of the estimation-driven ranking approach especially developed for workflow-supported social networks is named as RankCCWSSN (Rank Closeness Centrality Workflow-supported Social Network) algorithm. Based upon the algorithm, we conduct the performance evaluations, and compare the outcomes with the results from the pure algorithm. Additionally we extend the algorithm so as to be applied into weighted workflow-supported social networks that are represented by weighted matrices. After all, we confirmed that the time efficiency of the estimation-driven approach with our ranking algorithm is much higher (about 50% improvement) than the traditional approach.

• Journal of Korea Multimedia Society
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• v.3 no.3
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• pp.241-252
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• 2000

In this paper, a translation, rotation and scale invariant system for the recognition of closed 2-D images using the bispectrum of a contour sequence and a weighted fuzzy classifier is derived and compared with the recognition process using one of the competitive neural algorithm, called a LVQ( Loaming Vector Quantization). The bispectrum based on third order cumulants is applied to the contour sequences of an image to extract fifteen feature vectors for each planar image. These bispectral feature vectors, which are invariant to shape translation, rotation and scale transformation, can be used to the represent two-dimensional planar images and are fed into a weighted fuzzy classifier. The experimental processes with eight different shapes of aircraft images are presented to illustrate a relatively high performance of the proposed recognition system.

• Proceedings of the IEEK Conference
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• 2000.07a
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• pp.450-453
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• 2000

In this paper, an automated segmentation algorithm is proposed for MR brain images using T1-weighted, T2-weighted, and PD images complementarily. The proposed segmentation algorithm is composed of 3 steps. In the first step, cerebrum images are extracted by putting a cerebrum mask upon the three input images. In the second step, outstanding clusters that represent inner tissues of the cerebrum are chosen among 3-dimensional (3D) clusters. 3D clusters are determined by intersecting densely distributed parts of 2D histogram in the 3D space formed with three optimal scale images. Optimal scale image best describes the shape of densely distributed parts of pixels in 2D histogram. In the final step, cerebrum images are segmented using FCM algorithm with it’s initial centroid value as the outstanding cluster’s centroid value. The proposed segmentation algorithm complements the defect of FCM algorithm, being influenced upon initial centroid, by calculating cluster’s centroid accurately And also can get better segmentation results from the proposed segmentation algorithm with multi spectral analysis than the results of single spectral analysis.

• Journal of Korean Neurosurgical Society
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• v.46 no.4
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• pp.365-369
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• 2009

Objective : Susceptibility-weighted image (SWI) is a sensitive magnetic resonance image (MRI) technique to detect cerebral microbleeds (MBLs). which would not be detected by conventional MRI. We performed SWI to detect MBLs and investigated its usefulness in the evaluation of mild traumatic brain injury (MTBI) patients. Methods : From December 2006 to June 2007, twenty-one MTBI patients without any parenchymal hemorrhage on conventional MRI were selected. Forty-two patients without trauma were selected for control group. According to the presence of MBLs, we divided the MTBI group into MBLs positive [SWI (+)] and negative [SWI (-)] group. Regional distribution of MBLs and clinical factors were compared between groups. Results : Fifty-one MBLs appeared in 16 patients of SWI (+) group and 16 MBLs in 10 patients of control group [control (+)], respectively. In SWI (+) group, MBLs were located more frequently in white matters than in deep nucleus different from the control (+) group (p<0.05). Nine patients (56.3%) of SW (+) group had various neurological deficits (disorientation in 4, visual field defect in 2, hearing difficulty in 2 and Parkinson syndrome in 1). Initial Glasgow Coma Scale (GCS)/mean Glasgow Outcome Scale (GOS) were $13.9{\pm}1.5/4.7{\pm}0.8$ and $15.0{\pm}0.0/5.0{\pm}0.0$ in SWI (+) and SWI (-) groups, respectively (p<0.05). Conclusion : Traumatic cerebral MBLs showed characteristic regional distribution, and seemed to have an importance on the initial neurological status and the prognosis. SWI is useful for detection of traumatic cerebral MBLs, and can provide etiologic evidences for some post-traumatic neurologic deficits which were unexplainable with conventional MRI.