• Journal of applied mathematics & informatics
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• 제13권1_2호
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• pp.393-404
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• 2003

In general, a differential operator can be used as a tool of treating the local properties of given function. However, when the given function is varied with high frequency and has irregular form with non-stationary evolution it may not act its role sufficiently as in case of nowhere differentiable curves. In this paper we introduce a multi-scale derivative as a form of weakened global derivative so that it may explain its semi global diffusion properties as well as local ones for the various irregular diffusion phenomena.

• Interaction and multiscale mechanics
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• 제6권2호
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• pp.83-105
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• 2013

In this paper, a multi-scale meshfree-enriched finite element formulation is presented for the analysis of acoustic wave propagation problem. The scale splitting in this formulation is based on the Variational Multi-scale (VMS) method. While the standard finite element polynomials are used to represent the coarse scales, the approximation of fine-scale solution is defined globally using the meshfree enrichments generated from the Generalized Meshfree (GMF) approximation. The resultant fine-scale approximations satisfy the homogenous Dirichlet boundary conditions and behave as the "global residual-free" bubbles for the enrichments in the oscillatory type of Helmholtz solutions. Numerical examples in one dimension and two dimensional cases are analyzed to demonstrate the accuracy of the present formulation and comparison is made to the analytical and two finite element solutions.

• Journal of Multimedia Information System
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• 제8권4호
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• pp.203-210
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• 2021

Deep leaning convolutional neural networks (CNN) have successfully been applied to image super-resolution (SR). Despite their great performances, SR techniques tend to focus on a certain upscale factor when training a particular model. Algorithms for single model multi-scale networks can easily be constructed if images are upscaled prior to input, but sub-pixel convolution upsampling works differently for each scale factor. Recent SR methods employ multi-scale and multi-path learning as a solution. However, this causes unshared parameters and unbalanced parameter distribution across various scale factors. We present a multi-scale single-path upsample module as a solution by exploiting the advantages of sub-pixel convolution and interpolation algorithms. The proposed model employs sub-pixel convolution for the highest scale factor among the learning upscale factors, and then utilize 1-dimension interpolation, compressing the learned features on the channel axis to match the desired output image size. Experiments are performed for the single-path upsample module, and compared to the multi-path upsample module. Based on the experimental results, the proposed algorithm reduces the upsample module's parameters by 24% and presents slightly to better performance compared to the previous algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권9호
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• pp.3186-3203
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• 2021

In this paper, a multi-scale local difference directional number (MLDDN) pattern is proposed for pig identification. Firstly, the color images of individual pig are converted into grey images by the most significant bits (MSB) quantization, which makes the grey values have better discrimination. Then, Gabor amplitude and phase responses on different scales are obtained by convoluting the grey images with Gabor masks. Next, by calculating the main difference of local edge directions instead of traditionally edge information, the directional numbers of Gabor amplitude and phase responses are encoded. Finally, the block histograms of the encoded images are concatenated on each scale, and the maximum pooling is adopted on different scales to avoid the high feature dimension. Experimental results on two pigsties show that MLDDN impressively outperforms the other widely used local descriptors.

• 경영과학
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• 제24권2호
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• pp.57-72
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• 2007

Many Applications of scientific research have coupled with functional data signal clustering techniques to discover novel characteristics that can be used for the diagnoses of several issues. In this article we present an interpretable multi-scale cluster hierarchy framework for clustering functional data using its multi-aspect frequency information. The suggested method focuses on how to effectively select transformed features/variables in unsupervised manner so that finally reduce the data dimension and achieve the multi-purposed clustering. Specially, we apply our suggested method to mutual fund returns and make superior-performing funds group based on different aspects such as global patterns, seasonal variations, levels of noise, and their combinations. To promise our method producing a quality cluster hierarchy, we give some empirical results under the simulation study and a set of real life data. This research will contribute to financial market analysis and flexibly fit to other research fields with clustering purposes.

• Membrane and Water Treatment
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• 제2권2호
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• pp.105-119
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• 2011

This study presents the tests of an Immersed Membrane BioReactor (IMBR) equipped with a draft tube and focuses on the influence of hydrodynamic conditions on membrane fouling in a pilot-scale using a hollow fiber membrane module of ZW-10 under ambient conditions. In this system, the cross-flow velocities across the membrane surface were induced by a cylindrical draft-tube. The relationship between cross-flow velocity and aeration strength and the influence of the cross-flow on fouling rate (under various hydrodynamic conditions) were investigated using Multi-Dimension Scaling (MDS) analysis. MDS technique is especially suitable for samples with many variables and has relatively few observations, as the data about Membrane Bio-Reactor (MBR) often is. Observations and variables are analyzed simultaneously. According to the results, a specialized form of MDS, CoPlot enables presentation of the results in a two dimensional space and when plotting variables ratio (output/input) rather than original data the efficient units can be visualized clearly. The results indicate that: (i) aeration plays an important role in IMBR performance; (ii) implementing the MDS approach with reference to the variables ratio is consequently useful to characterize performance changes for data classification.

• 한국주거학회논문집
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• 제19권2호
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• pp.43-54
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• 2008

The aim of the research is to find out the direction of unit-plans in multi-dwellings for the future society. Shinonome Canal Court where residents actually live now are the objects in this study, and the residential floor plan of unit-plans were analyzed to find out the typical types. The analysis was focused on the unit-plans of 5 blocks of Shinonome Canal Court. Space Syntax Theory was used as the analysis method. As the first stage of the analysis, justified graphs were made to find out the characters of unit-plans through the classification of the graphs. Contents of the analysis are as follows: Relationship between classified justified graphs and dimension according to node number. Relationship between classified justified graph patterns and unit-plans. Characters of unit-plans in each blocks. Shinonome Canal Court consists of mainly small scale unit-plans and 30unit-plans are classified. 1LDK, 2LDK, 1LDK+S, 1LDK+f are typical unit-plans which are mainly supplied in the complex. It is noted that the results of the analysis by node, justified graph pattern and dimension are the same. It also presents diverse unit-plans which shows a change in nLDK pattern or add f (foyer), AN (annex), S (service room), Fs (free space) to basic nLDK type. In summary, it demonstrates the possibility of creating new residental floor plans in multi-dwellings.

• Tribology and Lubricants
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• 제33권6호
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• pp.303-308
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• 2017

Most automobile tire companies have not yet considered the geometric information of a road at the design stage of a tire because the topographical characterization of a road surface is very difficult owing to its vastness and randomness. A road surface shows variable surface roughness values according to magnification, and thus, the contact force between the road and tire significantly fluctuates with respect to the scale. In this study, we make an attempt to define a representative value for surface topographical information at multi-scale levels. To represent surface topography, we use a statistical method called power spectral density (PSD). We use the fast Fourier transform (FFT) and PSD to analyze the height profiles of a random surface. The FFT and PSD of a surface help in obtaining a fractal dimension, which is a representative value of surface topography at all length scales. We develop three surfaces with different fractal dimensions. We use finite element analysis (FEA) to observe the contact forces between a tire and the road surfaces with three different fractal dimensions. The results from FEA reveal that an increase in the fractal dimension decreases the contact length between the tire and road surfaces. On the contrary, the average contact force increases. This result indicates that designing and manufacturing a tire considering the fractal dimension of a road makes safe driving possible, owing to the improvement in service life and braking performance of the tire.

• 한국조사연구학회지:조사연구
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• 제11권1호
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• pp.63-85
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• 2010

이 연구는 다문화사회의 피할 수 없는 과제인 가치관 변화를 위한 출발점을 모색하기 위해 외국인에 대한 감정적 반응의 실체를 밝히고자 하였다. 연구 결과, 소수 민족에 대한 사회적 거리감을 측정하는 데 주로 사용되어 온 보가더스 척도는 '국민'과 '결혼을 통한 친족관계'에 대해 유독 배타성을 보이는 한국인들의 태도를 설명하는 데에는 충분하지 않은 것으로 나타났다. 이를 보완하기 위해 감정조절이론의 평가성과 권력성 그리고 활동성을 기준으로 각 출신지별 외국인에 대한 태도를 살펴보았다. 미국인이 세 차원 모두에서 높게 평가되었고, 일본인은 평가성은 낮고 권력성과 활동성이 높았다. 조선족과 새터민은 평가성은 높으나 나머지 점수가 낮다. 서아시아인은 세 차원 모두에서 부정적으로 평가되었다. 사회적 거리에 영향을 주는 요소들과 그 상대적인 영향력을 파악한 결과 평가성이 모든 집단에 영향을 미치는 공통된 변수였고, 일본인에 대해서는 권력성의 평가가, 중국인과 서아시아인에 대해서는 활동성에 대한 평가가 사회적 거리감에 영향을 미쳤다. 교육수준이 높을수록 미국인을 제외한 모든 외국인 집단에 대한 사회적 거리감이 줄어들었다. 미국인에 대해서는 여성이, 새터민의 경우에는 연령이 높을수록 사회적 거리감이 적었다.

• Environmental Sciences Bulletin of The Korean Environmental Sciences Society
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• 제10권S_1호
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• pp.23-28
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• 2001

A new numerical weather prediction and dispersion model, the Operational Multi-scale Environment model with Grid Adaptivity(OMEGA) including an embedded Atmospheric Dispersion Model(ADM), is introduced as a next generation atmospheric simulation system for real-time hazard predictions, such as severe weather or the transport of hazardous release. OMEGA is based on an unstructured grid that can facilitate a continuously varying horizontal grid resolution ranging from 100 km down to 1 km and a vertical resolution from 20 -30 meters in the boundary layer to 1 km in the free atmosphere. OMEGA is also naturally scale spanning and time. In particular, the unstructured grid cells in the horizontal dimension can increase the local resolution to better capture the topography or important physical features of the atmospheric circulation and cloud dynamics. This means the OMEGA can readily adapt its grid to a stationary surface, terrain features, or dynamic features in an evolving weather pattern. While adaptive numerical techniques have yet to be extensively applied in atmospheric models, the OMEGA model is the first to exploit the adaptive nature of an unstructured gridding technique for atmospheric simulation and real-time hazard prediction. The purpose of this paper is to provide a detailed description of the OMEGA model, the OMEGA system, and a detailed comparison of OMEGA forecast results with observed data.