• 한국전산유체공학회지
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• 제16권2호
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• pp.49-55
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• 2011

Turbulent flows with wavy wall are simulated using Residual-based Variational Multiscale Method (RB-VMS) which is proposed by Bazilves et al(2007) as new Large Eddy Simulation methodology. Incompressible Navier-Stokes equations are integrated using Isogeometric analysis which adopt the basis function as NURBS. The Reynolds number is 6760 based on the bulk velocity and averaged channel height. And the amplitude (${\alpha}/{\lambda}$) of wavy wall is 0.05. The computational domain is $2{\lambda}{\times}1.05{\lambda}{\times}{\lambda}$ in the streamwise, wall normal and spanwise direction. Mean quantities and turbulent statistics near wavy wall are compared with DNS results of Cherukat et al.(1998). The predicted results show good agreement with reference data.

• ETRI Journal
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• 제35권6호
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• pp.949-959
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• 2013

Since the recent launch of Microsoft Xbox Kinect, research on 3D human pose estimation has attracted a lot of attention in the computer vision community. Kinect shows impressive estimation accuracy and real-time performance on massive graphics processing unit hardware. In this paper, we focus on further reducing the computation complexity of the existing state-of-the-art method to make the real-time 3D human pose estimation functionality applicable to devices with lower computing power. As a result, we propose two simple approaches to speed up the random-forest-based human pose estimation method. In the original algorithm, the random forest classifier is applied to all pixels of the segmented human depth image. We first use a multi-scale approach to reduce the number of such calculations. Second, the complexity of the random forest classification itself is decreased by the proposed cascade approach. Experiment results for real data show that our method is effective and works in real time (30 fps) without any parallelization efforts.

• 한국멀티미디어학회논문지
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• 제20권9호
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• pp.1481-1490
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• 2017

This paper presents an image quality enhancement algorithm for dark image acquired under poor lighting condition. Various retinex algorithms which are human perception-based image processing methods were proposed to solve this problem. Although MSR(Multi-Scale Retinex) among these algorithm works well under most lighting condition, it shows color degradation because their separate nonlinear processing of RGB color channels. To compensate for the loss of the color, MSRCR(Multi-Scale Retinex with Color Restoration) was proposed. However, it requires high computational load and has additional parameters that need to be adjusted according to input image. In order to overcome this problem, a new retinex algorithm based on MSR is proposed in this paper. The proposed method consists of V channel MSR, saturation correction, and separate contrast enhancement process. Experimental results show that the subjective and objective image quality of the proposed method better than those of the conventional methods.

• 한국전산구조공학회논문집
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• 제12권4호
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• pp.681-690
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• 1999

본 논문에서는 요소를 사용하지 않은 수치해석기법인 무요소법 중에서 다중해상도(multi-resolution)특성이 내재되어 있는 Reproducing Kernel Particle Method (RKPM)의 이중스케일 분해기법을 사용하여 RKPM의 형상함수를 상단성분과 하단성분으로 분리하고 이를 3차원 선형탄성해석과정에 적용하여 von Mises 응력장의 상·하단성분을 유도하였다. 유도된 응력장의 상단성분을 이용하여 후처리과정을 거치지 않고도 응력의 고변화도 부위를 손쉽게 파악할 수 있는 기법을 개발하였으며 이를 이용한 효율적인 적응적 세분화기법의 적용가능성을 연구하였다. 대표적인 2차원 및 3차원 응력집중 문제에 적용하여 응력집중부위를 파악하고 간단한 적응적 세분화과정에 따른 절점추가를 통하여 해의 정도 향상을 파악해 본 결과, 본 연구에서 개발된 기법이 응력집중부위를 정확히 판정할 수 있었으며 효율적인 적응적 세분화기법의 유용한 도구로서 활용될 수 있음을 검증하였다.

• Geomechanics and Engineering
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• 제36권2호
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• pp.99-109
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• 2024

Studying the dynamic behavior of axially moving cylindrical shells in hygro-thermal environments has important theoretical and engineering value for aircraft design. Therefore, in this paper, considering hygro-thermal effect, the nonlinear forced vibration of an axially moving cylindrical shell made of functionally graded materials (FGM) is studied. It is assumed that the material properties vary continuously along the thickness and contain pores. The Donnell thin shell theory is used to derive the motion equations of FGM cylindrical shells with hygro-thermal loads. Under the four sides clamped (CCCC) boundary conditions, the Gallekin method and multi-scale method are used for nonlinear analysis. The effects of power law index, porosity coefficient, temperature rise, moisture concentration, axial velocity, prestress, damping and external excitation amplitude on nonlinear forced vibration are explored through parametric research. It can be found that, the changes in temperature and humidity have a significant effect. Increasing in temperature and humidity will cause the resonance position to shift to the left and increase the resonance amplitude.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권6호
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• pp.3037-3054
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• 2019

Public bicycle system (PBS) is a new emerging and popular mode of public transportation. PBS data can be adopted to analyze human movement patterns. Previous work usually focused on specific scales, and the relationships between different levels of hierarchies are ignored. In this paper, we introduce a multi-scale and interactive visual analytics system to investigate human cycling movement and PBS usage condition. The system supports level-of-detail explorative analysis of spatio-temporal characteristics in PBS. Visual views are designed from global, regional and microcosmic scales. For the regional scale, a bicycle network is constructed to model PBS data, and an flow-based community detection algorithm is applied on the bicycle network to determine station clusters. In contrast to the previous used Louvain algorithm, our method avoids producing super-communities and generates better results. We provide two cases to demonstrate how our system can help analysts explore the overall cycling condition in the city and spatio-temporal aggregation of stations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권8호
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• pp.2787-2800
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• 2022

The aging society increases emergency situations of the elderly living alone and a variety of social crimes. In order to prevent them, techniques to detect emergency situations through voice are actively researched. This study proposes CutPaste-based anomaly detection model using multi-scale feature extraction in time series streaming data. In the proposed method, an audio file is converted into a spectrogram. In this way, it is possible to use an algorithm for image data, such as CNN. After that, mutli-scale feature extraction is applied. Three images drawn from Adaptive Pooling layer that has different-sized kernels are merged. In consideration of various types of anomaly, including point anomaly, contextual anomaly, and collective anomaly, the limitations of a conventional anomaly model are improved. Finally, CutPaste-based anomaly detection is conducted. Since the model is trained through self-supervised learning, it is possible to detect a diversity of emergency situations as anomaly without labeling. Therefore, the proposed model overcomes the limitations of a conventional model that classifies only labelled emergency situations. Also, the proposed model is evaluated to have better performance than a conventional anomaly detection model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권10호
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• pp.3729-3749
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• 2021

At present, deep convolution network-based salient object detection (SOD) has achieved impressive performance. However, it is still a challenging problem to make full use of the multi-scale information of the extracted features and which appropriate feature fusion method is adopted to process feature mapping. In this paper, we propose a new adjacency auxiliary network (AANet) based on multi-scale feature fusion for SOD. Firstly, we design the parallel connection feature enhancement module (PFEM) for each layer of feature extraction, which improves the feature density by connecting different dilated convolution branches in parallel, and add channel attention flow to fully extract the context information of features. Then the adjacent layer features with close degree of abstraction but different characteristic properties are fused through the adjacent auxiliary module (AAM) to eliminate the ambiguity and noise of the features. Besides, in order to refine the features effectively to get more accurate object boundaries, we design adjacency decoder (AAM_D) based on adjacency auxiliary module (AAM), which concatenates the features of adjacent layers, extracts their spatial attention, and then combines them with the output of AAM. The outputs of AAM_D features with semantic information and spatial detail obtained from each feature are used as salient prediction maps for multi-level feature joint supervising. Experiment results on six benchmark SOD datasets demonstrate that the proposed method outperforms similar previous methods.

• The Journal of Asian Finance, Economics and Business
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• 제6권1호
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• pp.149-158
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• 2019

Due to the rapid development of IT (information technology) and internet, products become smart and able to collect, process and produce information and can think of themselves to provide better service to consumers. However, research on the characteristics of smart product is still sparse. In this paper, we report the systemic development of a scale to measure the perceived product smartness associated with smart product. To develop product smartness scale, this study follows systemic scale development processes of item generation, item reduction, scale validation, reliability and validity test consequently. And, after acquiring a large amount of qualitative interview data asking the definition of smart product, we add a unique process to reduce the initial items using both a text mining method using 'r' s/w and traditional reliability and validity tests including factor analysis. Based on an initial qualitative inquiry and subsequent quantitative survey, an eight-factor scale of product smartness is developed. The eight factors are multi-functionality, human-like touch, ability to cooperate, autonomy, situatedness, network connectivity, integrity, and learning capability consequently. Results from Korean samples support the proposed measures of product smartness in terms of reliability, validity, and dimensionality. Implications and directions for further study are discussed. The developed scale offers important theoretical and pragmatic implications for researchers and practitioners.

• 정보처리학회논문지B
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• 제17B권6호
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• pp.429-436
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• 2010

본 논문에서는 단일 카메라로부터 획득한 영상으로부터 물체의 3차원 윤곽선을 구성하는 방법을 제안한다. MOPS(Multi-Scale Oriented Patches) 알고리즘을 이용하여 물체의 대략적인 윤곽선을 검출하고 윤곽선 위에 분포한 특징점의 공간좌표를 획득한다. 동시에 SIFT(Scale Invariant Feature Transform) 알고리즘을 통하여 물체의 윤곽선 내부에 존재하는 특징점 공간좌표를 획득한다. 이러한 정보를 병합하여 물체의 전체 3차원 윤곽선 정보를 구성한다. 본 논문에서 제안하는 방법은 대략적인 물체의 윤곽선만 구성하기 때문에 빠른 계산이 가능하며 SIFT 특징점을 통해 윤곽선 내부 정보를 보완하기 때문에 물체의 자세한 3차원 정보를 얻을 수 있는 장점이 있다.