• 천문학회지
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• 제34권4호
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• pp.181-190
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• 2001

The advent of robust, reliable and accurate higher order Godunov schemes for many of the systems of equations of interest in computational astrophysics has made it important to understand how to solve them in multi-scale fashion. This is so because the physics associated with astrophysical phenomena evolves in multi-scale fashion and we wish to arrive at a multi-scale simulational capability to represent the physics. Because astrophysical systems have magnetic fields, multi-scale magnetohydrodynamics (MHD) is of especial interest. In this paper we first discuss general issues in adaptive mesh refinement (AMR), We then focus on the important issues in carrying out divergence-free AMR-MHD and catalogue the progress we have made in that area. We show that AMR methods lend themselves to easy parallelization. We then discuss applications of the RIEMANN framework for AMR-MHD to problems in computational astophysics.

• East Asian Economic Review
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• 제19권2호
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• pp.189-220
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• 2015

Multi-scale representations are effective in characterising the time-frequency characteristics of financial return series. They have the capability to reveal the properties not evident with typical time domain analysis. Given the aforesaid, this study derives crucial insights from multi scale analysis to investigate the co-movements between Indian and emerging Asian equity markets using wavelet correlation and wavelet coherence measures. It is reported that the Indian equity market is strongly integrated with Asian equity markets at lower frequency scales and relatively less blended at higher frequencies. On the other hand the results from cross correlations suggest that the lead-lag relationship becomes substantial as we turn to lower frequency scales and finally, wavelet coherence demonstrates that this correlation eventually grows strong in the interim of the crises period at lower frequency scales. Overall the findings are relevant and have strong policy and practical implications.

• 한국정보디스플레이학회:학술대회논문집
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• 한국정보디스플레이학회 2009년도 9th International Meeting on Information Display
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• pp.1546-1549
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• 2009

In recent studies on tone reproduction with the objective of reproducing natural looking colors in digital images, an integrated multi-scale retinex (IMSR) has produced great naturalness in the resulting images. Most methods, including IMSR, work in RGB or quasi-RGB color spaces. As such, this produces hue distortion from the perspective of the human visual system. Accordingly, this paper proposes the hue preserved multi-scale Retinex (HPMSR) method to obtain a high contrast and naturalness. The proposed method enhanced the $L^*$ and saturation values in CIELAB color space. As a result, the visibility in dark shadows in the resulting images was improved.

• 한국자동차공학회논문집
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• 제7권7호
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• pp.1-14
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• 1999

In the present study, the flame factor which primarily influence the simulation accuracy of the combustion process in a gasoline engine was modeled as a nonlinear function of turbulent intensity to laminar flame speed ratio. Multi-length-scale production rate model for turbulent kinetic energy equation was introduced to consider the different length scales of the swirling and tumbling motions in cylinder on the production rte of turbulent kinetic energy. By7 introducing the multi-length-scale production rate model for the turbulent kinetic energy equation, the predictions of turbulent burning velocity , cylinder pressure, mass burning rate and engine performance of a gasoline engine can much be improved.

• 한국멀티미디어학회논문지
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• 제21권12호
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• pp.1467-1472
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• 2018

For the problem that the face image of surveillance video cannot be accurately identified due to the low resolution, this paper proposes a low resolution face recognition solution based on convolutional neural network model. Convolutional Neural Networks (CNN) model for multi-scale input The CNN model for multi-scale input is an improvement over the existing "two-step method" in which low-resolution images are up-sampled using a simple bi-cubic interpolation method. Then, the up sampled image and the high-resolution image are mixed as a model training sample. The CNN model learns the common feature space of the high- and low-resolution images, and then measures the feature similarity through the cosine distance. Finally, the recognition result is given. The experiments on the CMU PIE and Extended Yale B datasets show that the accuracy of the model is better than other comparison methods. Compared with the CMDA_BGE algorithm with the highest recognition rate, the accuracy rate is 2.5%~9.9%.

• 대한전자공학회논문지
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• 제24권1호
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• pp.20-27
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• 1987

The hierarchical approach method is proposed to sperate each different time scale sub-systems from linear time invariant multi-parameter singular perturbation systems. By means of this proposal, the original multi-parameter singular perturbation systems is completely separated into independent subsystems with each different time scale. It is also investigated that the controllability of the system is invariant. And this paper applies singular perturbation methods to the minimum control effort problem for linear time invariant systems with constrained controls. Also near-optimum control theory, which is based on dividing the total time interval with the time scales respectively, is proposed. As a result, the time scale separation method is show to be particularly useful in a near optimum design which can be otained through a decentralized control structure.

• 한국멀티미디어학회논문지
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• 제19권12호
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• pp.1909-1918
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• 2016

This paper presents a unified framework for joint Convolutional Neural Network (CNN) based vehicle detection by leveraging multi-spectral image pairs. With the observation that under challenging environments such as night vision and limited light source, vehicle detection in a single color image can be more tractable by using additional far-infrared (FIR) image, we design joint CNN architecture for both RGB and FIR image pairs. We assume that a score map from joint CNN applied to overall image can be considered as confidence of vehicle existence. To deal with various scale ratios of vehicle candidates, multi-scale images are first generated scaling an image according to possible scale ratio of vehicles. The vehicle candidates are then detected on local maximal on each score maps. The generation of overlapped candidates is prevented with non-maximal suppression on multi-scale score maps. The experimental results show that our framework have superior performance than conventional methods with a joint framework of multi-spectral image pairs reducing false positive generated by conventional vehicle detection framework using only single color image.

• Structural Engineering and Mechanics
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• 제37권1호
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• pp.17-37
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• 2011

This paper presents a new Internet-based computational framework for the realistic simulation of multi-scale response of structural systems. Two levels of parallel processing are involved in this frame work: multiple local distributed computing environments connected by the Internet to form a cluster-to-cluster distributed computing environment. To utilize such a computing environment for a realistic simulation, the simulation task of a structural system has been separated into a simulation of a simplified global model in association with several detailed component models using various scales. These related multi-scale simulation tasks are distributed amongst clusters and connected to form a multi-level hierarchy. The Internet is used to coordinate geographically distributed simulation tasks. This paper also presents the development of a software framework that can support the multi-level hierarchical simulation approach, in a cluster-to-cluster distributed computing environment. The architectural design of the program also allows the integration of several multi-scale models to be clients and servers under a single platform. Such integration can combine geographically distributed computing resources to produce realistic simulations of structural systems.

• 한국전산유체공학회:학술대회논문집
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• 한국전산유체공학회 2006년도 PARALLEL CFD 2006
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• pp.368-371
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• 2006

• 한국지진공학회논문집
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• 제8권5호통권39호
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• pp.35-43
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• 2004

대형 구조물의 지진응답을 실험적으로 연구할 경우, 실험장비의 용량과 실험모형의 크기 제약으로 인하여 축소모형이 일반적으로 적용되고 있다. 그러나 구조물의 지진응답은 비탄성 거동을 나타내기 때문에 거동예측이 복잡함에도 불구하고, 축소모형의 지진실험 결과로부터 원형구조물의 지진응답을 유추하기 위한 상사법칙의 연구는 미비한 실정이다. 철근콘크리트 구조물의 축소모형 제작 시 상사율이 커지면 상대적으로 부가질량이 증가하며, 또한 굵은 골재 크기의 영향으로 원형구조물과 축소모형의 제작에 동일한 재료를 사용하지 않는 것이 바람직하다. 따라서 동일한 재료를 사용하지 않을 경우, 상사법칙은 기하학적인 상사율과 재료적인 등가탄성계수비에 의존하게 된다. 본 연구에서는 원형구조물과 축소모형에 각각 적용되는 normal-concrete와 micro-concrete의 재료 비선형성을 파악하기 위해 압축강도시험을 수행하여, 재료의 거동구간을 극한 변형률을 기준으로 등가의 다단계로 나누어 등가탄성계수비를 적용시킴으로써 지진손상의 정도를 고려할 수 있는 equivalent multi-phase similitude law를 유도하였다. 유도된 상사법칙을 고려한 유사동적실험 알고리즘을 구성하였으며, 실험적인 검증을 위하여 철근콘크리트 column에 대하여 원형구조물과 1/5축소모형을 재료시험에서 정의한 등가탄성계수비를 고려하여 설계, 제작하였다. 검증실험에서는 constant modulus ratio와 variable modulus ratio를 적용하여 준정적실험과 유사동적실험을 수행한 결과, equivalent multi-phase similitude law를 고려한 유사동적실험 알고리즘에 의한 축소모형의 응답결과가 원형구조물의 거동을 비교적 정확히 재현함을 확인하였다.