• Journal of The Korean Astronomical Society
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• v.34 no.4
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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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• v.19 no.2
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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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• 2009.10a
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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.

• Transactions of the Korean Society of Automotive Engineers
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• v.7 no.7
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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.

• Journal of Korea Multimedia Society
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• v.21 no.12
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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%.

• Journal of the Korean Institute of Telematics and Electronics
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• v.24 no.1
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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.

• Journal of Korea Multimedia Society
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• v.19 no.12
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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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• v.37 no.1
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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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• 2006.05a
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• pp.368-371
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• 2006

• Journal of the Earthquake Engineering Society of Korea
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• v.8 no.5 s.39
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• pp.35-43
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• 2004

Small-scale models have been frequently used for seismic performance tests because of limited testing facilities and economic reasons. However, there are not enough studies on similitude law for analogizing prototype structures accurately with small-scale models, although conventional similitude law based on geometry is not well consistent in the inelastic seismic behavior. When fabricating prototype and small-scale model of reinforced concrete structures by using the same material, added mass is demanded from a volumetric change and scale factor could be limited due to aggregate size. Therefore, it is desirable that different material is used for small-scale models. Thus, a modified similitude law could be derived depending on geometric scale factor, equivalent modulus ratio and ultimate strain ratio. In this study, compressive strength tests are conducted to analyze the equivalent modulus ratio of micro-concrete to normal-concrete. Then, equivalent modulus ratios are divided into multi-phase damage levels, which are basically dependent on ultimate strain level. Therefore, an algorithm adaptable to the pseudodynamic test, considering equivalent multi-phase similitude law based on seismic damage levels, is developed. Test specimens, consisted of prototype structures and 1/5 scaled models as a reinforced concrete column, were designed and fabricated based on the equivalent modulus ratios already defined. Finally quasistatic and pseudodynamic tests on the specimens are carried out using constant and variable modulus ratios, and correlation between prototype and small-scale model is investigated based on their test results. It is confirmed that the equivalent multi-phase similitude law proposed in this study could be suitable for seismic performance tests on small-scale models.