• Steel and Composite Structures
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• v.51 no.1
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• pp.1-8
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• 2024

Transient dynamic behavior of a sandwich beam under thermal and impulsive loads has been researched in the context of higher-order beam theory. The impulse load of blast type has been enforced on the top exponent of the sandwich beam while it is in a thermal environment. The core of the sandwich beam is cellular with auxetic rectangular pattern, whereas the layers have been built with the incorporation of graphene oxide powder (GOP) and are micromechanically introduced through Halpin-Tsai formulization. Governing equations for the sandwich beam have been solved through inverse Laplace transform style for obtaining the dynamical deflections. The connection of beam deflections on temperature variability, GOP quantity, pulse load situation and core relative density has been surveyed in detail.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.28 no.1
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• pp.1-21
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• 2024

This review comprehensively explores the evolution, theoretical underpinnings, variations, and applications of diffusion models. Originating as a generative framework, diffusion models have rapidly ascended to the forefront of machine learning research, owing to their exceptional capability, stability, and versatility. We dissect the core principles driving diffusion processes, elucidating their mathematical foundations and the mechanisms by which they iteratively refine noise into structured data. We highlight pivotal advancements and the integration of auxiliary techniques that have significantly enhanced their efficiency and stability. Variants such as bridges that broaden the applicability of diffusion models to wider domains are introduced. We put special emphasis on the ability of diffusion models as a crucial foundation model, with modalities ranging from image, 3D assets, and video. The role of diffusion models as a general foundation model leads to its versatility in many of the downstream tasks such as solving inverse problems and image editing. Through this review, we aim to provide a thorough and accessible compendium for both newcomers and seasoned researchers in the field.

• Journal of KIISE:Computer Systems and Theory
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• v.32 no.1
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• pp.1-8
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• 2005

Image morphing to generate 2D transitions between images may be difficult even to express simple 3D transformations. In addition, previous view morphing method requires control points for postwarping, and is much affected by self- occlusion. This paper presents a new morphing algorithm that can generate automatically in-between scenes from un-calibrated images. Our algorithm rectifies input images based on the fundamental matrix, which is followed by linear interpolation with bilinear disparity map. In final, we generate in-between views by inverse mapping of homography between the rectified images. The proposed method nay be applied to photographs and drawings, because neither knowledge of 3D shape nor camera calibration, which is complex process generally, is required. The generated in-between views can be used in various application areas such as simulation system of virtual environment and image communication.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.4
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• pp.257-267
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• 2017

Compressed Sensing(CS) deals with linear inverse problems. The theoretical results of CS have had an impact on inference problems and presented amazing research achievements in the related fields including signal processing and information theory. However, in order for CS to be applied in practical environments, there are two significant challenges to be solved. One is to guarantee in real time recovery of CS signals, and the other is that the signals have to be sparse. To this end, the latest researches using deep learning technology have emerged. In this paper, we consider CS problems based on deep learning and discuss the latest research results. And the approaches for CS signal reconstruction using deep learning show superior results in terms of recovery time and performance. It is expected that the approaches for CS reconstruction using deep learning shown in recent studies can not only raise the possibility of utilization of CS, but also be highly exploited in the fields of signal processing and communication areas.

• Journal of KIISE:Computer Systems and Theory
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• v.34 no.5_6
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• pp.228-237
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• 2007

displacement mapping enables us to add realistic details to polygonal meshes without changing geometry. We present a real-time artifacts-free inverse displacement mapping method. In each pixel, we construct a ray and trace the ray through the displacement map to find an intersection. To skip empty regions safely, we traverse the image pyramid of displacement map in top-down order. Furthermore, when the displacement map is enlarged, intersection with bilinear interpolated displacement map can be found. When the displacement map is at distance, our method supports mipmap-like prefiltering to enhance image quality and speed. Experimental results show that our method can produce correct images even at grazing view angles. Rendering speed of a test scene is over hundreds of frames per second and the influence of resolution of displacement map to rendering speed is little. Our method is simple enough to be added to existing virtual reality systems easily.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.25 no.9
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• pp.952-958
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• 2014

In this paper, we propose a parametric sparse recovery algorithm(SRA) applied to a radar signal model, based on the compressive sensing(CS), for the ISAR(Inverse Synthetic Aperture Radar) image reconstruction from an incomplete radar-cross-section(RCS) data and for the estimation of rotation rate of a target. As the SRA, the iteratively-reweighted-least-square(IRLS) is combined with the radar signal model including chirp components with unknown chirp rate in the cross-range direction. In addition, the particle swarm optimization(PSO) technique is considered for searching correct parameters related to the rotation rate. Therefore, the parametric SRA based on the IRLS can reconstruct ISAR image and estimate the rotation rate of a target efficiently, although there exists missing data in observed RCS data samples. The performance of the proposed method in terms of image entropy is also compared with that of the traditional interpolation methods for the incomplete RCS data.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.10 no.2
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• pp.67-78
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• 1990

An inverse self-iterative procedure is developed to determine layer moduli which are significant for the structural evaluation of pavements in developing rational and analytical rehabilitation technique. Falling weight deflectometer(FWD) is adopted as a non-destructive testing(NDT)device. The layer elastic theory is used to interpret NDT data. The theoretical deflection basins of pavement structures obtained by full factorial design are used for a parametric study on the characteristics of deflection basins and regression analyses. Regression equations to estimate layer moduli of flexible pavements are proposed through the regression analyses of theoretical deflection basins. The relationships between the rate of change of moduli and deflections are developed for the efficient iteration. An inverse self-iterative procedure to ensure the accuracy of the layer moduli is proposed. Validity and applicability of the developed procedure are verified through various numerical model tests.

• Transactions of the Korean Society of Mechanical Engineers
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• v.14 no.6
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• pp.1679-1688
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• 1990

The method of nonlinear parameter estimation(NPE), which is a statistical and an inverse method, is used to estimate the thermal diffusivity of the porous insulation material. In order to apply the NPE method for measuring the thermal diffusivity, and algorithm for programing suitable to IBM personal computer is established, and is studied the statistical treatment of experimental data and theory of estimation. The experimental data obtained by discrete measurement using a constant heat flux technique are used to find the boundary conditions, initial conditions, and the thermal diffusivity, and then the final values are compared with the values obtained by some different methods. The results are presented as follows:(1) NPE method is used to establish the estimation of the thermal diffusivity and compared results with experimental output shows, that this method can be applicable to define the thermal diffusivity without considering hear flux types. (2) Because of all of the temperatures obtained by the discrete measurement on each steps of time are used to estimate the thermal diffusivity. Although some error in the temperature measurements of temperature are included in estimating process, its influences on the final value are minimzed in NPE method. (3) NPE method can reduce the experimental time including the time of data collecting in a few minutes and can take smaller specimen compared with steady state method. If the tube-type furnace is used, also the adjusting time of surrounding temperature can be reduced.

• Journal of Korean Society of Steel Construction
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• v.18 no.5
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• pp.575-584
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• 2006

In this paper, a shear-flexible finite element model is developed for the buckling analysis of axially loaded, thin-walled composite I-beams. Based on an orthogonal Cartesian coordinate system, the displacement fields are defined using the first-order shear-deformable beam theory. The derived element takes into account flexural shear deformation and torsional warping deformation. Three different types of beam elements, namely, the two-noded, three-noded, and four-noded beam elements, were developed to solve the governing equations. An inverse iteration with shift eigenvalue solution was used to solve the resulting linearized buckling problem. A parametric study was conducted to show the importance of shear flexibility and fiber orientation on the buckling behavior of thin-walled composite beams. A good agreement was obtained among the proposed shear-flexible model, other results available in literature, and the finite element solution.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.5
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• pp.572-578
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• 2018

This paper considers Group Testing as one of combinatorial problems. The group testing first began to inspect soldier's syphilis infection during World War II and have long established an academic basis. Recently, there has been much interest in related areas because of the rediscovery of the value of the group testing. The group testing is the same as finding a few defect samples out of a large number of samples, which is similar to the inverse problem of Compressed Sensing. In this paper, we introduce the definition of the group testing, and specify the classes of the group testing and the bounds on performance of the group testing. In addition, we show a lower bound for the number of tests required to find defective samples using the theoretical theorem which is mainly used for relationship between conditional entropy and the probability of error in the information theory. We see how our result can be different from other related results.