• Journal of Broadcast Engineering
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• v.26 no.3
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• pp.247-257
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• 2021

This paper proposes a new technique for calibrating a multi-view RGB-D camera using a 3D (dimensional) skeleton. In order to calibrate a multi-view camera, consistent feature points are required. In addition, it is necessary to acquire accurate feature points in order to obtain a high-accuracy calibration result. We use the human skeleton as a feature point to calibrate a multi-view camera. The human skeleton can be easily obtained using state-of-the-art pose estimation algorithms. We propose an RGB-D-based calibration algorithm that uses the joint coordinates of the 3D skeleton obtained through the posture estimation algorithm as a feature point. Since the human body information captured by the multi-view camera may be incomplete, the skeleton predicted based on the image information acquired through it may be incomplete. After efficiently integrating a large number of incomplete skeletons into one skeleton, multi-view cameras can be calibrated by using the integrated skeleton to obtain a camera transformation matrix. In order to increase the accuracy of the calibration, multiple skeletons are used for optimization through temporal iterations. We demonstrate through experiments that a multi-view camera can be calibrated using a large number of incomplete skeletons.

• Journal of Navigation and Port Research
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• v.46 no.3
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• pp.280-288
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• 2022

This study examined the diagnostics of abnormalities and faults of equipment, whose rotational speed changes even during regular operation. The purpose of this study was to suggest a procedure that can properly apply machine learning to the time series data, comprising non-stationary characteristics as the rotational speed changes. Anomaly and fault diagnosis was performed using machine learning: k-Nearest Neighbor (k-NN), Support Vector Machine (SVM), and Random Forest. To compare the diagnostic accuracy, an autoencoder was used for anomaly detection and a convolution based Conv1D was additionally used for fault diagnosis. Feature vectors comprising statistical and frequency attributes were extracted, and normalization & dimensional reduction were applied to the extracted feature vectors. Changes in the diagnostic accuracy of machine learning according to feature selection, normalization, and dimensional reduction are explained. The hyperparameter optimization process and the layered structure are also described for each algorithm. Finally, results show that machine learning can accurately diagnose the failure of a variable-rotation machine under the appropriate feature treatment, although the convolution algorithms have been widely applied to the considered problem.

• Journal of the Korean Society for Precision Engineering
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• v.29 no.12
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• pp.1267-1271
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• 2012

Demand for optical glass device used for lighting could increase rapidly because of LED lighting market growth. The optical glass devices that have been formed by hot press molding process the desired optical performance without being subjected to mechanical processing such as curve generation or grinding. EL-Max material has been used for many engineering applications because of their high wear resistance, high compressive strength, corrosion resistant and very good dimensional stability. EL-Max is very useful for a glass lens mold especially at high temperature and pressure. The performance and reliability of optical components are strongly influenced by the surface damage of EL-Max during grinding process. Therefore, the severe process condition optimization shall be necessary for the highly qualified EL-Max glass lens mold. To get the required qualified surface of EL-Max, the selection of type of the diamond wheel is also important. In this paper, we report best grinding conditions of ultra-precision grinding machining. The grinding machining results of the form accuracy and surface roughness have been analyzed by using Form Talysurf and NanoScan.

• Journal of the Korean Society for Precision Engineering
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• v.29 no.12
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• pp.1376-1381
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• 2012

Optical devices are used extensively in the field of information network. Increasing demand for optical device, optical interconnection has been a important issue for commercialization. However many problems exist in the interconnection between optical device and optical fiber, and in the case of the multi-channel, problems of the optical alignment and optical array arise. For solving the alignment and array problem of optical device and the optical fiber, we fabricated fiber alignment and array by using imprint technology. Achieved higher precision of optical fiber alignment and array due to fabricating using imprint technology. The silicon stamp with different depth was fabricated using the conventional photolithography. Using the silicon stamp, a nickel stamp was fabricated by electroforming process. We conducted imprint process using the nickel stamp with different depth. The optical alignment and array by fabricating the patterns of optical device and fiber alignment and array using imprint process, and achieved higher precision of decreasing the dimensional error of the patterns by optimization of process. The fabricated optical interconnection of PLC device was measured 3.9 dB and 4.2 dB, lower than criteria specified by international standard.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.20 no.3
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• pp.365-370
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• 2007

Many engineering problems often require the large amount of computing resources for iterative simulations of problems treating many parameters and input files. In order to overcome the situation, this paper proposes an e-Science based computational system. The system exploits the Grid computing technology to establish an integrated web service environment which supports distributed high throughput computational simulations and remote executions. The proposed system provides an easy-to-use parametric study service where a computational service includes real time monitoring. To verify usability of the proposed system, two kinds of applications were introduced. The first application is an Aerospace Integrated Research System (e-AIRS). The e-AIRS adapts the proposed computational system to solve CFD problems. The second one is design and optimization of protein 3-dimensional structures in structural biology.

• JSTS:Journal of Semiconductor Technology and Science
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• v.14 no.5
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• pp.508-517
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• 2014

We design and analyze the n-channel junctionless fin-shaped field-effect transistor (JL FinFET) with 10-nm gate length and compare its performances with those of the conventional bulk-type fin-shaped FET (conventional bulk FinFET). A three-dimensional (3-D) device simulations were performed to optimize the device design parameters including the width ($W_{fin}$) and height ($H_{fin}$) of the fin as well as the channel doping concentration ($N_{ch}$). Based on the design optimization, the two devices were compared in terms of direct-current (DC) and radio-frequency (RF) characteristics. The results reveal that the JL FinFET has better subthreshold swing, and more effectively suppresses short-channel effects (SCEs) than the conventional bulk FinFET.

• Journal of Digital Contents Society
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• v.16 no.4
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• pp.605-613
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• 2015

This paper addresses the problem to make sparse the projection matrix in pattern recognition method. Recently, the size of computer program is often restricted in embedded systems. It is very often that developed programs include some constant data. For example, many pattern recognition programs use the projection matrix for dimension reduction. To improve the recognition performance, very high dimensional feature vectors are often extracted. In this case, the projection matrix can be very big. Recently, RSR(roated sparse regression) method[1] was proposed. This method has been proved one of the best algorithm that obtains the sparse matrix. We propose three methods to improve the RSR; outlier removal, sampling and elastic net RSR(E-RSR) in which the penalty term in RSR optimization function is replaced by that of the elastic net regression. The experimental results show that the proposed methods are very effective and improve the sparsity rate dramatically without sacrificing the recognition rate compared to the original RSR method.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.17 no.2 s.105
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• pp.158-164
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• 2006

In this paper, we proposed a new algorithm of the inverse scattering for the reconstruction of unknown dielectric scatterers using the finite-difference time-domain method and the design sensitivity analysis. We introduced the design sensitivity analysis based on the gradient information for the fast convergence of the reconstruction. By introducing the adjoint variable method for the efficient calculation, we derived the adjoint variable equation. As an optimal algorithm, we used the steepest descent method and reconstructed the dielectric targets using the iterative estimation. To verify our algorithm, we will show the numerical examples for the two-dimensional $TM^2$ cases.

• Proceedings of the Korean Society of Computational and Applied Mathematics Conference
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• 2003.09a
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• pp.1-1
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• 2003

In this talk, a reduced-order modeling methodology based on centroidal Voronoi tessellations (CVT's)is introduced. CVT's are special Voronoi tessellations for which the generators of the Voronoi diagram are also the centers of mass (means) of the corresponding Voronoi cells. The discrete data sets, CVT's are closely related to the h-means clustering techniques. Even with the use of good mesh generators, discretization schemes, and solution algorithms, the computational simulation of complex, turbulent, or chaotic systems still remains a formidable endeavor. For example, typical finite element codes may require many thousands of degrees of freedom for the accurate simulation of fluid flows. The situation is even worse for optimization problems for which multiple solutions of the complex state system are usually required or in feedback control problems for which real-time solutions of the complex state system are needed. There hava been many studies devoted to the development, testing, and use of reduced-order models for complex systems such as unsteady fluid flows. The types of reduced-ordered models that we study are those attempt to determine accurate approximate solutions of a complex system using very few degrees of freedom. To do so, such models have to use basis functions that are in some way intimately connected to the problem being approximated. Once a very low-dimensional reduced basis has been determined, one can employ it to solve the complex system by applying, e.g., a Galerkin method. In general, reduced bases are globally supported so that the discrete systems are dense; however, if the reduced basis is of very low dimension, one does not care about the lack of sparsity in the discrete system. A discussion of reduced-ordering modeling for complex systems such as fluid flows is given to provide a context for the application of reduced-order bases. Then, detailed descriptions of CVT-based reduced-order bases and how they can be constructed of complex systems are given. Subsequently, some concrete incompressible flow examples are used to illustrate the construction and use of CVT-based reduced-order bases. The CVT-based reduced-order modeling methodology is shown to be effective for these examples and is also shown to be inexpensive to apply compared to other reduced-order methods.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.33 no.9
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• pp.688-698
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• 2009

Using the multi-dimensional, multi-phase, nonisothermal Polymer Electrolyte Fuel Cell (PEFC) model presented in Part I, the effects of land/channel flow-field on temperature and liquid saturation distributions inside PEFCs are investigated in Part II. The focus is placed on exploring the coupled water transport and heat transfer phenomena within the nonisothermal and two-phase zone existing in the diffusion media (DM) of PEFCs. Numerical simulations are performed varying the land and channel widths and simulation results reveal that the water profile and temperature rise inside PEFCs are considerably altered by changing the land and channel widths, which indicates that oxygen supply and heat removal from the channel to the land regions and liquid water removal from the land toward the gas channels are key factors in determining the water and temperature distributions inside PEFCs. In addition, the adverse liquid saturation gradient along the thru-plane direction is predicted near the land regions by the numerical model, which is due to the vapor-phase diffusion driven by the temperature gradient in the nonisothermal two-phase DM where water evaporates at the hotter catalyst layer, diffuses as a vapor form and then condenses on the cooler land region. Therefore, the vapor phase diffusion exacerbates DM flooding near the land region, while it alleviates DM flooding near the gas channel.