• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.5
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• pp.697-704
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• 2018

Since the face in image content corresponds to individual information that can distinguish a specific person from other people, it is important to accurately detect faces not hidden in an image. In this paper, we propose a method to accurately detect a face from input images using a deep learning algorithm, which is one of the machine learning methods. In the proposed method, image input via the red-green-blue (RGB) color model is first changed to the luminance-chroma: blue-chroma: red-chroma ($YC_bC_r$) color model; then, other regions are removed using the learned skin color model, and only the skin regions are segmented. A CNN model-based deep learning algorithm is then applied to robustly detect only the face region from the input image. Experimental results show that the proposed method more efficiently segments facial regions from input images. The proposed face area-detection method is expected to be useful in practical applications related to multimedia and shape recognition.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.3
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• pp.297-303
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• 2009

In this paper, we propose the biomedical spectral pattern classification techniques by the fusion scheme based on the SpPCA and MLP in extended feature space. A conventional PCA technique for the dimension reduction has the problem that it can't find an optimal transformation matrix if the property of input data is nonlinear. To overcome this drawback, we extract features by the SpPCA technique in extended space which use the local patterns rather than whole patterns. In the classification step, individual classifier based on MLP calculates the similarity of each class for local features. Finally, biomedical spectral patterns is classified by the fusion scheme to effectively combine the individual information. As the simulation results to verify the effectiveness, the proposed method showed more improved classification results than conventional methods.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.37 no.7
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• pp.634-641
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• 2009

In this work, the behavior of vibratory hub loads induced due to the uncertainties of composite material properties for each of the participating rotor blades is investigated. The random material properties of composites available from the existing experimental data are processed by using the Monte-Carlo simulation technique to obtain the stochastic distribution of sectional stiffnesses of composite blades. The coefficients of variation (standard deviation divided by the mean) obtained from the sectional stiffness constants are used as an input to the comprehensive aeroelastic analysis code that can evaluate the hub loads of a rotor system. It is found that the uncertainty effects of composite material properties inevitably bring a dissimilarity to the rotor system. The influence of hub vibration response with respect to the individual stiffness (flatwise bending, chordwise bending and torsion) changes is also identified.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.35 no.7
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• pp.565-574
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• 2007

The flow control effect of blending Gurney flap with jet flap for flow around an NACA 0012 airfoil was numerically investigated through parameter variation of each flow control mechanism on unstructured meshes. The aerodynamic force and moment variations due to flow control were examined, and the results were compared between the blending control and each individual flow control. The results showed that the blending control required less energy input to achieve the same level of lift increment than that of the jet flap, and at the same time alleviated drag increment caused by introducing the Gurney flap.

• Journal of Astronomy and Space Sciences
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• v.24 no.1
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• pp.31-38
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• 2007

We have investigated the evolutionary status of 85 Peg within the framework of standard evolutionary theory. 85 Peg has been known to be a visual and spectroscopic binary system in the solar neighborhood. In spite of the accurate information of the total mass (${\sim}1.5M_{\odot}$) and the distance (${\sim}12pc$) from the HIPPARCOS parallax, it has been undetermined an individual mass, therefore the evolved status of the system. Moreover, the coupled uncertainties of chemical composition and age, make matters worse in predicting an evolutionary status of the system. Nevertheless, we computed the various possible models for 85 Peg, and then calibrated stellar parameters by adjusting to the recent observational data. Our modelling computation has included recently updated input physics and stellar theory such as opacity, equation of state, and chemical diffusion. Through a statistical assessment, we have derived a confident parameter set as the best solution which minimized $X^{2}$ within the observational error domain. Most of all, we found that 85 Peg is not a binary system but a triple system with an unseen companion 85 Peg $B_{b}\;{\sim}0.16M_{\odot}$. The aim of the present paper is (1) to provide a complete modelling of the stellar system based on the evolutionary theory, and (2) to constrain the physical dimensions such as mass, metallicity and age.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.6-10
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• 2007

Classification of hyperspectral images is challenging. A very high dimensional input space requires an exponentially large amount of data to adequately and reliably represent the classes in that space. In other words in order to obtain statistically reliable classification results, the number of necessary training samples increases exponentially as the number of spectral bands increases. However, in many situations, acquisition of the large number of training samples for these high-dimensional datasets may not be so easy. This problem can be overcome by using multiple classifiers. In this paper we compared the effectiveness of two approaches for creating multiple classifiers, feature selection and feature extraction. The methods are based on generating multiple feature subsets by running feature selection or feature extraction algorithm several times, each time for discrimination of one of the classes from the rest. A maximum likelihood classifier is applied on each of the obtained feature subsets and finally a combination scheme was used to combine the outputs of individual classifiers. Experimental results show the effectiveness of feature extraction algorithm for generating multiple classifiers.

• KIEE International Transactions on Power Engineering
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• v.3A no.3
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• pp.139-147
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• 2003

The paper presents a novel approach of constructing the state matrix of the multi-machine power system for SSR (subsynchronous resonance) analysis using the linearized equations of individual devices including electrical transmission network dynamics. The machine models in the local d-q reference frame are integrated with the network models in the common R-I reference frame by simply transforming their output equations into the R-I frame where the transformed output is used as the input to the network dynamics or vice versa. The salient feature of the formulation is that it allows for modular construction of various component models without rearranging the overall state space formulation. This universal SSR small signal stability program provides a flexible tool for systematic analyses of SSR small-signal stability impacts of both conventional devices such as generation systems and novel devices such as power electronic apparatus and their controllers. The paper also presents its application results to IEEE benchmark models.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.1
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• pp.68-75
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• 1999

Machine recognition of faces from still and video images is emerging as an active research area spanning several disciplines such as image processing, pattern recognition, computer vision and neural networks. In addition, human face identification has numerous applications such as human interface based systems and real-time video systems of surveillance and security. In this paper, we propose an algorithm that can identify a particular individual face. We consider human face identification system in color space, which hasn't often considered in conventional in conventional methods. In order to make the algorithm insensitive to luminance, we convert the conventional RGB coordinates into normalized CIE coordinates. The normalized-CIE-based facial images are KL-transformed. The transformed data are used as the input of multi-layered neural network and the network are trained using error-backpropagation methods. Finally, we verify the system performance of the proposed algorithm by experiments.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2007.11a
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• pp.508-509
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• 2007

For the safty driving of an automobile which is become individual requisites, a new Neural Network algorithm which recognized the load vehicles in real time is proposed. The proposed neural network technique is the real time computation method through the inter-node diffusion. The most reliable algorithm derived for real time recognition of vehicles, is a dynamic programming based algorithm based on sequence matching techniques that would process the data as it arrives and could therefore provide continuously updated neighbor information estimates.

• The Bulletin of The Korean Astronomical Society
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• v.39 no.1
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• pp.52.2-52.2
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• 2014

The Immersion Grating Infrared Spectrometer (IGRINS) is an unprecedentedly minimized infrared cross-dispersed echelle spectrograph with a high-resolution and high-sensitivity optical performance. A silicon immersion grating features the instrument for the first time in this field. IGRINS will cover the entire portion of the wavelength range between 1.45 and $2.45{\mu}m$ accessible from the ground in a single exposure with spectral resolution of 40,000. Individual volume phase holographic (VPH) gratings serve as cross-dispersing elements for separate spectrograph arms covering the H and K bands. On the 2.7m Harlan J. Smith telescope at the McDonald Observatory, the slit size is $1^{\prime\prime}{\times}15^{\prime\prime}$. IGRINS has a $0.27^{\prime\prime}$ pixel-1 plate scale on a $2048{\times}2048$ pixel Teledyne Scientific & Imaging HAWAII-2RG detector with SIDECAR ASIC cryogenic controller. The instrument includes four subsystems; a calibration unit, an input relay optics module, a slit-viewing camera, and nearly identical H and K spectrograph modules. The use of a silicon immersion grating and a compact white pupil design allows the spectrograph collimated beam size to be 25mm, which permits the entire cryogenic system to be contained in a moderately sized rectangular vacuum chamber. The fabrication and assembly of the optical and mechanical hardware components were completed in 2013. In this presentation, we describe the major design characteristics of the instrument and the early performance estimated from the first light commissioning at the McDonald Observatory.