• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.9C
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• pp.555-563
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• 2011

In this paper, we propose an object based 2D image to 3D conversion algorithm by using motion estimation, color labeling and non-local mean filtering methods. In the proposed algorithm, we first extract the motion vector of each object by estimating the motion between frames and then segment a given image frame with color labeling method. Then, combining the results of motion estimation and color labeling, we extract object regions and assign an exact depth value to each object to generate the right image. While generating the right image, occlusion regions occur but they are effectively recovered by using non-local mean filter. Through the experimental results, it is shown that the proposed algorithm performs much better than conventional conversion scheme by removing the eye fatigue effectively.

• The Journal of the Acoustical Society of Korea
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• v.17 no.2E
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• pp.26-30
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• 1998

In this paper, we compose neural network predictive HMM(NNPHMM) to provide the dynamic feature of the speech pattern for the HMM. The NNPHMM is the hybrid network of neura network and the HMM. The NNPHMM trained to predict the future vector, varies each time. It is used instead of the mean vector in the HMM. In the experiment, we compared the recognition abilities of the one hundred Korean syllables according to the variation of hidden layer, state number and prediction orders of the NNPHMM. The hidden layer of NNPHMM increased from 10 dimensions to 30 dimensions, the state number increased from 4 to 6 and the prediction orders increased from 10 dimensions to 30 dimension, the state number increased from 4 to 6 and the prediction orders increased from the second oder to the fourth order. The NNPHMM in the experiment is composed of multi-layer perceptron with one hidden layer and CMHMM. As a result of the experiment, the case of prediction order is the second, the average recognition rate increased 3.5% when the state number is changed from 4 to 5. The case of prediction order is the third, the recognition rate increased 4.0%, and the case of prediction order is fourth, the recognition rate increased 3.2%. But the recognition rate decreased when the state number is changed from 5 to 6.

• Journal of Electrical Engineering and Technology
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• v.13 no.6
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• pp.2511-2520
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• 2018

In this study, Multi-Radial Basis Function Support Vector Machine (Multi-RBF SVM) classifier is introduced based on a composite kernel function. In the proposed multi-RBF support vector machine classifier, the input space is divided into several local subsets considered for extremely nonlinear classification tasks. Each local subset is expressed as nonlinear classification subspace and mapped into feature space by using kernel function. The composite kernel function employs the dual RBF structure. By capturing the nonlinear distribution knowledge of local subsets, the training data is mapped into higher feature space, then Multi-SVM classifier is realized by using the composite kernel function through optimization procedure similar to conventional SVM classifier. The original training data set is partitioned by using some unsupervised learning methods such as clustering methods. In this study, three types of clustering method are considered such as Affinity propagation (AP), Hard C-Mean (HCM) and Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA). Experimental results on benchmark machine learning datasets show that the proposed method improves the classification performance efficiently.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.05a
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• pp.588-593
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• 2007

Fiber reinforced polymer(FRP) composite decks are new to bridge applications and hence not much literature exists on their structural mechanical behavior. As there are many differences between numerical displacements through static analysis of the primary model and experimental displacements through static load tests, system identification (SI)techniques such as Neural Networks (NN) and support vector machines (SVM) utilized in the optimization of the FE model. During the process of identification, displacements were used as input while stiffness as outputs. Through the comparison of numerical displacements after SI and experimental displacements, it can note that NN and SVM would be effective SI methods in modeling an FRP deck. Moreover, two methods such as response surface method and iteration were proposed to optimize the estimated stiffness. Finally, the results were compared through the mean square error (MSE) of the differences between numerical displacements and experimental displacements at 6 points.

• Proceedings of the KSRS Conference
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• v.2
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• pp.684-687
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• 2006

Airborne imagery must be precisely orthorectified to be used as geographical information data. GPS/INS (Global Positioning System/Inertial Navigation System) and LIDAR (LIght Detection And Ranging) data were employed to automatically orthorectify airborne images. In this study, 154 frame airborne images and LIDAR vector data were acquired. LIDAR vector data were converted to raster image for employing as reference data. To derive images with constant brightness, flat field correction was applied to the whole images. The airborne images were geometrically corrected by calculating internal orientation and external orientation using GPS/INS data and then orthorectified using LIDAR digital elevation model image. The precision of orthorectified images was validated using 50 ground control points collected in arbitrary selected five images and LIDAR intensity image. In validation results, RMSE (Root Mean Square Error) was 0.365 smaller then two times of pixel spatial resolution at the surface. It is possible that the derived mosaicked airborne image by this automatic orthorectification method is employed as geographical information data.

• Communications for Statistical Applications and Methods
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• v.25 no.2
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• pp.199-215
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• 2018

Interest in $PM_{10}$ concentrations have increased greatly in Korea due to recent increases in air pollution levels. Therefore, we consider a forecasting model for next day $PM_{10}$ concentration based on the principal elements of air pollution, weather information and Beijing $PM_{2.5}$. If we can forecast the next day $PM_{10}$ concentration level accurately, we believe that this forecasting can be useful for policy makers and public. This paper is intended to help forecast a daily mean $PM_{10}$, a daily max $PM_{10}$ and four stages of $PM_{10}$ provided by the Ministry of Environment using various data mining techniques. We use seven models to forecast the daily $PM_{10}$, which include five regression models (linear regression, Randomforest, gradient boosting, support vector machine, neural network), and two time series models (ARIMA, ARFIMA). As a result, the linear regression model performs the best in the $PM_{10}$ concentration forecast and the linear regression and Randomforest model performs the best in the $PM_{10}$ class forecast. The results also indicate that the $PM_{10}$ in Seoul is influenced by Beijing $PM_{2.5}$ and air pollution from power stations in the west coast.

• ETRI Journal
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• v.34 no.4
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• pp.542-552
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• 2012

In this paper, a gaze estimation method is proposed for use with a large-sized display at a distance. Our research has the following four novelties: this is the first study on gaze-tracking for large-sized displays and large Z (viewing) distances; our gaze-tracking accuracy is not affected by head movements since the proposed method tracks the head by using a near infrared camera and an infrared light-emitting diode; the threshold for local binarization of the pupil area is adaptively determined by using a p-tile method based on circular edge detection irrespective of the eyelid or eyelash shadows; and accurate gaze position is calculated by using two support vector regressions without complicated calibrations for the camera, display, and user's eyes, in which the gaze positions and head movements are used as feature values. The root mean square error of gaze detection is calculated as $0.79^{\circ}$ for a 30-inch screen.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2006.04a
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• pp.240-247
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• 2006

Piezoelectric sensor-based health monitoring technique using a two-step support vector machine (SYM) classifier is discussed for damage identification of a railroad track. An active sensing system composed of two PZT patches was investigated in conjunction with both impedance and guided wave propagation methods to detect two kinds of damage of the railroad track (one is a hole damage of 0.5cm in diameter at web section and the other is a transverse cut damage of 7.5cm in length and 0.5cm in depth at head section). Two damage-sensitive features were extracted one by one from each method; a) feature I: root mean square deviations (RMSD) of impedance signatures and b) feature II: wavelet coefficients for $A_0$ mode of guided waves. By defining damage indices from those damage-sensitive features, a two-dimensional damage feature (2-D DF) space was made. In order to minimize a false-positive indication of the current active sensing system, a two-step SYM classifier was applied to the 2-D DF space. As a result, optimal separable hyper-planes were successfully established by the two-step SYM classifier: Damage detection was accomplished by the first step-SYM, and damage classification was also carried out by the second step-SYM. Finally, the applicability of the proposed two-step SYM classifier has been verified by thirty test patterns.

• Korean Journal of Plant Resources
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• v.10 no.2
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• pp.107-113
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• 1997

Transformants of White Clover(Trifolium repens L.) were efficiently produced from immature seed derived callus cocultivated with Agrobacterium twnefaciens LBA4404 harboring plant binary vector. pBI121, using acetosyringone. The mean frequencies of transformants on the two kanamycin-containing media were 16 to 19% when the immature seed-derived calli were infected with bacteria cultured in the presence of 100$\mu$M acetosyringone compared with 7% in media without acetosyringone. Transgenic white clover was subject to molecular analysis for integration into plant nuclear genome and expression of $\beta$-glucuronidase(GUS) gene. PCR and Northern blot analyses demonstrated that GUS gene was integrated into white clover nuclear genome and expressed into its mRNA. The expression of GUS gene into its protein was confirmed by spectrophotometric assay of GUS activity.

• Bulletin of the Korean Mathematical Society
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• v.31 no.2
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• pp.193-200
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• 1994

Let x : $M^{n}$ .rarw. $E^{m}$ be an isometric immersion of a manifold $M^{n}$ into the Euclidean space $E^{m}$ and .DELTA. the Laplacian of $M^{n}$ defined by -div.omicron.grad. The family of such immersions satisfying the condition .DELTA.x = .lambda.x, .lambda..mem.R, is characterized by a well known result ot Takahashi (8]): they are either minimal in $E^{m}$ or minimal in some Euclidean hypersphere. As a generalization of Takahashi's result, many authors ([3,6,7]) studied the hypersurfaces $M^{n}$ in $E^{n+1}$ satisfying .DELTA.x = Ax + b, where A is a square matrix and b is a vector in $E^{n+1}$, and they proved independently that such hypersurfaces are either minimal in $E^{n+1}$ or hyperspheres or spherical cylinders. Since .DELTA.x = -nH, the submanifolds mentioned above satisfy .DELTA.H = .lambda.H or .DELTA.H = AH, where H is the mean curvature vector field of M. And the family of hypersurfaces satisfying .DELTA.H = .lambda.H was explored for some cases in [4]. In this paper, we classify space curves x : R .rarw. $E^{3}$ satisfying .DELTA.x = Ax + b or .DELTA.H = AH, and find conditions for such curves to be equivalent.alent.alent.