• Journal of Intelligence and Information Systems
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• v.10 no.2
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• pp.65-77
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• 2004

This paper presents a priori and the local font classification method. The font classification uses ascenders, descenders, and serifs extracted from a word image. The gradient features of those sub-images are extracted, and used as an input to a neural network classifier to produce font classification results. The font classification determines 2 font styles (upright or slant), 3 font groups (serif sans-serif or typewriter), and 7-font names (Postscript fonts such as Avant Garde, Helvetica, Bookman, New Century Schoolbook, Palatine, Times, and Courier). The proposed a priori and local font classification method allows an OCR system consisting of various font-specific character segmentation tools and various mono-font character recognizers. Experiments have shown font classification accuracies reach high performance levels of about 95.4 percent even with severely touching characters. The technique developed for tile selected 7 fonts in this paper can be applied to any other fonts.

• Geomechanics and Engineering
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• v.11 no.4
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• pp.571-585
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• 2016

The crest of the Pubugou central core rockfill dam (CCRD) cracked in the first and second impounding periods. To evaluate the safety of the Pubugou CCRD, an inversion analysis of the constitutive model parameters for rockfill materials is performed based on the in situ deformation monitoring data. The aim of this work is to truly reflect the deformation state of the Pubugou CCRD and determine the causes of the dam crest cracks. A novel real-coded genetic algorithm based upon the differences in gene fragments (DGFX) is proposed. It is used in combination with the radial based function neural network (RBFNN) to perform the parameters back analysis. The simulated settlements show good agreements with the monitoring data, illustrating that the back analysis is reasonable and accurate. Furthermore, the deformation gradient of the dam crest has been analysed. The dam crest has a great possibility of cracking due to the uncoordinated deformation, which agrees well with the field investigation. The deformation gradient decreases to the value lower than the critical one and reaches a stable state after the second full reservoir.

• Journal of Korea Multimedia Society
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• v.17 no.3
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• pp.300-311
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• 2014

According to fast change of the environment, the structured study of the ecosystem by analyzing the plant leaves are needed. Expecially, the methodology that searches and classifies the leaves from captured from the smart device have received numerous concerns in the field of computer science and ecology. In this paper, we propose a plant leaf classification technique using shape descriptor by combining Scale Invarinat Feature Transform (SIFT) and Histogram of Oriented Gradient (HOG) from the image segmented from the background via Graphcut algorithm. The shape descriptor is coded in the field of Locality-constrained Linear Coding to optimize the meaningful features from a high degree of freedom. It is connected to Support Vector Machines (SVM) for efficient classification. The experimental results show that our proposed approach is very efficient to classify the leaves which have similar color, and shape.

• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.7
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• pp.157-165
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• 1989

To recover three dimensional information in Shape from Texture, the distorting effects of projection must be distinguished from properties of the texture on which the distortion acts. In this paper, we show an approximated maximum likelihood estimation method in which we find surface orientation of the visible surface (hemisphere) in gaussian sphere using local analysis of the texture. In addition, assuming that an orthogonal projection and a circle is an image formation system and a texel (texture element) respectively, we drive the surface orientation from the distribution of variation by means of orthogonal projection of a tangent direction which exists regularly in the arc length of a circle. We present the orientation parameters of textured surface with slant and tilt in gradient space, and also the surface normal of the resulted surface orientationas as needle map. This algorithm is applied to geographic contour (artificially generated chejudo) and synthetic texture.

• Journal of Broadcast Engineering
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• v.25 no.3
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• pp.338-345
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• 2020

Versatile Video Coding (VVC), which has been developing as a next generation video coding standard, has adopted various techniques to achieve more than twice the compression performance of HEVC (High Efficiency Video Coding). The recently released VVC Test Model (VTM) shows 38% Bjontegaard Delta bitrate (BD-rate) improvement and 9x/1.6x encoding/decoding complexity over HEVC. In order to reduce such increased complexity, various fast algorithms have been proposed. In this paper, gradient-based methods of fast intra mode decision and block splitting are presented. Experimental results show that, compared to VTM6.0, the proposed method gives up to 65% encoding time reduction with 3.54% BD-rate loss in All-Intra (AI) configuration.

• Journal of the Korea Society of Computer and Information
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• v.17 no.7
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• pp.45-52
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• 2012

In this paper, an crowd density in elevator estimation method based on multi-class Adaboost classifier is proposed. The SOM (Self-Organizing Map) based conventional methods have shown insufficient performance in practical scenarios and have weakness for low reproducibility. The proposed method estimates the crowd density using multi-class Adaboost classifier with texture features, namely, GLDM(Grey-Level Dependency Matrix) or GGDM(Grey-Gradient Dependency Matrix). In order to classify into multi-label, weak classifier which have better performance is generated by modifying a weight update equation of general Adaboost algorithm. The crowd density is classified into four categories depending on the number of persons in the crowd, which can be 0 person, 1-2 people, 3-4 people, and 5 or more people. The experimental results under indoor environment show the proposed method improves detection rate by about 20% compared to that of the conventional method.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.6 no.4
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• pp.1-9
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• 1986

In this paper research results of developing a method of selecting design variables of an optimization problem from a finite set of pre-specified numbers, which can be utilized for the structural optimization with standardized structural members, is presented. The method first finds a continuous optimum under the assumption that design variables can be varied continuously. Then a pseudo-optimum is determined by selecting numbers from the set that are near to the continuous optimum and do not violate constraints. The pseudo-optimum is further improved to obtain the final discrete optimum from the set which minimizes cost function of the problem. In this research, the method is combined with the gradient projection optimization algorithm. The method is applied to several minimum weight truss optimization problems with constraints on the stresses, displacements, and design variables. As the results, it is found that the method can be efficiently applied to various optimization problems of which design variables must be chosen from a standard.

• Composites Research
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• v.12 no.1
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• pp.1-10
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• 1999

In order to predict the dynamic behavior of composite laminated plate accurately, the parametric identification is performed using its mechanical properties- $E_1,\;E_2,\;V_{12},\;G_{12}$ as design parameters. After natural frequencies are measured through simple vibration test, the objective function consists of the sum of errors between experimental and numerical frequencies of a structure. As optimization algorithm, conjugate gradient method is used to minimize the objective function. Sensitivity Analysis is performed to update design parameters during this process and can explain the result of parametric identification. In order to check the propriety of result, mode shapes are compared before and after identification. The improved prediction of natural frequencies of composite laminated plate is obtained with updated properties. For the application of result, updated properties is applied to the composite laminated plate that has different stacking sequence.

• The Korean Journal of Applied Statistics
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• v.30 no.6
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• pp.1015-1025
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• 2017

Bayesian personalized ranking (BPR) is a state-of-the-art recommendation system techniques for implicit feedback data. Unfortunately, there might be a loss of information because the BPR model considers only the binary transformation of implicit feedback that is non-binary data in most cases. We propose a modified BPR method using a level of confidence based on the size or strength of implicit feedback to overcome this limitation. The proposed method is useful because it still has a structure of interpretable models for underlying personalized ranking i.e., personal pairwise preferences as in the BPR and that it is capable to reflect a numerical size or the strength of implicit feedback. We propose a computation algorithm based on stochastic gradient descent for the numerical implementation of our proposal. Furthermore, we also show the usefulness of our proposed method compared to ordinary BPR via an analysis of steam video games data.

• Journal of Korea Water Resources Association
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• v.31 no.1
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• pp.45-57
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• 1998

The purpose of this paper is to develop a neural network model in order to forecast flood inflow into the reservoir that has the nature of uncertainty and nonlinearity. The model has the features of multi-layered structure and parallel multi-connections. To develop the model. backpropagation learning algorithm was used with the Momentum and Levenberg-Marquardt techniques. The former technique uses gradient descent method and the later uses gradient descent and Gauss-Newton method respectively to solve the problems of local minima and for the speed of convergency. Used data for learning are continuous fixed real values of input as well as output to emulate the real physical aspects. after learning process. a reservoir inflows forecasting model at flood period was constructed. The data for learning were used to calibrate the developed model and the results were very satisfactory. applicability of the model to the Chungju Mlultipurpose Reservoir proved the availability of the developed model.