• Structural Engineering and Mechanics
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• v.33 no.4
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• pp.429-446
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• 2009

Three-scale homogenization procedure is proposed in this paper to provide estimates of the effective thermal conductivities of porous carbon-carbon textile composites. On each scale - the level of fiber tow (micro-scale), the level of yarns (meso-scale) and the level of laminate (macro-scale) - a two step homogenization procedure based on the Mori-Tanaka averaging scheme is adopted. This involves evaluation of the effective properties first in the absence of pores. In the next step, an ellipsoidal pore is introduced into a new, generally orthotropic, matrix to make provision for the presence of crimp voids and transverse and delamination cracks resulting from the thermal transformation of a polymeric precursor into the carbon matrix. Other sources of imperfections also attributed to the manufacturing processes, including non-uniform texture of the reinforcements, are taken into consideration through the histograms of inclination angles measured along the fiber tow path together with a particular shape of the equivalent ellipsoidal inclusion proposed already in Sko ek (1998). The analysis shows that a reasonable agreement of the numerical predictions with experimental measurements can be achieved.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.4
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• pp.443-450
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• 2007

In this paper, we propose a novel threshold selection method based on statistical information on gray-levels of given images and interval-valued fuzzy sets. In the proposed threshold selection method, the interval-valued fuzzy set is used to represent more definitely the relationship between a pixel and its belonging region, that is, the object and the background. Also the statistical information on gray-level is used to determine the rules and partitions of interval-valued fuzzy sets. To show the validity of the proposed method, we compared the performance of the proposed with those of conventional methods such as Otsu's method, Huang and Wang's method applied to 5 test images with various types of histograms.

• Journal of Advanced Research in Ocean Engineering
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• v.2 no.4
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• pp.202-208
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• 2016

In this paper, we propose novel seabed sediment classification algorithm using feature obtained by continuous wavelet transform (CWT). Contrast to previous researches using direct reflection coefficient of seabed which is function of frequency and is highly influenced by sediment types, we develop an algorithm using both direct reflection signal and backscattering signal. In order to obtain feature vector, we employ CWT of the signal and obtain histograms extracted from local binary patterns of the scalogram. The proposed algorithm also adopts principal component analysis (PCA) to reduce dimension of the feature vector so that it requires low computational cost to classify seabed sediment. For training and classification, we adopts K-means clustering algorithm which can be done with low computational cost and does not require prior information of the sediment. To verify the proposed algorithm, we obtain field data measured at near Jeju island and show that the proposed classification algorithm has reliable discrimination performance by comparing the classification results with actual physical properties of the sediments.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.10
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• pp.497-502
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• 2016

Shape Contexts Recognition(SCR) is a technology recognizing shapes such as figures and objects, greatly supporting technologies such as character recognition, motion recognition, facial recognition, and situational recognition. However, generally SCR makes histograms for all contours and maps the extracted contours one to one to compare Shape A and B, which leads to slow progress speed. Thus, this paper has made simple yet more effective algorithm with optimized contour, finding the outlines according to shape figures and using the improved Douglas-Peucker algorithm and Harris corner detector. With this improved method, progress speed is recognized as faster.

• Journal of Satellite, Information and Communications
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• v.9 no.4
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• pp.32-37
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• 2014

The panorama is a good alternative to overcome narrow FOV under study in robot vision, stereo camera and panorama image registration and modeling. The panorama can materialize view with angles wider than human view and provide realistic space which make feeling of being on the scene based on realism. If we use all correspondence, it is too difficult to find strong features and correspondences and assume accurate homography matrix in geographic changes in images as load of calculation increases. Accordingly, we used SURF algorithm to estimate overlapping areas with high similarity by comparing and analyzing the input images' histograms and to detect features. And we solved the problem of input order so we can make panorama by input images without order.

• Journal of Korean Institute of Industrial Engineers
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• v.26 no.4
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• pp.363-375
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• 2000

In this paper we propose a content-based image retrieval method that can search large image databases efficiently by color, texture, and shape content. Quantized RGB histograms and the dominant triple (hue, saturation, and value), which are extracted from quantized HSV joint histogram in the local image region, are used for representing global/local color information in the image. Entropy and maximum entry from co-occurrence matrices are used for texture information and edge angle histogram is used for representing shape information. Relevance feedback approach, which has coupled proposed features, is used for obtaining better retrieval accuracy. Simulation results illustrate the above method provides 77.5 percent precision rate without relevance feedback and increased precision rate using relevance feedback for overall queries. We also present a new indexing method that supports fast retrieval in large image databases. Tree structures constructed by k-means algorithm, along with the idea of triangle inequality, eliminate candidate images for similarity calculation between query image and each database image. We find that the proposed method reduces calculation up to average 92.9 percent of the images from direct comparison.

• Journal of the Institute of Convergence Signal Processing
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• v.10 no.2
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• pp.132-135
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• 2009

The video similarity measure is very important factor to index and to retrieve for video data. In this paper, we propose the luminance projection model to measure the video similarity efficiently. Most algorithms for video indexing have been commonly used histograms, edges, or motion features, whereas in this paper, the proposed algorithm is employed an efficient measure using the luminance projection. To index effectively the video sequences and to decrease the computational complexity, we calculate video similarity using the key frames extracted by the cumulative measure, and compare the set of key frames using the modified Hausdorff distance. Experimental results show that the proposed luminance projection model yields the remarkable accuracy and performance than the conventional algorithm.

• Water for future
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• v.22 no.4
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• pp.441-451
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• 1989

Using the wave data by typhoons LEE, VERA, THELMA which gave great damages in the Korean penisula, the significant waves based on zero-up & down crossing and Tucker-Draper method are compared with those from the wave energy spectrum. And the histograms of individual waves obtained from zero-up crossing method are presented and compared with the Rayleigh, Weibull, Gluhovski, Ibrageemov and Goda distributions, and also the Chi-square goodness of fit test is applied to each theoretical distributions. It is shown that the significant wave heights by zero-up crossing method are very well agreed to those by energy spectrum method. The wave heights are found to well follow the Rayleigh and Goda distributions by the Chi-square test.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.729-734
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• 2012

Clustering analysis has been widely used in numerous applications like pattern recognition, data analysis, intrusion detection, image processing, bioinformatics and so on. Much of previous work has been based on the numeric data only. However, symbolic data analysis has emerged to deal with variables that can have intervals, histograms, and even functions as values. In this paper, we propose a non symmetric proximity based clustering approach for symbolic objects. A method for clustering symbolic patterns based on the average similarity value(ASV) is explored. The results of the proposed clustering method differ from those of the existing methods and the results are very encouraging.

• Journal of Radiation Industry
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• v.12 no.4
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• pp.297-302
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• 2018

The wedge filter has two movements, fixed and dynamic. In this study, the depth dose distribution was analyzed to determine the stability of the dose distribution and dose volume histograms obtained by evaluating the usability of the critical normal tissue dose around the tumor dose. The depth dose was analyzed from the dose distribution from a Linac (6 MV and 10 MV irradiation field of energy $20{\times}20cm^2$, wedge filter with a SSD of 100 cm and $15^{\circ}$, $30^{\circ}$, $45^{\circ}$ Y1-in (Left -7 cm), Y2-out(Right +7 cm). To analyze the fluctuations of the depth dose, a fixed wedge and dynamic wedge toe portion was examined according to the energy and angle because the size of the fluctuations was included in the error bound and did not show significant differences. The neck, breast, and pelvic dosimetry in tumor tissue are measured more commonly with a dynamic wedge than a fixed wedge presumably due to the error range. On the other hand, dosimetry of the surrounding normal tissue is more common using a fixed wedge than with a dynamic wedge.