• Applied Chemistry for Engineering
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• v.20 no.6
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• pp.681-684
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• 2009

The turbidity of flow type samples has a nonlinear relation to brightness of laser scattered light, but the shape of images in laser scattered light is different from each turbidity samples. The turbidity measurement will be easy if it uses a pattern of images in laser scattered light. But the excessive analysis load comes from the turbidity measured by red, green, blue intensity (intensity) of all pixels of images in laser scattered light. Therefore the images in laser scattered light were divided by appropriate block to decrease excessive analysis load. The shape of divided images in laser scattered light was different from each turbidity sample. The real turbidity has a linear relation to turbidity measured by the artificial neural network learned with the intensity of divided images in laser scattered light and turbidity.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.5
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• pp.744-752
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• 2012

In this study, the Polynomial-based Radial Basis Function Neural Networks is proposed as an one of the recognition part of overall face recognition system that consists of two parts such as the preprocessing part and recognition part. The design methodology and procedure of the proposed pRBFNNs are presented to obtain the solution to high-dimensional pattern recognition problems. In data preprocessing part, Principal Component Analysis(PCA) which is generally used in face recognition, which is useful to express some classes using reduction, since it is effective to maintain the rate of recognition and to reduce the amount of data at the same time. However, because of there of the whole face image, it can not guarantee the detection rate about the change of viewpoint and whole image. Thus, to compensate for the defects, Linear Discriminant Analysis(LDA) is used to enhance the separation of different classes. In this paper, we combine the PCA&LDA algorithm and design the optimized pRBFNNs for recognition module. The proposed pRBFNNs architecture consists of three functional modules such as the condition part, the conclusion part, and the inference part as fuzzy rules formed in 'If-then' format. In the condition part of fuzzy rules, input space is partitioned with Fuzzy C-Means clustering. In the conclusion part of rules, the connection weight of pRBFNNs is represented as two kinds of polynomials such as constant, and linear. The coefficients of connection weight identified with back-propagation using gradient descent method. The output of the pRBFNNs model is obtained by fuzzy inference method in the inference part of fuzzy rules. The essential design parameters (including learning rate, momentum coefficient and fuzzification coefficient) of the networks are optimized by means of Differential Evolution. The proposed pRBFNNs are applied to face image(ex Yale, AT&T) datasets and then demonstrated from the viewpoint of the output performance and recognition rate.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.1
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• pp.195-201
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• 2014

In this study, face recognition system was designed based on polynomial Radial Basis Function Neural Networks(pRBFNNs) pattern classifier using 2-directional 2-dimensional principal component analysis algorithm. Existing one dimensional PCA leads to the reduction of dimension of image expressed by the multiplication of rows and columns. However $(2D)^2PCA$(2-Directional 2-Dimensional Principal Components Analysis) is conducted to reduce dimension to each row and column of image. and then the proposed intelligent pattern classifier evaluates performance using reduced images. The proposed pRBFNNs consist of three functional modules such as the condition part, the conclusion part, and the inference part. In the condition part of fuzzy rules, input space is partitioned with the aid of fuzzy c-means clustering. In the conclusion part of rules. the connection weight of RBFNNs is represented as the linear type of polynomial. The essential design parameters (including the number of inputs and fuzzification coefficient) of the networks are optimized by means of Differential Evolution. Using Yale and AT&T dataset widely used in face recognition, the recognition rate is obtained and evaluated. Additionally IC&CI Lab dataset is experimented with for performance evaluation.

• Journal of the Korean Data and Information Science Society
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• v.14 no.3
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• pp.535-543
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• 2003

Cluster analysis has been widely used in many applications, such as pattern analysis or recognition, data analysis, image processing, market research on on-line or off-line and so on. Clustering can identify dense and sparse regions among data attributes or object attributes. But it requires many hours to get clusters that we want, because clustering is more primitive, explorative and we make many data an object of cluster analysis. In this paper we propose a new method of clustering using sample based on grid. It is more fast than any traditional clustering method and maintains its accuracy.

• 한국데이터정보과학회:학술대회논문집
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• 2003.05a
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• pp.97-108
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• 2003

Cluster analysis has been widely used in many applications, such that pattern analysis or recognition, data analysis, image processing, market research on on-line or off-line and so on. Clustering can identify dense and sparse regions among data attributes or object attributes. But it requires many hours to get clusters that we want, because of clustering is more primitive, explorative and we make many data an object of cluster analysis. In this paper we propose a new method of clustering using sample based on grid. It is more fast than any traditional clustering method and maintains its accuracy. It reduces running time by using grid-based sample. And other clustering applications can be more effective by using this methods with its original methods.

• KSTLE International Journal
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• v.1 no.1
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• pp.34-42
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• 2000

A method and results of classification of four different metallic wear debris were presented by using their color features. The color image of wear debris was used far the initial data, and the color properties of the debris were specified by HSI color model. Particles were characterized by a set of statistical features derived from the distribution of HSI color model components. The initial feature set was optimized by a principal component analysis, and multidimensional scaling procedure was used fer the definition of a classification plane. It was found that five features, which include mean values of H and S, median S, skewness of distribution of S and I, allow to distinguish copper based alloys, red and dark iron oxides and steel particles. In this work, a method of probabilistic decision-making of class label assignment was proposed, which was based on the analysis of debris-coordinates distribution in the classification plane. The obtained results demonstrated a good availability for the automated wear particle analysis.

• Korean Journal of Remote Sensing
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• v.35 no.6_2
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• pp.1095-1106
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• 2019

Damage of forest fire should be investigated quickly and accurately for recovery, compensation and prevention of secondary disaster. Using remotely sensed data, burn severity is investigated based on the difference of reflectance or spectral indices before and after forest fire. Recently, the use of high resolution satellite and UAV imagery is increasing, but it is not easy to obtain an image before forest fire that cannot be predicted where and when. This study tried to analyze availability of high-resolution images and supervised classifiers on the burn severity classification. Two supervised classifiers were applied to the KOMPSAT-3A image and the UAV multispectral image acquired after the forest fire. The maximum likelihood (MLH) classifier use absolute value of spectral reflectance and the spectral angle mapper (SAM) classifier use pattern of spectra. As a result, in terms of spatial resolution, the classification accuracy of the UAV image was higher than that of the satellite image. However, both images shown very high classification accuracy, which means that they can be used for classification of burn severity. In terms of the classifier, the maximum likelihood method showed higher classification accuracy than the spectral angle mapper because some classes have similar spectral pattern although they have different absolute reflectance. Therefore, burn severity can be classified using the high resolution multispectral images after the fire, but an appropriate classifier should be selected to get high accuracy.

• Journal of the Institute of Convergence Signal Processing
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• v.22 no.2
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• pp.71-78
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• 2021

In order to classify a target image in a cluster of images, the difference in brightness between the object and the background is mainly concerned, which is not easy to classify if the shape of the object is blurred and the sharpness is low. However, there are a few studies attempted to solve these problems, and there is still the problem of not properly distinguishing between wrong pattern and right pattern images when applied to actual data analysis. In this paper, we propose an algorithm that classifies 3D films into sharp and blurry using the width of the pixel values histogram. This algorithm determines the width of the right and wrong images based on the width of the pixel distributions. The larger the width histogram, the sharp the image, while the shorter the width histogram the blurry the image. Experiments show that the proposed algorithm reflects that the characteristics of these histograms allows classification of all wrong images and right images. To determine the reliability and validity of the proposed algorithm, we compare the results with the other obtained from preprocessed 3D films. We then trained the 3D films using few-shot learning algorithm for accurate classification. The experiments verify that the proposed algorithm can perform higher without complicated computations.

• Horticultural Science & Technology
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• v.28 no.4
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• pp.656-663
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• 2010

In order to improve the consumer acceptance of fragrant yellow hybrids of $Phalaenopsis$ ($Phal.$), crosses between yellow hybrid varieties were made and the obtained hybrids were investigated for horticultural characteristics as well as difference in fragrance patterns. Cross combination of $Phal.$ Brother lawrence and $Phal.$ Brother saragold yielded good seedling population of 500 plants. Segregation was noticed in color density, spot and stripe patterns on yellow color base of petal and also in fragrance. Six lines with multi-branch on flower stalk and strong fragrance flower were finally selected. Volatile fragrance components were compared among $Phal.$ Brother lawrence, $Phal.$ Brother saragold and their hybrids by GC/SAW electronic nose. In the derivative pattern of chromatogram and polar derivative pattern of fragrance, similar dominant peaks appeared on retention time 7-9 s and some hybrid lines had two strong peaks on retention time 20-25 s, respectively. Also in polar frequency pattern of fragrance obtained by $VaporPrint^{TM}$ image analysis among parent flowers and hybrids, an identical strong peak near 8 s of retention time was shown. This single fragrance component is considered a key element of fragrance in $Phal.$ Brother lawrence, $Phal.$ Brother saragold and their hybrids. This peak could be used as a marker for the breeding of fragrance in $Phalaenopsis$.

• Proceedings of the Technology Innovation Conference
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• 1997.12a
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• pp.177-196
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• 1997

The engineering industry plays an important role for national competitive, since it has an high impact on other industries. With its importance, the engineering industry development largely depends on its technical manpower ather than capital factor. This study aims at estimating the additional requirement on technical manpower based on the forecasted market size which represents the structure change corresponding to economic growth in related industry. Research scope includes the twelve of fifteen field except three with insufficient historical data and technical manpower above bachelor degree. Specialty, we forecast market size with determinants resulted from historical data analysis on each field. The demand on technical manpower is derived from the forecasted market. We also estimate an additional requirement with the supply analysis. The research results show different patterns over time period. The relative ratio on chemical and construction to total market will steadily grow over short term, while applied, environment, electronic and information will rapidly grow This pattern will be stabilized over mid or long term. The additional requirement on technical manpower represents the similar pattern to market growth. The research result implies manpower policy for having high inflow of technical engineer from educational institute and the related industries through the image improvement.