• Korean Journal of Geomatics
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• v.4 no.1
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• pp.31-37
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• 2004

The aim of this research is comparing the existing approximation models (e.g. Affine Transformation and Direct Linear Transformation) with Rational Function Model as a substitute of rigorous sensor model of linear array scanner, especially push-broom sensor. To do so, this research investigates the mathematical model of each approximation method. This is followed by the assessments of accuracy of transformation from object space to image space by using simulated data generated by collinearity equations which incorporate or depict the physical aspects of linear array sensor.

• Journal of the Korea Society of Computer and Information
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• v.13 no.5
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• pp.53-67
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• 2008

At present, for reuse of similar domains between feature model and class model. researches of transformation at the model level and of transformation using ontology between two models are being made. but consistent transformation through metamodel is not made. And the factors of modeling transformation targets are not sufficient, and especially, automatic transformation algorithm and supporting tools are not provided so reuse of domains between models is not activated. This paper proposes a method of transformation from feature model to class model using ontology on the metamodel. For this, it re-establishes the metamodel of feature model, class model, and ontology, and it defines the properties of modelling factors for each metamodel. Based on the properties, it defines the profiles of transformation rules between feature mndel and ontology, and between ontology and class model, using set theory and propositional calculus. For automation of the transformation, it creates transformation algorithm and supporting tools. Using the proposed transformation rules and tools, real application is made through Electronic Approval System. Through this, it is possible to transform from the existing constructed feature model to the class model and to use it again for a different development method. Especially, it is Possible to remove ambiguity of semantic transformation using ontology, and automation of transformation maintains consistence between models.

• Korean Journal of Breeding Science
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• v.41 no.3
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• pp.252-260
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• 2009

Rice is the most important cereal crop not only in supplying the basic staple food for more than half of the world's population but also as a model plant for functional genomic studies of monocotyledons. Although rice transformation method using A. tumefaciens has already been widely used to generate transgenic plants, the transformation rate is still low in most Korean elite cultivars. We made several modifications of the standard protocol especially in the co-cultivation step to improve the efficiency of the rice transformation. The co-culture medium was modified by the addition of three antioxidant compounds (10.5 mg/L L-cysteine, 1 mM sodium thiosulfate, 1 mM dithiothreitol) and of Agrobacterium growth-inhibiting agent (5 mg/L silver nitrate). Co-cultivation temperature ($23.5^{\circ}C$ for 1 day, $26.5^{\circ}C$ for 6 days) and duration (7 days) were also changed. The plasmid of pMJC-GB-GUS carrying the GUS reporter gene and the bar gene as the selectable marker was used to evaluate the efficiency of the transformation. After co-cultivation, a high level of GUS gene expression was observed in calli treated with the modified method. It is likely that those newly added compounds helped to minimize the damage due to oxidative bursts during plant cell-Agrobacterium interaction and to prevent necrosis of rice cells. And the transformation rate under the modified method was also remarkably increased approximately 8-fold in Heungnambyeo and 2-fold in Ilmibyeo as compared to the corresponding standard method. Furthermore, we could produce the transgenic plants stably from Ilpumbyeo which is a high-quality rice but its transformation rate is extremely low. Transformation and the copy number of transgenes were confirmed by PCR, bar strip and Southern blot analysis. The improved method would attribute reducing the effort and the time required to produce a large number of transgenic rice plants.

• Journal of KIISE
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• v.42 no.10
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• pp.1185-1194
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• 2015

A spherical panoramic image does not conform to the pin-hole camera model, and, hence, it is not possible to utilize previous techniques consisting of plane-to-plane transformation. In this paper, we propose a new method to estimate the planar geometric transformation between the planar image and a spherical panoramic image. Our proposed method estimates the transformation parameters for latitude, longitude, rotation and scaling factors when the matching pairs between a spherical panoramic image and a planar image are given. A planar image is projected into a spherical panoramic image through two steps of nonlinear coordinate transformations, which makes it difficult to compute the geometric transformation. The advantage of using our method is that we can uncover each of the implicit factors as well as the overall transformation. The experiment results show that our proposed method can achieve estimation errors of around 1% and is not affected by deformation factors, such as the latitude and rotation.

• Journal of the Korean Society of Radiology
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• v.13 no.4
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• pp.509-516
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• 2019

In the linear transformation method, the original image is divided into a plurality of range blocks, and a partial transform system for finding an optimal domain block existing in the image for each range block is used to adjust the performance of the compression ratio and the picture quality, The nonlinear transformation method uses only the rotation transformation among eight shuffle transforms. Since the search is performed only in the limited domain block, the coding time is faster than the linear transformation method of searching the domain block for any block in the image, Since the optimal domain block for the range block can not be selected in the image, the performance may be lower than other methods. Therefore, the nonlinear transformation method improves the performance by increasing the approximation degree of the brightness coefficient conversion instead of selecting the optimal domain block, The smaller the size of the block, the higher the PSNR value, The higher the compression ratio is increased groups were quadtree block divided to encode the image at best.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.2
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• pp.18-27
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• 2019

The process control methods based on the statistical analysis apply the analysis method or mathematical model under the assumption that the process characteristic is normally distributed. However, the distribution of data collected by the automatic measurement system in real time is often not followed by normal distribution. As the statistical analysis tools, the process capability index (PCI) has been used a lot as a measure of process capability analysis in the production site. However, PCI has been usually used without checking the normality test for the process data. Even though the normality assumption is violated, if the analysis method under the assumption of the normal distribution is performed, this will be an incorrect result and take a wrong action. When the normality assumption is violated, we can transform the non-normal data into the normal data by using an appropriate normal transformation method. There are various methods of the normal transformation. In this paper, we consider the Box-Cox transformation among them. Hence, the purpose of the study is to expand the analysis method for the multivariate process capability index using Box-Cox transformation. This study proposes the multivariate process capability index to be able to use according to both methodologies whether data is normally distributed or not. Through the computational examples, we compare and discuss the multivariate process capability index between before and after Box-Cox transformation when the process data is not normally distributed.

• The Transactions of the Korea Information Processing Society
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• v.6 no.11
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• pp.3140-3149
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• 1999

Skew correction for document images can be using a rotational transformation of pixel coordinates. In this paper we propose a method which corrects the document skew, by an amount of $\theta$ degrees, using block information, where the block is defined as a rectangular area containing adjacent black pixels. Processing speed of the proposed method is faster than that of the method using pixel transformation, since the number of floating-point operations can be reduced significantly. In the proposed method, we rotate only the four corner points of each block, and then identify the pixels inside the block. Two methods for inside pixel identification are proposed; the first method finds two points intersecting the boundary of the rotated block in each row, and determines the pixels between the two intersection points as the inside pixel. The second method finds boundary points based on Bresenham's line drawing algorithm, using fixed-point operation, and fills the region surrounded by these boundaries as black pixels. We have measured the performance of the proposed method by experimenting it with 2,016 images of various English and Korean documents. We have also proven the superiority of our algorithm through performance comparison with respect to existing methods based on pixel transformation.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2393-2398
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• 2003

In this paper, we proposes a new fuzzy inference system for modeling nonlinear systems given input and output data. In the suggested fuzzy inference system, the number of fuzzy rules and parameter values of membership functions are automatically decided by using the kernel-based method. The kernel-based method individually performs linear transformation and kernel mapping. Linear transformation projects input space into linearly transformed input space. Kernel mapping projects linearly transformed input space into high dimensional feature space. The structure of the proposed fuzzy inference system is equal to a Takagi-Sugeno fuzzy model whose input variables are weighted linear combinations of input variables. In addition, the number of fuzzy rules can be reduced under the condition of optimizing a given criterion by adjusting linear transformation matrix and parameter values of kernel functions using the gradient descent method. Once a structure is selected, coefficients in consequent part are determined by the least square method. Simulated result illustrates the effectiveness of the proposed technique.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.10a
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• pp.323-335
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• 1999

In this paper, we propose a new hybrid model of artificial neural networks(ANNs) and genetic algorithm (GA) to optimal feature transformation and feature weighting. Previous research proposed several variants of hybrid ANNs and GA models including feature weighting, feature subset selection and network structure optimization. Among the vast majority of these studies, however, ANNs did not learn the patterns of data well, because they employed GA for simple use. In this study, we incorporate GA in a simultaneous manner to improve the learning and generalization ability of ANNs. In this study, GA plays role to optimize feature weighting and feature transformation simultaneously. Globally optimized feature weighting overcome the well-known limitations of gradient descent algorithm and globally optimized feature transformation also reduce the dimensionality of the feature space and eliminate irrelevant factors in modeling ANNs. By this procedure, we can improve the performance and enhance the generalisability of ANNs.

• ETRI Journal
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• v.34 no.6
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• pp.847-857
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• 2012

Intrusion detection systems (IDSs) have an important effect on system defense and security. Recently, most IDS methods have used transformed features, selected features, or original features. Both feature transformation and feature selection have their advantages. Neighborhood component analysis feature transformation and genetic feature selection (NCAGAFS) is proposed in this research. NCAGAFS is based on soft computing and data mining and uses the advantages of both transformation and selection. This method transforms features via neighborhood component analysis and chooses the best features with a classifier based on a genetic feature selection method. This novel approach is verified using the KDD Cup99 dataset, demonstrating higher performances than other well-known methods under various classifiers have demonstrated.