• Asia pacific journal of information systems
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• v.29 no.4
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• pp.717-730
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• 2019

The era of digital transformation (or the fourth industrial revolution) has been triggered by the rapid development of software (SW) technologies. In this era, several studies suspected rapid changes in job structures occurring around the world. Thus, there is a growing need for acquiring the skill sets required for the future. However, there are no specific studies on how existing jobs are changing. To cope with this ambiguity of job changes, this paper aims to investigate how the current job structure is changing in response to digital transformation. To identify the dynamic nature of job change over time, we conducted an analysis based on job posting data. As a result, nine job occupations and fifteen jobs were found.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.6
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• pp.305-310
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• 2016

In these days, XML is a de facto standard language for representing and exchanging data. In order to define the conceptual model of the XML, we need to define the representation rules expressed in the diagram and propose the transformation algorithm that converts the diagram into a logical model of XML. This paper proposes a transformation methodology for generating a logical model from the conceptual model of the XML. We use CMXML as a conceptual model and generate XML schema definition as a logical model. For this, we define transformation rules and data structures for XML schema, and propose a transformation algorithm.

• The Korean Journal of Applied Statistics
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• v.14 no.1
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• pp.137-148
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• 2001

Compositional data found in many practical applications consist of non-negative vectors of proportions with the constraint which the sum of the elements of each vector is unity. It is well-known that the statistical analysis of compositional data suffers from the unit-sum constraint. Moreover, the non-linear pattern frequently displayed by the data does not facilitate the application of the linear multivariate techniques such as principal component analysis. In this paper we develop new type of principal component analysis for compositional data using Box-Cox contrast transformation. Numerical illustrations are provided for comparative purpose.

• Journal of Astronomy and Space Sciences
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• v.38 no.3
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• pp.185-192
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• 2021

This technical paper deals the practical transformation algorithms between several lunar reference frames which will be used for Korea pathfinder lunar orbiter (KPLO) flight operation. Despite of various lunar reference frame definitions already exist, use of a common transformation algorithm while establishing lunar reference frame is very important for all members related to KPLO mission. This is because use of slight different parameters during frame transformation may result significant misleading while reprocessing data based on KPLO flight dynamics. Therefore, details of practical transformation algorithms for the KPLO mission specific lunar reference frames is presented with step by step implementation procedures. Examples of transformation results are also presented to support KPLO flight dynamics data user community which is expected to give practical guidelines while post processing the data as their needs. With this technical paper, common understandings of reference frames that will be used throughout not only the KPLO flight operation but also science data reprocessing can be established. It is expected to eliminate, or at least minimize, unnecessary confusion among all of the KPLO mission members including: Korea Aerospace Research Institute (KARI), National Aeronautics and Space Administration (NASA) as well as other organizations participating in KPLO payload development and operation, or further lunar science community world-wide who are interested in KPLO science data post processing.

• Journal of the Korean Association of Geographic Information Studies
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• v.7 no.4
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• pp.24-33
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• 2004

Advances in remote sensing technologies are resulting in the rapid increase of the number of spectral channels, and thus, growing data volumes. This creates a need for developing faster techniques for processing such data. One application in which such fast processing is needed is the dimension reduction of the multispectral data. Principal component transformation is perhaps the mostpopular dimension reduction technique for multispectral data. In this paper, we discussed the processing procedures of principal component transformation. And we presented and discussed the results of the principal component transformation of the multispectral data. Moreover principal components image data are classified by the Maximum Likelihood method and Multilayer Perceptron method. In addition, the performances of two classification methods and data reduction effects are evaluated and analyzed based on the experimental results.

• Korean Journal of Crystallography
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• v.7 no.2
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• pp.191-195
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• 1996

Geometrical calculations are often more easily made in an orthonormal than in a crystallographic frame. In this paper a rational for the transformation from a triclinic cell data and atomic coordinates to orthonormal ones has been shown and a computer program has been made for the transformation.

• Communications for Statistical Applications and Methods
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• v.28 no.1
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• pp.89-97
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• 2021

Due to boundedness and sum constraint, compositional data are often transformed by logratio transformation and their transformed data are put into traditional binary classification or discriminant analysis. However, it may be problematic to directly apply traditional multivariate approaches to the transformed data because class distributions are not Gaussian and Bayes decision boundary are not polynomial on the transformed space. In this study, we propose to use flexible classification approaches to transformed data for compositional data classification. Empirical studies using synthetic and real examples demonstrate that flexible approaches outperform traditional multivariate classification or discriminant analysis.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1244-1248
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• 2005

In this paper, we propose fuzzy c-means (FCM) to solve recognition errors in invariant range image, multi-pose face recognition. Scale, center and pose error problems were solved using geometric transformation. Range image face data was digitized into range image data by using the laser range finder that does not depend on the ambient light source. Then, the digitized range image face data is used as a model to generate multi-pose data. Each pose data size was reduced by linear reduction into the database. The reduced range image face data was transformed to the gradient face model for facial feature image extraction and also for matching using the fuzzy membership adjusted by fuzzy c-means. The proposed method was tested using facial range images from 40 people with normal facial expressions. The output of the detection and recognition system has to be accurate to about 93 percent. Simultaneously, the system must be robust enough to overcome typical image-acquisition problems such as noise, vertical rotated face and range resolution.

• The Mathematical Education
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• v.40 no.1
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• pp.93-102
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• 2001

The world we live in is called the age of information. Thus communication and computers are doing the central role in it. When one studies the mathematical problem, the use of tools such as computers, calculators and technology is available for all students, and then students are actively engaged in reasoning, communicating, problem solving, and making connections with mathematics, between mathematics and other disciplines. The use of technology extends to include computer algebra systems, spreadsheets, dynamic geometry software and the Internet and help active learning of students by analyzing data and realizing mathematical models visually. In this paper, we explain concepts of transformation, linear transformation, congruence transformation and homothety, and introduce interesting, meaningful and visual models for teaching of a plane transformation geomeoy which are obtained by using Mathematica. Moreover, this study will show how to visualize linear transformation for student's better understanding in teaching a plane transformation geometry in classroom. New development of these kinds of teaching-learning methods can simulate student's curiosity about mathematics and their interest. Therefore these models will give teachers the active teaching and also give students the successful loaming for obtaining the concept of linear transformation.

• The KIPS Transactions:PartA
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• v.9A no.1
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• pp.37-44
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• 2002

Most researches of transforming sequential programs into parallel programs have been based on the loop structure transformation method. However, most programs have implicit interprocedure parallelism. This paper suggests a way of extracting parallelism from the loops with procedure calls using the data dependency elimination method. Most parallelization of the loop with procedure calls have been conducted for extracting parallelism from the uniform code. In this paper, we propose interprocedural transformation, which can be apply to both uniform and nonuniform code. We show the examples of uniform, nonuniform, and complex code parallelization. We then evaluated the performance of the various transformation methods using the CRAY-T3E system. The comparison results show that the proposed algorithm out-performs other conventional methods.