• Journal of the Ergonomics Society of Korea
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• v.12 no.1
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• pp.57-73
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• 1993

In this paper, methods of human figure representation in computer graphics are described. Many applications of the human figure representation are found in areas including industry, advertisement, and cartoon production and further research for the methods that show the human figure more realistically is ex- pected. Two analytic methods for human model, kinematics and dynamics, are ex- plained and the characteristics of the man-machine interface systems that include human figure representation are presented. Various techniques of the human figure representation based on kinematics or(and) dynamics are discussed and representation methods of human body segments such as hand, face, spine are introduced in this paper.

• Korean Journal of Remote Sensing
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• v.21 no.5
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• pp.393-404
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• 2005

As multi-source spatial data fusion mainly deal with various types of spatial data which are specific representations of real world with unequal reliability and incomplete knowledge, proper data representation and uncertainty analysis become more important. In relation to this problem, this paper presents and applies an advanced data representation methodology for different types of spatial data such as categorical and continuous data. To account for the uncertainties of both categorical data and continuous data, fuzzy boundary representation and smoothed kernel density estimation within a fuzzy logic framework are adopted, respectively. To investigate the effects of those data representation on final fusion results, a case study for landslide hazard mapping was carried out on multi-source spatial data sets from Jangheung, Korea. The case study results obtained from the proposed schemes were compared with the results obtained by traditional crisp boundary representation and categorized continuous data representation methods. From the case study results, the proposed scheme showed improved prediction rates than traditional methods and different representation setting resulted in the variation of prediction rates.

• Journal of Korea Game Society
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• v.6 no.4
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• pp.39-45
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• 2006

In game developments, the design results are often modified not only in the design phase but also in the implementation and test phases. The results of game design are consisted of the results of game rule design and the results of game contend design. The results of game rule design should be correctly understood to all the participants, be efficiently managed by the given configuration controls, and be accurately verified. In this study, we carry out a survey of representation methods of game rules in game design. We have the comparison analysis of the written representation, the UML representation, the Petri net representation, and script-language representation methods about the suitability of the representation method for game rule designs. The comparison analysis is about the representation scope, the visual representation, the automated verification, and the configuration management. The analysis results show that the UML representation is the best method but it needs more convenient automated verification method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.9
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• pp.3745-3761
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• 2020

Low-rank representation methods already achieve many applications in the image reconstruction. However, for high-gradient image patches with rich texture details and strong edge information, it is difficult to find sufficient similar patches. Existing low-rank representation methods usually destroy image critical details and fail to preserve edge structure. In order to promote the performance, a new representation-based image super-resolution reconstruction method is proposed, which combines gradient domain guided image filter with the structure-constrained low-rank representation so as to enhance image details as well as reveal the intrinsic structure of an input image. Firstly, we extract the gradient domain guided filter of each atom in high resolution dictionary in order to acquire high-frequency prior information. Secondly, this prior information is taken as a structure constraint and introduced into the low-rank representation framework to develop a new model so as to maintain the edges of reconstructed image. Thirdly, the approximate optimal solution of the model is solved through alternating direction method of multipliers. After that, experiments are performed and results show that the proposed algorithm has higher performances than conventional state-of-the-art algorithms in both quantitative and qualitative aspects.

• Journal of Korean Elementary Science Education
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• v.39 no.2
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• pp.204-218
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• 2020

This study aims to investigate the representation patterns used by elementary teachers and their meaning-making process in electromagnetic experiments. In particular, we analyzed the representations depending on three levels of their abstractness: enactive representation (action based), iconic representation (image based) and symbolic representation (language based). For this, four experiment activities of two teachers were analyzed and the results are as follows. First, as an overall pattern of representation, an experiment subject is presented as the form of symbolic representation and the related concepts, experimental materials and methods are embodied through iconic representation. Then, through enactive representation, the actual experiments are implemented. The experimental results are primarily recorded through iconic representations and abstracted into symbolic representations to draw conclusions. The different levels of representations complement each other to expand their meanings, however, sometimes they also make inconsistent meanings among different levels. Based on these results, educational implications were discussed in terms of supporting and improving electromagnetic experiment activities.

• Journal of KIISE
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• v.45 no.3
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• pp.211-223
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• 2018

In this paper, we propose a robot-centered direction relation representation and the relevant reasoning methods for indoor service robots. Many conventional works on qualitative spatial reasoning, when deciding the relative direction relation of the target object, are based on the use of position information only. These reasoning methods may infer an incorrect direction relation of the target object relative to the robot, since they do not take into consideration the heading direction of the robot itself as the base object. In this paper, we present a robot-centered direction relation representation and the reasoning methods. When deciding the relative directional relationship of target objects based on the robot in an indoor environment, the proposed methods make use of the orientation information as well as the position information of the robot. The robot-centered reasoning methods are implemented by extending the existing cone-based, matrix-based, and hybrid methods which utilized only the position information of two objects. In various experiments with both the physical Turtlebot and the simulated one, the proposed representation and reasoning methods displayed their high performance and applicability.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.6
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• pp.2709-2729
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• 2016

Representation based classification, kernel method and sparse representation have received much attention in the field of face recognition. In this paper, we proposed an improved kernel principal component analysis method based on sparse representation to improve the accuracy and robustness for face recognition. First, the distances between the test sample and all training samples in kernel space are estimated based on collaborative representation. Second, S training samples with the smallest distances are selected, and Kernel Principal Component Analysis (KPCA) is used to extract the features that are exploited for classification. The proposed method implements the sparse representation under ℓ₂ regularization and performs feature extraction twice to improve the robustness. Also, we investigate the relationship between the accuracy and the sparseness coefficient, the relationship between the accuracy and the dimensionality respectively. The comparative experiments are conducted on the ORL, the GT and the UMIST face database. The experimental results show that the proposed method is more effective and robust than several state-of-the-art methods including Sparse Representation based Classification (SRC), Collaborative Representation based Classification (CRC), KCRC and Two Phase Test samples Sparse Representation (TPTSR).

• Journal of Educational Research in Mathematics
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• v.14 no.1
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• pp.39-69
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• 2004

This paper investigates the teaching and learning of Linear function relating functional contexts and suggests the improved methods of representation-shift through this analysis. The methods emphasize the link between students' preacquired knowledge of mathematical representations and the way of using those. This methods are explanatory teaching, teaching and teaming based on modelling perspectives or tasks (interpretation, prediction, translation and scaling). We categorize the 8th grade middle school students' errors on the linear function relating real contexts and make a comparative study of the error-remedial effects and the teaching and teaming methods. We present the results of a study in which representation-shift methods based on modelling perspectives and tasks are more effective in terms of flexible connection of representations and error remediation. Also, We describe how students used modelling perspective-taking to explain and justify their conceptual models, to assess the quality of their models and to make connection to other mathematical representation during the problem solving focusing on the students' self-diagnosis.

• Education of Primary School Mathematics
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• v.8 no.1
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• pp.23-32
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• 2004

It is not too much to say that problem solving is still the focus of school mathematics though the trend of mathematics education for ten year from the one of 1980 is problem solving and the one of mathematics education for ten year from the one of 1990 is standards and constructivism. There are so many crucial clues or methods in good problem solving but I think that one of them is a representation. So, the purpose of this study is to investigate what is the meaning of representation in general and why representation is so important in elementary mathematics learning, Moreover, I have analyzed the gifted children's thinking of representation which is appeared in the previous internet home task of 40 gifted children who are selected through the examination of 1st, 2nd with paper and pencil and 3rd with practical skill and interview and finally I have presented some examples of children's representation how they use representation to model, investigate and understand special concept more easily in elementary school mathematics class.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.283-286
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• 2001

Based on a fuzzy system representation of gray scale images, we derive an edge detection algorithm whose convolution kernel is different from the known kernels such as those of Roberts', Prewitt's or Sobel's gradient. Our fuzzy system representation is an exact representation of the bicubic spline function which represents the gray scale image approximately. Hence the fuzzy system is a continuous function and it provides a natural way to define the gradient and the Laplacian operator. We show that the gradient at grid points can be evaluated by taking the convolution of the image with a 3 3 kernel. We also show that our gradient coupled with the approximate value of the continuous function generates an edge detection method which creates edge images clearer than those by other methods. A few examples of applying our methods are included.