• The Mathematical Education
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• v.47 no.1
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• pp.49-59
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• 2008

In this paper, we discuss two representations of a curve. One is a static representation as set of points, the other is a dynamic representation using time parameter. And we suggest needs of designing a computer microword where we can represent a curve both statically and dynamically. We also emphasize the importance of translation activity from a static representation to a dynamic representation. For this purpose, we first consider constructionism and 'computers and mathematics education' as a theoretical backgrounds. We focus the curve of a parabola in this paper since this is common in mathematics curriculum and is related to realistic situation such as throwing ball. And we survey the mathematics curriculum about parabola representation. And we introduce JavaMAL microworld that is integrated microworld between LOGO and DGS. In this microworld, we represent a parabola using a dynamic action, and connect this dynamic parabola action to recursive patterns. Finally, we remake a parabola for a realistic situation using this dynamic representation. And we discuss the educational meaning of dynamic representation and its computer microworld.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.10
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• pp.1731-1737
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• 2016

In this paper, a method is proposed to recognize human actions as nonverbal expression; the proposed method is composed of two steps which are action representation and action recognition. First, MHI(Motion History Image) is used in the action representation step. This method includes segmentation based on depth information and generates spatio-temporal templates to describe actions. Second, CNN(Convolution Neural Network) which includes feature extraction and classification is employed in the action recognition step. It extracts convolution feature vectors and then uses a classifier to recognize actions. The recognition performance of the proposed method is demonstrated by comparing other action recognition methods in experimental results.

• Computers and Concrete
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• v.12 no.1
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• pp.19-36
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• 2013

In the finite element analysis of reinforced concrete structures, discrete representation of the steel reinforcing bars is considered advantageous over smeared representation because of the more realistic modelling of their bond-slip behaviour. However, there is up to now limited research on how to simulate the dowel action of discrete reinforcing bars, which is an important component of shear transfer in cracked concrete structures. Herein, a numerical model for the dowel action of discrete reinforcing bars is developed. It features derivation of the dowel stiffness based on the beam-on-elastic-foundation theory and direct assemblage of the dowel stiffness matrix into the stiffness matrices of adjoining concrete elements. The dowel action model is incorporated in a nonlinear finite element program based on secant stiffness formulation and application to deep beams tested by others demonstrates that the incorporation of dowel action can improve the accuracy of the finite element analysis.

• ETRI Journal
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• v.44 no.2
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• pp.327-338
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• 2022

Classification models for human action recognition require robust features and large training sets for good generalization. However, data augmentation methods are employed for imbalanced training sets to achieve higher accuracy. These samples generated using data augmentation only reflect existing samples within the training set, their feature representations are less diverse and hence, contribute to less precise classification. This paper presents new data augmentation and action representation approaches to grow training sets. The proposed approach is based on two fundamental concepts: virtual video generation for augmentation and representation of the action videos through robust features. Virtual videos are generated from the motion history templates of action videos, which are convolved using a convolutional neural network, to generate deep features. Furthermore, by observing an objective function of the genetic algorithm, the spatiotemporal features of different samples are combined, to generate the representations of the virtual videos and then classified through an extreme learning machine classifier on MuHAVi-Uncut, iXMAS, and IAVID-1 datasets.

• Journal of the Korean Mathematical Society
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• v.45 no.2
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• pp.575-585
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• 2008

Suppose that D is a $C^*$-discrete quantum group and $D_0$ a discrete quantum group associated with D. If there exists a continuous action of D on an operator algebra L(H) so that L(H) becomes a D-module algebra, and if the inner product on the Hilbert space H is D-invariant, there is a unique $C^*$-representation $\theta$ of D associated with the action. The fixed-point subspace under the action of D is a Von Neumann algebra, and furthermore, it is the commutant of $\theta$(D) in L(H).

• Journal of Electrical Engineering and Technology
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• v.10 no.4
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• pp.1851-1863
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• 2015

– In the paper, to handle the problem of traditional bag-of-features model ignoring the spatial relationship of local features in human action recognition, we proposed a Multiscale Spatial Position Coding under Locality Constraint method. Specifically, to describe this spatial relationship, we proposed a mixed feature combining motion feature and multi-spatial-scale configuration. To utilize temporal information between features, sub spatial-temporal-volumes are built. Next, the pooled features of sub-STVs are obtained via max-pooling method. In classification stage, the Locality-Constrained Group Sparse Representation is adopted to utilize the intrinsic group information of the sub-STV features. The experimental results on the KTH, Weizmann, and UCF sports datasets show that our action recognition system outperforms the classical local ST feature-based recognition systems published recently.

• 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.

• Cartoon and Animation Studies
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• s.11
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• pp.41-53
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• 2007

Due to the development of digital cinema, Animations are no longer a peripheral part of movies and become major role in making films, including live action movies. This kind of change makes the distinctive line between animations and live action movies vague. In order to prevent such side-effect, this study is aimed at building solid territory again between the two by reviewing the difference and, based on it, looking for effective cinematic techniques to produce synthesized and digitalized images. First of all, consideration on mise-en-scene is crucially required to tell this line. The mise-en-scene is a director's own unique style in making films. In other words, it is a symbol expressed by him/her. With the mise-en-scene, competitive directors explore huge possibility of image expression and know how to use it audiences can understand. Therefore, I look into a set of studies on the mise-en-scene and methodological problems because it is thought that the mise-en-scene is an important element to distinguish way of expression in animations, live action movies and digital cinema. In addition, owing to these fundamental differences, both movies have their own limitation on representation even though they imitate it each other. Synthesized images produced by both representation may not overcome that limitation and even worse bring up the lack of expression and the increase on unfamiliarity, which reduce audiences' interest. But ironically speaking, digital cinema accept each representation. And it serves as hindrance to narrative's delivery not to balance each of it. Therefore, digital cinema that integrate animations and live action movies should keep an eye on the overuse of images and pursue balanced mise-en-scene.

• Journal of the Korea Computer Graphics Society
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• v.27 no.5
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• pp.55-61
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• 2021

The character motion control based on physics simulation using reinforcement learning continue to being carried out. In order to solve a problem using reinforcement learning, the network structure, hyperparameter, state, action and reward must be properly set according to the problem. In many studies, various combinations of states, action and rewards have been defined and successfully applied to problems. Since there are various combinations in defining state, action and reward, many studies are conducted to analyze the effect of each element to find the optimal combination that improves learning performance. In this work, we analyzed the effect on reinforcement learning performance according to the state representation, which has not been so far. First we defined three coordinate systems: root attached frame, root aligned frame, and projected aligned frame. and then we analyze the effect of state representation by three coordinate systems on reinforcement learning. Second, we analyzed how it affects learning performance when various combinations of joint positions and angles for state.

• Journal of information and communication convergence engineering
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• v.11 no.2
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• pp.124-131
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• 2013

Extraction and representation of postures and/or gestures from human activities in videos have been a focus of research in this area of action recognition. With various applications cropping up from different fields, this paper seeks to improve the performance of these action recognition machines by proposing a shape-based silhouette-edge descriptor for the human body. Information entropy, a method to measure the randomness of a sequence of symbols, is used to aid the selection of vital key postures from video frames. Morphological operations are applied to extract and stack edges to uniquely represent different actions shape-wise. To classify an action from a new input video, a Hausdorff distance measure is applied between the gallery representations and the query images formed from the proposed procedure. The method is tested on known public databases for its validation. An effective method of human action annotation and description has been effectively achieved.