• 한국수학교육학회지시리즈A:수학교육
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• 제47권1호
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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.

• 전기학회논문지
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• 제65권10호
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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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• 제12권1호
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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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• 제44권2호
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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.

• 대한수학회지
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• 제45권2호
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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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• 제10권4호
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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.

• 한국초등과학교육학회지:초등과학교육
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• 제39권2호
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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.

• 만화애니메이션 연구
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• 통권11호
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• pp.41-53
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• 2007

최근의 영화는 기존의 애니메이션과 실사영화라는 범주로 나눌 수 없을 만큼 그 경계가 모호하다. 디지털영화의 등장이 애니메이션과 실사영화의 경계를 허물고 있는 것이다. 이 논문은 애니메이션과 실사영화, 디지털영화의 프레임과 이를 근간으로 한 미장센의 특성을 다루고 있다. 애니메이션과 실사영화는 각각 회화적, 사진적 프레임 특성을 가지고 있지만, 합성이미지를 근간으로 하는 디지털영화는 두 양식의 프레임 특성을 모두 갖게 된다. 이는 결국 영화작가의 표현스타일인 미장센의 차이로 나타난다.

• 한국컴퓨터그래픽스학회논문지
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• 제27권5호
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• pp.55-61
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• 2021

물리 시뮬레이션 기반의 캐릭터 동작 제어 문제를 강화학습을 이용하여 해결해나가는 연구들이 계속해서 진행되고 있다. 강화학습을 사용하여 문제를 풀기 위해서는 네트워크 구조, 하이퍼파라미터 튜닝, 상태(state), 행동(action), 보상(reward)이 문제에 맞게 적절히 설정이 되어야 한다. 많은 연구들에서 다양한 조합으로 상태, 행동, 보상을 정의하였고, 성공적으로 문제에 적용하였다. 상태, 행동, 보상을 정의함에 다양한 조합이 있다보니 학습 성능을 향상시키는 최적의 조합을 찾기 위해서 각각의 요소들이 미치는 영향을 분석하는 연구도 진행되고 있다. 우리는 지금까지 이뤄지지 않았던 상태 표현 방식에 따른 강화학습성능에 미치는 영향을 분석하였다. 첫째로, root attached frame, root aligned frame, projected aligned frame 3가지로 좌표계를 정의하였고, 이에 대해 표현된 상태를 이용하여 강화학습에 미치는 영향을 분석하였다. 둘째로, 상태를 정의 할 때, 관절의 위치, 각도로 다양하게 조합하는 경우에 학습성능에 어떠한 영향을 미치는지 분석하였다.

• Journal of information and communication convergence engineering
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• 제11권2호
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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.