• Journal of Gifted/Talented Education
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• v.13 no.4
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• pp.119-140
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• 2003

The purpose of this study is to examine the dispute over creativity domain at schools in Korea. In other words, this study is to examine the creativity domain among all subjects, a group of high level performance, and a group of low level performance in creativity. Based on ability-differentiation hypothesis, correlation among 4 domains, correlation between divergent thinking variables of domain-generality and 4 domains of domain-speciality, and multiple linear regression have been computed. The results are opposed to the hypothesis. The group of low level performance is related to domain-speciality. The group of high level performance, however, has relationships neither domain-generality nor domain-speciality. Instead of differentiation between domain-generality and domain-speciality, the results support the domain-complementarity.

• Journal of information and communication convergence engineering
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• v.21 no.2
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• pp.139-144
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• 2023

Engineers prefer deep neural networks (DNNs) for solving computer vision problems. However, DNNs pose two major problems. First, neural networks require large amounts of well-labeled data for training. Second, the covariate shift problem is common in computer vision problems. Domain adaptation has been proposed to mitigate this problem. Recent work on adversarial-learning-based unsupervised domain adaptation (UDA) has explained transferability and enabled the model to learn robust features. Despite this advantage, current methods do not guarantee the distinguishability of the latent space unless they consider class-aware information of the target domain. Furthermore, source and target examples alone cannot efficiently extract domain-invariant features from the encoded spaces. To alleviate the problems of existing UDA methods, we propose the mixup regularization in adversarial discriminative domain adaptation (ADDA) method. We validated the effectiveness and generality of the proposed method by performing experiments under three adaptation scenarios: MNIST to USPS, SVHN to MNIST, and MNIST to MNIST-M.

• Journal of Elementary Mathematics Education in Korea
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• v.16 no.2
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• pp.253-267
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• 2012

In this paper, we primarily focus on the perspectives about creative process, which is how mathematical creativity emerged, as one aspect of mathematical creativity and then present a desirable task characteristic to measure and program characteristics to develop mathematical creativity. At first, we describe domain-generality perspective and domain-specificity perspective on creativity. The former regard divergent thinking skill as a key cognitive process embedded in creativity of various discipline domain involving language, science, mathematics, art and so on. In contrast the researchers supporting later perspective insist that the mechanism of creativity is different in each discipline. We understand that the issue on this two perspective effect on task and program to foster and measure creativity in mathematics education beyond theoretical discussion. And then, based on previous theoretical review, we draw a desirable characteristic on instruction program and task to facilitate and test mathematical creativity, and present an applicable task and instruction cases based on Geneplor model at the mathematics class in elementary school. In conclusion, divergent thinking is necessary but sufficient to develop mathematical creativity and need to consider various mathematical reasoning such as generalization, ion and mathematical knowledge.

• Journal of Families and Better Life
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• v.26 no.5
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• pp.333-354
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• 2008

This study aimed to investigate the role of domain-specific causal mechanism information and domain-general conditional probability in young children's causal reasoning on psychology and biology. Participants were 121 3-year-olds and 121 4-year-olds recruited from seven childcare centers in Seoul, Kyonggi Province, and Busan. After participants watched moving pictures on psychological and biological phenomena, they were asked to choose appropriate cause and justify their choices. Results of this study were as follows: First, young children made different inferences according to domain-specific causal mechanisms. Second, the developmental level of causal mechanisms has a gap between psychology and biology, and biological knowledge was proved to be separate from psychological knowledge during the preschool period. Third, young children's causal reasoning was different depending on the interaction effect of domain-specific mechanisms and domain-general conditional probability: children could make more inferences based on domain-specific causal mechanisms if conditional probability between domain-appropriate cause and effect was evident. To conclude, it can be inferred that the role of domain-specific causal mechanisms and domain-general conditional probability is not competitive but complementary in young children's causal reasoning.

• Structural Engineering and Mechanics
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• v.36 no.3
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• pp.279-299
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• 2010

A methodology on application of the discrete singular convolution (DSC) technique to the free vibration analysis of thin plates with curvilinear quadrilateral platforms is developed. In the proposed approach, irregular physical domain is transformed into a rectangular domain by using geometric coordinate transformation. The DSC procedures are then applied to discretization of the transformed set of governing equations and boundary conditions. For demonstration of the accuracy and convergence of the method, some numerical examples are provided on plates with different geometry such as elliptic, trapezoidal having straight and parabolic sides, sectorial, annular sectorial, and plates with four curved edges. The results obtained by the DSC method are compared with those obtained by other numerical and analytical methods. The method is suitable for the problem considered due to its generality, simplicity, and potential for further development.

• Journal of the Korea Safety Management & Science
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• v.15 no.3
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• pp.133-141
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• 2013

As a work analysis technique, Work Domain Analysis (WDA) aims to identify the design knowledge structure of a work domain that human operators interact with through human-system interfaces. Abstraction hierarchy (AH) is a multi-level, hierarchical knowledge representation framework for modeling the functional structure of any kinds of systems. Thus, WDA based on AH aims to identify the functional knowledge structure of a work domain. AH has been used in a range of work domains and problems to model their functional knowledge structure and has proven its generality and usefulness. However, many of researchers and system designers have reported that it is never easy to understand the concepts underlying AH and use it effectively for WDA. This would be because WDA is a form of work analysis that is different from other types of work analysis techniques such as task analysis and AH has several unique characteristics that are differentiated from other types of function analysis techniques used in systems engineering. With this issue in mind, this paper introduces the concepts of WDA based on AH and offers a comprehensive list of references. Next, this paper proposes a set of principles for effectively applying AH for work domain analysis, which are developed based on the author's experiences, consultation with experts, and literature reviews.

• Korean Journal of Computational Design and Engineering
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• v.4 no.2
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• pp.79-86
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• 1999

A general approach to automatic generation of triangular meshes on three-dimensional surfaces is presented in this paper. The approach, developed with emphasis on program generality and interface with CAD/CAM systems, is based on the double mapping method and the looping method. The double mapping method is introduced and anew splitting scheme is proposed for the looping method employed for triangular mesh generation on the parametric domain. Several application examples are given.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.4024-4041
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• 2022

Robust reversible watermarking has attracted widespread attention in the field of information hiding in recent years. It should not only have robustness against attacks in transmission but also meet the reversibility of distortion-free transmission. According to our best knowledge, the most recent robust reversible watermarking methods adopt a single image as the carrier, which might lead to low efficiency in terms of carrier utilization. To address the issue, a novel dual-image robust reversible watermarking framework is proposed in this paper to effectively utilize the correlation between both carriers (namely dual images) and thus improve the efficiency of carrier utilization. In the dual-image robust reversible watermarking framework, a two-layer robust watermarking mechanism is designed to further improve the algorithm performances, i.e., embedding capacity and robustness. In addition, an optimization model is built to determine the parameters. Finally, the proposed framework is applied in different domains (namely domain-independent), i.e., Slantlet Transform and Singular Value Decomposition domain, and Zernike moments, respectively to demonstrate its effectiveness and generality. Experimental results demonstrate the superiority of the proposed dual-image robust reversible watermarking framework.

• International Journal of Naval Architecture and Ocean Engineering
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• v.13 no.1
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• pp.75-85
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• 2021

The broadband noise can be dominant or important for total characteristics for marine propeller noise representing the minimum base of self-noise. Accurate prediction of such noise is crucial for survivability of underwater military vessels. While the FW-H Formulation 1B can be used to predict broadband trailing edge noise, the method required experiment measurements of surface pressure correlations, showing its limitations in generality. Therefore, in this study, the methods are developed to utilize wall pressure spectrum models to overcome those limitations. Chase model is adopted to represent surface pressure along with the developed formulations to reproduce pressure statistics. Newly developed method is validated with the experiments of airfoils at different velocities. Thereafter, with its feasibility and generality, the procedure incorporating computational fluid dynamics is established and performed for a propeller behind submarine hull. The results are compared with the experiments conducted at Large Cavitation Tunnel, thus showing its usability and robustness.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.3
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• pp.141-148
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• 2022

Recently, reinforcement learning combined with deep neural network technology has achieved remarkable success in various fields such as board games such as Go and chess, computer games such as Atari and StartCraft, and robot object manipulation tasks. However, such deep reinforcement learning describes states, actions, and policies in vector representation. Therefore, the existing deep reinforcement learning has some limitations in generality and interpretability of the learned policy, and it is difficult to effectively incorporate domain knowledge into policy learning. On the other hand, dNL-RRL, a new relational reinforcement learning framework proposed to solve these problems, uses a kind of vector representation for sensor input data and lower-level motion control as in the existing deep reinforcement learning. However, for states, actions, and learned policies, It uses a relational representation with logic predicates and rules. In this paper, we present dNL-RRL-based policy learning for transportation mobile robots in a manufacturing environment. In particular, this study proposes a effective method to utilize the prior domain knowledge of human experts to improve the efficiency of relational reinforcement learning. Through various experiments, we demonstrate the performance improvement of the relational reinforcement learning by using domain knowledge as proposed in this paper.