• Korean Journal of Child Studies
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• v.33 no.2
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• pp.203-222
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• 2012

The purpose of this study was to examine the factors influencing their mathematical learning abilities and mathematical learning potential in an attempt to assist their learning at the preschool level. The findings of the study were as follows : First. the female children performed at a much higher level than their male counterparts in terms of mathematical learning ability and mathematical learning potential training. The young children generally improved in their mathematical learning abilities and mathematical learning potential with age. Second, it was found that the participants had higher levels of both mathematical learning ability and mathematical learning potential when their mathematical attitudes and learning motivation were better. Third, there were significant differences in terms training-test and transfer-test scores between the 4 groups based on their relative levels of mathematical abilities and attitudes.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.5
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• pp.502-506
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• 2008

The recently proposed "Potential-based" reinforcement learning (RL) method made it possible to combine multiple learnings and expert advices as supervised knowledge within an RL framework. The effectiveness of the approach has been established by a theoretical convergence guarantee to an optimal policy. In this paper, the potential-based RL method is applied to a dynamic channel assignment (DCA) problem in a cellular networks. It is empirically shown that the potential-based RL assigns channels more efficiently than fixed channel assignment, Maxavail, and Q-learning-based DCA, and it converges to an optimal policy more rapidly than other RL algorithms, SARSA(0) and PRQ-learning.

• Journal of Distribution Science
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• v.21 no.4
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• pp.47-56
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• 2023

Purpose: the research aims to analyze the impact of the distribution of knowledge through online learning on the intellectual potential of PhD students and produce recommendations for policy to improve intellectual capacity. During the literature review, it was determined that a large number of studies examined the impact of online learning on the quality of education at different levels. Research design, data and methodology: the research methodology is based on subjective assessment and studying the students' opinions. The basis of the study was a comprehensive analysis of primary data obtained through a sociological survey of PhD students. 324 respondents from humanitarian, medical and natural faculties participated in the survey. Results: the study revealed that online learning helps increase students' intellectual potential. PhD students had a positive attitude towards the transition from traditional education to online learning. It should be noted that, according to the results, the most popular gadgets were laptops and smartphones, which were characterized by high mobility and ease of use. Based on the obtained results, recommendations were developed for the formation of online learning with a focus on increasing students' intellectual potential. Conclusions: based on the results of the assessment of educational and innovative potential, policy recommendations and further research in this area were proposed.

• Transactions of the Korean Society of Pressure Vessels and Piping
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• v.19 no.2
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• pp.93-101
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• 2023

In this study, a deep learning algorithm was used to diagnose electric potential signals obtained through CIPS and DCVG, used indirect inspection methods to confirm the soundness of buried pipes. The deep learning algorithm consisted of CNN(Convolutional Neural Network) model for diagnosing the electric potential signal and Grad CAM(Gradient-weighted Class Activation Mapping) for showing the flaw prediction point. The CNN model for diagnosing electric potential signals classifies input data as normal/abnormal according to the presence or absence of flaw in the buried pipe, and for abnormal data, Grad CAM generates a heat map that visualizes the flaw prediction part of the buried pipe. The CIPS/DCVG signal and piping layout obtained from the 3D finite element model were used as input data for learning the CNN. The trained CNN classified the normal/abnormal data with 93% accuracy, and the Grad-CAM predicted flaws point with an average error of 2m. As a result, it confirmed that the electric potential signal of buried pipe can be diagnosed using a CNN-based deep learning algorithm.

• Journal of Environmental Health Sciences
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• v.47 no.5
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• pp.462-471
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• 2021

Background: The number of synthesized chemicals has rapidly increased over the past decade. For many chemicals, there is a lack of information on toxicity. With the current movement toward reducing animal testing, the use of toxicity big data and deep learning could be a promising tool to screen potential toxicants. Objectives: This study identified potential chemicals related to reproductive and estrogen receptor (ER)-mediated toxicities for 1135 cleaning products and 886 laundry products. Methods: We listed chemicals contained in cleaning and laundry products from a publicly available database. Then, chemicals that potentially exhibited reproductive and ER-mediated toxicities were identified using the European Union Classification, Labeling and Packaging classification and ToxCast database, respectively. For chemicals absent from the ToxCast database, ER activity was predicted using deep learning models. Results: Among the 783 listed chemicals, there were 53 with potential reproductive toxicity and 310 with potential ER-mediated toxicity. Among the 473 chemicals not tested with ToxCast assays, deep learning models indicated that 42 chemicals exhibited ER-mediated toxicity. A total of 13 chemicals were identified as causing reproductive toxicity by reacting with the ER. Conclusions: We demonstrated a screening method to identify potential chemicals related to reproductive and ER-mediated toxicities utilizing chemical toxicity big data and deep learning. Integrating toxicity data from in vivo, in vitro, and deep learning models may contribute to screening chemicals in consumer products.

• Korean Journal of Child Studies
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• v.34 no.1
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• pp.123-140
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• 2013

The purpose of this study was to examine the effects of counting ability on young children's mathematical ability and mathematical learning potential. The subjects in this study were 75 young children of 4 & 5 years old who attended kindergartens and child care center in the city of B. They were evaluated in terms of counting ability, mathematical ability and mathematical learning potential(training and transfer) and the correlation between sub-factors and their relative influence on the partipants' mathematical ability was then analyzed. The findings of the study were as follows : First, there was a close correlation between the sub-factors of counting and those of mathematical ability. As a result of checking the relative influence of the sub-factors of counting on mathematical ability, reverse counting was revealed to have the largest impact on total mathematical ability scores and each sub-factors including algebra, number and calculation, geometry and measurement. Second, the results revealed a strong correlation between counting ability and mathematical learning ability. Regarding the size of the relative influence of the sub-factors of counting ability on training scores, reverse counting was found to be most influential, followed by continuous counting. While in relation to transfer scores, reverse counting was found to exert the greatest influence.

• Educational Technology International
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• v.19 no.2
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• pp.199-228
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• 2018

The main purposes of this study were (a) to analyze the research trend of educational use of tangible technology, (b) to identify tangible learning mechanisms, and potential benefits of learning with tangible technology, and (c) to provide references and future research directions. We conducted a systematic literature review to search for academic papers published in recent five years (from 2013 to 2017) in the major databases. Forty papers were coded and analyzed by the established coding framework in four dimensions: (a) basic publication information, (b) learning context, (c) learning mechanism, and (d) learning benefits. Overall, the results show that tangible technology has been used more for young learners in the kindergarten and primary school contexts mainly for science learning, to achieve both cognitive and affective learning outcomes, by coupling tangible objects with tabletops and desktop computers. From the synthesis of the review findings, this study suggests that the affordances of tangible technology useful for learning include embodied interaction, physical manipulations, and the physical-digital representational mapping. With such technical affordances, tangible technologies have the great potential in three particular areas in education: (a) learning spatial relationships, (b) making the invisible visible, and (c) reinforcing abstract concepts through the correspondence of representations. In conclusion, we suggest some areas for future research endeavors.

• Korean Journal of Radiology
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• v.21 no.10
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• pp.1126-1137
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• 2020

Imaging plays a key role in the management of brain tumors, including the diagnosis, prognosis, and treatment response assessment. Radiomics and deep learning approaches, along with various advanced physiologic imaging parameters, hold great potential for aiding radiological assessments in neuro-oncology. The ongoing development of new technology needs to be validated in clinical trials and incorporated into the clinical workflow. However, none of the potential neuro-oncological applications for radiomics and deep learning has yet been realized in clinical practice. In this review, we summarize the current applications of radiomics and deep learning in neuro-oncology and discuss challenges in relation to evidence-based medicine and reporting guidelines, as well as potential applications in clinical workflows and routine clinical practice.

• Korean Journal of Radiology
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• v.21 no.5
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• pp.511-525
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• 2020

Chest X-ray radiography and computed tomography, the two mainstay modalities in thoracic radiology, are under active investigation with deep learning technology, which has shown promising performance in various tasks, including detection, classification, segmentation, and image synthesis, outperforming conventional methods and suggesting its potential for clinical implementation. However, the implementation of deep learning in daily clinical practice is in its infancy and facing several challenges, such as its limited ability to explain the output results, uncertain benefits regarding patient outcomes, and incomplete integration in daily workflow. In this review article, we will introduce the potential clinical applications of deep learning technology in thoracic radiology and discuss several challenges for its implementation in daily clinical practice.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.12
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• pp.978-982
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• 2009

This paper proposes a potential-based reinforcement learning, called "RLs-CBDT", which combines multiple RL agents and case-base decision theory designed for decision making in uncertain environment as an expert knowledge in RL. We empirically show that RLs-CBDT converges to an optimal policy faster than pre-existing RL algorithms through a Tetris experiment.