• Journal of the Korean Society for information Management
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• v.29 no.3
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• pp.215-234
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• 2012

Recently, the importance of team based learning has emerged as the method for conducting the constructivist learning theory. College students, however, have the low preference toward team projects. Thus, this research suggested that the information literacy education should be designed to overcome the problems in team project activities after analyzing the college students' information problem solving process. The in-depth interviews were conducted twice with 10 subjects. As a result, the main problems during team project activities were task definition, judgement on relevant information, evaluation of result and process, absence of accountability and synthesis. The recommendations for information literacy course are as follows: introduction to different types of information sources, support for communication problems between team members, education of credibility judgment on information and criteria for evaluating the results.

• Journal of KIISE
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• v.44 no.4
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• pp.399-410
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• 2017

We present a system that automatically annotates unverified Web sentences with information from credible sources. The system turns to neural theorem proving for an annotating task for cancer related Wikipedia data (1,486 propositions) with Korean National Cancer Center data (19,304 propositions). By switching the recursive module in a neural theorem prover to a word embedding module, we overcome the fundamental problem of tremendous learning time. Within the identical environment, the original neural theorem prover was estimated to spend 233.9 days of learning time. In contrast, the revised neural theorem prover took only 102.1 minutes of learning time. We demonstrated that a neural theorem prover, which encodes a proposition in a tensor, includes a classic theorem prover for exact match and enables end-to-end differentiable logic for analogous words.

• The Journal of the Korea Contents Association
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• v.17 no.10
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• pp.30-40
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• 2017

The purpose of this study is to analyze the long term mediating effects of Self-regulated Learning(SRL) in the Relationships among the Perceptions of Middle School Students on the Democratic Attitude of Parenting(DAP) and their Relationships with Teachers(RT), and their Self-esteem(SE). The data sources were from the first (2010) and the third (2012) Korean Children & Youth Panel Survey(KCYPS). As a result, first, the perceived DAP had significant effects on the students' SE both directly and indirectly through SRL. Second, the perception on RT had indirect effects on their SE mediated by SRL. Third, this pattern in first year continued in two-year-later-SE. This study implies that DAP, RT, and SRL play important roles in the continuous development of adolescents' self-esteem.

• Journal of the Korean Chemical Society
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• v.46 no.6
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• pp.550-560
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• 2002

This study was to explore students' thought patterns on problem and problem solving in the course of general chemistry. The participants were 9 students taking the course of general chemistry in a university in Seoul. Data were collected from various sources; three individual interviews, classroom observations, and essays written by students.Data were all transcribed and then analyzed circularly in constant component analysis. As the results of this study, six thought patterns of students in the context of learning general chemistry were presented. These thought patterns were common and existed important component within most of students' conceptual ecologies about learning chemistry. Implications of chemistry and science learning related to this results were discussed.

• Journal of Korean Society on Water Environment
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• v.33 no.3
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• pp.326-333
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• 2017

The lakes and reservoirs have been reported as important sources of carbon emissions to the atmosphere in many countries. Although field experiments and theoretical investigations based on the fundamental gas exchange theory have proposed the quantitative amounts of Net Atmospheric Flux (NAF) in various climate regions, there are still large uncertainties at the global scale estimation. Mechanistic models can be used for understanding and estimating the temporal and spatial variations of the NAFs considering complicated hydrodynamic and biogeochemical processes in a reservoir, but these models require extensive and expensive datasets and model parameters. On the other hand, data driven machine learning (ML) algorithms are likely to be alternative tools to estimate the NAFs in responding to independent environmental variables. The objective of this study was to develop random forest (RF) and multi-layer artificial neural network (ANN) models for the estimation of the daily $CO_2$ NAFs in Daecheong Reservoir located in Geum River of Korea, and compare the models performance against the multiple linear regression (MLR) model that proposed in the previous study (Chung et al., 2016). As a result, the RF and ANN models showed much enhanced performance in the estimation of the high NAF values, while MLR model significantly under estimated them. Across validation with 10-fold random samplings was applied to evaluate the performance of three models, and indicated that the ANN model is best, and followed by RF and MLR models.

• The Mathematical Education
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• v.49 no.2
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• pp.125-148
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• 2010

This study was to find the perception of mathematical & scientific learning of North Korean students who lived in Korea. To understand their perception, three groups as the focus group for clinical interview, consisting of North Korean students, their teaches and their parents, were investigated through narrative description of qualitative method, North Korean students experienced the gap between what they had learned and what they learned in Korea, due to visiting the 3rd country before they came to Korea. So they were in need of well developed instructional instruments based on a precise diagnosis of language ability to help them get over their difficulties. Second, they have difficulties in math & science classes due to differences between curricular and to the differences between the ways of expression of terminologies used in two countries. They expressed that the group work in learning and a great deal of number of problems could be helpful for their needs. Third, the community-service center should be operated in a systematic way to compensate their lack of getting a private education. Fourth, they thought that the supplemental materials should provide some sources that might help them to get over the language barrier and difficulties from the differences, because they depended on them.

• Journal of Korea Water Resources Association
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• v.54 no.12
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• pp.1275-1284
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• 2021

This paper presents a machine learning technique applied to prediction of time-dependent local scour around bridge piers in both non-cohesive and cohesive beds. The support vector machines (SVM), which is known to be free from overfitting, is used. The time-dependent scour depths are expressed by 7 and 9 variables for the non-cohesive and cohesive beds, respectively. The SVM models are trained and validated with time series data from different sources of experiments. Resulting Mean Absolute Percentage Error (MAPE) indicates that the models are trained and validated properly. Comparisons are made with the results from Choi and Choi's formula and Scour Rate in Cohesive Soils (SRICOS) method by Briaud et al., as well as measured data. This study reveals that the SVM is capable of predicting time-dependent local scour in both non-cohesive and cohesive beds under the condition that sufficient data of good quality are provided.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.1
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• pp.54.2-54.2
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• 2019

Removing noise which occurs inevitably when taking image data has been a big concern. There is a way to raise signal-to-noise ratio and it is regarded as the only way, image stacking. Image stacking is averaging or just adding all pixel values of multiple pictures taken of a specific area. Its performance and reliability are unquestioned, but its weaknesses are also evident. Object with fast proper motion can be vanished, and most of all, it takes too long time. So if we can handle single shot image well and achieve similar performance, we can overcome those weaknesses. Recent developments in deep learning have enabled things that were not possible with former algorithm-based programming. One of the things is generating data with more information from data with less information. As a part of that, we reproduced stacked image from single shot image using a kind of deep learning, conditional generative adversarial network (cGAN). r-band camcol2 south data were used from SDSS Stripe 82 data. From all fields, image data which is stacked with only 22 individual images and, as a pair of stacked image, single pass data which were included in all stacked image were used. All used fields are cut in $128{\times}128$ pixel size, so total number of image is 17930. 14234 pairs of all images were used for training cGAN and 3696 pairs were used for verify the result. As a result, RMS error of pixel values between generated data from the best condition and target data were $7.67{\times}10^{-4}$ compared to original input data, $1.24{\times}10^{-3}$. We also applied to a few test galaxy images and generated images were similar to stacked images qualitatively compared to other de-noising methods. In addition, with photometry, The number count of stacked-cGAN matched sources is larger than that of single pass-stacked one, especially for fainter objects. Also, magnitude completeness became better in fainter objects. With this work, it is possible to observe reliably 1 magnitude fainter object.

• Journal of IKEEE
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• v.24 no.4
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• pp.1122-1128
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• 2020

Bandwidth Extension refers to restoring and expanding a narrow band signal(NB) that is damaged or damaged in the encoding and decoding process due to the lack of channel capacity or the characteristics of the codec installed in the mobile communication device. It means converting to a wideband signal(WB). Bandwidth extension research mainly focuses on voice signals and converts high bands into frequency domains, such as SBR (Spectral Band Replication) and IGF (Intelligent Gap Filling), and restores disappeared or damaged high bands based on complex feature extraction processes. In this paper, we propose a model that outputs an bandwidth extended signal based on an autoencoder among deep learning models, using the residual connection of one-dimensional convolutional neural networks (CNN), the bandwidth is extended by inputting a time domain signal of a certain length without complicated pre-processing. In addition, it was confirmed that the damaged high band can be restored even by training on a dataset containing various types of sound sources including music that is not limited to the speech.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.8
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• pp.1222-1230
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• 2022

As the number of signal sources with low detectability by using various modulation techniques increases, research to classify signal modulation methods is steadily progressing. Recently, a Convolutional Neural Network (CNN) deep learning technique using FFT as a preprocessing process has been proposed to improve the performance of received signal classification in signal interference or noise environments. However, due to the characteristics of the FFT in which the window is fixed, it is not possible to accurately classify the change over time of the detection signal. Therefore, in this paper, we propose a CNN model that has high resolution in the time domain and frequency domain and uses wavelet transform as a preprocessing process that can express various types of signals simultaneously in time and frequency domains. It has been demonstrated that the proposed wavelet transform method through simulation shows superior performance regardless of the SNR change in terms of accuracy and learning speed compared to the FFT transform method, and shows a greater difference, especially when the SNR is low.