• Smart Media Journal
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• v.12 no.2
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• pp.27-35
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• 2023

Currently, online user authentication is perform using joint certificates issued by accredited certification authorities and simple certificates issued by private agency. In such a PKI(Public Key Infrastructure) system, various cryptographic technologies are used, and in particular, digital signatures are used as a core technology. The digital signature scheme is equally used in DID(Decentralized Identity), which is attracting attention to replace the existing centralized system. As such, the digital signature-based user authentication used in current online services is also applied in the metaverse, which is attracting attention as the next-generation online world. Metaverse, a compound word of "meta," which means virtual and transcendent, and "universe," means a virtual world that includes the existing online world. Due to various developments of the metaverse, it is expted that new authentication technologies including biometric authentication will be used, but existing authentication technologies are still being used. Therefore, in this study, we study digital signature scheme that can be efficiently used for user authentication in the developing metaverse. In particular, we experimentally analyze the effectiveness of ECDSA, which is currently used as a standard for digital signatures, and Schnorr signatures, which can quickly verify a large amount of signatures.

• Applied Chemistry for Engineering
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• v.34 no.4
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• pp.412-420
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• 2023

In this study, red mud/fly ash based geopolymer adsorbents (RFGPA) were prepared with calcination temperatures of 200, 400, and 600 ℃, and the effects of these calcination temperatures on the adsorption of methylene blue (MB) were investigated. In addition, the prepared RFGPA was characterized using X-ray fluorescence (XRF), scanning electron microscopy (SEM), X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FT-IR) spectroscopy, and Brunauer-EmmettTeller (BET) analysis. The results of the adsorption kinetics of MB at RFGPA prepared calcination temperatures indicated that the adsorption equilibrium of MB was reached after about 72 h. From the results of the adsorption isotherm, we verified that the degree of adsorption increased with increasing MB concentrations. In addition, the adsorption amount (Q) of MB decreased with an increase in calcination temperature. The experimental adsorption isotherm data were well fitted to the Freundlich and Sips equations compared to the Langmuir equation. In order to verify the effects of photocatalytic decomposition (C/C₀) of MB on Fe₂O₃ present in prepared RFGPA, the degree of decomposition of MB was examined under dark and visible conditions. Results indicated that the decomposition of MB in visible conditions was about 3.0 times faster than that in dark conditions.

• Applied Chemistry for Engineering
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• v.34 no.2
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• pp.161-169
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• 2023

In this study, we prepared naproxen (NP) imprinted biodegradable polymer based multi-layer biomaterials using allbanggae starch (ABS), polyvinyl alcohol (PVA), and alginic acid (SA), and investigated their physicochemical properties and the controlled drug release effects. In addition, the prepared multi-layer biomaterials were characterized by FE-SEM and FT-IR. In order to confirm the controlled drug release effect for the transdermal drug delivery system (TDDS), the NP release properties of NP imprinted multi-layer biomaterials were investigated using various pH buffer solutions and artificial skin at 36.5 ℃. The results of NP release in various pH buffer solutions indicated that the NP release at high pH was about 1.3 times faster than that at low pH. In addition, NP release in multi-layer biomaterials was about 4.0 times slower than that in single-layer biomaterials. It was confirmed that the NP release rate in triple-layer biomaterials was 4.0 times slower than that in single-layer biomaterials while using artificial skin. Also, it could be found that NP in double-layer biomaterials and triple-layer biomaterials was released sustainably for 12 h. The NP release mechanism in pH buffer solutions followed the Fickian diffusion mechanism, but followed the non-Fickian diffusion mechanism with artificial skin.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.5B
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• pp.429-439
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• 2009

When Catastrophic extreme flood occurs due to dam break, the response time for flood warning is much shorter than for natural floods. Numerical models can be powerful tools to predict behaviors in flood wave propagation and to provide the information about the flooded area, wave front arrival time and water depth and so on. But flood wave propagation due to dam break can be a process of difficult mathematical characterization since the flood wave includes discontinuous flow and dry bed propagation. Nevertheless, a lot of numerical models using finite volume method have been recently developed to simulate flood inundation due to dam break. As Finite volume methods are based on the integral form of the conservation equations, finite volume model can easily capture discontinuous flows and shock wave. In this study the numerical model using Riemann approximate solvers and finite volume method applied to the conservative form for two-dimensional shallow water equation was developed. The MUSCL scheme with surface gradient method for reconstruction of conservation variables in continuity and momentum equations is used in the predictor-corrector procedure and the scheme is second order accurate both in space and time. The developed finite volume model is applied to 2D partial dam break flows and dam break flows with triangular bump and validated by comparing numerical solution with laboratory measurements data and other researcher's data.

• Journal of Practical Engineering Education
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• v.15 no.1
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• pp.73-80
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• 2023

The advancement of online technology in the 21st century has increased online courses and web-based communication in higher education. This type of education is not limited by time or location and has made it possible to expand university campuses globally and broaden the reach of university education to the general public and students from other universities. Changes such as a decrease in the school-age population and a reorganization of the university structure have also created an opportunity to change the perception of online education. In this paper, we conducted surveys on K University students, high school seniors, and the general public to assess their satisfaction with online courses, identify areas that require massive online courses, and determine students' needs for the operation of massive online courses. The survey showed that K University students are generally satisfied with online courses. However, improvements are needed to ensure a smooth online course-taking environment, increase system uniformity, and enhance the overall online course environment. High school students have a strong preference for natural science and should be offered online courses in subjects such as mathematics and physics as prerequisites to prepare for their major classes. The general public prefers the humanities, which is evident in the purpose of the liberal arts lectures.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.1
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• pp.229-249
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• 2022

This paper investigates machine learning models for predicting the designation of administrative issues in the KOSDAQ market through various techniques. When a company in the Korean stock market is designated as administrative issue, the market recognizes the event itself as negative information, causing losses to the company and investors. The purpose of this study is to evaluate alternative methods for developing a artificial intelligence service to examine a possibility to the designation of administrative issues early through the financial ratio of companies and to help investors manage portfolio risks. In this study, the independent variables used 21 financial ratios representing profitability, stability, activity, and growth. From 2011 to 2020, when K-IFRS was applied, financial data of companies in administrative issues and non-administrative issues stocks are sampled. Logistic regression analysis, decision tree, support vector machine, random forest, and LightGBM are used to predict the designation of administrative issues. According to the results of analysis, LightGBM with 82.73% classification accuracy is the best prediction model, and the prediction model with the lowest classification accuracy is a decision tree with 71.94% accuracy. As a result of checking the top three variables of the importance of variables in the decision tree-based learning model, the financial variables common in each model are ROE(Net profit) and Capital stock turnover ratio, which are relatively important variables in designating administrative issues. In general, it is confirmed that the learning model using the ensemble had higher predictive performance than the single learning model.

• KIPS Transactions on Software and Data Engineering
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• v.13 no.1
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• pp.1-16
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• 2024

Over recent years, 6 Sigma has become a key methodology in manufacturing for quality improvement and cost reduction. However, challenges have arisen due to the difficulty in analyzing large-scale data generated by smart factories and its traditional, formal application. To address these limitations, a big data-based 6 Sigma approach has been developed, integrating the strengths of 6 Sigma and big data analysis, including statistical verification, mathematical optimization, interpretability, and machine learning. Despite its potential, the practical impact of this big data-based 6 Sigma on manufacturing processes and management performance has not been adequately verified, leading to its limited reliability and underutilization in practice. This study investigates the efficiency impact of DX SS, a big data-based 6 Sigma, on manufacturing processes, and identifies key success policies for its effective introduction and implementation in enterprises. The study highlights the importance of involving all executives and employees and researching key success policies, as demonstrated by cases where methodology implementation failed due to incorrect policies. This research aims to assist manufacturing companies in achieving successful outcomes by actively adopting and utilizing the methodologies presented.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.139-156
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• 2021

The biggest reason for using a deep learning model in image classification is that it is possible to consider the relationship between each region by extracting each region's features from the overall information of the image. However, the CNN model may not be suitable for emotional image data without the image's regional features. To solve the difficulty of classifying emotion images, many researchers each year propose a CNN-based architecture suitable for emotion images. Studies on the relationship between color and human emotion were also conducted, and results were derived that different emotions are induced according to color. In studies using deep learning, there have been studies that apply color information to image subtraction classification. The case where the image's color information is additionally used than the case where the classification model is trained with only the image improves the accuracy of classifying image emotions. This study proposes two ways to increase the accuracy by incorporating the result value after the model classifies an image's emotion. Both methods improve accuracy by modifying the result value based on statistics using the color of the picture. When performing the test by finding the two-color combinations most distributed for all training data, the two-color combinations most distributed for each test data image were found. The result values were corrected according to the color combination distribution. This method weights the result value obtained after the model classifies an image's emotion by creating an expression based on the log function and the exponential function. Emotion6, classified into six emotions, and Artphoto classified into eight categories were used for the image data. Densenet169, Mnasnet, Resnet101, Resnet152, and Vgg19 architectures were used for the CNN model, and the performance evaluation was compared before and after applying the two-stage learning to the CNN model. Inspired by color psychology, which deals with the relationship between colors and emotions, when creating a model that classifies an image's sentiment, we studied how to improve accuracy by modifying the result values based on color. Sixteen colors were used: red, orange, yellow, green, blue, indigo, purple, turquoise, pink, magenta, brown, gray, silver, gold, white, and black. It has meaning. Using Scikit-learn's Clustering, the seven colors that are primarily distributed in the image are checked. Then, the RGB coordinate values of the colors from the image are compared with the RGB coordinate values of the 16 colors presented in the above data. That is, it was converted to the closest color. Suppose three or more color combinations are selected. In that case, too many color combinations occur, resulting in a problem in which the distribution is scattered, so a situation fewer influences the result value. Therefore, to solve this problem, two-color combinations were found and weighted to the model. Before training, the most distributed color combinations were found for all training data images. The distribution of color combinations for each class was stored in a Python dictionary format to be used during testing. During the test, the two-color combinations that are most distributed for each test data image are found. After that, we checked how the color combinations were distributed in the training data and corrected the result. We devised several equations to weight the result value from the model based on the extracted color as described above. The data set was randomly divided by 80:20, and the model was verified using 20% of the data as a test set. After splitting the remaining 80% of the data into five divisions to perform 5-fold cross-validation, the model was trained five times using different verification datasets. Finally, the performance was checked using the test dataset that was previously separated. Adam was used as the activation function, and the learning rate was set to 0.01. The training was performed as much as 20 epochs, and if the validation loss value did not decrease during five epochs of learning, the experiment was stopped. Early tapping was set to load the model with the best validation loss value. The classification accuracy was better when the extracted information using color properties was used together than the case using only the CNN architecture.

• Journal of Korean Society of Forest Science
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• v.102 no.1
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• pp.82-89
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• 2013

Harvest is one of the major disturbances affecting the soil carbon (C) dynamics in forests. However, researches on the long-term impact of periodic harvest on the soil C dynamics are limited since they requires rigorous control of various factors. Therefore, we adopted a modeling approach to determine the long-term impacts of harvest interval, harvest intensity and post-harvest residue management on soil C dynamics by using the Korean Forest Soil Carbon model (KFSC model). The simulation was conducted on Pinus densiflora S. et Z. stands in central Korea, and twelve harvest scenarios were tested by altering harvest intervals (50, 80, and 100-year interval), intensities (partial-cut harvest: 30% and clear-cut harvest: 100% of stand volume), and the residue managements after harvest (collection: 0% and retention: 100% of aboveground residue). We simulated the soil carbon stock for 400 years for each scenario. As a result, the soil C stocks in depth of 30 cm after 400 years range from 50.3 to 55.8 Mg C $ha^{-1}$, corresponding to 98.1 to 108.9% of the C stock at present. The soil C stock under the scenarios with residue retention was 2.5-11.0% higher than that under scenarios with residue collection. However, there was no significant impact of harvest interval and intensity on the soil C stock. The soil C dynamics depended on the dead organic matter dynamics derived from the amount of dead organic matter and growth pattern after harvest.

• Journal of The Korean Association For Science Education
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• v.19 no.4
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• pp.604-621
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• 1999

We analyze the admission test problems used in 1998 at the Science Education Center for Gifted Youth at Kyungpook National University (SECGY, KNU). The test consists of two parts, an evaluation of the scientific thinking skills and an evaluation of the achievement for Mathematics and Sciences, the former of which includes evaluations of scientific process skills and logical thinking skills. The problems for the test of scientific thinking skills were developed and standardized by the Korea Education Development Center, while those of the achievement for Mathematics and Sciences were made at SECGY. We calculate the indices of the difficulty and discrimination for each problem to determine whether or not the test is appropriate to apply for selecting number of gifted students among the recommended students from 389 middle schools in Taegu-city and Kyungsang-pook-do Province. We find that both indices of most problems for the test of scientific thinking skills were out range of the appropriate level and. moreover, even those problems which fall into the appropriate range showed very low efficiencies for distractors. We, thus, conclude that the problems of the test of scientific thinking skills are inappropriate to use as a test for admission to SECGY. On the other hand, the problems of the achievement test showed extreme results; the Mathematics problems appeared to be too difficult, whereas the Physics problems appeared too easy. However, overall scores showed a normal distribution, indicating that those problems played crucial role in selecting gifted students. We finally propose several suggestions in developing the test problems and in selecting students at the SECGY.