• Journal of Wellness
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• v.14 no.2
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• pp.207-223
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• 2019

The purpose This study aimed at proposing class module elements for creativity and convergence and class models for creativity and convergence by integrating content elements by physical activity field(health, challenge, competition, ) for physical education and STEAM. For this, literature review, focus group interview(FGI) and discussions with experts were conducted, and the following study results have been drawn up: First, concerning the class module elements for creativity and convergence, total 11 class module elements in the health field were suggested including detecting risks by posture analysis and analyzing and designing amount of physical activity. Second, total 7 module elements in the challenge field were deduced such as anticipation of obstacles to target achievement and modeling of effective exercise. There were 17 convergence elements in the competition field including game record analysis and creation of game data storage application. Third, total 9 creativity and convergence module elements in the field include modeling of technology improvement for motion and symbolization for motion records. In addition, class modules related to convergence with engineering in the health field, convergence with technology in the challenge field, convergence with art in the competition field and convergence with art and mathematical symbols were proposed.

• 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.

• Economic and Environmental Geology
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• v.56 no.5
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• pp.533-545
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• 2023

In this study, an investigation was conducted on the features, events, and processes (FEP) that could impact the long-term safety of the natural barriers constituting high-level radioactive waste geological repositories. The FEP list was developed utilizing the IFEP list 3.0 provided by the Nuclear Energy Agency (NEA) as foundational data, supplemented by geological investigations and research findings from leading countries in this field. A total of 49 FEPs related to the performance of the natural barrier were identified. For each FEP, detailed definitions, classifications, impacts on long-term safety, significance in domestic conditions, and feasibility of quantification were provided. Moreover, based on the compiled FEP list, three scenarios that could affect the long-term safety of the disposal facility were developed. Geological factors affecting the performance of the natural barrier in each scenario were selected and their relationships were visualized. The constructed FEP list and the visualization of interrelated factors in various scenarios are anticipated to provide essential information for selecting and organizing factors that must be considered in the development of mathematical models for quantitatively evaluating the long-term safety of deep geological repositories. In addition, these findings could be effectively utilized in establishing criteria related to the key performance of natural barriers for the confirmation of repository sites.

• Journal of the Korean GEO-environmental Society
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• v.16 no.4
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• pp.5-12
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• 2015

Civil engineers have taken the numerous slope monitoring data for an engineering project subjected to hazard potential of slide. However, the topics on how to deal with and draw out proper information from the data related to the slope behavior have not been widely discussed. Recently, several researchers had installed the real-time monitoring system to cope with slope failure; however they are mainly focused on the hardware system installation. Therefore, this study tries to show how the measured data could be grouped and connected each other. The basic idea of analyzing method studied in this paper came from the clustering, which is the part of data mining analysis. Therefore, at the base of classification of time series data, the authors suggest three mathematical data analyzing methods; Average Index of different displacement ($AD_{i,j}$), Difference of average relative displacement ($\overline{RD}_{i,j}$) and Coordinate system of average and relative displacement ($\overline{RD}$, AD). These analyzing methods are based on the statistical method and failure mechanism of slope. Therefore they showed clustering relationships of the similar parts of the slope which makes the same sliding mechanism.

• Journal of the International Relations & Interdisciplinary Education
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• v.4 no.1
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• pp.85-111
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• 2024

Since the release of the 2000 PISA results, Finland's education has consistently been regarded as a competitor or benchmark for South Korea's educational system. However, recent indicators of division, opposition, and discontent within our educational sphere suggest a considerable departure from Finland's ethos of happiness in education. Against this backdrop, this study aims to analyze the trends in Finnish education-related research appearing in Korean academic journals. Utilizing network text analysis, we examined 160 papers indexed in RISS with titles containing "Finland" and "education". Key findings are as follows. Firstly, research on Finnish education has been steadily increasing, albeit showing recent signs of decline. Secondly, the majority of research topics were micro-level, with literature review-based methodologies predominating. Thirdly, a minority of researchers accounted for one-third of the total research output. Fourthly, countries compared with Finland predominantly included neoliberal states such as Japan, the United States, the United Kingdom, Australia, and Singapore. Fifthly, research themes and subjects primarily focused on primary and secondary education, particularly in domains such as mathematics and science, influenced by PISA. Future research on Finnish education should transcend localized and fragmented areas of inquiry, undertaking comprehensive investigations into the processes and history of Finland's happiness-oriented education. Such endeavors are essential for deriving insights crucial for our learning. Particularly, consideration should be given to moving beyond literature-based methodologies, fostering international collaborative discussions facilitated online, and linking the Finnish education community with educators, parents, students, local councils, and governmental stakeholders to collectively discuss and research.

• Journal of the Korean Society of Marine Environment & Safety
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• v.30 no.2
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• pp.200-208
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• 2024

The occurrence a fire on a training ship with a large number of people on board can lead to severe casualties. Hence the Seafarers' Act and Safety Life At Sea(SOLAS) emphasizes the importance of the abandon ship drill. Therefore, in this study, the training ship of Mokpo National Maritime University, Segero, which has a large number of people on board, was selected as the target ship and the likelihood and severity of fire accidents on each deck were predicted through the preliminary hazard analysis(PHA) qualitative risk assessment. Additionally, assuming a fire in a high-risk area, a simulation of evacuation time and population density was performed to quantitatively predict the risk. The the total evacuation time was predicted to be the longest at 501s in the meal time scenario, in which the population distribution was concentrated in one area. Depending on the scenario, some decks had relatively high population densities of over 1.4pers/m², preventing stagnation in the number of evacuees. The results of this study are expected to be used as basic data to develop training scenarios for training ships by quantifying evacuation time and population density according to various evacuation scenarios, and the research can be expanded in the future through comparison of mathematical models and experimental values.

• Applied Chemistry for Engineering
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• v.35 no.3
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• pp.192-199
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• 2024

In this study, carbamazepine (CBZ) imprinted starch/PVA-based biomaterials were prepared by the casting method and UV irradiation, and their physicochemical properties, CBZ adsorption ability, and release properties were investigated. The surface properties of the prepared biomaterials were characterized using FE-SEM, while the stability of CBZ under UV irradiation and the functional groups of the biomaterials were characterized using FT-IR analysis. The adsorption properties of CBZ on the biomaterials were evaluated by binding isotherm and Scatchard plot. Results indicate that CBZ imprinted biomaterials possess a specific binding site of CBZ. To evaluate the applicability of the transdermal drug delivery system, the release properties of CBZ from prepared biomaterials using various pH buffers and artificial skin at 36.5 ℃ were investigated. Results indicated that the CBZ release at high pH was faster than at low pH. In addition, CBZ was released continuously for 12 h in the artificial skin test. The drug release mechanism of CBZ followed a pseudo-Fickian diffusion mechanism in buffer solution, whereas the release from artificial skin exhibited a non-Fickian diffusion mechanism.

• Economic and Environmental Geology
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• v.22 no.4
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• pp.303-314
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• 1989

The electrum-silver-sulfide mineralization of the Geojae island area was deposited in three stages (I, II, and carbonate) of quartz and calcite veins that crosscut Late Cretaceous volcanic rocks and granodiorite(83 m.y.). Stages I and II were terminated by the onset of fractunng and breCCIation events. Fluid inclusion data suggest that the gold-sulfide-bearing stages I and II each evolved from an initial high temperature( near $370^{\circ}C$) to a later low temperature(near $200^{\circ}C$). Each of those stages represented a separate mineralizing system which cooled prior to the onset of the next stage. The relationship between homogenization temperature and salinity in stages I and II suggests a complex history of boiling, cooling and dilution. Evidence of boiling indicates a pressure of < 100 bars, corresponding to a depth of 500 to 1,250m assummg hthostatlc and hydrostatic pressure regimes, respectively. Fluid inclusion and mineralogical evidence suggest that the electrum-silver mineralization was deposited at a temperature of $220-260^{\circ}C$ from ore fluids with salinities between 1.9 and 8.1 equivalent wt.% NaCl. Total sulfur concentration is estimated to be $10^{-3}$ to $10^{-4}$ molal. The estimated $fs_2$ and $fo_2$ range from $10^{-11.8}$ to $10^{-14}$ atm and $10^{-35}$ to $10^{-36}$ atm, respectively. The chemical conditions indicate that the dominant sulfur species in the ore forming fluids was a reduced form($H_2S$). Rapid cooling and dilution of ore-forming fluids by mixing with less-evolved meteoric waters led to gold-silver deposition through the breakdown of the bisulfide complex($Au(HS)_2$) as the activity of $H_2S$ decreased.

• 한국지구물리탐사학회:학술대회논문집
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• 2005.09a
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• pp.21-36
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• 2005

Although the main geology of Korea consists of granite and gneiss, it Is not uncommon to encounter anisotropy Phenomena in crosshole radar tomography even when the basement is crystalline rock. To solve the anisotropy Problem, we have developed and continuously upgraded an anisotropic inversion algorithm assuming a heterogeneous elliptic anisotropy to reconstruct three kinds of tomograms: tomograms of maximum and minimum velocities, and of the direction of the symmetry axis. In this paper, we discuss the developed algorithm and introduce some case histories on the application of anisotropic radar tomography in Korea. The first two case histories were conducted for the construction of infrastructure, and their main objective was to locate cavities in limestone. The last two were performed In a granite and gneiss area. The anisotropy in the granite area was caused by fine fissures aligned in the same direction, while that in the gneiss and limestone area by the alignment of the constituent minerals. Through these case histories we showed that the anisotropic characteristic itself gives us additional important information for understanding the internal status of basement rock. In particular, the anisotropy ratio defined by the normalized difference between maximum and minimum velocities as well as the direction of maximum velocity are helpful to interpret the borehole radar tomogram.

• 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.