• Clean Technology
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• v.28 no.1
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• pp.63-78
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• 2022

Electronics industrial wastewater treatment facilities release organic wastewaters containing high concentrations of organic pollutants and more than 20 toxic non-biodegradable pollutants. One of the major challenges of the fourth industrial revolution era for the electronics industry is how to treat electronics industrial wastewater efficiently. Therefore, it is necessary to develop an electronics industrial wastewater modeling technique that can evaluate the removal efficiency of organic pollutants, such as chemical oxygen demand (COD), total nitrogen (TN), total phosphorous (TP), and tetramethylammonium hydroxide (TMAH), by digital twinning an electronics industrial organic wastewater treatment facility in a cyber physical system (CPS). In this study, an electronics industrial wastewater activated sludge model (e-ASM) was developed based on the theoretical reaction rates for the removal mechanisms of electronics industrial wastewater considering the growth and decay of micro-organisms. The developed e-ASM can model complex biological removal mechanisms, such as the inhibition of nitrification micro-organisms by non-biodegradable organic pollutants including TMAH, as well as the oxidation, nitrification, and denitrification processes. The proposed e-ASM can be implemented as a Water Digital Twin for real electronics industrial wastewater treatment systems and be utilized for process modeling, effluent quality prediction, process selection, and design efficiency across varying influent characteristics on a CPS.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.4
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• pp.41-54
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• 2021

It is very essential to estimate the water body area using remote exploration for water resource management, analysis and prediction of water disaster damage. Hydrophysical detection using satellites has been mainly performed on large satellites equipped with optical and SAR sensors. However, due to the long repeat cycle, there is a limitation that timely utilization is impossible in the event of a disaster/disaster. With the recent active development of Micro satellites, it has served as an opportunity to overcome the limitations of time resolution centered on existing large satellites. The Micro satellites currently in active operation are ICEYE in Finland and Capella satellites in the United States, and are operated in the form of clusters for earth observation purposes. Due to clustering operation, it has a short revisit cycle and high resolution and has the advantage of being able to observe regardless of weather or day and night with the SAR sensor mounted. In this study, the operation status and characteristics of micro satellites were described, and the water area estimation technology optimized for micro SAR satellite images was applied to the Daecheong Dam basin on the Korean Peninsula. In addition, accuracy verification was performed based on the reference value of the water generated from the optical satellite Sentinel-2 satellite as a reference. In the case of the Capella satellite, the smallest difference in area was shown, and it was confirmed that all three images showed high correlation. Through the results of this study, it was confirmed that despite the low NESZ of Micro satellites, it is possible to estimate the water area, and it is believed that the limitations of water resource/water disaster monitoring using existing large SAR satellites can be overcome.

• Journal of Korea Water Resources Association
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• v.55 no.10
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• pp.723-736
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• 2022

In this study, monthly precipitation forecasting models that can predict up to 12 months in advance were constructed for the Geum River basin, and two statistical techniques, multiple linear regression (MLR) and artificial neural network (ANN), were applied to the model construction. As predictor candidates, a total of 47 climate indices were used, including 39 global climate patterns provided by the National Oceanic and Atmospheric Administration (NOAA) and 8 meteorological factors for the basin. Forecast models were constructed by using climate indices with high correlation by analyzing the teleconnection between the monthly precipitation and each climate index for the past 40 years based on the forecast month. In the goodness-of-fit test results for the average value of forecasts of each month for 1991 to 2021, the MLR models showed -3.3 to -0.1% for the percent bias (PBIAS), 0.45 to 0.50 for the Nash-Sutcliffe efficiency (NSE), and 0.69 to 0.70 for the Pearson correlation coefficient (r), whereas, the ANN models showed PBIAS -5.0~+0.5%, NSE 0.35~0.47, and r 0.64~0.70. The mean values predicted by the MLR models were found to be closer to the observation than the ANN models. The probability of including observations within the forecast range for each month was 57.5 to 83.6% (average 72.9%) for the MLR models, and 71.5 to 88.7% (average 81.1%) for the ANN models, indicating that the ANN models showed better results. The tercile probability by month was 25.9 to 41.9% (average 34.6%) for the MLR models, and 30.3 to 39.1% (average 34.7%) for the ANN models. Both models showed long-term predictability of monthly precipitation with an average of 33.3% or more in tercile probability. In conclusion, the difference in predictability between the two models was found to be relatively small. However, when judging from the hit rate for the prediction range or the tercile probability, the monthly deviation for predictability was found to be relatively small for the ANN models.

• Journal of Korean Tunnelling and Underground Space Association
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• v.23 no.6
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• pp.451-468
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• 2021

Predicting hazardous ground conditions ahead of a TBM (Tunnel Boring Machine) tunnel face is essential for efficient and stable TBM advance. Although there have been several studies on the electrical resistivity survey method for TBM tunnelling, sufficient experimental data considering TBM advance were not established yet. Therefore, in this study, the laboratory-scale model experiments for simulating TBM excavation were carried out to analyze the applicability of an electrical resistivity survey for predicting hazardous ground conditions ahead of a TBM tunnel face. The trend of electrical resistivity during TBM advance was experimentally evaluated under various hazardous ground conditions (fault zone, seawater intruded zone, soil to rock transition zone, and rock to soil transition zone) ahead of a tunnel face. In the course of the experiments, a scale-down rock ground was provided using granite blocks to simulate the rock TBM tunnelling. Based on the experimental data, the electrical resistivity tends to decrease as the tunnel approaches the fault zone. While the seawater intruded zone follows a similar trend with the fault zone, the resistivity value of the seawater intrude zone decreased significantly compared to that of the fault zone. In case of the soil-to-rock transition zone, the electrical resistivity increases as the TBM approaches the rock with relatively high electrical resistivity. Conversely, in case of the rock-to-soil transition zone, the opposite trend was observed. That is, electrical resistivity decreases as the tunnel face approaches the rock with relatively low electrical resistivity. The experiment results represent that hazardous ground conditions (fault zone, seawater intruded zone, soil-to-rock transition zone, rock-to-soil transition zone) can be efficiently predicted by utilizing an electrical resistivity survey during TBM tunnelling.

• Journal of Conservation Science
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• v.37 no.6
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• pp.689-700
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• 2021

This study aims to optimize the measurement conditions of handmade Hanji paper to reduce the time required for measurement while meeting folding endurance standards, for various basis weights. Thirteen types were selected, including 12 types of Hanji with grammages of 17 g/m², 30 g/m², and 45 g/m², and one type of 75 g/m² copy paper, and 24 sheets were prepared in the laid and chain directions. Subsequently, folding endurance experiments at different load (14.72 N, 9.81 N, 4.91 N) and specimen width conditions (15 mm, 10 mm, 5 mm) were performed, using a 4-MIT folding endurance tester. The results showed that, for specimen widths of 15 mm and 10 mm under a load of 4.91 N, the folding endurance falls within the reference range (10~10000 times). In particular, reduced folding endurance range and measurement time were observed at a width of 10 mm under a load of 4.91 N. Moreover, the endurance trend lines based on the average values of load and specimen width ranges enabled the prediction of the folding endurance within those ranges, according to the decreasing slope. Furthermore, for a specimen width of 15 mm under a load of 4.91 N, the folding endurance in the comparison group (35 to 17723 times) significantly exceeds the upper limit of the reference range (10~10000 times). Therefore, the measurement conditions of 4.91 N with a specimen width of 10 mm are proposed.

• Journal of Korean Tunnelling and Underground Space Association
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• v.23 no.6
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• pp.469-484
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• 2021

Rock mass classification results have a great influence on construction schedule and budget as well as tunnel stability in tunnel design. A total of 3,526 tunnels have been constructed in Korea and the associated techniques in tunnel design and construction have been continuously developed, however, not many studies have been performed on how to assess rock mass quality and grade more accurately. Thus, numerous cases show big differences in the results according to inspectors' experience and judgement. Hence, this study aims to suggest a more reliable rock mass classification (RMR) model using machine learning algorithms, which is surging in availability, through the analyses based on various rock and rock mass information collected from boring investigations. For this, 11 learning parameters (depth, rock type, RQD, electrical resistivity, UCS, V_p, V_s, Young's modulus, unit weight, Poisson's ratio, RMR) from 13 local tunnel cases were selected, 337 learning data sets as well as 60 test data sets were prepared, and 6 machine learning algorithms (DT, SVM, ANN, PCA & ANN, RF, XGBoost) were tested for various hyperparameters for each algorithm. The results show that the mean absolute errors in RMR value from five algorithms except Decision Tree were less than 8 and a Support Vector Machine model is the best model. The applicability of the model, established through this study, was confirmed and this prediction model can be applied for more reliable rock mass classification when additional various data is continuously cumulated.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.4
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• pp.99-112
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• 2021

This study was performed to predict spatial change of future forestland area in South Korea at regional level for supporting forest-related plans established by local governments. In the study, land use was classified to three types which are forestland, agricultural land, and urban and other lands. A logistic regression model was developed using transitional interaction between each land use type and topographical factors, land use restriction factors, socioeconomic indices, and development infrastructures. In this model, change probability from a target land use type to other land use types was estimated using raster dataset(30m×30m) for each variable. With priority order map based on the probability of land use change, the total annual amount of land use change was allocated to the cells in the order of the highest transition potential for the spatial analysis. In results, it was found that slope degree and slope standard value by the local government were the main factors affecting the probability of change from forestland to urban and other land. Also, forestland was more likely to change to urban and other land in the conditions of a more gentle slope, lower slope criterion allowed to developed, and higher land price and population density. Consequently, it was predicted that forestland area would decrease by 2027 due to the change from forestland to urban and others, especially in metropolitan and major cities, and that forestland area would increase between 2028 and 2050 in the most local provincial cities except Seoul, Gyeonggi-do, and Jeju Island due to locality extinction with decline in population. Thus, local government is required to set an adequate forestland use criterion for balanced development, reasonable use and conservation, and to establish the regional forest strategies and policies considering the future land use change trends.

• Journal of Korea Entertainment Industry Association
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• v.13 no.3
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• pp.193-202
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• 2019

This study focuses on what changes are required as to a curriculum of dance department in the university in the age of the 4th industrial revolution. By comparing and analyzing the curricula of dance department in the five universities in Seoul, five academic subjects as to curricula of dance department, which covers what to learn for dance education in the age of the 4th industrial revolution, are presented. First, dance integrative education, the integration of creativity and science education, can be referred to as a subject that stimulates ideas and creativity and raises artistic sensitivity based on STEAM. Second, the curriculum characterized by prediction of the future prospect through Big Data can be utilized well in dealing with dance performance, career path of dance-majoring people, and job creation by analyzing public opinion, evaluation, and feelings. Third, video education. Seeing the images as modern major media tends to occupy most of the expressive area of art, dance by dint of video enables existing dance work to be created as new form of art, expanding dance boundaries in academic and performing art viewpoint. Fourth, VR and AR are essential techniques in the era of smart media. Whether upcoming dance studies are in the form of performance or education or industry, for VR and AR to be digitally applied into every relevant field, keeping with the time, learning about VR and AR is indispensable. Last, the 4th industrial revolution and the curriculum of dance art are needed to foresee the changes in the 4th industrial revolution and to educate changes, development and seeking in dance curriculum.

• Journal of Environmental Impact Assessment
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• v.32 no.2
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• pp.94-111
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• 2023

In order to establish an effective management strategy for invasive species early detection and regular monitoring are required to assess their introduction or dispersal. Environmental DNA (eDNA) is actively applied to evaluate the fauna including the presence of invasive species as it has high detection sensitivity and can detect multiple species simultaneously. In Korea, the applicability evaluation of metabarcoding is being conducted mainly on fish, and research on other taxa is insufficient. Therefore, this study identified the feasibility of detecting invasive species in Korea using eDNA metabarcoding. In addition, to confirm the possibility of detection by taxa, the detection of target species was evaluated using four universal primers (MiFish, MiMammal, Mibird, Amp16S) designed for fish, mammals, birds, and amphibians. As a result, target species (Trachemys scripta, 3 sites; Cervus nippon, 3 sites; Micropterus salmoides, 7 sites; Rana catesbeiana, 4 sites) were detected in 17 of the total 55 sites. Even in the selection of dense sampling sites within the study area, there was a difference in the detection result by reflecting the ecological characteristics of the target species. A comparison of community structures (species richness, abundance and diversity) based on the presence of invasive species focused on M.salmoides and T.scripta, showed higher diversity at the point where invasive species were detected. Also, 1 to 4 more species were detected and abundance was also up to 1.7 times higher. The results of invasive species detection through metabarcoding and the comparison of community structures indicate that the accumulation of large amounts of monitoring data through eDNA can be efficiently utilized for multidimensional ecosystem evaluation. In addition, it suggested that eDNA can be used as major data for evaluation and prediction, such as tracking biological changes caused by artificial and natural factors and environmental impact assessment.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.263-286
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• 2022

This study acquires NBA statistical information for a total of 32 years from 1990 to 2022 using web crawling, observes variables of interest through exploratory data analysis, and generates related derived variables. Unused variables were removed through a purification process on the input data, and correlation analysis, t-test, and ANOVA were performed on the remaining variables. For the variable of interest, the difference in the mean between the groups that advanced to the playoffs and did not advance to the playoffs was tested, and then to compensate for this, the average difference between the three groups (higher/middle/lower) based on ranking was reconfirmed. Of the input data, only this year's season data was used as a test set, and 5-fold cross-validation was performed by dividing the training set and the validation set for model training. The overfitting problem was solved by comparing the cross-validation result and the final analysis result using the test set to confirm that there was no difference in the performance matrix. Because the quality level of the raw data is high and the statistical assumptions are satisfied, most of the models showed good results despite the small data set. This study not only predicts NBA game results or classifies whether or not to advance to the playoffs using machine learning, but also examines whether the variables of interest are included in the major variables with high importance by understanding the importance of input attribute. Through the visualization of SHAP value, it was possible to overcome the limitation that could not be interpreted only with the result of feature importance, and to compensate for the lack of consistency in the importance calculation in the process of entering/removing variables. It was found that a number of variables related to three points and errors classified as subjects of interest in this study were included in the major variables affecting advancing to the playoffs in the NBA. Although this study is similar in that it includes topics such as match results, playoffs, and championship predictions, which have been dealt with in the existing sports data analysis field, and comparatively analyzed several machine learning models for analysis, there is a difference in that the interest features are set in advance and statistically verified, so that it is compared with the machine learning analysis result. Also, it was differentiated from existing studies by presenting explanatory visualization results using SHAP, one of the XAI models.