• Journal of Korea Entertainment Industry Association
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• v.15 no.5
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• pp.55-68
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• 2021

The purpose of this study is to analyze the characters and narratives of series films and to extract corresponding elements of film music to compare and analyze how musical elements were utilized. The scope of the study was analyzing the story structure and characters of the films "Mary Poppins" and "Mary Poppins Returns" and the corresponding film music. After comparing the contents of the film "Mary Poppins" and "Mary Poppins Returns," the film matched the film music equivalent to the similar scenes of the two films. As a result, seven of the 11 songs of "Mary Poppins" overlap with those used in similar narratives of "Mary Poppins Returns", and eight songs overlap in "Mary Poppins Returns". Seven songs from "Mary Poppins" and eight songs from "Mary Poppins Returns" can be divided into nine scenes in total when connected to a common narrative. Among them, "A Spoonful of Sugar" from "Mary Poppins", "Jolly Holiday", "A cover is not the Book" from "Mary Poppins Returns" and "Triple light fantastic" were overlapping songs with narratives. Based on this, it analyzes leitmotiv film music, focusing on characters from the films "Mary Poppins" and "Mary Poppins Returns." The common leitmotivs in the two films are Mary Poppins leitmotiv, Lesson leitmotiv, Lullaby leitmotiv, World leitmotiv, Chimney Sweeper leitmotiv, Up & Down leitmotiv, Chimney Sweeper leitmotiv, and Sky leitmotiv. The characteristic rhythm and pitch used in Mary Poppins leitmotivs were used in the overall song featuring Mary Poppins. Through this, the elements of music symbolizing Mary Poppins, a key figure, were matched to the films "Mary Poppins" and "Mary Poppins Returns" and modified according to the narrative flow. The analysis results of this work have theoretical significance in that it is necessary to analyze the narratives and film music of series films to discover common features and consider how they are matched in theoretical terms.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.5
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• pp.431-449
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• 2022

As many tunnels generally have been constructed, various experiences and techniques have been accumulated for tunnel design as well as tunnel construction. Hence, there are not a few cases that, for some usual tunnel design works, it is sufficient to perform the design by only modifying or supplementing previous similar design cases unless a tunnel has a unique structure or in geological conditions. In particular, for a tunnel blast design, it is reasonable to refer to previous similar design cases because the blast design in the stage of design is a preliminary design, considering that it is general to perform additional blast design through test blasts prior to the start of tunnel excavation. Meanwhile, entering the industry 4.0 era, artificial intelligence (AI) of which availability is surging across whole industry sector is broadly utilized to tunnel and blasting. For a drill and blast tunnel, AI is mainly applied for the estimation of blast vibration and rock mass classification, etc. however, there are few cases where it is applied to blast pattern design. Thus, this study attempts to automate tunnel blast design by means of machine learning, a branch of artificial intelligence. For this, the data related to a blast design was collected from 25 tunnel design reports for learning as well as 2 additional reports for the test, and from which 4 design parameters, i.e., rock mass class, road type and cross sectional area of upper section as well as bench section as input data as well as16 design elements, i.e., blast cut type, specific charge, the number of drill holes, and spacing and burden for each blast hole group, etc. as output. Based on this design data, three machine learning models, i.e., XGBoost, ANN, SVM, were tested and XGBoost was chosen as the best model and the results show a generally similar trend to an actual design when assumed design parameters were input. It is not enough yet to perform the whole blast design using the results from this study, however, it is planned that additional studies will be carried out to make it possible to put it to practical use after collecting more sufficient blast design data and supplementing detailed machine learning processes.

• Journal of the Korean Society of Radiology
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• v.16 no.6
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• pp.779-786
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• 2022

This study is designed to examine the effects of Dioscorea Quinqueloba extract as a natural radiation protection agent on the prostate and heart of male rats. Dioscorea Quinqueloba extract is well known to prevent the male-specific disease and heart disease. In this study, the Gamma-ray 10 Gy was irradiated in whole body of male rat to identify radioprotective effect by Dioscorea Quinqueloba extract. After irradiation, tissue change, SOD (Superoxide Dismutase) activity changes and hematological changes were observed. DQ+IR group showed higher lymphocyte, white blood cell, platelet levels than the IR group. In the NC and DQ groups, the number of prostate gland cells and the gap between cells were relatively narrow. But in the IR group, the cells died significantly and the gap widened. In the DQ+IR group, the gap between cells increased similarly to the IR group, but the number of dead cells was noticeably smaller. In the NC and DQ groups, the cardiovascular and myocardium are clearly separated, and cell nuclei are in good condition. But in the IR group, the cardiovascular and myocardium boundaries were disrupted, and the number of dead cell nuclei was high. In the DQ+IR group, although the boundaries were widened, but not disrupted and the number of dead cell nuclei was high. Therefore, Dioscorea Quinqueloba extract is judged to have radioprotective properties for the prostate and cardiovascular.

• Journal of Service Research and Studies
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• v.12 no.2
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• pp.65-79
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• 2022

Non-face-to-face classes continue due to Covid-19. There have been e-learning classes since the past, but the difference is that the current non-face-to-face classes are blended classes that combine real-time and recording classes or combine face-to-face and non-face classes. It is also characterized by being able to self-filmed or choose various lecture platforms in a place other than a dedicated studio. The advantages of non-face-to-face classes can be learned beyond time and space, and repetitive viewing and learning speed can be adjusted. Greening classes have no time and place constraints, and real-time classes have the advantage of high communication effects with learners. Evaluating whether non-face-to-face classes provide sufficient quality compared to face-to-face classes or e-learning will be necessary if branded classes are considered for post Covid. In this paper, for the evaluation of the service quality of non-face-to-face classes, the essential attributes desired by the instructors were derived from the viewpoint of Kano quality attributes and a quality improvement plan was proposed. After expressing the degree of functions that non-face-to-face classes should have on the X-axis and the satisfaction of learners on the Y-axis, 23 quality attributes were classified into 6 quality dimensions. In addition, satisfaction coefficient, dissatisfaction coefficient, and customer satisfaction improvement index were derived. As a result, 50% of learners were satisfied with non-face-to-face classes, but the preference was slightly higher than satisfaction, suggesting the sustainability of non-face-to-face classes. In terms of the customer satisfaction improvement index, the ranking of attributes with the largest increase in satisfaction when improving class quality was as follows. Professors' quick answers to learners' questions, content that can fully explain the subject, what the professor explains easily, develop high-quality content that can be learned on mobile phones, fairness of attendance checks, and real-time classes should start on time.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.6
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• pp.21-35
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• 2022

With the support of local and central governments, various incentive policies for "green" cars have been established, and the number of electric vehicle users has been rapidly increasing in recent years. As a result, much attention is being given to establishing a user-centered charging infrastructure. A standard for the number of electric vehicle chargers to be supplied is being prepared based on building characteristics, but there is quite limited research on the appropriate ratio of slow and fast chargers based on the characteristics of each place. Therefore, this study derived an appropriate penetration ratio based on data about the distribution ratio of common slow chargers. These data were collected using a survey of actual electric vehicle users. Next, an analysis was done on how to categorize the needs of charging environments and to determine what criteria or characteristics to use for categorization. Based on the results of the survey analysis, three types of places were derived. Type-1 places require 10% of chargers to be slow chargers, Type-2 places require 40-60% of chargers to be slow chargers (i.e., around equal distribution of slow and fast chargers), and Type-3 places require more than 80% of chargers to be slow chargers. The required levels of slow chargers were classified by place type and by individual using latent class cluster analysis, which made it possible to categorize them into five clusters related to socioeconomic variables, vehicle characteristics, traffic, and charging behaviors. It was found that there was a high correlation between charging behavior, weekend travel behavior, gender, and income. The results and insights from this study could be used to establish charging infrastructure policies in the future and to prepare standards for supplying charging infrastructure according to changes in the electric vehicle market.

• Economic and Environmental Geology
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• v.55 no.6
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• pp.737-760
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• 2022

For the geological disposal of high-level radioactive wastes (HLW), an understanding of deep subsurface environment is essential through geological, hydrogeological, geochemical, and geotechnical investigations. Although South Korea plans the geological disposal of HLW, only a few studies have been conducted for characterizing the geochemistry of deep subsurface environment. To guide the hydrogeochemical research for selecting suitable repository sites, this study overviewed the status and trends in hydrogeochemical characterization of deep groundwater for the deep geological disposal of HLW in developed countries. As a result of examining the selection process of geological disposal sites in 8 countries including USA, Canada, Finland, Sweden, France, Japan, Germany, and Switzerland, the following geochemical parameters were needed for the geochemical characterization of deep subsurface environment: major and minor elements and isotopes (e.g., ³⁴S and ¹⁸O of SO₄^2-, ¹³C and ¹⁴C of DIC, ²H and ¹⁸O of water) of both groundwater and pore water (in aquitard), fracture-filling minerals, organic materials, colloids, and oxidation-reduction indicators (e.g., Eh, Fe²⁺/Fe³⁺, H₂S/SO₄^2-, NH₄⁺/NO₃^-). A suitable repository was selected based on the integrated interpretation of these geochemical data from deep subsurface. In South Korea, hydrochemical types and evolutionary patterns of deep groundwater were identified using artificial neural networks (e.g., Self-Organizing Map), and the impact of shallow groundwater mixing was evaluated based on multivariate statistics (e.g., M3 modeling). The relationship between fracture-filling minerals and groundwater chemistry also has been investigated through a reaction-path modeling. However, these previous studies in South Korea had been conducted without some important geochemical data including isotopes, oxidationreduction indicators and DOC, mainly due to the lack of available data. Therefore, a detailed geochemical investigation is required over the country to collect these hydrochemical data to select a geological disposal site based on scientific evidence.

• The Korean Journal of Applied Statistics
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• v.36 no.1
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• pp.49-62
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• 2023

Solar energy, which is rapidly increasing in proportion, is being continuously developed and invested. As the installation of new and renewable energy policy green new deal and home solar panels increases, the supply of solar energy in Korea is gradually expanding, and research on accurate demand prediction of power generation is actively underway. In addition, the importance of solar radiation prediction was identified in that solar radiation prediction is acting as a factor that most influences power generation demand prediction. In addition, this study can confirm the biggest difference in that it attempted to predict solar radiation using medium-term forecast weather data not used in previous studies. In this paper, we combined the multi-linear regression model, KNN, random fores, and SVR model and the clustering technique, K-means, to predict solar radiation by hour, by calculating the probability density function for each cluster. Before using medium-term forecast data, mean absolute error (MAE) and root mean squared error (RMSE) were used as indicators to compare model prediction results. The data were converted into daily data according to the medium-term forecast data format from March 1, 2017 to February 28, 2022. As a result of comparing the predictive performance of the model, the method showed the best performance by predicting daily solar radiation with random forest, classifying dates with similar climate factors, and calculating the probability density function of solar radiation by cluster. In addition, when the prediction results were checked after fitting the model to the medium-term forecast data using this methodology, it was confirmed that the prediction error increased by date. This seems to be due to a prediction error in the mid-term forecast weather data. In future studies, among the weather factors that can be used in the mid-term forecast data, studies that add exogenous variables such as precipitation or apply time series clustering techniques should be conducted.

• Journal of the Korean Society of Radiology
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• v.17 no.3
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• pp.315-325
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• 2023

The purpose of this study was to predict abdominal obesity with 3-Dimensional computed tomography (3D CT) measurements of kidneys by analyzing the correlation between kidney sizes and abdominal obesity level. The subjects were 178 healthy adults without underlying diseases who had a comprehensive health examination at the Health Medical Center of Jesus Hospital in Jeonju. Abdominal obesity was measured by CT cross-sectional image at the level of the umbilicus and divided into visceral fat area, subcutaneous fat area, visceral fat/total fat ratio. The average comparison of kidney sizes classified according to abdominal obesity were performed through one-way analysis of variance (ANOVA) and Scheffe test. Pearson correlation analysis was performed to correlate all measurement values. The results of kidney size ANOVA analysis according to abdominal obesity were as follows. The means of kidney measurements according to visceral fat classification were significantly different in all kidney measurements (p<0.05). And in case of subcutaneous fat classification, the means of kidney measurements by 3D CT of the severe obesity group were significantly different in the right kidney width (p<0.05). In case of visceral fat area/total fat area ratio, the means of kidney measurements by 3D CT of the severe obesity group were significantly different in both kidneys width (p<0.05). Pearson correlation between kidneys measurements and CT abdominal obesity showed that visceral fat area had the highest correlation with the left kidney width measured by 3D CT (r=0.467) and subcutaneous fat area had correlation with the right kidney width measured by 3D CT (r=0.249). The visceral fat area/total fat area ratio had correlation with the left kidney width measured by 3D CT (r=0.291).

• Journal of Practical Agriculture & Fisheries Research
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• v.21 no.1
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• pp.99-114
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• 2019

In this study we examined the preferences of eight college living factors for students at Korea National College of Agriculture and Fisheries(KNCAF). Analytical techniques of unstructured data used opinion mining and text mining techniques, and the analysis results of text mining were visualized as word cloud. The college life factors included eight topics that were closely related to students: 'my present', 'my 10 years later', 'friendship', 'college festival', 'student restaurant', 'college dormitory', 'KNCAF', and 'long-term field practice'. In the text submitted by the students, we have established a dictionary of positive words and negative words to evaluate the preference by classifying the emotions of positive and negative. As a result, KNCAF students showed more than 85% positive emotions about the theme of 'student restaurant' and 'friendship'. But students' positive feelings about 'long-term field practice' and 'college dormitory' showed the lowest satisfaction rate of not exceeding 60%. The rest of the topics showed satisfaction of 69.3~74.2%. The gender differences showed that the positive emotions of male students were high in the topics of 'my present', 'my 10 years later', 'friendship', 'college dormitory' and 'long-term field practice'. And those of female were high in 'college festival', 'student restaurant' and 'KNCAF'. In addition, using text mining technique, the main words of positive and negative words were extracted, and word cloud was created to visualize the results.

• Korean Journal of Culture and Social Issue
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• v.9 no.spc
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• pp.107-122
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• 2003

The object of this study is to evaluate the prevailing physical and psychosocial conditions regarding occupational low back injury. This study consists of two parts. In the first part of the study, analytic biomechanical model and NIOSH guidelines are applied to evaluate risk levels of low back injury for automobile assembly jobs. Total of 246 workers are analysed. There are 20 jobs having greater back compressive forces than 300kg at L5/S1. Also, there are 44 jobs over Action Limit with respect to 1981 NIOSH guidelines. The relationship between psychosocial factors and low back injury was examined in the second part of the study. A battery of questionnaires concerning the psychosocial stress based on PWI (Psychosocial Well-being Index) and musculoskeletal pain symptoms at low back was completed by 246 workers at the same plant. Results showed that 207 out 246 workers experienced the symptoms and 27 workers were diagnosed as patients. Two groups(low stressed, high stressed) based on PWI score had no significant relationships with both symptoms and results of diagnosis. The relationships between physical work load and psychosocial stress were also analysed. Specifically, some postural factors(vertical deviation angle of forearm, horizontal deviation angle of upperarm, vertical deviation angle of thigh, etc) were highly correlated with psychosocial stress. The results illustrated that PWI scores were associated with some physical workloads. However, psychosocial stress levels couldn't be well related with the pain symptom as well as the actual incidence of low back injury since pain or discomfort regarding low back injury were more complex than that of other musculoskeletal disorders.