• Journal of Korea Entertainment Industry Association
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• v.14 no.3
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• pp.63-73
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• 2020

The purpose of this study was to investigate the relationship of academic resilience, academic burnout and major satisfaction and to identify the influencing factors on major satisfaction of nursing students. Data were collected using questionnaires from 215 students who were in 3rd and 4th year of the nursing college in G city, from May to June 2019. The collected data was analyzed using descriptive statistics, pearson's correlation coefficient and stepwise multiple regression with IBM SPSS 25.0 program. The study results showed that university students in nursing scored 3.71±.56 points for academic resilience, 2.96±.52 for academic burnout, 3.78±.54 for major satisfaction. The higher the academic Resilience, the lower the academic burnout and the higher the level of major satisfaction. The factors affection the relationship of academic resilience, academic burnout and major satisfaction are interpersonal relationships and admission motive which resulted in the major satisfaction level of 44.3%. In conclusion, to enhance major satisfaction for nursing students, efficient education program development is required considering the factors that explain the major satisfaction of nursing students. Also It should be considered that required the students' talent and aptitude. So it makes sure that this will require an advanced method of studying to students' capabilities.

• Journal of Internet Computing and Services
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• v.23 no.4
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• pp.21-33
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• 2022

Recently, the recommender system has been widely used in various fields such as movies, music, online shopping, and social media, and in the meantime, the recommender model has been developed from correlation analysis through the Apriori model, which can be said to be the first-generation model in the recommender system field. In 2005, many models have been proposed, including deep learning-based models, which are receiving a lot of attention within the recommender model. The recommender model can be classified into a collaborative filtering method, a content-based method, and a hybrid method that uses these two methods integrally. However, these basic methods are gradually losing their status as methodologies in the field as they fail to adapt to internal and external changing factors such as the rapidly changing user-item interaction and the development of big data. On the other hand, the importance of deep learning methodologies in recommender systems is increasing because of its advantages such as nonlinear transformation, representation learning, sequence modeling, and flexibility. In this paper, among deep learning methodologies, RNN, CNN, and GAN-based models suitable for sequential modeling that can accurately and flexibly analyze user-item interactions are classified, compared, and analyzed.

• The Journal of the Acoustical Society of Korea
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• v.41 no.3
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• pp.326-334
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• 2022

In this paper, we propose speech enhancement algorithm as a pre-processing for robust speech recognition in noisy environments. Auxiliary-function-based Independent Vector Analysis (AuxIVA) is performed with weighted covariance matrix using time-varying variances with scaling factor from target masks representing time-frequency contributions of target speech. The mask estimates can be obtained using Neural Network (NN) pre-trained for speech extraction or diffuseness using Coherence-to-Diffuse power Ratio (CDR) to find the direct sounds component of a target speech. In addition, outputs for omni-directional noise are closely chained by sharing the time-varying variances similarly to independent subspace analysis or IVA. The speech extraction method based on AuxIVA is also performed in Independent Low-Rank Matrix Analysis (ILRMA) framework by extending the Non-negative Matrix Factorization (NMF) for noise outputs to Non-negative Tensor Factorization (NTF) to maintain the inter-channel dependency in noise output channels. Experimental results on the CHiME-4 datasets demonstrate the effectiveness of the presented algorithms.

• Journal of Christian Education in Korea
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• v.70
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• pp.141-173
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• 2022

The purpose of this study is to illuminate depersonalization in the flow of technological revolution and to present a Christian SARAMDAUM education that aims for a new human image. It represents the Christian SARAMDAUM education that adapts to, mediates, and offers alternatives to the technological and human evolutionary flow of the machine age. The purpose of education for this purpose is to aim for 'Homo Creators', creative human beings presented as a new human image in the age of technological revolution. The educational goal is to nurture creative human beings through creative interpretation, creative integration between disciplines, and personal dialogue in the post-mechanical/ post-conventional paradigm. The content of the education is a conversation with the SARAMDAUM that consiliences the characteristics of post-machine and post-convention. The educational method utilizes Edu-Tech and AIED(Artificial Intelligence in Education) to realize systemic thinking and SARAMDAUM dialogue of technology. In addition, the composition of teachers and learners, educational environment and educational evaluation is presented. The significance of this study is that from the point of view of Christian education, the identity of human beings in the era of the technological revolution has been identified, and research on the creative image of the human being is newly attempted, and the direction of Christian SARAMDAUM education aimed at this is presented. This can be said to be a Christian education that emphasizes the essential characteristics of human beings while accommodating the era of technological revolution.

• Korean Journal of Cognitive Science
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• v.34 no.1
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• pp.39-66
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• 2023

New media moved the place of social exchange to the Internet, allowing large groups to communicate in one place beyond the limits of time and space. Recent studies have also reported cases in which human social abilities do not keep up with the expansion of group scale through social media. In this context, current study investigated how human perception of social information is affected by the expansion of the group scale in the context of majority bias. Using Internet-based task, the psychological processes that group ratio and group size are perceived and affect majority-biased social information use were investigated, and whether group scale moderates those processes was examined. The group ratio has a positive effect on the majority bias, and the relationship was partially mediated by ratio perception. Group scale did not moderate the relationship between group ratio and ratio perception. On the other hand, the correlation between group size and majority-biased social information use was not significant. Group scale moderates group size perception. The group size and size perception showed positive correlation under the smaller group scale condition. However under the extended group scale condition, the perceived group size became significantly lower and lost its correlation with group size. These results provide evidence that the psychological mechanism related to group size perception was not properly responding to the expansion of the group scale. Furthermore, the possibility of a specific psychological mechanism for processing group size information and the form of information input specifically accepted by majority bias were discussed from perspective of evolutionary psychology.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.1B
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• pp.61-67
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• 2006

Various methods have been applied for the research to model the relationship between rainfall-runoff, which shows a strong nonlinearity. In particular, most researches to model the relationship between rainfall-runoff using artificial neural networks have used back propagation algorithm (BPA), Levenberg Marquardt (LV) and radial basis function (RBF). and They have been proved to be superior in representing the relationship between input and output showing strong nonlinearity and to be highly adaptable to rapid or significant changes in data. The theory of artificial neural networks is utilized not only for prediction but also for classifying the patterns of data and analyzing the characteristics of the patterns. Thus, the present study applied self?organizing map (SOM) based on Kohonen's network theory in order to classify the patterns of rainfall-runoff process and analyze the patterns. The results from the method proposed in the present study revealed that the method could classify the patterns of rainfall in consideration of irregular changes of temporal and spatial distribution of rainfall. In addition, according to the results from the analysis the patterns between rainfall-runoff, seven patterns of rainfall-runoff relationship with strong nonlinearity were identified by SOM.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.13 no.1
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• pp.117-128
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• 2002

Objectives：For the purpose of obtaining the more vivid present status and prevention program of teenage pregnancy, this survey was done by Obstetricians, as study subject, who manage the pregnant teenager in real clinical situation. Methods：Structured survey form about teenage pregnancy was sent to 2,800 obstetricians. That form contained frequency, characteristics, decision making processes, and psychiatric aspects of the teenage pregnancy. 349 obstetricians replied that survey form and we analysed these datas. Results：(1) The trend of teenage pregnancy was mildly increased. (2) The most common cases were unwanted pregnancy by continuing sexual relationship with boyfriends rather than by forced, accidental sexual relationship with multiple partners. (3) The most common reason of labor was loss the time of artificial abotion. (4) Problems of pregnant girls' were conduct behaviors and poor informations about contraception rather than sexual abuse or mental retardation. (5) Most obstetricians percepted the necessity of psychiatric consultation, however psychiatric consultation was rare due to parents refusal and abscense of available psychiatric facility. (6) For the prevention of teenage pregnancy, the most important thing was practical education about contraception. Conclusions：Based on the result of this study, further study using structured interview schedule with pregnant girl is needed for the detecting risk factor of teenage pregnancy and effective systematic approach to pregnant girl.

• Journal of the Korean Chemical Society
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• v.64 no.6
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• pp.416-428
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• 2020

This study analyzed the uses of external representations presented in the matter units of the 7th-grade science digital textbooks developed under the 2015 revised national curriculum. The level, form, presentation, and interactivity of external representations presented in 5 types of digital textbooks were analyzed. As for the level, the macroscopic level of representations was mainly presented. The macroscopic level and microscopic level of representations were presented together in the particle description. As for the form, visual-verbal and visual-nonverbal representations were usually presented across the board. Very few audial-verbal and audial-nonverbal representations were presented. Visual-verbal and audial-verbal representations were mostly presented in formal form, and visual-nonverbal representations were mostly presented in illustration without movement. The presentation of representations was analyzed in three aspects. First, visual-verbal and visual-nonverbal representations were mainly presented together and none of audial-verbal and visual-nonverbal representations were presented together. When the representations of the audial-verbal, visual-nonverbal, and visual-verbal were presented together, some of the information presented in audial-verbal representations was repeatedly presented in the visual-verbal representations. Second, audial-nonverbal representations not related to learning content were presented along with other representations. Third, there were few cases of arranging visual-verbal and visual-nonverbal representations on the next pages. Audialverbal and visual-nonverbal representations were always presented synchronized. As for the interactivity, the manipulation level was mainly presented in the main area, and the feedback level was mainly presented in the activity area. The adaptation level and the communication level of interactivity were presented very few. Based on the results, the implications for the direction of constructing digital textbooks were discussed.

• Journal of The Korean Society of Grassland and Forage Science
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• v.42 no.2
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• pp.127-136
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• 2022

This study was conducted to estimate the damage of Whole Crop Maize (WCM) according to abnormal climate using machine learning and present the damage through mapping. The collected WCM data was 3,232. The climate data was collected from the Korea Meteorological Administration's meteorological data open portal. Deep Crossing is used for the machine learning model. The damage was calculated using climate data from the Automated Synoptic Observing System (95 sites) by machine learning. The damage was calculated by difference between the Dry matter yield (DMY)_normal and DMY_abnormal. The normal climate was set as the 40-year of climate data according to the year of WCM data (1978~2017). The level of abnormal climate was set as a multiple of the standard deviation applying the World Meteorological Organization(WMO) standard. The DMY_normal was ranged from 13,845~19,347 kg/ha. The damage of WCM was differed according to region and level of abnormal climate and ranged from -305 to 310, -54 to 89, and -610 to 813 kg/ha bnormal temperature, precipitation, and wind speed, respectively. The maximum damage was 310 kg/ha when the abnormal temperature was +2 level (+1.42 ℃), 89 kg/ha when the abnormal precipitation was -2 level (-0.12 mm) and 813 kg/ha when the abnormal wind speed was -2 level (-1.60 m/s). The damage calculated through the WMO method was presented as an mapping using QGIS. When calculating the damage of WCM due to abnormal climate, there was some blank area because there was no data. In order to calculate the damage of blank area, it would be possible to use the automatic weather system (AWS), which provides data from more sites than the automated synoptic observing system (ASOS).

• Journal of The Korean Society of Grassland and Forage Science
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• v.43 no.1
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• pp.11-21
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• 2023

This study was conducted to estimate the damage of Whole Crop Corn (WCC; Zea Mays L.) according to abnormal climate using machine learning as the Representative Concentration Pathway (RCP) 4.5 and present the damage through mapping. The collected WCC data was 3,232. The climate data was collected from the Korea Meteorological Administration's meteorological data open portal. The machine learning model used DeepCrossing. The damage was calculated using climate data from the automated synoptic observing system (ASOS, 95 sites) by machine learning. The calculation of damage was the difference between the dry matter yield (DMY)_normal and DMY_abnormal. The normal climate was set as the 40-year of climate data according to the year of WCC data (1978-2017). The level of abnormal climate by temperature and precipitation was set as RCP 4.5 standard. The DMY_normal ranged from 13,845-19,347 kg/ha. The damage of WCC which was differed depending on the region and level of abnormal climate where abnormal temperature and precipitation occurred. The damage of abnormal temperature in 2050 and 2100 ranged from -263 to 360 and -1,023 to 92 kg/ha, respectively. The damage of abnormal precipitation in 2050 and 2100 was ranged from -17 to 2 and -12 to 2 kg/ha, respectively. The maximum damage was 360 kg/ha that the abnormal temperature in 2050. As the average monthly temperature increases, the DMY of WCC tends to increase. The damage calculated through the RCP 4.5 standard was presented as a mapping using QGIS. Although this study applied the scenario in which greenhouse gas reduction was carried out, additional research needs to be conducted applying an RCP scenario in which greenhouse gas reduction is not performed.