• Journal of the Korea Society of Computer and Information
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• v.25 no.6
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• pp.171-181
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• 2020

This is an empirical study that analyzes factors affecting the work-family compatibility of female wage workers with underage children. The analysis was conducted with 1,113 women from the 7th wave of the Korean Longitudinal Survey of Women & Families by Korean Women's Development Institute. As for research methods, multiple regression analysis was used in order to analyze the effects of 'social support(home support, maternity protection support, childcare and education services)', 'job characteristics', 'socio-demographic variables' and 'husband characteristics' on 'work-family compatibility and conflict'. As a result, it was analyzed that the husband's support for work life, gender inequality at work and women's educational training were the factors that strengthen work-family compatibility. It was also analyzed that gender inequality, use of childcare and education facility, number of underage children, age of husband, husband's satisfaction with caring support and husband's support for work life were the factors that cause conflict between work and family. Thus, if the policy of strengthening the work-family compatibility is a long-term policy, it appears that it is necessary to supplement and strengthen policies that can reduce conflict factors in the short term. It is hoped that the results of the study will be used as objective and academic data to strengthen the maternity protection and work-family compatibility of female workers with underage children.

• The Journal of Korean Society for School & Community Health Education
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• v.4
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• pp.79-95
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• 2003

This dissertation aims to analyze various safety accidents taking place during physical education class according to physical education teacher's value orientation, to identify teacher's value orientation that can minimize safety accidents, and to provide basic materials for safe and smooth class management. For this purpose, data have been collected from 261 physical education teachers at some middle and high schools in G City in Kyung-Ki Province and Busan Metropolitan City. The materials were treated as follows: The variables on demographic characteristics of physical education teachers are determined by t-test ; The analysis of one-way ANOVA and relationship between value orientation and safety accident prevention activities was conducted through Pearson's linear correlation analysis and multiple regression; The analysis of the relationship between value orientation and actual conditions of safety accidents was conducted through logistic regression. First, there is almost no awareness difference of physical education teachers' value orientation according to demographical variables. The value orientation physical education teachers consider to be the most important is, however, mainly 'mastery of disciplinary lesson.' There is a statistically significant difference in safety accident prevention activities according to demographical variables. Teachers' focuses in class contents showed a significant difference according to teaching experience and working area, while the dependency on facility has a significant difference according to teaching experience and school type. Second, there is no correlation between physical education teacher's value orientation and safety accident prevention activities because there is virtually no statistically significant difference between them. It means that safety accident prevention activities are not related with on which teachers place emphasis among mastery of disciplinary lesson, social reconstruction, self-realization, ecological integration and value orientation on learning process. Third, the analysis of safety accident prevention activities according to physical education teachers' value orientation revealed that the lower value orientation in social reconstruction is, the more safety accidents teachers experience. It is also found that crashes among students, ball games and leg injuries are inter-related with social reconstruction in value orientation, over-motivation and unskilled motor function ; athletic sports with value orientation on learning process and safety prevention training ; unskilled motor functions with value orientation in ecological integration and disobedience to teacher's directions ; winter accidents with mastery of disciplinary lesson in value orientation. In conclusion, the research indicates that physical education teacher's value orientation according to demographical variables didn't show any significant difference, while one according to safety accident prevention activities showed significant difference. Besides, physical education teachers' value orientation is not related to safety accident prevention activities, but the relationship between value orientation and actual conditions of safety accidents showed correlations according to each variable. Especially, teachers with lower value orientation in social reconstruction experienced more safety accidents. Therefore, physical education teachers can manage physical education class more safely with more emphasis on value orientation in social reconstruction.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.5
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• pp.574-583
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• 2021

Predicting shipping markets is an important issue. Such predictions form the basis for decisions on investment methods, fleet formation methods, freight rates, etc., which greatly affect the profits and survival of a company. To this end, in this study, we propose a shipping freight rate prediction model for container ships using gated recurrent units (GRUs) and long short-term memory structure. The target of our freight rate prediction is the China Container Freight Index (CCFI), and CCFI data from March 2003 to May 2020 were used for training. The CCFI after June 2020 was first predicted according to each model and then compared and analyzed with the actual CCFI. For the experimental model, a total of six models were designed according to the hyperparameter settings. Additionally, the ARIMA model was included in the experiment for performance comparison with the traditional analysis method. The optimal model was selected based on two evaluation methods. The first evaluation method selects the model with the smallest average value of the root mean square error (RMSE) obtained by repeating each model 10 times. The second method selects the model with the lowest RMSE in all experiments. The experimental results revealed not only the improved accuracy of the deep learning model compared to the traditional time series prediction model, ARIMA, but also the contribution in enhancing the risk management ability of freight fluctuations through deep learning models. On the contrary, in the event of sudden changes in freight owing to the effects of external factors such as the Covid-19 pandemic, the accuracy of the forecasting model reduced. The GRU1 model recorded the lowest RMSE (69.55, 49.35) in both evaluation methods, and it was selected as the optimal model.

• Journal of Korea Water Resources Association
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• v.54 no.12
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• pp.1285-1294
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• 2021

In recent years, as rainfall is concentrated and rainfall intensity increases worldwide due to climate change, the scale of flood damage is increasing. Rainfall of a previously unobserved magnitude falls, and the rainy season lasts for a long time on record. In particular, these damages are concentrated in ASEAN countries, and at least 20 million people among ASEAN countries are affected by frequent flooding due to recent sea level rise, typhoons and torrential rain. Korea supports the domestic flood warning system to ASEAN countries through various ODA projects, but the communication network is unstable, so there is a limit to the central control method alone. Therefore, in this study, an artificial intelligence-based flood prediction model was developed to develop an observation station that can observe water level and rainfall, and even predict and warn floods at once at one observation station. Training, validation and testing were carried out for 0.5, 1, 2, 3, and 6 hours of lead time using the rainfall and water level observation data in 10-minute units from 2009 to 2020 at Junjukbi-bridge station of Seolma stream. LSTM was applied to artificial intelligence algorithm. As a result of the study, it showed excellent results in model fit and error for all lead time. In the case of a short arrival time due to a small watershed and a large watershed slope such as Seolma stream, a lead time of 1 hour will show very good prediction results. In addition, it is expected that a longer lead time is possible depending on the size and slope of the watershed.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1131-1141
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• 2021

In this study, the artificial neural network model is applied for real-time dam inflow prediction and then evaluated for the prediction lead times (1, 3, 6 hr) in dam basins in Korea. For the training and testing the model, hourly precipitation and inflow are used as input data according to average annual inflow. The results show that the model performance for up to 6 hour is acceptable because the NSE is 0.57 to 0.79 or higher. Totally, the predictive performance of the model in dry seasons is weaker than the performance in wet seasons, and this difference in performance increases in the larger basin. For the 6 hour prediction lead time, the model performance changes as the sequence length increases. These changes are significant for the dry season with increasing sequence length compared to the wet season. Also, with increasing the sequence length, the prediction performance of the model improved during the dry season. Comparison of observed and predicted hydrographs for flood events showed that although the shape of the prediction hydrograph is similar to the observed hydrograph, the peak flow tends to be underestimated and the peak time is delayed depending on the prediction lead time.

• Journal of Digital Convergence
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• v.19 no.12
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• pp.531-539
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• 2021

Given the rapid development of information and communication technology (ICT) and the deepening of the information gap phenomenon in the context of the COVID-19 pandemic, researchers and practitioners need to understand the changing perceptions of new phenomena such as COVID-19 information gap on the existing information-vulnerable population. In this study, an empirical analysis was performed with the digital information gap survey data in 2020 to understand the potential impact of COVID-19 on the information gap according to the information-vulnerable class. This study is to verify the effect of information gap, marginalized groups, gender, and major factors of information services (contents, social relations, life services, information production, networking, social participation, non-face-to-face services) on the change in perception of digital information technology after Corona. Hierarchical regression analysis was performed. As a result of the analysis, it was found that the higher the content, social relationship, life service, networking, and digital non-face-to-face service, the higher the change in perception of digital information technology after Corona. Therefore, in light of the evolving phenomenon of COVID-19, it is considered that the government needs to provide education and training to strengthen the capabilities of the information-vulnerable class in order to resolve the digital information gap.

• Journal of Music and Human Behavior
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• v.18 no.2
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• pp.67-85
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• 2021

This study reviewed the research in China on music interventions for adult brain injury patients. Eighty-three studies that met the inclusion criteria were included for analysis. Our review revealed that the number of intervention studies using music with adult brain injury patients has been on the rise since 2012, and random control research design methods have been dominant. Studies focused on the physical domain and emotional domain together were most common. Researchers in fields outside of music therapy conducted 43 of the studies, and music therapists carried out 14 of the studies as intervention providers. Most of the studies carried out by experts in fields other than music therapy used listening activities involving preexisting recorded music. However, most of the studies conducted by music therapists adopted reconstructed music and played it live during their intervention. The specificity of the described content of the interventions and level and relevance of stated rationale to the target goal of the intervention suggests that high quality of intervention was conducted with a therapist/investigator who has completed adequate professional education/training, which would emphasize the importance of music therapy professionalism. This study provides the baseline data for how music intervention research has been implemented in China and presents implications for future clinical practice and research.

• Journal of the Korea Convergence Society
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• v.12 no.9
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• pp.11-19
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• 2021

A software system is required to change during its life cycle due to various requirements such as adding functionalities, fixing bugs, and adjusting to new computing environments. Such program code modification should be considered as carefully as a new system development becase unexpected software errors could be introduced. In addition, when reusing open source programs, we can expect higher quality software if code changes of the open source program are predicted in advance. This paper proposes a Convolutional Neural Network (CNN)-based deep learning model to predict source code changes. In this paper, the prediction of code changes is considered as a kind of a binary classification problem in deep learning and labeled datasets are used for supervised learning. Java projects and code change logs are collected from GitHub for training and testing datasets. Software metrics are computed from the collected Java source code and they are used as input data for the proposed model to detect code changes. The performance of the proposed model has been measured by using evaluation metrics such as precision, recall, F1-score, and accuracy. The experimental results show the proposed CNN model has achieved 95% in terms of F1-Score and outperformed the multilayer percept-based DNN model whose F1-Score is 92%.

• Journal of the Korea Convergence Society
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• v.12 no.10
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• pp.137-144
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• 2021

The purpose of this study is to analyze the factors affecting the education experience, education needs, and knowledge level of calculation criteria for dental workers. It was conducted on dental workers in Daegu and Gyeongbuk province and an online survey was conducted using Google Survey. We used frequency analysis, crossover analysis, and ANOVA analysis method to find out general characteristic, education experience, education needs, and knowledge level according to education experience and education needs of candidates. As a result of in the knowledge level survey of dental health insurance, there were high rates of incorrect answers to the calculation criteria when the claim program automatically processes it or notifies you through an error window. The level of knowledge of candidates who are experienced, on a claim, and with experience in dental insurance training in the last six months was high. In conclusion, it seems that accurate and correct insurance claims are possible when the dental workers are familiar with the calculation criteria changed through regular dental health insurance education. We look forward to this study providing basic data in preparation of education system for professional dental insurance claims for dental workers.

• Journal of Practical Engineering Education
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• v.13 no.2
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• pp.327-332
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• 2021

Materials used for education include SM20C, Al6061, and acrylic. SM20C materials are used a lot in certification tests and functional competitions as carbon steel, but they are also used in industrial sites. Al6061 is said to be a material that produces a lot of tools because it has lower hardness than carbon steel and is highly flexible. When practical guidance is given to students using acrylic materials, it is a material that causes vibration and tool damage due to excessive cutting. In this process, we examine how impact on the 5-axis equipment 2NC head can affect precision control. The weakest part of a five-axis equipment is the head that controls the AC axis. In the event of precision and cumulative tolerances in this area, the precision of all products is reduced. Thus, a key part of the 2NC head, the spindle housing was carried out using Al7075 T6 (U.S. Alcoasa) material and the entire body using FCD450 (spherical graphite cast iron). In the vibration and cutting process acting on these two materials, the analysis was carried out to determine the value of applying the force as a finite element analysis under extreme conditions. We hope that using these analytical data will help students see and understand the structure of 5-axis machining rather than 5-axis cutting.