• Journal of Digital Convergence
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• v.19 no.12
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• pp.29-42
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• 2021

As the low fertility intensifies in Korea, this study investigated fundamental differences between the government's low fertility policy and public perception of it. To this end, we selected four times 'Aging Society and Population Policy' documents and news comments for two weeks immediately after announcement of the third and fourth Policy as analysis targets. Then we conducted word frequency analysis, co-occurrence analysis and CONCOR analysis. As a result of analyses, first, direct childcare support during the first and second periods, and a social structural approach during third and fourth periods were noticeable. Second, it was revealed that both policies and comments aim for the work-family compatibility in 'parenting'. Lastly it was showed public interest in environment of raising children and the critical mind to effectiveness of the policy. This study is meaningful in that it confirmed the public perception using big data analysis, and it will help improve the direction for the future low fertility policy.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.3
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• pp.407-416
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• 2019

This study is about a Customized Nursery School where working women can support work - life balance and a reduction in retirement or turnover. The research method is to identify the current status of Customized Nursery services and to recognize the recognition and need of the operation of Customized Nursery School. The importance of securing skilled nurses and preventing them from changing their jobs for the health and safety of people cannot be emphasized enough. A Customized Nursery School must be opened to reduce the retirement or change of jobs of working women nurses and to provide care for continuous work in three shifts from 365days to support the balance between the working mother and family. It is considered that nurses will put their children in relief when using retired nurses who have the ability to work 24hour rotation in a Customized Nursery School and when a Customized Nursery School be ran suited for the condition and demand of working women nurses, it is expected to reduce retirement and the change of jobs, also to give positive effect on marrige and family planning which would make improvement in low birthrate. To activate the Customized Nursery School, Creating a secure learning environment and qualification of educators great effort should be put. A program curriculum based on 'basic life and habits' should be the center of education. Continuous management and effort will need to be placed in continuous development of educators.

• The Journal of the Korea Contents Association
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• v.19 no.8
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• pp.572-583
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• 2019

This study sought to find out what the most ideal and appropriate native English speakers-Korean English teacher cooperative class model and the defining factors for organizing effective cooperative classes in the English education environment of our country. To achieve this goal, a total of 165 sixth graders of five elementary schools in Seoul were subject to the study. For about a month from April 1 to April 30, 2019, the survey and statistical analysis were conducted, including multiple return analysis, correlation analysis, cross analysis, and t/F verification. In summary, the results of the study are as follows. First, it was found that among the recognition of cooperative classes between native English speakers and Korean English teachers, it affected the defining factors in the order of class-related skills, task orientation, teaching-learning strategies, and motivation. Second, based on learner characteristics, the difference in perception of cooperative classes between native English speakers and Korean English teachers was verified, and the perception of native-Korean English teachers' cooperative classes was different depending on gender and the type of English cooperative classes currently participating, but the recognition of native-Korean English-Korean English cooperative classes, which were statistically significant, was not confirmed. Third, according to learner characteristics, the difference in the definition factors of the learner was verified and the difference between the sexes occurred, but the learner-defined factors according to the current type of English cooperative class did not occur. Also, there was no difference in the definition factors of scholars according to the type of English cooperative classes desired.

• The Journal of the Korea Contents Association
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• v.19 no.3
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• pp.313-327
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• 2019

The purpose of this study was to identify the effect of new graduate nurses' empathy and their perceived preceptors' empathy on job satisfaction, job stress and turnover intention. For this purpose, a survey was conducted on new graduate nurses who were under 12 months old while working at three medical institutions that operated the preceptorship. In the preceptorship according to adult learning theory, the preceptors need to create a psychological environment for the new graduate nurses, and their empathy should be perceived by new graduate nurses. The study revealed that the new graduate nurses group with high level of empathy had high levels of job satisfaction, low job stress, and low degree of turnover intention, and the level of empathy affects job satisfaction of new graduate nurses. The new graduate nurses group with low level of perceived preceptor's empathy had low job satisfaction, high job stress, and high degree of turnover intention, and their perceived preceptor's empathy affected job stress and turnover intention. Based on these results, we suggest the introduction of a program to enhance empathy for new graduate nurses and preceptors.

• Korean Journal of Remote Sensing
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• v.35 no.2
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• pp.299-316
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• 2019

The purpose of this study was to analyze landslide susceptibility in the Pyeongchang area using Weight of Evidence (WOE) and Evidential Belief Function (EBF) as probability models and Artificial Neural Networks (ANN) as a machine learning model in a geographic information system (GIS). This study examined the widespread shallow landslides triggered by heavy rainfall during Typhoon Ewiniar in 2006, which caused serious property damage and significant loss of life. For the landslide susceptibility mapping, 3,955 landslide occurrences were detected using aerial photographs, and environmental spatial data such as terrain, geology, soil, forest, and land use were collected and constructed in a spatial database. Seventeen factors that could affect landsliding were extracted from the spatial database. All landslides were randomly separated into two datasets, a training set (50%) and validation set (50%), to establish and validate the EBF, WOE, and ANN models. According to the validation results of the area under the curve (AUC) method, the accuracy was 74.73%, 75.03%, and 70.87% for WOE, EBF, and ANN, respectively. The EBF model had the highest accuracy. However, all models had predictive accuracy exceeding 70%, the level that is effective for landslide susceptibility mapping. These models can be applied to predict landslide susceptibility in an area where landslides have not occurred previously based on the relationships between landslide and environmental factors. This susceptibility map can help reduce landslide risk, provide guidance for policy and land use development, and save time and expense for landslide hazard prevention. In the future, more generalized models should be developed by applying landslide susceptibility mapping in various areas.

• Management & Information Systems Review
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• v.38 no.3
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• pp.55-80
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• 2019

Recently, many companies improving their management performance by building a powerful brand value which is recognized for trademark rights. However, as growing up the size of online commerce market, the infringement of trademark rights is increasing. According to various studies and reports, cases of foreign and domestic companies infringing on their trademark rights are increased. As the manpower and the cost required for the protection of trademark are enormous, small and medium enterprises(SMEs) could not conduct preliminary investigations to protect their trademark rights. Besides, due to the trademark image search service does not exist, many domestic companies have a problem that investigating huge amounts of trademarks manually when conducting preliminary investigations to protect their rights of trademark. Therefore, we develop an intelligent similar trademark search model to reduce the manpower and cost for preliminary investigation. To measure the performance of the model which is developed in this study, test data selected by intellectual property experts was used, and the performance of ResNet V1 101 was the highest. The significance of this study is as follows. The experimental results empirically demonstrate that the image classification algorithm shows high performance not only object recognition but also image retrieval. Since the model that developed in this study was learned through actual trademark image data, it is expected that it can be applied in the real industrial environment.

• Journal of the Korean School Mathematics Society
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• v.21 no.4
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• pp.419-443
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• 2018

The purpose of this study was to analyze the problem solving strategies of ordinary students, gifted students, pre-service teachers, and in-service teachers with the 'chicken and pig problem,' which has multiple strategies to obtain the solution. For this study, 98 students in the 6th grade elementary schools, 96 gifted students in a gifted institution, 72 pre-service teachers, and 60 in-service teachers were selected. The researcher presented the "chicken and pig" problem and requested them the solution strategies as many as possible for 30 minutes in a free atmosphere. As a result of the study, the gifted students used relatively various and efficient strategies compared to the ordinary students, and there was a difference in the most used strategies among the groups. In addition, the percentage of respondents who suggested four or more strategies was 1% for the ordinary students, 54% for the gifted students, 42% for the pre-service teachers, and 43% for the in-service teachers. As suggestions, the researcher asserted that various kinds of high-quality mathematical problems and solving experiences should be provided to students and teachers and have students develop multi-strategy problems. As a follow-up study, the researcher suggested that multi-strategy mathematical problems should be applied to classroom teaching in a collaborative learning environment and reflected them in teacher training program.

• Journal of Korean Society of Forest Science
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• v.110 no.2
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• pp.155-164
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• 2021

Many studies have been conducted on developing automatic plant identification algorithms using machine learning to various plant features, such as leaves and flowers. Unlike other plant characteristics, barks show only little change regardless of the season and are maintained for a long period. Nevertheless, barks show a complex shape with a large variation depending on the environment, and there are insufficient materials that can be utilized to train algorithms. Here, in addition to the previously published bark image dataset, BarkNet v.1.0, images of barks were collected, and a dataset consisting of 53 tree species that can be easily observed in Korea was presented. A convolutional neural network (CNN) was trained and tested on the dataset, and the factors that interfere with the model's performance were identified. For CNN architecture, VGG-16 and 19 were utilized. As a result, VGG-16 achieved 90.41% and VGG-19 achieved 92.62% accuracy. When tested on new tree images that do not exist in the original dataset but belong to the same genus or family, it was confirmed that more than 80% of cases were successfully identified as the same genus or family. Meanwhile, it was found that the model tended to misclassify when there were distracting features in the image, including leaves, mosses, and knots. In these cases, we propose that random cropping and classification by majority votes are valid for improving possible errors in training and inferences.

• Journal of Digital Convergence
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• v.19 no.2
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• pp.1-12
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• 2021

With the recent 4th Industrial Revolution, the growth and value of big data are continuously increasing, and the government is also actively making efforts to open and utilize public data. However, the situation still does not reach the level of demand for public data use by citizens, At this point, it is necessary to identify research trends in the public data field and seek directions for development. In this study, in order to understand the research trends related to public data, the analysis was performed using topic modeling, which is mainly used in text mining techniques. To this end, we collected papers containing keywords of 'Public data' among domestic and foreign research papers (1,437 domestically, 9,607 overseas) and performed topic modeling based on the LDA algorithm, and compared domestic and foreign public data research trends. After analysis, policy implications were presented. Looking at the time series by topic, research in the fields of 'personal information protection', 'public data management', and 'urban environment' has increased in Korea. Overseas, it was confirmed that research in the fields of 'urban policy', 'cell biology', 'deep learning', and 'cloud·security' is active.

• The Journal of Korean Institute of Next Generation Computing
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• v.14 no.5
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• pp.19-30
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• 2018

Visual information understanding is known as one of the most difficult and challenging problems in the realization of machine intelligence. This paper proposes deriving visual verb and construction of ActionNet database as a video database for video semantic understanding. Even though development AI (artificial intelligence) algorithms have contributed to the large part of modern advances in AI technologies, huge amount of database for algorithm development and test plays a great role as well. As the performance of object recognition algorithms in still images are surpassing human's ability, research interests shifting to semantic understanding of video contents. This paper proposes candidates of visual verb requiring in the construction of ActionNet as a learning and test database for video understanding. In order to this, we first investigate verb taxonomy in linguistics, and then propose candidates of visual verb from video description database and frequency of verbs. Based on the derived visual verb candidates, we have defined and constructed ActionNet schema and database. According to expanding usability of ActionNet database on open environment, we expect to contribute in the development of video understanding technologies.