• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.2
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• pp.219-227
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• 2021

This study aimed to determine the importance of the policies formulated through the Ministry of Maritime Affairs and Fisheries' master plan for Seafarer's policy. To confirm the importance of the Seafarer's policy, AHP analysis was conducted on the 3 main policies and 15 auxiliary policies established by the master plan for Seafarer's policy. To analyze the reliability of the survey, we investigated the inconsistency ratio of 34 respondents retrieved. We found that 25 respondents validated the reliability of the findings. The analysis showed that the policy of establishing a stable supply and demand system was the most important among the main policies for seafarers, while the policy of fostering seafarers linked to jobs was the most important one among the auxiliary policies. By group, the government recognized establishing a stable supply and demand system as a priority of policy. Dif erences were found on the fact that improving working conditions and expanding welfare were important for seafarers. This study, which analyzed the importance of the Seafarer's policy through AHP analysis, has the following implications. We established guidelines to enhance efficiency, such as budget allocation by policy according to the importance in implementing Seafarer's policies. Furthermore, our study can be used as basic data for establishing a master plan for Seafarer's policy by comparing differences in importance per group.

• The Journal of the Korea Contents Association
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• v.21 no.7
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• pp.317-327
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• 2021

The purpose of this study is to identify the priority factors for sustainable management of social enterprises related to early childhood education. To achieve the purpose of this study, sustainable management factors were extracted by examining previous studies related to social enterprises, and the priorities of factors were derived using the AHP method after conducting surveys of those who are related to early childhood education and social enterprises. As a result of the study, the priorities of the four major factors for sustainable management of social enterprises were confirmed in the following order: strategic factors, organizational factors, management capability factors, and social environment factors. And the priorities of the 16 sub-factors were confirmed in the following order: program originality, excellence of program content, CEO's leadership, expertise in operation and education, securing information on early childhood education market, etc. This study is meaningful in that it discovered the basic success factors for the performance and sustainability of social enterprises related to early childhood education and suggested directions for establishing management strategies for social enterprises related to early childhood education.

• The Journal of the Korea Contents Association
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• v.21 no.11
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• pp.389-402
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• 2021

The purpose of this study is to examine early childhood teacher's perception and current implementation toward autonomy and to provide basic data for related research. The survey was conducted with 121 public and private kindergarten teachers. The study results are as follows: First, as for teachers' perception of autonomy, the majority of the survey respondents answered that "I am very interested in the concept of autonomy and have tried to practice it." In terms of areas for which they expect to be given autonomy, "autonomy in developing and operating education plans" was most answered. Second, as for teachers' practice of autonomy, teacher autonomy was answered most for "work and operation" in the development and implementation of education plans among "institutional autonomy." In terms of communication between parents and the local community, "operation of the kindergarten website" and "operation of teachers' personal e-mail" were most answered. In addition, "participation in teacher training" was most answered for the development of teachers' expertise, and "interaction with infants" for "autonomy in educational activity." Lastly, it is expected for follow-up research to perform case studies to understand the context of the implementation of teacher autonomy.

• The Journal of the Korea Contents Association
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• v.21 no.11
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• pp.135-144
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• 2021

Recently, data-driven decision-making technology has become a key technology leading the data industry, and machine learning technology for this requires high-quality training datasets. However, real-world data contains missing values for various reasons, which degrades the performance of prediction models learned from the poor training data. Therefore, in order to build a high-performance model from real-world datasets, many studies on automatically imputing missing values in initial training data have been actively conducted. Many of conventional machine learning-based imputation techniques for handling missing data involve very time-consuming and cumbersome work because they are applied only to numeric type of columns or create individual predictive models for each columns. Therefore, this paper proposes a new data imputation technique called 'Denoising Self-Attention Network (DSAN)', which can be applied to mixed-type dataset containing both numerical and categorical columns. DSAN can learn robust feature expression vectors by combining self-attention and denoising techniques, and can automatically interpolate multiple missing variables in parallel through multi-task learning. To verify the validity of the proposed technique, data imputation experiments has been performed after arbitrarily generating missing values for several mixed-type training data. Then we show the validity of the proposed technique by comparing the performance of the binary classification models trained on imputed data together with the errors between the original and imputed values.

• Journal of Digital Convergence
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• v.20 no.2
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• pp.499-509
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• 2022

This study was conducted to understand the effect of health assessment practical education on class participation, problem-solving process, academic self-efficacy, and academic achievement of nursing students. This study used a nonequivalent control group pretest-posttest design. The participants were 69 nursing university students located in C city. Data were collected on two separate occasions before and after the application of the program from February 2021 to July 2021. Data were analyzed by chi-square test, independent t-test, and ANCOVA using SPSS WIN 23.0. There were significant differences in class participation(F=15.003, p<.001), academic self-efficacy(F=13.288, p=.001) and academic achievement(F=19.755, p<.001) between the experimental group and the control group. In the problem-solving process, the experimental group was significantly higher than the control group in decision-making(F=6.948, p=.010), applying the solution(F=6.232, p=.015) and evaluation-reflection(F=5.364, p=.024). It is necessary to expand case-based learning to increase the problem-solving process, class participation, academic self-efficacy, and academic achievement of nursing students.

• Journal of the Korean Association of Geographic Information Studies
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• v.25 no.1
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• pp.77-100
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• 2022

In the era of low growth, many provincial cities are experiencing population decline and aging. Population decline phenomena such as reduction of productive manpower, reduction of finances, deterioration of quality of life, and collapse of the community base are occurring in a chain and are being pushed to the brink of extinction of the cities. This study aims to propose a methodology to objectively estimate the employment creation centers and setting the basic unit of industrial-centered zoning by applying spatial statistical techniques and GIS for the application of the compact city plan as an efficient spatial management policy in a city with a declining population. In details, based on reviewing previous studies on compact city, 'employment complex index(ECI)' were defined considering the number of workers, the number of settlers, and the area of development land, the employment creation center was estimated by applying the 'Local Moran's I' and 'Getis-Ord's Hot-Spot Analysis'. As a case study, changes in the four years of 2013, 2015, 2017, and 2019 were compared and analyzed for Changwon City. As a result, it was confirmed that the employment creation center is becoming compacted and polycentric, which is a significant result that reflects the actual situation well. This results provide the basic data for functional and institutional territorial governance for the regional revitalization platform, and provide meaningful information necessary for spatial policy decision-making, such as population reduction, regional gross domestic product, and public facility arrangement that can respond to energy savings, transportation plans, and medical and health plans.

• Journal of the Korean Association of Geographic Information Studies
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• v.25 no.1
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• pp.56-76
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• 2022

This study shared with community mapping participants spatial analysis information, collected using smart devices, to give them an opportunity to objectively review their opinions. The study examined the impact of sharing such spatial information on residents' decision-making and perceptions. Yeongju-dong in Jung-gu district of Busan Metropolitan City, South Korea was selected for the case study; community mapping was carried out in Yeongju-dong to identify hazardous areas to improve pedestrian safety of primary school students. The community mapping participants drew a preliminary hazard map based on their experience and perception. Then, they drew a second hazard map after being given spatial information on pedestrian safety installations and pedestrian flow collected with smart devices including drones and sensors. Numerous changes in ranking across various sections occurred when the two maps were compared. There was a climb in the ranking of areas where the pedestrian flow was higher and lacked safety installations based on objective measurements over the perceptions of the participating people. Furthermore, according to a survey conducted among the participants, the provision of spatial analysis information using smart devices during community mapping process not only helped them recognize local community problems, but also raised their expectations that their submitted opinions would be reflected in policies. Moreover, the participants demonstrated increased self-confidence and faith in themselves as they were able to have more trust in the outcome they created.

• Journal of Navigation and Port Research
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• v.46 no.6
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• pp.562-569
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• 2022

The alarm monitoring technology applied to existing operating ships manages data items such as temperature and pressure with AMS (Alarm Monitoring System) and provides an alarm to the crew should these sensing data exceed the normal level range. In addition, the maintenance of existing ships follows the Planned Maintenance System (PMS). whereby the sensing data measured from the equipment is monitored and if it surpasses the set range, maintenance is performed through an alarm, or the corresponding part is replaced in advance after being used for a certain period of time regardless of whether the target device has a malfunction or not. To secure the reliability and operational safety of ship engine operation, it is necessary to enable advanced diagnosis and prediction based on real-time condition monitoring data. To do so, comprehensive measurement of actual ship data, creation of a database, and implementation of a condition diagnosis monitoring system for condition-based predictive maintenance of auxiliary equipment and piping must take place. Furthermore, the system should enable management of auxiliary equipment and piping status information based on a responsive web, and be optimized for screen and resolution so that it can be accessed and used by various mobile devices such as smartphones as well as for viewing on a PC on board. This update cost is low, and the management method is easy. In this paper, we propose CBM (Condition Based Management) technology, for autonomous ships. This core technology is used to identify abnormal phenomena through state diagnosis and monitoring of pumps and purifiers among ship auxiliary equipment, and seawater and steam pipes among pipes. It is intended to provide performance diagnosis and failure prediction of ship auxiliary equipment and piping for convergence analysis, and to support preventive maintenance decision-making.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.6
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• pp.25-40
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• 2022

This study is about how the quality of M2M big data that provides real-time information affects users. Recently, there are many difficulties in acquiring and managing data because data types such as variety, data volume, and data velocity are changing rapidly and diversified. This not only leads to a decrease in data quality but also it can give a negative impact when making decisions using data. Generally, the quality of data is defined as 'suitability for use', which means that data quality must meet the expectations of user needs. Therefore, data providers need activities to improve data quality for this purpose, and the key is to identify data quality dimensions in each field where data is used and provide data suitable for the level of user needs. In this study, the relationship between the quality area of real-time M2M data used in the traffic information system and user satisfaction was analyzed. Research models and hypotheses were established to analyze the effects between variables related to M2M big data. In order to test the hypothesis, a causal relationship between the major factors was identified by conducting a survey and analyzing the data users.

• Journal of Korean Society of Forest Science
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• v.111 no.4
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• pp.603-612
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• 2022

The study aims were to evaluate a machine-learning, algorithm-based, forest biomass-estimation model to estimate subnational forest biomass and to comparatively analyze REDD+ forest reference emission levels. Time-series Landsat satellite imagery and ESA Biomass Climate Change Initiative information were used to build a machine-learning-based biomass estimation model. The k-nearest neighbors algorithm (kNN), which is a non-parametric learning model, and the tree-based random forest (RF) model were applied to the machine-learning algorithm, and the estimated biomasses were compared with the forest reference emission levels (FREL) data, which was provided by the Paraguayan government. The root mean square error (RMSE), which was the optimum parameter of the kNN model, was 35.9, and the RMSE of the RF model was lower at 34.41, showing that the RF model was superior. As a result of separately using the FREL, kNN, and RF methods to set the reference emission levels, the gradient was set to approximately -33,000 tons, -253,000 tons, and -92,000 tons, respectively. These results showed that the machine learning-based estimation model was more suitable than the existing methods for setting reference emission levels.