• Journal of Fisheries and Marine Sciences Education
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• v.29 no.3
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• pp.677-688
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• 2017

The purpose of this study is to analyze the action learning lesson about the improvement process of the job support program of P university students. As a research method, we applied the related classes during the semester to the students who took courses in the course of 'Human Resource Development', which is a subject of P university, and analyzed the learner's reflection journal, interview data. As a result of the research, we went through the problem selection stage, the team construction and the team building stage. And then we searched for the root cause of the problem, clarified the problem, derived the possible solution, determined the priority and created the action plan. There are 10 solutions to the practical problems of poor job camps. Through two interviews with field experts it offered final solutions focused on promoting employment and Camp students participate in the management of post-employment into six camps. According to the first rank, job board integration, vendor selection upon student feedback, reflecting improved late questionnaire, public relations utilizing KakaoTalk, recruiting additional selection criteria, the camp provides recorded images in order. The results of this study suggest that the university's employment support program will strengthen the competitiveness of students' employment and become the basic data for the customized employment support program.

• The Journal of Bigdata
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• v.6 no.1
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• pp.127-132
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• 2021

Korea is both a resource-poor country and a energy-consuming country. In addition, the use and dependence on electricity is very high, and more than 20% of total energy use is consumed in buildings. As research on deep learning and machine learning is active, research is underway to apply various algorithms to energy efficiency fields, and the introduction of building energy management systems (BEMS) for efficient energy management is increasing. In this paper, we constructed a database based on energy usage by device per household directly collected using smart plugs. We also implement algorithms that effectively analyze and predict the data collected using RNN and LSTM models. In the future, this data can be applied to analysis of power consumption patterns beyond prediction of energy consumption. This can help improve energy efficiency and is expected to help manage effective power usage through prediction of future data.

• The Journal of the Convergence on Culture Technology
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• v.8 no.1
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• pp.1-8
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• 2022

This study examined whether the integrated education social studies textbooks developed for students with disabilities were properly implemented in terms of universal design for learning. For analysis, "Teaching and learning materials for inclusive education of students with disabilities: grade 3~6 social studies textbooks", which were instructional adaptation, were selected for students with disabilities who are unable to learn the contents of general textbooks for the 3rd to 6th grade of the elementary school social course in the 2015 revised curriculum. The social curriculum grades are composed of 20 units, including general public, geography and history. The content analysis standard was based on detailed items of 9 definitions according to the 3 principles of UDL presented in CAST (2018). Overall, the aspect of providing multiple means of action and expression was the most frequently observed, followed by providing multiple means of representation and providing multiple means of engagement. Special education teachers and textbook developers can use these results as a resource for designing curricula and lessons for students with disabilities in the inclusive classroom.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.179-179
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• 2023

This study suggests a new approach of water level forecasting for extended lead times using original data preprocessing with variational mode decomposition (VMD). Here, two machine learning algorithms including light gradient boosting machine (LGBM) and random forest (RF) were considered to incorporate extended lead times (i.e., 5, 10, 15, 20, 25, 30, 40, and 50 days) forecasting of water levels. At first, the original data at two water level stations (i.e., SW173 and SW269 in Bangladesh) and their decomposed data from VMD were prepared on antecedent lag times to analyze in the datasets of different lead times. Mean absolute error (MAE), root mean squared error (RMSE), and mean squared error (MSE) were used to evaluate the performance of the machine learning models in water level forecasting. As results, it represents that the errors were minimized when the decomposed datasets were considered to predict water levels, rather than the use of original data standalone. It was also noted that LGBM produced lower MAE, RMSE, and MSE values than RF, indicating better performance. For instance, at the SW173 station, LGBM outperformed RF in both decomposed and original data with MAE values of 0.511 and 1.566, compared to RF's MAE values of 0.719 and 1.644, respectively, in a 30-day lead time. The models' performance decreased with increasing lead time, as per the study findings. In summary, preprocessing original data and utilizing machine learning models with decomposed techniques have shown promising results for water level forecasting in higher lead times. It is expected that the approach of this study can assist water management authorities in taking precautionary measures based on forecasted water levels, which is crucial for sustainable water resource utilization.

• Environmental Engineering Research
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• v.25 no.4
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• pp.545-553
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• 2020

Rainfall-Runoff modeling plays a crucial role in various aspects of water resource management. It helps significantly in resolving the issues related to flood control, protection of agricultural lands, etc. Various Machine learning and statistical-based algorithms have been used for this purpose. These techniques resulted in outcomes with an acceptable rate of success. One of the pertinent machine learning algorithms namely Adaptive Neuro Fuzzy Inference System (ANFIS) has been reported to be a very effective tool for the purpose. However, the computational complexity of ANFIS is a major hindrance in its application. In this paper, we resolved this problem of ANFIS by incorporating one of the evolutionary algorithms known as Particle Swarm Optimization (PSO) which was used in estimating the parameters pertaining to ANFIS. The results of the modified ANFIS were found to be satisfactory. The performance of this modified ANFIS is then compared with conventional ANFIS and another popular statistical modeling technique namely ARIMA model with respect to the forecasting of runoff. In the present investigation, it was found that proposed PSO-ANFIS performed better than ARIMA and conventional ANFIS with respect to the prediction accuracy of runoff.

• Journal of National Security and Military Science
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• s.15
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• pp.31-55
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• 2018

It has been fifteen years since military science was introduced into private and public universities since 2004. The department focuses on the improvements of the South Korean Army quality based on the Korean Army's traits including: an increase of power in the armed force and operations through research, development, and the expansion of a cooperation between the public (civilians) and military. Approximately, four hundred students from various universities in the military science department graduate in order to become an officer. The fourth industrial revolution causes structural transformation to our lives. Through the use of Artificial Intelligence (AI,) war and the military as a whole will be altered significantly particularly with regard to efficiency. Nevertheless, it is important for us to train officers in creative ways so that they can deal with situations where machines will be unable to handle situations. Considering this change in our lives, it is necessary for the military science departments to change the way to teach and train their students. In order to accomplish this goal, we need to introduce a method called "Flipped Learning" and during the process all the members need to participate and communicate in an interactive way. By doing this, the military science departments will play an important role by improving human resource in terms of military and national security.

• International Journal of Computer Science & Network Security
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• v.21 no.4
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• pp.241-248
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• 2021

The article reviews the social media as an Internet phenomenon, determines their place and level of popularity in the society, as a result of which the social networks are a resource with perspective pedagogical potential. The analysis of social media from the point of view of studying a foreign language and the possibility of their usage as a learning medium has been carried out. The most widespread and popular platforms have been considered and, based on their capabilities in teaching all types of speech activities, the "Instagram", "Twitter", and "Facebook" Internet resources have been selected as the subject of the research. The system of tasks of teaching all types of speech activities and showing the advantages of the "Instagram", "Twitter", and "Facebook" platforms has been proposed and briefly reviewed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.9
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• pp.2573-2589
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• 2023

As 5G mobile systems carry multiple services and applications, numerous user, and application types with varying quality of service requirements inside a single physical network infrastructure are the primary problem in constructing 5G networks. Radio Access Network (RAN) slicing is introduced as a way to solve these challenges. This research focuses on optimizing RAN slices within a singular physical cell for vehicle-to-everything (V2X) and enhanced mobile broadband (eMBB) UEs, highlighting the importance of adept resource management and allocation for the evolving landscape of 5G services. We put forth two unique strategies: one being offline network slicing, also referred to as standard network slicing, and the other being Online reinforcement learning (RL) network slicing. Both strategies aim to maximize network efficiency by gathering network model characteristics and augmenting radio resources for eMBB and V2X UEs. When compared to traditional network slicing, RL network slicing shows greater performance in the allocation and utilization of UE resources. These steps are taken to adapt to fluctuating traffic loads using RL strategies, with the ultimate objective of bolstering the efficiency of generic 5G services.

• Culinary science and hospitality research
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• v.22 no.1
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• pp.141-152
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• 2016

Recently, not only development of curriculum associated directly with job, the development of new education model is in great need. So, the purpose of this study is to develop the instructional design guideline utilizing goal-based scenario(GBS) for college students who major in culinary arts. First, to achieve this goal, we recognized the 7 core elements(learning goal, mission, cover story, role activity, scenario operating, resource, feedback) composing GBS through literature review and case study. Second, we drew a conclusion about the problem and guideline for traditional culinary practice course by conducting inspection about culinary practice environment and needs with professors who are teaching culinary arts. Third, we applied the instructional design guideline for culinary practice to regular classes according to GBS's factors, and then we did formative evaluation with content experts and educational technology expert. Finally, we designed the final instructional design guideline for culinary practice by modifying early model reflected the result of formative evaluation. The results of this study are as following. First, when we applied GBS to culinary practice, professors have to focus on process of materialization by developing easy scenario to students. Also, they have to prepare the class circumstance to feel about sense of realism in advance. Second, to give a conjugally new skill at working, professors's effort is important. culinary practice education at college has responsibility to carry out the vocational training that has competitiveness and difference with labor market's needs. Therefore, it is necessary for us to develop the teaching and learning model for culinary practice which is suitable for major based on the manpower demand for industry without causing job mismatch from demand for industry.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.3
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• pp.120-131
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• 2020

Water body detection using remote sensing based on machine interpretation of satellite image is efficient for managing water resource, drought and flood monitoring. In this study, water body detection with SAR satellite image based on machine learning was performed. However, non water body area can be misclassified to water body because of shadow effect or objects that have similar scattering characteristic comparing to water body, such as roads. To decrease misclassifying, 8 combination of morphology open filtered band, DEM band, curvature band and Cosmo-SkyMed SAR satellite image band about Mokpo region were trained to semantic segmentation machine learning models, respectively. For 8 case of machine learning models, global accuracy that is final test result was computed. Furthermore, concordance rate between landcover data of Mokpo region was calculated. In conclusion, combination of SAR satellite image, morphology open filtered band, DEM band and curvature band showed best result in global accuracy and concordance rate with landcover data. In that case, global accuracy was 95.07% and concordance rate with landcover data was 89.93%.