• Journal of the Korean Society for Library and Information Science
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• v.57 no.2
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• pp.379-408
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• 2023

This case study reports on the redevelopment of a course, Local Culture Information Theory offered by the Department of Library and Information Science at C University, into a capstone design course using a project-based learning approach. In collaboration with a local community youth organization, the redesigned course provided an opportunity for LIS students to develop and implement a digital literacy program that enabled high school students to use a variety of digital multimedia technologies to complete a project of digital Human Library featuring video, audio, and digital are such as webtoons. Through semi-structured interviews with 5 students and 3 staff from partner organizations, this study reports on course development process, the establishment of local partnerships, project outcome, as well as suggestions for improvements. In addition, a qualitative analysis of the participating students' interview responses using the Framework for 21st Century Learning (P21) found they developed and improved 11 skills across three core areas: life and career skills including self-direction, project management, collaboration with diverse teams, flexibility, responsibility, leadership; learning and innovation skills including communication and collaboration, problem-solving, creativity, and critical thinking; and information, media, and technology skills through media creation. Lessons learned and recommendations from this case study may be useful for other LIS programs and faculty interested in implementing project-based learning or developing capstone design courses.

• Journal of The Korean Association of Information Education
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• v.16 no.3
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• pp.373-382
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• 2012

Of computing skills in the field of multi-media, particularly augmented reality technology contents may provide realistic learning experiences with 3D pictures through the learners' manipulation activities. However, the marker systems in the existing studies were not well developed as to maintain the students' interest and concentration. In this study, we have designed the first lesson ("Earth and Moon") of 5th graders' science with augmented reality combined system so that we could deal with manipulation activities of the relationship between augmented objects, From the experimental study, using combined augmented reality contents made a significant difference in their learning achievement and motivation. Thus augmented reality combined system can be utilized for a variety of topics to maintain students' learning motivation.

• Journal of the Korean Society of Industry Convergence
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• v.24 no.1
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• pp.19-29
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• 2021

In the Port Logistics system, Container Stowage planning is an important issue for cost-effective efficiency improvements. At present, Planners are mainly carrying out Stowage planning by manual or semi-automatically. However, as the trend of super-large container ships continues, it is difficult to calculate an efficient Stowage plan with manpower. With the recent rapid development of artificial intelligence-related technologies, many studies have been conducted to apply enhanced learning to optimization problems. Accordingly, in this paper, we intend to develop and present a Deep Q-Learning Network model for the Master Stowage planning of Container ships.

• The Journal of the Acoustical Society of Korea
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• v.39 no.5
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• pp.441-446
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• 2020

In this paper, we propose the α-Feature Map Scaling (α-FMS) method which extends the FMS method that was designed to enhance the discriminative power of feature maps of deep neural networks in Speaker Verification (SV) systems. The FMS derives a scale vector from a feature map and then adds or multiplies them to the features, or sequentially apply both operations. However, the FMS method not only uses an identical scale vector for both addition and multiplication, but also has a limitation that it can only add a value between zero and one in case of addition. In this study, to overcome these limitations, we propose α-FMS to add a trainable parameter α to the feature map element-wise, and then multiply a scale vector. We compare the performance of the two methods: the one where α is a scalar, and the other where it is a vector. Both α-FMS methods are applied after each residual block of the deep neural network. The proposed system using the α-FMS methods are trained using the RawNet2 and tested using the VoxCeleb1 evaluation set. The result demonstrates an equal error rate of 2.47 % and 2.31 % for the two α-FMS methods respectively.

• The Journal of Engineering Geology
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• v.31 no.1
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• pp.31-42
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• 2021

Database of Cut-slope management system (CSMS) has been constructed based on investigations of all slopes on the roads of the whole country. The investigation data is documented by human, so it is inevitable to avoid human-error such as missing-data and incorrect entering data into computer. The goal of this paper is constructing a prediction model based on several machine-learning algorithms to solve those imperfection problems of the CSMS data. First of all, the character-type data in CSMS data must be transformed to numeric data. After then, two algorithms, i.g., multinomial logistic regression and deep-neural-network (DNN), are performed, and those prediction models from two algorithms are compared. Finally, it is identified that the accuracy of DNN-model is better than logistic model, and the DNN-model will be utilized to improve data-quality.

• Journal of the Korea Institute of Military Science and Technology
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• v.23 no.4
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• pp.399-406
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• 2020

In this paper, we propose a time-varying proportional navigation guidance law that determines the proportional navigation gain in real-time according to the operating situation. When intercepting a target, an unidentified evasion strategy causes a loss of optimality. To compensate for this problem, proper proportional navigation gain is derived at every time step by solving an optimal control problem with the inferred evader's strategy. Recently, deep reinforcement learning algorithms are introduced to deal with complex optimal control problem efficiently. We adapt the actor-critic method to build a proportional navigation gain network and the network is trained by the Proximal Policy Optimization(PPO) algorithm to learn an evasion strategy of the target. Numerical experiments show the effectiveness and optimality of the proposed method.

• Journal of Practical Engineering Education
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• v.10 no.2
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• pp.95-102
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• 2018

Many undergraduate engineering freshmen have difficulties in attending major courses due to their poor basic academic ability. Regardless of the university level it is a reality in universities all over the country. In order to solve the problems of poor learning and basic academic ability, in this study, students who want to major in mechanical engineering at A university, it was confirmed the effectiveness and surveyed the satisfaction with the questionnaire. The pre and post test results showed that the A group improved scores by 40.1% and the B group by 18.9%. Questionnaire survey and in-depth interviews conducted after the completion of the program. It showed that the basic learning ability achievement program was highly satisfied with the overall average of 90.6% (4.53/5.0) and an useful program which not only contributed to the interest in the major subjects and the confidence in the academic achievement but also build positive relationships between the student and professor.

• The Journal of the Convergence on Culture Technology
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• v.7 no.3
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• pp.261-268
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• 2021

After developing literacy education programs using coaching techniques, applying them to literacy learners, in order to see how they have an impact on improving learning achievement, we selected 13 senior literacy learners in U city and chose qualitative research method based on in-depth interviews, observation journals, and learning materials. Literature education programs using coaching techniques are a process-oriented model consisting of four stages of mind-opening, introducing positivity, strengthening learning competence and assistance, confidence and persistence. You can find the results as following. Firstly, communication between teachers and learners was expanded in the first stage, and secondly, self-directed learning ability was strengthened in the second stage by forming a positive mind. Thirdly, the results of utilizing the three-stage balanced literacy teaching method and interaction teaching method resulted in confidence in reading and writing, leading to an increase in self-efficacy. Fourthly, the fourth stage showed the results of improving learning achievement, which overcame the fear of learning with active praise and continuous encouragement and implied hope for higher courses. As a result of the above-mentioned research, I think literacy education programs using coaching techniques can be useful as an educational method for learners in the field of literacy education.

• The Journal of the Korea Contents Association
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• v.20 no.11
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• pp.422-434
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• 2020

This study aims to analyze the types of college admission that should be strengthened or reflected in universities and to suggest the direction of entrance examination by identifying the actual implementation of the literature-science integrated 2015 revised national curriculum. In order to do so, in-depth interviews on the current state were executed to five curriculum experts. As a result of the interview, it was found that the introduction and adoption of clear admission types look into the inner side of high school life are necessary. Also, it is required to establish specific criteria for student selection expand in-depth interviews related to learning activities in high school, strengthen evaluation competence of admission staffs and recruit more evaluation personnel. In addition, in order to revitalize the 2015 revised curriculum, it is necessary to evaluate how many subjects, especially in career-related subjects, students have taken in order to expand the school record-focused system. For this, it is required to extract evaluation elements and criteria of universities that can grasp continuous and active role performance, and to design a typical design that can objectively judge them. This study can contribute to the settlement of the selection process that can revitalize public education. And it is expected that the selection of the talents desired by the university will be used as a possible basic data.

• Journal of Korean Society for Quality Management
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• v.51 no.4
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• pp.573-588
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• 2023

Purpose: This study presents a research approach that utilizes deep reinforcement learning to construct optimal portfolios based on the business cycle for stocks and other assets. The objective is to develop effective investment strategies that adapt to the varying returns of assets in accordance with the business cycle. Methods: In this study, a diverse set of time series data, including stocks, is collected and utilized to train a deep reinforcement learning model. The proposed approach optimizes asset allocation based on the business cycle, particularly by gathering data for different states such as prosperity, recession, depression, and recovery and constructing portfolios optimized for each phase. Results: Experimental results confirm the effectiveness of the proposed deep reinforcement learning-based approach in constructing optimal portfolios tailored to the business cycle. The utility of optimizing portfolio investment strategies for each phase of the business cycle is demonstrated. Conclusion: This paper contributes to the construction of optimal portfolios based on the business cycle using a deep reinforcement learning approach, providing investors with effective investment strategies that simultaneously seek stability and profitability. As a result, investors can adopt stable and profitable investment strategies that adapt to business cycle volatility.