• Journal of Korean Society of Industrial and Systems Engineering
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• v.47 no.2
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• pp.116-123
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• 2024

In the manufacturing industry, dispatching systems play a crucial role in enhancing production efficiency and optimizing production volume. However, in dynamic production environments, conventional static dispatching methods struggle to adapt to various environmental conditions and constraints, leading to problems such as reduced production volume, delays, and resource wastage. Therefore, there is a need for dynamic dispatching methods that can quickly adapt to changes in the environment. In this study, we aim to develop an agent-based model that considers dynamic situations through interaction between agents. Additionally, we intend to utilize the Q-learning algorithm, which possesses the characteristics of temporal difference (TD) learning, to automatically update and adapt to dynamic situations. This means that Q-learning can effectively consider dynamic environments by sensitively responding to changes in the state space and selecting optimal dispatching rules accordingly. The state space includes information such as inventory and work-in-process levels, order fulfilment status, and machine status, which are used to select the optimal dispatching rules. Furthermore, we aim to minimize total tardiness and the number of setup changes using reinforcement learning. Finally, we will develop a dynamic dispatching system using Q-learning and compare its performance with conventional static dispatching methods.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.10a
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• pp.303-308
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• 2004

Fuzzy logic, neural network, fuzzy-neural network play an important as the key technology of linguistic modeling for intelligent control and decision making in complex systems. The fuzzy-neural network (FNN) learning represents one of the most effective algorithms to build such linguistic models. This paper proposes learning approach of fuzzy-neural network by immune algorithm. The proposed learning model is presented in an immune based fuzzy-neural network (FNN) form which can handle linguistic knowledge by immune algorithm. The learning algorithm of an immune based FNN is composed of two phases. The first phase used to find the initial membership functions of the fuzzy neural network model. In the second phase, a new immune algorithm based optimization is proposed for tuning of membership functions and structure of the proposed model.

• Journal of Digital Convergence
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• v.17 no.4
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• pp.19-27
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• 2019

The purpose of this study is to develop play education programs based on the instructional systems development (ISD). To achieve the goal, play education programs for elementary school students were developed based on the ADDIE model. Internal researchers, teaching design experts, and subject matter expert reviewed, modified, and developed play and education programs having three modules: design and scheduling, making a model, and preparing an exhibition. Students set up a program to realize the play space they want through collaboration with their peers. The program was applied to fifth grade elementary school students in Chungju for verification of the play and education program. At the end of the play education program, the FGI was conducted on 13 students to measure the overall value of the play education program, including its sustainability. Through these processes, we drew the effects and implications of the play education program and presented future research directions of play education and projects for elementary school students.

• Journal of Korean Academy of Nursing
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• v.48 no.6
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• pp.743-753
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• 2018

Purpose: This study aimed to explore the effects of a community-based first and third Intergenerational Exchange Program (IGEP) on older adults' health-related quality of life (HRQoL), loneliness, depression, and walking speed, and on 4~5-year-old preschool children's learning-related social skills. Methods: This study employed a non-equivalent control group pre-post-test design. The experimental group included 42 older adults and 42 children who participated in the IGEP for 8 weeks, and the control group included 39 older adults. The experimental group participated in the IGEP once a week for 8 weeks. It comprised a traditional play program based on the intergroup contact theory. Results: Compared to the control group, there was a significant increase in scores on the HRQoL-Visual analogue scale (VAS) and a decrease in loneliness and depression in older adults in the experimental group (p<.05). Children who participated in the IGEP showed an improvement in their learning-related social skills (p<.001). Conclusion: These results confirm that the IGEP is an effective intervention to improve HRQoL-VAS, loneliness, and depression among older adults and learning-related social skills among preschool children in the community.

• Cross-Cultural Studies
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• v.38
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• pp.461-500
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• 2015

This study aims to investigate the effective method to improve the competence of the Vocabulary by the image and listening towards the ELF. In the first part, we observed the problems and point improvement on learning vocabulary based on learner survey. In the second part, we analyzed two remarkable studies: - consistent and adapt method, communicational context - method based on the lexical, morphological semantical, notional and thematic field Then we proposed effective methods that are applicable to the vocabulary's learning in the class : - learning vocabulary by combining the words - learning vocabulary based on the meaning field - learning vocabulary as concrete characters - learning vocabulary by the descriptive character - learning vocabulary with the type "who am I?" - learning vocabulary by listening For teachers, one of the difficulties to the conduct of vocabulary course is that learners take passive position. Specifically, it is the teachers who play an important role because it runs in the direction of the course. However, learners do not show the active attitude for vocabulary lessons despite the course to take to improve their vocabulary skills. Therefore, teachers must prepare course materials that can both improve the competence of the vocabulary of learners and cause their interest or desire on the current vocabulary. This is why teachers should exploit various materials depending on the skill level of the learner vocabulary.

• The Journal of Korean Association of Computer Education
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• v.8 no.5
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• pp.73-83
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• 2005

Recently, Internet utilization ratio of elementary school students is urgently increasing. On the other hand, elementary school students are not doing personal information management properly in the Internet. As a result, it is causing problem of information leakage. In this paper, we proposed a instructional learning method to teach importance of personal information management to an elementary school student. Proposed method is based on role-play instructional learning method. And applied to the third-year class in elementary school. We show that proposed method is effect in education than traditional teaching method about personal information management's importance.

• The Journal of Korean Association of Computer Education
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• v.5 no.2
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• pp.29-37
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• 2002

The purpose of this study is to examine the effects of the level of background knowledge and the metacognition supporting tool (MST) on the learning activities and outcomes in a web problem based learning environment from the theoretical perspectives of PBL (problem-based-learning) in the Web and metacognition. Results suggest that the level of learners' background knowledge could play a slight role in problem oriented learning activities. This was because learning tasks were characterized by ill-structured problems in Web-based problem learning and this sort of learning might have been a somewhat newer experience for the selected students. Providing MST for learners' PBL activities, however, was highly beneficial for learners who practice PBL in Web based learning environments. In addition, for PBL outcomes, variables like problem solution reporting documents. the use of MST had a more positive effect on the lower level learners' background knowledge than the higher level learners'.

• Proceedings of the Korean Statistical Society Conference
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• 2004.11a
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• pp.307-312
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• 2004

In our fast-moving information and knowledge society, skills and know-how rapidly become outdated. Virtual learning environments play a key role in meeting today's growing demand for customized educational and vocational training and lift-long teaming. The scope of multimedia-based and web-supported education is illustrated by means of an interdisciplinary multimedia project 'New Statistics' funded by the German government. The project output contains more than 70 learning modules covering the complete curriculum of an introductory statistics course. All modules are based on a statistical laboratory and on a multitude of Java applets, animations and case studies. The paper focuses on presenting the statistical laboratory and the applets. These components present the main project pillars and are particularly suitable for international use, independently from the original project framework. This article also demonstrates the application of Java applets and other multimedia developments from the educational world to official statistics for interactive presentation of statistical information.

• Electronics and Telecommunications Trends
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• v.35 no.5
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• pp.14-29
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• 2020

The 6G mobile communication system will become a backbone infrastructure around 2030 for the future digital world by providing distinctive services such as five-sense holograms, ultra-high reliability/low-latency, ultra-high-precision positioning, ultra-massive connectivity, and gigabit-per-second data rate for aerial and maritime terminals. The recent remarkable advances in machine learning (ML) technology have recognized its efficiency in wireless networking fields such as resource management and cell-configuration optimization. Further innovation in ML is expected to play an important role in solving new problems arising from 6G network management and service delivery. In contrast, an approach to apply ML to a physical-layer (PHY) target tackles the basic problems in radio links, such as overcoming signal distortion and interference. This paper reviews the methodologies of ML-based PHY, relevant industrial trends, and candiate technologies, including future research directions and standardization impacts.

• Journal of the Semiconductor & Display Technology
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• v.22 no.3
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• pp.161-165
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• 2023

One of the difficult works in an autonomous driving system is detecting road lanes or objects in the road boundaries. Detecting and tracking a vehicle is able to play an important role on providing important information in the framework of advanced driver assistance systems such as identifying road traffic conditions and crime situations. This paper proposes a vehicle detection scheme based on deep learning to classify and tracking vehicles in a complex and diverse environment. We use the modified YOLO as the object detector and polynomial regression as object tracker in the driving video. With the experimental results, using YOLO model as deep learning model, it is possible to quickly and accurately perform robust vehicle tracking in various environments, compared to the traditional method.