• Journal of Wellbeing Management and Applied Psychology
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• v.5 no.2
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• pp.15-19
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• 2022

Purpose: This pilot study aimed to clarify features of motor module during walking in exercise experts who experienced lately repeated training for sports skill. To identify motor modules, autoencoder machine learning algorithm was used, and modules were extracted from muscle activities of lower extremities. Research design, data and methodology: A total of 10 university students were participated. 5 students did not experience any sports training before, and 5 students did experience sports training more than 5 years. Eight muscle activities of dominant lower extremity were measured. After modules were extracted by autoencoder, the numbers of modules and spatial muscle weight values were compared between two groups. Results: There was no significant difference in the minimal number of motor modules that explain more than 90% of original data between groups. However, in similarity analysis, three motor modules were shown high similarity (r>0.8) while one module was shown low similarity (r<0.5). Conclusions: This study found not only common motor modules between exercise novice and expert during walking, but also found that a specific motor module, which would be associated with high motor control ability to distinguish the level of motor performance in the field of sports.

• Journal of Multimedia Information System
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• v.8 no.4
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• pp.203-210
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• 2021

Deep leaning convolutional neural networks (CNN) have successfully been applied to image super-resolution (SR). Despite their great performances, SR techniques tend to focus on a certain upscale factor when training a particular model. Algorithms for single model multi-scale networks can easily be constructed if images are upscaled prior to input, but sub-pixel convolution upsampling works differently for each scale factor. Recent SR methods employ multi-scale and multi-path learning as a solution. However, this causes unshared parameters and unbalanced parameter distribution across various scale factors. We present a multi-scale single-path upsample module as a solution by exploiting the advantages of sub-pixel convolution and interpolation algorithms. The proposed model employs sub-pixel convolution for the highest scale factor among the learning upscale factors, and then utilize 1-dimension interpolation, compressing the learned features on the channel axis to match the desired output image size. Experiments are performed for the single-path upsample module, and compared to the multi-path upsample module. Based on the experimental results, the proposed algorithm reduces the upsample module's parameters by 24% and presents slightly to better performance compared to the previous algorithm.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.3
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• pp.133-139
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• 2023

This paper proposes a two-stage Water Shield Automation System (WSAS) to predict the possibility of river overflow and vehicle flooding due to sudden rainfall. The WSAS uses a two-stage Deep Neural Network (DNN) model. First, a river overflow prediction module is designed with LSTM to decide whether the river is flooded by predicting the river's water level rise. Second, a vehicle flooding prediction module predicts flooding of underground parking lots by detecting flooded tires with YOLOv5 from CCTV images. Finally, the WSAS automatically installs the water barrier whenever the river overflow and vehicle flooding events happen in the underground parking lots. The only constraint to implementing is that collecting training data for flooded vehicle tires is challenging. This paper exploits the Image C&S data augmentation technique to synthesize flooded tire images. Experimental results validate the superiority of WSAS by showing that the river overflow prediction module can reduce RMSE by three times compared with the previous method, and the vehicle flooding detection module can increase mAP by 20% compared with the naive detection method, respectively.

• Fourth Industrial Review
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• v.4 no.1
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• pp.11-18
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• 2024

Purpose: The purpose of this study is to develop a geography class teaching and learning guide that enables learners to realistically explore the characteristics of the world's climate and geographical environment using digital tools. Research design, data and methodology: We review previous research on classes using goal-based scenario learning models, gamification, and digital tools, and explore tools that can be applied to world geography classes. Based on the exploration results, a goal-based scenario learning module is designed and a strategy for promoting educational gamification is established based on the ADDIE instructional design model. Results: The study comprises four sessions. Sessions 1-3 involve performance evaluations using a goal-based scenario learning module. Learners create game characters reflecting geographical characteristics, present results, and proceed with 3D modeling. In Session 4, a gamification class using Google Sites on the CoSpaces metaverse platform will be conducted. Conclusions: The study introduces a goal-based scenario learning model and a gamification class using digital tools to empower learners in exploring geographical diversity and its impact on lifestyles. Utilizing an accessible online platform, the study provides practical measures for integrating digital tools into geography education, addressing the current importance of digital technology in teaching.

• Proceedings of the Korea Society for Simulation Conference
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• 2001.10a
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• pp.321-324
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• 2001

The robot soccer simulation game is a dynamic multi-agent environment. In this paper we suggest a new reinforcement learning approach to each agent's dynamic positioning in such dynamic environment. Reinforcement Beaming is the machine learning in which an agent learns from indirect, delayed reward an optimal policy to choose sequences of actions that produce the greatest cumulative reward. Therefore the reinforcement loaming is different from supervised teaming in the sense that there is no presentation of input-output pairs as training examples. Furthermore, model-free reinforcement loaming algorithms like Q-learning do not require defining or loaming any models of the surrounding environment. Nevertheless it can learn the optimal policy if the agent can visit every state-action pair infinitely. However, the biggest problem of monolithic reinforcement learning is that its straightforward applications do not successfully scale up to more complex environments due to the intractable large space of states. In order to address this problem, we suggest Adaptive Mediation-based Modular Q-Learning(AMMQL) as an improvement of the existing Modular Q-Learning(MQL). While simple modular Q-learning combines the results from each learning module in a fixed way, AMMQL combines them in a more flexible way by assigning different weight to each module according to its contribution to rewards. Therefore in addition to resolving the problem of large state space effectively, AMMQL can show higher adaptability to environmental changes than pure MQL. This paper introduces the concept of AMMQL and presents details of its application into dynamic positioning of robot soccer agents.

• Journal of Broadcast Engineering
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• v.24 no.6
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• pp.1076-1084
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• 2019

In this paper, we introduced a system that extracts metadata by recognizing characters and objects in media using deep learning technology. In the field of broadcasting, multimedia contents such as video, audio, image, and text have been converted to digital contents for a long time, but the unconverted resources still remain vast. Building media archives requires a lot of manual work, which is time consuming and costly. Therefore, by implementing a deep learning-based metadata generation system, it is possible to save time and cost in constructing media archives. The whole system consists of four elements: training data generation module, object recognition module, character recognition module, and API server. The deep learning network module and the face recognition module are implemented to recognize characters and objects from the media and describe them as metadata. The training data generation module was designed separately to facilitate the construction of data for training neural network, and the functions of face recognition and object recognition were configured as an API server. We trained the two neural-networks using 1500 persons and 80 kinds of object data and confirmed that the accuracy is 98% in the character test data and 42% in the object data.

• Journal of Engineering Education Research
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• v.5 no.1
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• pp.50-58
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• 2002

The most important point in electronic learning(e-learning) is to gain the learning sympathy by improving interaction among the learners, instructors and operators around instruction contents. But it is necessary for the instructor to have active assistance of an administrator as a supporter and an operator because instructors do not accept all the learner's demands. So, operator's activity is very critical in success of e-learning. In this paper, wamine the theory of constructionism to effectively reflect the characteristic of WWW, and to build up a foundation of the web-based integrated e-learning circumstance. The circumstance is composed of 3 modules of the learner, instructor and the administrator. This aims to coordinate instruction functions in order to improve the effect of learning and to intensify the interaction. This paper presents the design and implementation of an e-learning system which is focused on the administrator's module to effectively support the operator's activity. As a result of this research, the system can be used in building up a variety of e-learning in university, that is, general training course and technical training course.

• The KIPS Transactions:PartA
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• v.10A no.2
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• pp.173-180
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• 2003

This paper proposes Remote Educating Communication Tool (RECT) that allows students and teachers to communicate using Web-based Bulletin Board System. The distance teaching using DCOM (Distributed Component Object Model) voice module is used to enhance academic accomplishments for students in computer class. The DCOM voice module to be used in distance learning is designed, implemented and applied to teachers and students in the computer class in order to measure and analyze academic results. The RECT server provides Q&A sessions between students and teachers in the BBS using recording and playback functions. The client RECT includes recording and playback functions. The client module of RECT receives and uses DCOM module. When recording, the client transmits voice files with the recorded content to the server.

• Journal of Distribution Science
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• v.21 no.2
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• pp.45-52
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• 2023

Purpose: The Malaysia Education Blueprint is primarily concerned with the transformation of students' minds through the curriculum offered at the school level (2013-2025). Diversity in the application of teaching and learning methods is one means of achieving the transformation of students' minds through the Secondary School Standard Curriculum. Consequently, the production of ExSheetLink's Module for Accounting Education is the primary outcome of this study, which had three objectives: the need for ExSheetLink's Module in the process of producing financial statements for Accounting Students in secondary school to the Accounting Teacher; and the design of ExSheetLink's Module that meets the entire process in the production of financial statements for Accounting Students in secondary school based on the Documents Curriculum and the Accounting Students' needs. Research design, data and methodology: This study outlines the research framework for module development in accordance with the Design and Development Research Method, which combines multiple research techniques (Mixed Method). Results: The development of ExSheetLink's Module is completed and can be used for the level of effectiveness purposes. Conclusion: The transformation of Accounting Students' minds is a success thanks to the ExSheetLink Module. Researchers also suggested that all Malaysian Secondary School accounting students test the ExSheetLink Module.

• Journal of the Korean earth science society
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• v.25 no.6
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• pp.409-417
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• 2004

This study investigated the effects and development of instruction module using ICT on earth field at elementary school science. The effects by 5th graders appeared as follows; First. ICT-applied teaching method proved to enhance the science teaming achievement regardless of their grades compared to the ordinary one. Second, Instruction module using ICT devoted to improve 'self-directed learning characteristics' at all grades by comparition of the ordinary teaching method. The 5th graders showed the improvements in the fields of' openness', 'self-conception', 'initiative', 'future inclination', 'creativity', 'self-assessment ability' all of which belong to self-directed teaming characteristics. They did not, however, show meaningful effect on improving 'learning eagerness' and 'responsibility' improvement. Thirdly, ICT-applied teaching method proved that it is more effective for developing 'creativity' than the ordinary one at all sample grades. The effectiveness was presented highly at 'fluency', 'originality' all of which belong to creativity. They did not, however, show meaningful effect on improving 'flexibility'.