• Journal of Korea Game Society
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• v.16 no.3
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• pp.109-116
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• 2016

Distance learning systems that teacher and learner(s) are located at remote have been in progress in a way that directly transfer the video and audio. To get the interest of learners and effectiveness of education or to overcome the poor network environment, various methods utilizing computer graphics in the distance learning system have been attempted. This paper describes a design and implementation of a distance learning system using 3D animation control technology based on Kinect and network game technology. Distance learning system designed and implemented in this paper is a good example of combining education and game technology. And I expect to be used at various educational contents in the future.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.45-47
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• 2022

Mobile edge computing (MEC), which enables mobile terminals to offload computational tasks to a server located at the user's edge, is considered an effective way to reduce the heavy computational burden and achieve efficient computational offloading. In this paper, we study a multi-user MEC system in which multiple user devices (UEs) can offload computation to the MEC server via a wireless channel. To solve the resource allocation and task offloading problem, we take the total cost of latency and energy consumption of all UEs as our optimization objective. To minimize the total cost of the considered MEC system, we propose an DRL-based method to solve the resource allocation problem in wireless MEC. Specifically, we propose a Asynchronous Advantage Actor-Critic (A3C)-based scheme. Asynchronous Advantage Actor-Critic (A3C) is applied to this framework and compared with DQN, and Double Q-Learning simulation results show that this scheme significantly reduces the total cost compared to other resource allocation schemes

• Journal of Internet Computing and Services
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• v.8 no.6
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• pp.127-133
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• 2007

The u-learning environment has been developed through a number of iterations, and has now been formally evaluated, through analysis of student learning results and the use of quantitative and qualitative measures, Generally, for advance learning effect and analysis of student learning results, the most learning system be use to the item analysis method. But, nowadays, it has using the IRT(Item Response Theory) instead of the item analysis method, The IRT adopts explicit models for the probability of each possible response to a test. Therefore, I proposed the lightweight component based u-learning system using the IRT. Applied device of u-learning is PDA which is in Windows mobile 5.0 environments.

• Proceedings of the Korean Society of Computer Information Conference
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• 2014.01a
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• pp.189-191
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• 2014

This paper provides an overview of the E-learning service education on the last decade, In the early 2000's the emphasis of educational technology was on interactive multimedia- stand alone packages on computer hard disks or portable memory, which integrated a range of media forms in the lately. Customers handle finding the best sources of content.The system then uses social signals such as those coming from Facebook, Twitter, LinkedIn, delicious as well as clicks and views. The SNS and network infrastructure is sufficiently mature that the focus should shift to how to use the technology most appropriately to facilitate learning. As we study environmental conditions of the traditional internet and the mobile internet users in some ways. In this paper, analyze the nature of learning, role of educational and suggest alternative policy, innovation of e-learning service and effective e-learning environment in developing technology.

• KIPS Transactions on Computer and Communication Systems
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• v.6 no.7
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• pp.315-320
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• 2017

In this paper, I design a new scheme that is immune to malicious attack based on the weighted voting game. By using stochastic learning, the proposed scheme can revoke the certification of malicious node. Through the revocation process, the proposed scheme can effectively adapt the dynamic Mobile Ad hoc network situation. Simulation results clearly indicate that the developed scheme has better performance than other existing schemes under widely diverse network environments.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.3
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• pp.176-181
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• 2007

This paper presents evolvable neural networks based on a developmental model for navigation control of autonomous mobile robots in dynamic operating environments. Bio-inspired mechanisms have been applied to autonomous design of artificial neural networks for solving practical problems. The proposed neural network architecture is grown from an initial developmental model by a set of production rules of the L-system that are represented by the DNA coding. The L-system is based on parallel rewriting mechanism motivated by the growth models of plants. DNA coding gives an effective method of expressing general production rules. Experiments show that the evolvable neural network designed by the production rules of the L-system develops into a controller for mobile robot navigation to avoid collisions with the obstacles.

• Smart Media Journal
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• v.12 no.7
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• pp.9-17
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• 2023

Plant diseases and pests affect the growth of various plants, so it is very important to identify pests at an early stage. Although many machine learning (ML) models have already been used for the inspection and classification of plant pests, advances in deep learning (DL), a subset of machine learning, have led to many advances in this field of research. In this study, disease and pest inspection of abnormal crops and maturity classification were performed for normal crops using YOLOX detector and MobileNet classifier. Through this method, various plant pest features can be effectively extracted. For the experiment, image datasets of various resolutions related to strawberries, peppers, and tomatoes were prepared and used for plant pest classification. According to the experimental results, it was confirmed that the average test accuracy was 84% and the maturity classification accuracy was 83.91% in images with complex background conditions. This model was able to effectively detect 6 diseases of 3 plants and classify the maturity of each plant in natural conditions.

• Journal of Positioning, Navigation, and Timing
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• v.11 no.4
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• pp.361-368
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• 2022

An indoor navigation system that utilizes long-term evolution (LTE) signals has the benefit of no additional infrastructure installation expenses and low base station database management costs. Among the LTE signal measurements, received signal strength (RSS) is particularly appealing because it can be easily obtained with mobile devices. Propagation channel models can be used to estimate the position of mobile devices with RSS. However, conventional channel models have a shortcoming in that they do not discriminate between line-of-sight (LOS) and non-line-of-sight (NLOS) conditions of the received signal. Accordingly, a previous study has suggested separated LOS and NLOS channel models. However, a method for determining LOS and NLOS conditions was not devised. In this study, a machine learning-based LOS/NLOS classification method using RSS measurements is developed. We suggest several machine-learning features and evaluate various machine-learning algorithms. As an indoor experimental result, up to 87.5% classification accuracy was achieved with an ensemble algorithm. Furthermore, the range estimation accuracy with an average error of 13.54 m was demonstrated, which is a 25.3% improvement over the conventional channel model.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.2
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• pp.525-531
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• 2010

In this paper, a mobile quiz system using SMS has been developed, which can be conveniently used on the spot of education. The system immediately not only provides each individual student with the results of his/her quizzes but also a instructor with the statistics of the results by SMS. Field tests of the quiz system have shown that it can be effectively used as a good teaching tool for teachers getting immediate feedback from participants in the lecture and adjusting teaching level as well as for inducing more interest in learning from participants and raising their concentration on the class.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.156-159
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• 2019

The damage from voice phishing reaches one trillion won in the past 5 years following report of Business Korea on August 28, 2018. Voice phishing and mobile phone scams are recognized as a top concern not only in Korea but also in over the world in recent years. In this paper, we propose an efficient system to identify the caller and alert or prevent of dangerous to users. Our system includes a mobile application and web server using client and server architecture. The main purpose of this system is to automatically display the information of unidentified callers when a user receives a call or message. A mobile application installs on a mobile phone to automatically get the caller phone number and send it to the server through web services to verify. The web server applies a machine learning to a global phone book with Blacklist and Whitelist to verify the phone number getting from the mobile application and returns the result.