• Journal of the Korea Society of Computer and Information
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• v.26 no.3
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• pp.9-17
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• 2021

In this paper, an autonomous multi-agent path planning using reinforcement learning for monitoring of infrastructures and resources in a computationally distributed system was proposed. Reinforcement-learning-based multi-agent exploratory system in a distributed node enable to evaluate a cumulative reward every action and to provide the optimized knowledge for next available action repeatedly by learning process according to a learning policy. Here, the proposed methods were presented by (a) approach of dynamics-based motion constraints multi-agent path-planning to reduce smaller agent steps toward the given destination(goal), where these agents are able to geographically explore on the environment with initial random-trials versus optimal-trials, (b) approach using agent sub-goal selection to provide more efficient agent exploration(path-planning) to reach the final destination(goal), and (c) approach of reinforcement learning schemes by using the proposed autonomous and asynchronous triggering of agent exploratory phases.

• Journal of Cadastre & Land InformatiX
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• v.52 no.1
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• pp.81-93
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• 2022

Recently, there have been many studies on object recognition around the vehicle and recognition of traffic signs and traffic lights in autonomous driving. In particular, such the recognition of traffic lights is one of the core technologies in autonomous driving. Therefore, many studies for such the recognition of traffic lights have been performed, the studies based on various deep learning models have increased significantly in recent. In addition, as a high-quality AI training data set for voice, vision, and autonomous driving is released on AIHub, it makes it possible to develop a recognition model for traffic lights suitable for the domestic environment using the data set. In this study, we developed a recognition model for traffic lights that can be used in Korea using the AIHub's training data set. In particular, in order to improve the recognition performance, we used various models of YOLOv4 and YOLOv5, and performed our recognition experiments by defining various classes for the training data. In conclusion, we could see that YOLOv5 shows better performance in the recognition than YOLOv4 and could confirm the reason from the architecture comparison of the two models.

• The Journal of Korean Association of Computer Education
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• v.8 no.6
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• pp.45-53
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• 2005

In this paper, we propose an improved method of differentiated curriculum in social studies class that can be immediately used in the class. There are two major reasons that differentiated curriculum has not been applied to actual educational programs. One reason is the inefficient progress of differentiated curriculum derived from students' self-study based on individual projects and teacher's excessive investment of time in the development and management of individual researches. The other attribute is the difficulty in distinguishing students' academic level due to the lake of distinct criteria, because students are classified into merely two groups, in-depth group and supplementary class. To cope with these problems, we adopted a cooperative learning to enhance the educational effect of students of the similar level. Experimental results validate that the proposed method is effective in the course of social studies.

• Proceedings of the Korea Society of Mathematical Education Conference
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• 2006.10a
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• pp.225-230
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• 2006

• Journal of Engineering Education Research
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• v.13 no.6
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• pp.14-23
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• 2010

Whether the engineering department of universities employs ideal usage of e-learning or not needs to be investigated as many engineering departments diversify the use of the e-learning elements for educational purpose. Applying the teaching and learning methods and characteristics would lead to better strategies which are applied to development of contents and deployment of the e-learning courses. This study examines the characteristics and approaches of the usage of e-learning elements used by some instructors who use multimedia contents in offline teaching and learning environment. The results of this study shows that the e-learning elements assist the face-to-face course and the interactions are manifested in the classroom rather than in online setting. Lecture, hands-on-practice, simulation, and PBL(Problem-based learning) are turned out to be the major teaching and learning methods. This study signifies the need for use of various teaching and learning methods by the instructors and provision of PBL environment.

• Journal of Internet Computing and Services
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• v.22 no.4
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• pp.13-28
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• 2021

Reinforcement Learning (RL) is a machine learning algorithm that repeat the closed-loop process that agents perform actions specified by the policy, the action is evaluated with a reward function, and the policy gets updated accordingly. The key benefit of RL is the ability to optimze the policy with action evaluation. Hence, it can effectively be applied to developing advanced intelligent systems and autonomous systems. Conventional RL incoporates a single policy, a reward function, and relatively simple policy update, and hence its utilization was limited. In this paper, we propose an extended RL model that considers multiple instances of RL elements. We define a formal model of the key elements and their computing model of the extended RL. Then, we propose design methods for applying to system development. As a case stud of applying the proposed formal model and the design methods, we present the design and implementation of an advanced car navigator system that guides multiple cars to reaching their destinations efficiently.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.2
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• pp.227-234
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• 2012

Development of Computer science and internet technoloy have led changes all over the social area. Educational markets based on this circumstance are offering various services named remote education, cyber lecture, e-Learning, etc. Due to these products, systems for computer based teaching and evaluating student's achievement are wide spread. But in many systems we can find functional restrictions. In this paper we propose a RTE system offering interactive self control mode based education so as to provide customized education for each individual by realtime feedback of the level of the student's comprehension we expect that this system provides customized education environment considering student's achievement level and maximizes their motivation.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.211-214
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• 2000

This paper describes a lateral guidance system of an autonomous vehicle, using a neural network model of magneto-resistive sensor and magnetic fields. The model equation was compared with experimental sensing data. We found that the experimental result has a negligible difference from the modeling equation result. We verified that the modeling equation can be used in simulations. As the neural network controller acquires magnetic field values(B$\sub$x/, B$\sub$y/, B$\sub$z/) from the three-axis, the controller outputs a steering angle. The controller uses the back-propagation algorithms of neural network. The learning pattern acquisition was obtained using computer simulation, which is more exact than human driving. The simulation program was developed in order to verify the acquisition of the teaming pattern, learning itself, and the adequacy of the design controller. Also, the performance of the controller can be verified through simulation.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.6
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• pp.117-122
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• 2021

Autonomous driving allows cars to drive without people and is being studied very actively thanks to the recent development of artificial intelligence technology. Among artificial intelligence technologies, deep reinforcement learning is used most effectively. Deep reinforcement learning requires us to build a neural network using an appropriate activation function. So far, many activation functions have been suggested, but different performances have been shown depending on the field of application. This paper compares and evaluates the performance of which activation function is effective when using deep reinforcement learning to learn autonomous driving on highways. To this end, the performance metrics to be used in the evaluation were defined and the values of the metrics according to each activation function were compared in graphs. As a result, when Mish was used, the reward was higher on average than other activation functions, and the difference from the activation function with the lowest reward was 9.8%.

• Journal of Digital Convergence
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• v.17 no.11
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• pp.305-313
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• 2019

This study was a quasi-experimental design with a non-equivalent pre-post test that verified the effect of formative feedback practice on practice satisfaction, learning motivation and academic self efficacy for nursing students. 37 were assigned to an intervention group and 41 to a control group. Formative feedback practice was applied to intervention group and peer review smartphone videos was applied to control group. Data were analyzed using the SPSS/WIN 22.0 program. There was no significant difference in learning motivation between the groups, but there were a significant difference in practice satisfaction(t=-2.79, p=.007) and academic self efficacy(t=2.30, p=.024) between the pre-post scores of the experimental group. This is meaningful in that it is more effective to provide formative feedback practice than to provide peer review smartphone videos. Therefore, it is necessary to consider formative feedback practice for the acquisition of core fundamental nursing skills of nursing students.