• Journal of Engineering Education Research
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• v.22 no.1
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• pp.22-28
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• 2019

The purpose of this study is to develop instructional design model of flipped learning suitable for engineering education field and to draw out effects and improvements by applying it to actual lessons for engineering college students. Literature review and case studies were conducted to achieve the purpose of the study. For a case study, flipped learning was applied to 'creative problem solving methodology' which is a liberal arts course of engineering college at D university in Gyeonggi-do. As a result of the literature review, the PARTNER model was applied and weekly instructional guide was presented by each stage. In addition, the results of analysis on the reflection journal showed that the students were more able to achieve the deepening learning stage through active participation in class than the existing class, and found that they had a more challenging plan after the class.

• Journal of the Semiconductor & Display Technology
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• v.21 no.3
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• pp.110-113
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• 2022

Non-playable game character plays an important role in improving the concentration of the game and the interest of the user, and recently implementation of NPC with reinforcement learning has been in the spotlight. In this paper, we estimate an AI target tracking method via reinforcement learning, and implement an AI-based tracking agency of specific target object with avoiding traps through Unity ML-Agents Toolkit. The implementation is built in Unity game engine, and simulations are conducted through a number of experiments. The experimental results show that outstanding performance of the tracking target with avoiding traps is shown with good enough results.

• Journal of Engineering Education Research
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• v.13 no.6
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• pp.3-13
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• 2010

The purpose of this study was to investigate between learning styles and communication apprehension of Engineering students. Participants were 405 first-year Engineering cohort. Following were the results: First, 80 percent were classified as Reflective learners, 61 percent were classified as Sensing learners, 73.1 percent were classified as Visual learners, and 66.7 percent were classified as Global learners. Second, the result showed that there was a significant difference in learning style by gender. Most female learners were Reflective, while most male learners were Active. Lastly, the finding revealed that there were significant differences in communication apprehension on Perception and Processing dimension. Sensing students demonstrated higher level of communication apprehension than Intuitive students and Reflective students shown higher level of communication apprehension than Active students. For the program developing Engineering students' communication skills, implications for reducing students' communication apprehension based on the type of learning styles were discussed.

• Journal of Digital Contents Society
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• v.18 no.4
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• pp.671-679
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• 2017

According to studies of many language education researchers including R. Ellis, a representative English education specialist, it is an effective and important method to improve the authenticity of English by learning a foreign language repeatedly in learner's actual situation. In this paper, we propose a foreign language learning service combined with context-awareness service, and develop foreign language learning contents which improves the authenticity of English learning based on the service. This approach is based on a study on digital education contents that helps learners to acquire a foreign language in an unconscious state in their real environment and a study to analyze the empirical characteristics of users based on real-time multi-sensed data.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.12
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• pp.8249-8261
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• 2015

The purpose of this study was to examine the effects of learning community on communication skills and self-directed learning(SDL) ability. Data were collected from 147 members at N university, they participated in learning community activities for 8 weeks. To verify program's effect, pre and post tests of communication skills and SDL ability were examined. The result were as follows. Learning community activities were positive influenced on the improvement of communication skills and SDL ability. Especially, it was effective on the improvement of interpretation ability that includes diverse information collection or listen to other's opinion. And it was effective on the improvement of learning plan ability. Learning plan includes that learning needs diagnosis, goal setting and grasp learning resources. We were suggested that student customized learning community development and activation for sustainable core competency improvement.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.2
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• pp.28-37
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• 1999

Differentially Responsible Adaptive Critic Learning technique is proposed for learning the control technique with multiple control inputs as in robot system using reinforcement learning. The reinforcement learning is a self-learning technique which learns the control skill based on the critic information Learning is a after a long series of control actions. The Adaptive Critic Learning (ACL) is the representative reinforcement learning structure. The ACL maximizes the learning performance using the two learning modules called the action and the critic modules which exploit the external critic value obtained seldomly. Drawback of the ACL is the fact that application of the ACL is limited to the single input system. In the proposed Differentially Responsible Action Dependant Adaptive Critic learning structure, the critic function is constructed as a function of control input elements. The responsibility of the individual control action element is computed based on the partial derivative of the critic function in terms of each control action element. The proposed learning structure has been constructed with the CMAC neural networks and some simulations have been done upon the two dimensional Cart-Role system and robot squatting problem. The simulation results are included.

• Annual Conference on Human and Language Technology
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• 2019.10a
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• pp.185-189
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• 2019

관계추출기의 학습을 위해서는 많은 학습 데이터가 필요한데, 사람이 모으게 되면 많은 비용이 필요하여 원격 지도 학습을 이용한 데이터 수집이 많은 연구에서 사용되고 있다. 원격 지도 학습은 지식베이스를 기반으로 학습 데이터를 자동으로 만들어 내는 방식이기에 비용이 거의 들지 않지만, 지식베이스의 질과 양에 영향을 받는다. 본 연구는 원격 지도 학습을 기본으로 관계추출기의 성능을 향상 시키고, 지식베이스를 확장하는 방안으로 반복학습을 제안한다. 실험을 적은 비용으로 빠르게 진행하기 위해 반복학습을 자동화 하는 시스템을 설계하여 실험을 하였고, 이 시스템으로 관계추출기의 성능이 향상 될 수 있는 가능성을 보였으며, 반복학습을 통한 지식베이스의 확장 방안을 제시한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.924-926
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• 2021

AI (Artificial Intelligence) is being utilized in various fields and services to give convenience to human life. Unfortunately, there are many security vulnerabilities in today's ML (Machine Learning) systems, causing various privacy concerns as some AI models need individuals' private data to train them. Such concerns lead to the interest in ML systems which can preserve the privacy of individuals' data. This paper introduces the latest research on various attacks that infringe data privacy and the corresponding defense techniques.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.6
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• pp.547-557
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• 2017

This study analyzed the differences depending on gender differences, the years for graduation, college majors, and grades by the level of students' learning process about the student engagement of college students. Subjects were 684 students who responded to a student engagement survey questionnaire among the students attending the regular curriculum at the K-college located in Geoje-si. For measurement, the college students' learning process analysis scale was modified and supplemented prior to use. Frequency analysis was adopted to search individual backgrounds of college students. One-way ANOVA and Post-hoc test were conducted in order to find differences according to gender differences, the years for graduation, college majors, and grades by the level of learning process. The study results are as follows. First, the college students' learning process on gender had significant differences in involvement in and out of instruction, teaching-learning outcomes, and college facility system and service. Second, the college students' learning process according to the years for graduation had significant differences in involvement in and out of instruction, class satisfaction, and college facility system and service. Third, the college students' learning process according to major differences had significant differences in involvement in and out of instruction, study interactions, academic achievement, and college facility system and service. Fourth, the college students' learning process according to grades had significant differences in total sub-components (involvement in and out of instruction, class satisfaction, study interactions, academic achievement, and college facility system and service). Lastly, the study discussions and implications are described.

• Journal of Korea Multimedia Society
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• v.23 no.5
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• pp.650-657
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• 2020

The rapid growth of internet users and faster network speed are driving the new ICT services. ICT Technology has improved our way of thinking and style of life, but it has created security problems such as malware, ransomware, and so on. Therefore, we should research against the increase of malware and the emergence of malicious code. For this, it is necessary to accurately and quickly detect and classify malware family. In this paper, we analyzed and classified visualization technology, which is a preprocessing technology used for deep learning-based malware classification. The first method is to convert each byte into one pixel of the image to produce a grayscale image. The second method is to convert 2bytes of the binary to create a pair of coordinates. The third method is the method using LSH. We proposed improving the technique of using the entire existing malicious code file for visualization, extracting only the areas where important information is expected to exist and then visualizing it. As a result of experimenting in the method we proposed, it shows that selecting and visualizing important information and then classifying it, rather than containing all the information in malicious code, can produce better learning results.