• Journal of the Korea Convergence Society
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• v.12 no.2
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• pp.121-127
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• 2021

This study is to propose a communication strengthening teaching model using action learning for Spanish learners, and to verify its effectiveness through a case study of Spanish lessons. This study was conducted under the same conditions by dividing 91 students from September 1 to December 20, 2019 into experiment and control classes. As a result of the experiment, both classes improved their writing ability to some extent, but the learners in the experimental class applying action learning showed more meaningful results in terms of the content, expressions, fluency of the text, and the affective domain test also showed a significant difference. The development of this teaching model, which is necessary for learner-centered convergence activities, is expected to be of academic significance as it can be used for other foreign language class activities as well as improving Spanish communication.

• Journal of the Korea Computer Graphics Society
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• v.30 no.2
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• pp.21-29
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• 2024

Although there is an increasing demand for capturing various natural motions, collecting climbing motion data is difficult due to technical complexities, related to obscured markers. Additionally, scanning climbing structures and preparing diverse routes further complicate the collection of necessary data. To tackle this challenge, this paper proposes a climbing motion synthesis using reinforcement learning. The method comprises two learning stages. Firstly, the hanging policy is trained to grasp holds in a natural posture. Once the policy is obtained, it is used to extract the positions of the holds, postures, and gripping states, thus forming a dataset of favorable initial poses. Subsequently, the climbing policy is trained to execute actual climbing maneuvers using this initial state dataset. The climbing policy allows the character to move to the target location using limbs more evenly in a natural posture. Experiments have shown that the proposed method can effectively explore the space of good postures for climbing and use limbs more evenly. Experimental results demonstrate the effectiveness of the proposed method in exploring optimal climbing postures and promoting balanced limb utilization.

• Proceedings of the Korean Information Science Society Conference
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• 2002.10d
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• pp.400-402
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• 2002

지금까지의 코스웨어는 주로 우수아와 일반아를 대상으로 제작되어 왔다. 그러나 본 연구는 초등학교 학습 장애아들을 대상으로 개별화 학습 ICT 활용을 위한 멀티미디어 코스웨어를 제작하였다. 특히 학습 동기와 흥미도를 강화하여 학습 부진 요소를 제거할 수 있는 방안을 모색하였다. 그리고 다양한 교육정보화 매체를 활용하여 자기 주도의 학습을 할 수 있도록 멀티미디어 저작도구를 사용하여 단계별 개별화 학습자료를 설계하고 개발하였다. 이를 통해 학습 장애아들이 정확한 지식을 습득할 수 있고, 사물에 대한 정확한 개념과 관심을 가질 수 있도록 하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.722-723
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• 2023

연합학습은 중앙 서버에서 데이터를 수집하는 방식이 아닌 로컬 디바이스 또는 클라이언트에서 학습을 진행하고 중앙 서버로 모델 업데이트만 전송하는 분산 학습 기법으로 데이터 보안 및 개인정보보호를 강화하는 동시에 효율적인 분산 학습을 수행할 수 있다. 그러나, 연합학습 대부분의 시나리오는 클라이언트의 서로 다른 분포 형태인 non-IID 데이터를 대상으로 학습함에 따라 중앙집중식 모델에 비하여 낮은 성능을 보이게 된다. 이에 본 연구에서는 연합학습 모델의 성능을 개선하기 위하여 non-IID 의 환경에서 참여 후보자 중에서 적합한 클라이언트 선택의 최적화 기법을 분석한다.

• The Journal of the Convergence on Culture Technology
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• v.7 no.4
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• pp.35-42
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• 2021

The college English teaching-learning model in this study is intended to effectively apply dictogloss activities to enhance competencies such as communication, self-directedness, and cooperation by upgrading the utilization of various online platform functions. Dictogloss is a language teaching and learning activity that combines four functions (listening, speaking, reading, and writing) of communication. College English classes in this study focus on communication-oriented integrated English education. In this study, the teaching and learning is an online-based English integrated teaching-learning method based on constructivism theory. The model presented the roles of learners and teachers according to the seven procedures.

• Annual Conference on Human and Language Technology
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• 2022.10a
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• pp.289-294
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• 2022

의존구문분석은 문장을 의존관계(의존소-지배소)로 분석하는 구문분석 방법론이다. 현재 사전학습모델을 사용한 전이 학습의 딥러닝이 좋은 성능을 보이며 많이 연구되지만, 데이터셋에 의존적이며 그로 인한 자료부족 문제와 과적합의 문제가 발생한다는 단점이 있다. 본 논문에서는 언어학적 지식에 기반한 강화된 지배소-의존소 제약규칙 에지 알고리즘을 심층학습과 결합한 모델을 제안한다. TTAS 표준 가이드라인 기반 모두의 말뭉치로 평가한 결과, 최대 UAS 96.28, LAS 93.19의 성능을 보였으며, 선행연구 대비 UAS 2.21%, LAS 1.84%의 향상된 결과를 보였다. 또한 적은 데이터셋으로 학습했음에도 8배 많은 데이터셋 학습모델 대비 UAS 0.95%의 향상과 11배 빠른 학습 시간을 보였다. 이를 통해 심층학습과 언어지식의 결합이 딥러닝의 문제점을 해결할 수 있음을 확인하였다.

• Journal of Energy Engineering
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• v.21 no.1
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• pp.33-42
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• 2012

In this study, long-term greenhouse gas reductions expected passenger sector was used for the MESSAGE. Green Car road map proposed BAU scenario, Enhanced diffusion green car scenario, and price 1, 2 scenarios was configured with four scenarios. Enhanced diffusion green car in the scenario, in 2050 compared to BAU scenario 13% of the emissions will decrease. Price 1 and Price 2 scenario is emissions reduction of 14% compared to BAU. This study consists of six chapters. Introduction of MESSAGE, creation and RES in the year and the target year set a different base line and the passenger building materials sector activities, steps for passenger sector scenario and Based on the results of running the emissions reductions were to describe.

• Journal of Energy Engineering
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• v.27 no.2
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• pp.26-31
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• 2018

This paper introduces a study to improve the thermal efficiency of the district heating user control facility based on reinforcement learning. As an example, it is proposed a general method of constructing a deep Q learning network(DQN) using deep Q learning, which is a reinforcement learning algorithm that does not specify a model. In addition, it is also introduced the big data platform system and the integrated heat management system which are specialized in energy field applied in processing huge amount of data processing from IoT sensor installed in many thermal energy control facilities.

• Journal of the Korea Society for Simulation
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• v.21 no.4
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• pp.65-73
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• 2012

Due to the development of technology related to a weapon system and the info-communication, the battle system of a warship has to manage many kinds of human intervention tactics according to the complicated battlefield environment. Therefore, many kinds of studies about M&S(Modeling & Simulation) have been carried out recently. The previous M&S system based on an agent, however, has simply used non-flexible(or fixed) tactics. In this paper, we propose an agent modeling methodology which has reinforcement learning function for spontaneous(active) reaction and generation evolution learning Function using Genetic Algorithm for more proper reaction for warship battle. We experiment with virtual 1:1 warship combat simulation on the west sea so as to test validity of our proposed methodology. We consequently show the possibility of both reinforcement and evolution learning in a warship battle.

• Journal of Internet of Things and Convergence
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• v.9 no.6
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• pp.11-16
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• 2023

The technology of 3D reconstruction, primarily relying on point cloud data, is essential for digitizing objects or spaces. This paper aims to utilize reinforcement learning to achieve the acquisition of point clouds in a given environment. To accomplish this, a simulation environment is constructed using Unity, and reinforcement learning is implemented using the Unity package known as ML-Agents. The process of point cloud acquisition involves initially setting a goal and calculating a traversable path around the goal. The traversal path is segmented at regular intervals, with rewards assigned at each step. To prevent the agent from deviating from the path, rewards are increased. Additionally, rewards are granted each time the agent fixates on the goal during traversal, facilitating the learning of optimal points for point cloud acquisition at each traversal step. Experimental results demonstrate that despite the variability in traversal paths, the approach enables the acquisition of relatively accurate point clouds.