• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.990-993
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• 2019

인공지능(AI)은 최근 다양한 산업과 사회에서 패러다임을 바꾸고 있지만, 최첨단 AI 가 제조업에서는 즉각적인 성과를 보이지 못 하고 있다. 다시 말해, Industry 4.0 시점에서 기존의 접근 방법과 차별화되는 실용적인 방법론이 필요하다. 여기서 중요한 점은 '어떤' 데이터를 '어떻게' 활용하여 '어느' 부분에 적용할 것 인가이다. 제조업은 게임과 같이 가상의 캐릭터가 하나의 객체 단위로 구동되는 것이 아니라 수많은 하드웨어가 물리적으로 조합되어 연동한다. 따라서, 현실 세계에서는 물리적 마모, 고장 등으로 인해 엔지니어의 개입 없이 수천만 번 이상의 반복 학습이 불가능하다. 또, 제조업은 학습을 위한 방대한 양의 데이터를 수집하고 레이블링 하는 것이 매우 어렵다. 이 두 가지 한계를 극복할 수 있는 방법은 현실과 매우 유사한 환경을 시뮬레이션으로 재연 후 강화 학습을 사용하는 것이다. 제조 분야에서 아주 복잡한 환경 중 하나로 이송 설비가 있으며, 본 논문에서는 그리드 분류 시스템을 개발하고 강화 학습을 적용시킬 수 있는 환경을 설계한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.05a
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• pp.552-555
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• 2019

디지털 트윈이란 현실 세계의 물리적인 사물을 컴퓨터 상에 동일하게 가상화 시키는 기술을 의미하는 것으로, 물리적 사물이나 시스템을 모델링하거나 IoT 기술에 접목되어 활용되고 있는 기술이다. 디지털 트윈 기술은 가상의 모델을 무한정 시뮬레이션을 통해 동작을 튜닝하고 환경변화에 대한 대응을 미리 실험하여 리스크를 최소화할 수 있는 장점을 지닌다. 최근 인공지능이나 기계학습에 관련된 기술들이 주목받기 시작하면서, 이와 같은 물리적인 사물의 모델링 작업을 데이터 기반으로 수행하려는 시도가 증가하고 있다. 특히, 산업현장에서 많이 활용되는 인더스트리 4.0 공장 자동화의 핵심인 협력 로봇의 디지털 트윈을 구축하기 위해서는 로봇의 동작을 인지하는 과정이 필수적으로 요구된다. 그러나 현재 협력 로봇의 동작을 인지하기 위한 시도는 미비하며, 센서 데이터를 기반으로 동작을 역으로 예측하는 기술은 더욱 그렇다. 따라서 본 논문에서는 로봇의 동작을 인지하기 위해 가정용 협력 로봇에서 전류 및 관성 데이터를 수집하기 위한 실험 환경을 구축하고, 수집한 센서 데이터를 기반으로 한 동작 예측 모델을 제안하고자 한다. 제안하는 방식은 로봇의 동작 명령어를 조인트 위치 기반으로 분류하고 전류와 위치 센서 값을 사용하여 학습을 통해 예측하는 방식이다. SVM 을 이용하여 학습한 결과, 모델의 성능은 평균적으로 정확도, 정밀도, 및 재현율이 모두 96%로 평가되었다.

• The Journal of the Korea Contents Association
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• v.10 no.11
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• pp.1-9
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• 2010

In comparison with the acquirement of knowledge by expression of symbol or numerical formula, the experience based knowledge acquirement give stable and clear understanding of information to a learner. Various educational systems based on computer such as virtual reality, intelligent learning system, visualization of concept, simulation-based system, and microworld have so far been developed. In this paper, a method of two dimensional visualization of a physical law by simulation has been suggested to educate a learner. Especially, learning the law of gravity by simulation based educational method gives a learner indirect experience that enhance the efficiency of knowledge acquirement clearly. The visualization methods of a real-time physical law and a virtual anomalies' physical law by simulation are proposed in this paper to deliver information or knowledge clearly. Then we show that a learner can acquire some knowledge effectively by the proposed method.

• Journal of the Korea Computer Graphics Society
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• v.27 no.5
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• pp.55-61
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• 2021

The character motion control based on physics simulation using reinforcement learning continue to being carried out. In order to solve a problem using reinforcement learning, the network structure, hyperparameter, state, action and reward must be properly set according to the problem. In many studies, various combinations of states, action and rewards have been defined and successfully applied to problems. Since there are various combinations in defining state, action and reward, many studies are conducted to analyze the effect of each element to find the optimal combination that improves learning performance. In this work, we analyzed the effect on reinforcement learning performance according to the state representation, which has not been so far. First we defined three coordinate systems: root attached frame, root aligned frame, and projected aligned frame. and then we analyze the effect of state representation by three coordinate systems on reinforcement learning. Second, we analyzed how it affects learning performance when various combinations of joint positions and angles for state.

• Transactions of the KSME C: Technology and Education
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• v.2 no.1
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• pp.15-20
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• 2014

Intuitive learning method was applied at the beginning of each lecture to induce students to draw their interests. Avoiding simple explanation of equations and problem solving by using them, we repeated theoretical concepts verbally and applied physical meanings when we developed and wrote equations. By these methods we expected to find a way to increase students' learning effects. We also took a group investigation on pursuing term projects. Students choose their own subjects individually and submitted reports according to the time schedule. The reports included the contents that they learned during classes. After choosing best reports for each group by instructor, students at each group divided roles and prepared presentations. Thorough these methods they increased their scores from mid-term to final exams, and got aquatinted with responsibilities among group and organizations. They also experienced physical meanings from the usual daily life phenomena which could be connected to the engineering concepts and improved abilities as junior engineers.

• Journal of Gifted/Talented Education
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• v.22 no.3
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• pp.729-755
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• 2012

As an effort to understand alienated gifted students, we investigated learning characteristics and learning tactics of a scientifically gifted student with economic difficulty and physical disadvantage. The student we studied is attending the Saturday Physics Class which is an after school science activity offered by our university. We adopted techniques of qualitative case study. Participant observation was carried out at the field and the interview was done with the participant, his mother, and his teacher of 5th grade. Field documents and self-reports were used to understand the student synthetically. As a result, learning characteristics of the participant could be summarized as a spontaneous learning which originated from the internal motivation and struggle for learning to overcome the sense of inferiority and isolation from the peers. The participant adopted a strategic method for learning to satisfy his learning desire given the circumstance of socioeconomic and physical disadvantage: the three tactics we found were various learning routes, meta-cognitive ability and fervent response.

• Geophysics and Geophysical Exploration
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• v.24 no.3
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• pp.89-97
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• 2021

Fault detection in seismic data is well suited to the application of machine learning algorithms. Accordingly, various machine learning techniques are being developed. In recent studies, machine learning models, which utilize synthetic data, are the particular focus when training with deep learning. The use of synthetic training data has many advantages; Securing massive data for training becomes easy and generating exact fault labels is possible with the help of synthetic training data. To interpret real data with the model trained by synthetic data, the synthetic data used for training should be geologically realistic. In this study, we introduce a method to generate realistic synthetic seismic data. Initially, reflectivity models are generated to include realistic fault structures, and then, a one-way wave equation is applied to efficiently generate seismic stack sections. Next, a migration algorithm is used to remove diffraction artifacts and random noise is added to mimic actual field data. A convolutional neural network model based on the U-Net structure is used to verify the generated synthetic data set. From the results of the experiment, we confirm that realistic synthetic data effectively creates a deep learning model that can be applied to field data.

• Journal of Educational Research in Mathematics
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• v.16 no.1
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• pp.59-78
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• 2006

As a theoretical background for this research, the literatures which focus on the rationale of teaching and learning of connecting with mathematics and science in calculus were investigated. And teaching and learning material of connecting with mathematics and science in calculus was developed. And then, based on the case study using this material, the research questions were analyzed in depth. Students could understand mean-velocity, instant-velocity, and acceleration in the experimenting process of constant acceleration movement. Also Students could understand fundamental ideas that instant-velocity means slope of the tangent line at one point on the time-displacement graph and rate of distance change means rate of area change under a time-velocity graph.

• Physical Therapy Korea
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• v.8 no.4
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• pp.91-99
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• 2001

뇌 손상 후 급성기에 기능의 자발적인 회복이 일어나지만 환자들은 환측의 상지를 잘 사용하지 못하게 된다. 그 결과 원하는 움직임을 억제하는 상황을 발생시키는데 이것을 학습 무사용 증후군(learned nonu se syndrome)이라 한다. 이러한 학습 무사용 증후군을 치료하기 위해 억제-유도 치료(constraint-induced therapy)가 고안되었다. 억제-유도 치료는 연속되는 몇 주간에 걸쳐 매일 많은 시간 동안 건측의 상지를 묶어두고 환측 상지를 사용하게 하여 기능을 반복 학습하게 함으로써 기능을 증진시키는 방법이다. 이미 여러 연구자들이 경두개 자기자극(transcranial magnetic stimulation), 움직임 관련 피질전위(movement-related cortical potential), 기능적 자기공명 영상기법(functional magnetic resonance imaging) 등을 통하여 억제-유도 치료 후 운동피질영역에서의 재조직화를 보고함으로써 기능 증진과 관련된 회복 기전을 뒷받침하고 있다. 억제-유도 치료의 영역은 확대되어 뇌졸중, 척수손상, 고관절 치환술 후로 하지에서의 기능증진을 위하여 연구가 진행되고 있으며 특히 뇌졸중 후 실어증 환자에서 새로운 방법으로 제시되고 있다. 따라서, 억제-유도 치료는 신경학적인 손상 후 움직임의 재활에 있어서 치료-유도를 통한 중추신경계의 회복에 효과적으로 작용할 수 있다.

• Journal of the Korean Society for information Management
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• v.39 no.2
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• pp.61-86
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• 2022

The purpose of this study is to investigate the effect of learning space Servicescape on the user satisfaction level and continuance intention and to identify moderating effect of the learning activity. The six Servicescape factors are selected after literature review and in-depth interviews; cleanliness, comfort, convenience, aesthetics, accessibility, and flexibility. The online survey is given to the university students at four-year private universities in Seoul metropolitan area. The result shows that among the learning space Servicescape factors, cleanliness, comfort, convenience, and accessibility have a significant impact on the user's satisfaction and the user's satisfaction response determines the continuance intention to the learning space. It is also found that the factors of cleanliness and comfort have a negative moderating effect on user satisfaction. This study implies that the result provides methods to develop the space arrangement for university libraries that provide the better-support to students' learning experience.