• East Asian mathematical journal
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• 제26권2호
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• pp.115-140
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• 2010

The purpose of this paper was to develop manipulative materials to teach the angle concepts and construct a teaching-learning program by using that. Furthermore, this study analyzed how does the program affect students understanding of the angle concepts. To check the effects of learning activities with manipulative materials on the understanding of an angle concepts, applied observation during class and write a mathematics journal writing, a description of students impressions at the end of the class and analyzed before and after test paper. We find that students approached the subject more friendly and knew well about the mathematical concepts by using materials. Furthermore, this activity helped that way to solve add and subtract of the angle, estimate ability, round angle concept, positive response in mathematics learning.

• 한국인공지능학회지
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• 제11권4호
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• pp.15-20
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• 2023

Machine learning is comprised of supervised learning, unsupervised learning and reinforcement learning as the type of data and processing mechanism. In this paper, as input and output are unclear and it is difficult to apply the concrete modeling mathematically, reinforcement learning method are applied for crawling robot in this paper. Especially, Q-Learning is the most effective learning technique in model free reinforcement learning. This paper presents a method to implement a crawling robot that is operated by finding the most optimal crawling method through trial and error in a dynamic environment using a Q-learning algorithm. The goal is to perform reinforcement learning to find the optimal two motor angle for the best performance, and finally to maintain the most mature and stable motion about EV3 Crawling robot. In this paper, for the production of the crawling robot, it was produced using Lego Mindstorms with two motors, an ultrasonic sensor, a brick and switches, and EV3 Classroom SW are used for this implementation. By repeating 3 times learning, total 60 data are acquired, and two motor angles vs. crawling distance graph are plotted for the more understanding. Applying the Q-learning reinforcement learning algorithm, it was confirmed that the crawling robot found the optimal motor angle and operated with trained learning, and learn to know the direction for the future research.

• 한국군사과학기술학회지
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• 제26권4호
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• pp.352-360
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• 2023

Recently, AI pilots using reinforcement learning are developing to a level that is more flexible than rule-based methods and can replace human pilots. In this paper, a curriculum was used to help head-on combat with reinforcement learning. It is not easy to learn head-on with a reinforcement learning method without a curriculum, but in this paper, through the two circle-based head-on air combat learning technique, ownship gradually increase the difficulty and become good at head-on combat. On the two-circle, the ATA angle between the ownship and target gradually increased and the AA angle gradually decreased while learning was conducted. By performing reinforcement learning with and w/o curriculum, it was engaged with the rule-based model. And as the win ratio of the curriculum based model increased to close to 100 %, it was confirmed that the performance was superior.

• The Journal of Korean Physical Therapy
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• 제22권6호
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• pp.43-51
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• 2010

Purpose: This study examined the influence of visuomotor congruency on learning a relative phase relationship between a single joint movement and an external signal. Methods: Participants (N=5) were required to rhythmically coordinate elbow flexion-extension movements with a continuous sinusoidal wave (0.375 Hz) at a $90^{\circ}$ relative phase relationship. The congruent group was provided online feedback in which the elbow angle decreased (corresponding to elbow flexion) as the angle trajectory was movingup, and vice versa. The incongruent group was provided online feedback in which the elbow angle decreased as the angle trajectory was moving down, and vice versa. There were two practice sessions (day 1 and 2) and each session consisted of 6 trials per block (5 blocks per session). Retention tests were performed 24 hours after session 2, and only the external sinusoidal wave was provided. Repeated ANOVAs were used for statistical analysis. Results: During practice, the congruent group was significantly less variable than the incongruent group. Phase variability in the incongruent group did not significantly change across blocks, while variability decreased significantly in the congruent group. In retention, the congruent group produced the required $90^{\circ}$ relative phase pattern with significantly less phase variability than the incongruent group. Conclusions: Congruent visual feedback facilitates learning. Moreover, the deprivation of online feedback does not affect the congruent group but does affect the incongruent group in retention.

• 한국컴퓨터그래픽스학회논문지
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• 제23권3호
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• pp.95-103
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• 2017

본 논문은 딥 러닝(deep learning)을 이용하여 입력 영상의 기울어진 정도를 측정하고 수평에 맞게 바로 세우는 방법을 제시한다. 기존 방법들은 일반적으로 영상 내에서 선분, 평면 등 하위 레벨의 특징들을 추출한 후 이를 이용해 영상의 기울어진 정도를 측정한다. 이러한 방법들은 영상 내에 선이나 평면이 존재하지 않는 경우에는 제대로 동작하지 않는다. 본 논문에서는 대규모 데이터 셋을 통해 영상의 다양한 특징들에 대해 학습 가능한 Convolutional Neural Network (CNN)를 이용하여 인물이나 복잡한 배경으로 구성된 기울어진 영상에 대해서도 강인하게 동작하는 프레임워크를 제시한다. 또한, 네트워크에 가변 공간적 (adaptive spatial) pooling 레이어를 추가하여 영상의 다중 스케일 특징을 동시에 고려할 수 있게 하여 영상의 기울어진 정도를 측정하는 성능을 높인다. 실험 결과를 통해 다양한 콘텐츠를 포함한 영상의 기울어짐을 높은 정확도로 바로 세울 수 있음을 확인할 수 있다.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2001년도 추계학술대회논문집B
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• pp.365-370
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• 2001

In order to minimize unstable flow occurred at a multiblade fan/scroll system, optimal angle and shape of cut-off was determined by using two-dimensional turbulent fluid field analyses and neural network. The results of CFD analyses were used for learning as data of input and output of neural network. After learning neural network optimization process was accomplished for design variables, the angle and the shape of cut-off, in the design domain. As a result of optimization, the optimal angle and shape were obtained as 71 and 0.092 times the outer diameter of impeller, respectively, which are very similar values to previous studies. Finally, it was verified that the fluid field is very stable for optimal angle and shape of cut-off by two-dimensional CFD analysis.

• International Journal of Naval Architecture and Ocean Engineering
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• 제13권1호
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• pp.848-857
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• 2021

This paper proposes an efficient approach based on a machine learning technique to predict the local stresses on mooring chain links. Three-link and multi-link finite element analyses were conducted for a target chain link of D107 with steel grade R4; 24,000 and 8000 analyses were performed, respectively. Two serial Artificial Neural Network (ANN) models based on a deep multi-layer perceptron technique were developed. The first ANN model corresponds to multi-link analyses, where the input neurons were the tension force and angle and the output neurons were the interlink angles. The second ANN model corresponds to the three-link analyses with the input neurons of the tension force, interlink angle, and the local stress positions, and the output neurons of the local stress. The predicted local stresses for the untrained cases were reliable compared to the numerical simulation results.

• 한국수학교육학회지시리즈E:수학교육논문집
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• 제33권3호
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• pp.295-317
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• 2019

각 개념은 교육과정 전반에 걸쳐 나타나는 개념이며, 기하 단원에서 기본적인 개념이다. 각은 다면적인 성격을 갖고 있으며 이후 학습에 영향을 주므로 학생들이 다양한 각 개념을 이해하는 것이 필요하다. 본 연구에서는 싱가포르, 영국, 호주, 미국을 비교 대상국가로 정하여 교육과정에서 나타나는 각 관련 내용 요소와 학습시기를 전체적으로 살펴 본 뒤 각에 대한 관점과 각의 크기 측면을 상세하게 살펴보고 이를 바탕으로 우리나라 교육과정에 시사점을 주고자 한다. 분석 결과 우리나라를 제외한 4개국은 보각, 여각, 직선 위의 각, 한 점에서의 각, 각도 구하기를 교육과정에 명시하여 다루고 있으며, 특정 학년에서 집중적으로 각 관련 내용을 학습하는 우리나라에 비해 대부분의 국가가 여러 학년에 걸쳐 점진적으로 각 관련 내용을 다루고 있었다. 대부분의 국가가 각의 정의는 정적인 관점에서, 각의 크기는 동적인 관점에서 서술하고 있었으며, 동적인 관점을 초등학교에서 도입하는 다른 국가에 비해 우리나라는 비교적 늦은 중학교에서 동적인 관점이 처음으로 나타났다. 교육과정에서 다루는 각의 크기의 범위는 우리나라가 다른 국가보다 좁았다. 이를 통해 우리나라 교육과정에 각의 성질과 관련된 다양한 내용 요소를 어떻게 배치하고 전개해 나갈지 논의할 것, 각의 다면적인 성격을 고려하여 정적인 관점뿐만 아니라 동적인 관점을 모두 활용하여 각을 다룰 것, 회전량으로서 각의 크기를 도입하여 우각 및 $180^{\circ}$, $360^{\circ}$ 크기의 각을 학습할 것을 제안한다.

• 대한공업교육학회지
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• 제32권1호
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• pp.117-133
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• 2007

이 연구는 중등학교 학생들에게 비행의 원리를 이해하고 항공 기술 분야에 흥미를 가질 수 있도록 하기 위해 학교 현장에 적용 가능한 교수-학습 자료인 학습용 풍동의 힘 측정 장치를 개발하였다. 연구의 내용은 학습용 풍동의 힘 측정 장치 개발과 실험으로 이루어져 있다. 이 연구에서 얻은 주요 결과를 정리하면 다음과 같다. 공과대학 기계계열 학과나 항공연구소 등에서 사용하는 고가의 Load cell을 이용한 장치 대신에 지렛대 원리를 활용한 간단한 구조이다. 종합된 하나의 장치로 양력, 항력 및 유체 저항 비교 실험이 가능하다. 에어포일 받음각에 따른 양력 계수는 실험값과 이론값이 전체적으로 비슷한 경향성을 갖으며, 실속 현상은 실험값이 이론값보다 더 큰 받음각에서 나타났다. 에어포일 받음각에 따른 항력 계수는 실험값과 이론값이 전체적으로 비슷한 경향성을 갖으며, 실험값은 이론값에 비해 항력 계수의 증가 비율이 완만하게 증가하였다.

• Structural Engineering and Mechanics
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• 제57권4호
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• pp.763-783
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• 2016

A methodology based on Teaching Learning-Based Optimization (TLBO) algorithm is proposed for optimum design of reinforced concrete retaining walls. The objective function is to minimize total material cost including concrete and steel per unit length of the retaining walls. The requirements of the American Concrete Institute (ACI 318-05-Building code requirements for structural concrete) are considered for reinforced concrete (RC) design. During the optimization process, totally twenty-nine design constraints composed from stability, flexural moment capacity, shear strength capacity and RC design requirements such as minimum and maximum reinforcement ratio, development length of reinforcement are checked. Comparing to other nature-inspired algorithm, TLBO is a simple algorithm without parameters entered by users and self-adjusting ranges without intervention of users. In numerical examples, a retaining wall taken from the documented researches is optimized and the several effects (backfill slope angle, internal friction angle of retaining soil and surcharge load) on the optimum results are also investigated in the study. As a conclusion, TLBO based methods are feasible.