The purpose of this study was to show the efficient aspect and the proper time of feedback for the ball movement teaching when the rhythmic gymnastics exercises are practiced in the class room situation, concerning that the method and the time of feedback

Motor skill learning can be acquired implicitly without consciousness of what is being learned. The purpose of this study was to examine the characteristics of implicit motor learning in young and elderly people using a perceptual-motor task. Forty normal young and elderly subjects participated. A modified version of the Serial Reaction Time Task (SRTT) using six blocks of twelve perceptual motor sequences was administered. The paradigm consisted of the first random sequence block followed by the four patterned blocks and another random block. In each block, the go signal consisted of an asterisk displayed in the one of the four parallel arrayed boxes in the middle of the screen. Subjects were instructed to push the corresponding response buttons as quickly as possible. Young subjects demonstrated shorter reaction times during the consecutive patterned blocks reflecting appropriate learning accomplished. Elderly subjects were able to learn a perceptual-motor task with implicit knowledge, but the performance was lower than that of the young persons. These results indicated that implicit sequence learning is still preserved in elderly adults, but the rate of learning is slower.

본 연구는 실시간 온라인으로 진행되는 학부 교양 초급 한국 어 수업에서 강의식 수업과 플립 러닝식 수업을 실시하고 각 수업 방 식에 대한 학습자 인식 및 수업 효율성을 살펴보는 데 목적이 있다. 이를 위해 ZOOM에서 8주간 각 수업 방식을 적용한 한국어 수업을 진행하고 사후 설문 조사를 실시하였다. 그 결과 학습자들은 강의식 수업보다는 플립 러닝식 수업에 긍정적인 반응을 보였으며, 강의식 수 업에서는 ‘어휘 및 문법’ 영역, 플립 러닝식 수업에서는 ‘듣기, 읽기, 쓰 기, 말하기’ 영역의 수업 효율성이 높게 나타났다. 이에 실시간 온라인 으로 진행되는 초급 한국어 수업의 효율을 높이기 위해서는 수업 환 경과 언어 영역의 특성을 고려한 혼합된 수업 방식이 필요할 것이다.

목적: 이 연구는 체육수업에 참여하는 학생들을 대상으로 체육교사와 동료의 관계성지지가 학생들의 심리적 욕구, 학습참여와 여가시간 신체활동에 어떻게 영향을 미치는지를 규명하였다. 방법: 연구 참여자들은 서울 및 경기도 소재 중학교에 재학 중인 1,004명의 학생이다. 연구 참여자들을 대상으로 체육수업 상황에서 지각하고 있는 교사와 동료의 관계성지지, 심리적 욕구, 학습 참여(학습 목표)와 여가시간 신체활동 의도에 관한 설문조사를 진행하였다. 수집된 자료는 통계 프로그램을 이용하여, 기술통계, 신뢰도 분석, 타당도 분석, 상관 분석과 경로 분석을 진행했다. 결과: 체육교사와 동료의 관계성지지는 학생들의 심리적 욕구, 학습참여와 신체활동 의도와 정적 상관 및 영향력을 행사하는 것으로 밝혀졌다. 무엇보다 동료의 관계성지지는 교사의 관계성지지 보다 학생들의 심리적 욕구, 학습참여와 신체활동 의도와 더 높은 상관 및 예측력을 지닌 것으로 나타났다. 결론: 체육수업 상황에서 교사만큼이나 동료의 관계성지지가 학생들의 긍정적인 정서 및 심리적 경험을 유도하는 선행변인이라는 것을 보여준다. 본 연구의 결과는 체육수업 및 스포츠 상황에서 교사와 더불어 동료의 영향력에 관한 정보를 교육 현장에 제공하여 실제적인 도움을 줄 것으로 기대된다.

This paper proposes a model and train method that can real-time detect objects and distances estimation based on a monocular camera by applying deep learning. It used YOLOv2 model which is applied to autonomous or robot due to the fast image processing speed. We have changed and learned the loss function so that the YOLOv2 model can detect objects and distances at the same time. The YOLOv2 loss function added a term for learning bounding box values x, y, w, h, and distance values z as 클래스ification losses. In addition, the learning was carried out by multiplying the distance term with parameters for the balance of learning. we trained the model location, recognition by camera and distance data measured by lidar so that we enable the model to estimate distance and objects from a monocular camera, even when the vehicle is going up or down hill. To evaluate the performance of object detection and distance estimation, MAP (Mean Average Precision) and Adjust R square were used and performance was compared with previous research papers. In addition, we compared the original YOLOv2 model FPS (Frame Per Second) for speed measurement with FPS of our model.

Collecting a rich but meaningful training data plays a key role in machine learning and deep learning researches for a self-driving vehicle. This paper introduces a detailed overview of existing open-source simulators which could be used for training self-driving vehicles. After reviewing the simulators, we propose a new effective approach to make a synthetic autonomous vehicle simulation platform suitable for learning and training artificial intelligence algorithms. Specially, we develop a synthetic simulator with various realistic situations and weather conditions which make the autonomous shuttle to learn more realistic situations and handle some unexpected events. The virtual environment is the mimics of the activity of a genuine shuttle vehicle on a physical world. Instead of doing the whole experiment of training in the real physical world, scenarios in 3D virtual worlds are made to calculate the parameters and training the model. From the simulator, the user can obtain data for the various situation and utilize it for the training purpose. Flexible options are available to choose sensors, monitor the output and implement any autonomous driving algorithm. Finally, we verify the effectiveness of the developed simulator by implementing an end-to-end CNN algorithm for training a self-driving shuttle.

This paper proposes real-time image-based damage detection method for concrete structures using deep learning. The proposed method is composed of three steps: (1) collection of a large volume of images containing damage information from internet, (2) development of a deep learning model (i.e., convolutional neural network (CNN)) using collected images, and (3) automatic selection of damage images using the trained deep learning model. The whole procedure of the proposed method has been applied to some figures taken in a real structure. This method is expected to facilitate the regular inspection and speed up the assessment of detailed damage distribution the without losing accuracy.

As the development of autonomous vehicles becomes realistic, many automobile manufacturers and components producers aim to develop ‘completely autonomous driving’. ADAS (Advanced Driver Assistance Systems) which has been applied in automobile recently, supports the driver in controlling lane maintenance, speed and direction in a single lane based on limited road environment. Although technologies of obstacles avoidance on the obstacle environment have been developed, they concentrates on simple obstacle avoidances, not considering the control of the actual vehicle in the real situation which makes drivers feel unsafe from the sudden change of the wheel and the speed of the vehicle. In order to develop the ‘completely autonomous driving’ automobile which perceives the surrounding environment by itself and operates, ability of the vehicle should be enhanced in a way human driver does. In this sense, this paper intends to establish a strategy with which autonomous vehicles behave human-friendly based on vehicle dynamics through the reinforcement learning that is based on Q-learning, a type of machine learning. The obstacle avoidance reinforcement learning proceeded in 5 simulations. The reward rule has been set in the experiment so that the car can learn by itself with recurring events, allowing the experiment to have the similar environment to the one when humans drive. Driving Simulator has been used to verify results of the reinforcement learning. The ultimate goal of this study is to enable autonomous vehicles avoid obstacles in a human-friendly way when obstacles appear in their sight, using controlling methods that have previously been learned in various conditions through the reinforcement learning.

Solar radiation forecasts are important for predicting the amount of ice on road and the potential solar energy. In an attempt to improve solar radiation predictability in Jeju, we conducted machine learning with various data mining techniques such as tree models, conditional inference tree, random forest, support vector machines and logistic regression. To validate machine learning models, the results from the simulation was compared with the solar radiation data observed over Jeju observation site. According to the model assesment, it can be seen that the solar radiation prediction using random forest is the most effective method. The error rate proposed by random forest data mining is 17%.

이 연구는 운동기술의 학습에 있어 결과지식 제시시기에 따른 학습효과를 알아보는데 그 목적이 있으며 2가지 실험(실험Ⅰ, 실험Ⅱ)으로 구성되었다. 실험Ⅰ은 결과지식의 제공시기가 전체 연습시행의 초기, 중기, 후기에 집중됨으로써 나타나는 기술 학습효과를 실험적으로 검증하기 위해 각 집단별로 8시행을 1분단으로 반복시행 측정하는 3×20(결과지식의 제시조건×분단) 요인 설계하에서 이루어졌다. 실험Ⅱ는 실험Ⅰ에 대한 결과를 지지하기 위해 결과지식의 제시방법에 있어 집단별로 각기 다른 점감절차(fading procedure)를 이용하였으며, 연습시행의 초반, 중반, 후반 중 한 부분에서 점진적으로 집중되는 절차와 전체시행중 골고루 결과지식을 부여하는 조건의 4가지 집단별로 10회 시행을 1분단으로 하여 반복측정에 의한 4×16(결과지식의 제시조건×분단)요인 설계하에서 이루어졌으며 각 실험이 끝난 1시간후에 전이실험이 실시되었다. 실험Ⅰ의 결과에서는 결과지식이 연습회기의 중간부분에서 제공되어지는 집단이 가장 큰 수행효과를 보였으며 전이효과는 40msec조건에서 유의성이 나타났으나 200msec조건에서는 유의한 차이가 없었다. 실험Ⅱ의 결과에서도 결과지식이 전체 연습회기의 중간부분에 점진적으로 집중되어지는 조건이 연습단계에서 가장 큰 수행효과를 나타냈다. 이러한 결과를 종합해 볼때 연습단계에서 결과지식의 제시시기는 유의한 학습변인은 아니며, 동작수행에 있어 가장 효율적인 결과지식의 제공시기는 전체 시행의 중간부분에서 제공되는 것이라고 할 수 있다.

The purpose of this research is to find the effect of motor learning if quantitative knowledge of results and qualitative knowledge of results are presented according to task natures. As the experimental method, I execute a kind of linear positioning task adopting 36 college students as objects of this study. After measuring movement time and distant error using a touch pad adhered a circuit and a programmed personal computer system, I calculate the mean of distant error and standard deviation. I come to get these results as followings to perform factorial ANOVA with repeated measures providing a basis for this computation. The task natures and forms of knowledge of results have not influences on the effect of motor learning. The performance effect according to task natures makes a meaningful difference, that is students show high accuracy of motion when they have enough time to perform the task voluntarily than under the condition that they have to do the task as soon as possible.