• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 1993년도 한국자동제어학술회의논문집(국내학술편); Seoul National University, Seoul; 20-22 Oct. 1993
• /
• pp.14-19
• /
• 1993

In this paper, we proposed an indirect learning and direct adaptive control schemes using neural networks, i.e., composite adaptive neural control, for a class of continuous nonlinear systems. With the indirect learning method, the neural network learns the nonlinear basis of the system inverse dynamics by a modified backpropagation learning rule. The basis spans the local vector space of inverse dynamics with the direct adaptation method when the indirect learning result is within a prescribed error tolerance, as such this method is closely related to the adaptive control methods. Also hash addressing technique, similar to the CMAC functional architecture, is introduced for partitioning network hidden nodes according to the system states, so global neuro control properties can be organized by the local ones. For uniform stability, the sliding mode control is introduced when the neural network has not sufficiently learned the system dynamics. With proper assumptions on the controlled system, global stability and tracking error convergence proof can be given. The performance of the proposed control scheme is demonstrated with the simulation results of a nonlinear system.

• 컴퓨터교육학회논문지
• /
• 제4권1호
• /
• pp.135-143
• /
• 2001

학습자 중심의 학습환경으로 하이퍼미디어에 대한 많은 관심이 증가하고 있지만 학습자의 방향상실(Disorientation)로 인해 학습자가 적절한 문제 해결을 하지 못하는 문제에 직면하고 있다. 이에 대한 해결책으로서 방향상실에 대한 단편적 접근을 지양하고, 중요한 관련 변인에 대한 종합적 접근 방법으로서 학습자 통제의 문제, 학습자의 메타인지 능력문제, 하이퍼미디어의 구조문제, 인터페이스 설계문제에 관한 종합적 접근방안에 대해 개괄적 논의를 시도하였다.

• 한국주거학회논문집
• /
• 제9권3호
• /
• pp.25-32
• /
• 1998

This study has carried out two steps. Firstly, the questionnaire was carried out in order to extract visual interference factors in the TV learning spaces. Secondly, on the basis of the questionnaire, it has been carried out two experiments in the TV learning space. In the experiment I, the preferable luminance of the characters and the preferable luminance ratios between the characters and backgrounds on the TV screen are extracted. In the experiment II, the preferable luminance distributions on the TV screen and its surrounding surfaces is found out. The data made in this study is expected to utilize in the lighting design on the TV learning spaces as guides.

• 대한전기학회논문지
• /
• 제39권9호
• /
• pp.985-996
• /
• 1990

An inverse kinematic solution of a robot manipulator using multilayer perceptrons is proposed. Neural networks allow the solution of some complex nonlinear equations such as the inverse kinematics of a robot manipulator without the need for its model. However, the back-propagation (BP) learning rule for multilayer perceptrons has the major limitation of being too slow in learning to be practical. In this paper, a new algorithm named Dynamically Reconfiguring BP is proposed to improve its learning speed. It uses a modified version of Kohonen's Self-Organizing Feature Map (SOFM) to partition the input space and for each input point, select a subset of the hidden processing elements or neurons. A subset of the original network results from these selected neuron which learns the desired mapping for this small input region. It is this selective property that accelerates convergence as well as enhances resolution. This network was used to learn the parity function and further, to solve the inverse kinematic problem of a robot manipulator. The results demonstrate faster learning than the BP network.

• 한국실내디자인학회:학술대회논문집
• /
• 한국실내디자인학회 2005년도 춘계학술발표대회 논문집
• /
• pp.251-252
• /
• 2005

The present design, which is English Zone Development Project for 'G' Elementary School at Seo gu, Incheon, contained various booths for experiential learning corners as well as spaces of teaching learning through group study, dramas and role plays, breaking away from the structure and atmosphere of traditional language labs, and at the same time it include a school building as an affiliated space where the whole students can gather for discussion and learning. The general design concept adopted the atmosphere of an exotic street, installing five theme booths (airport, bank, hospital, book/game store and shop) along the wall and applying the image of road to the floor in order to perform role plays. The blackboard and furniture were also designed to produce the atmosphere of street so that elementary students take interest and actively participate in learning.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 1996년도 추계학술대회 논문집 학회본부
• /
• pp.386-388
• /
• 1996

This paper presents the learning controller for robot manipulators to track the desired trajectory exactly. The learning controller, based on the Lyapunov theory, consists of a fixed PD action and a repetitive action for the purpose of feedforward compensation which is adjusted utilizing a linear combination of the velocity and position errors. The learning controller Is often used In case of the desired trajectories are periodic tasks, and has advantage that it periodically converges to zero even if we don't know the exact dynamic parameters. In this paper, we show that the position and velocity errors of robot manipulators converge to zero sa time goes infinite for the input is periodic function and show a good trajectory tracking performance In the cartesian space.

• International Journal of Advanced Culture Technology
• /
• 제8권4호
• /
• pp.51-57
• /
• 2020

A propeller-based seesaw system is a system that can represent one of axis of four propeller drones and its stabilization has been replaced by intelligent control system instead of often used control methods such as PID and state space. Today, robots are increasingly use machine learning methods to adapt to their environment and learn to perform the right actions. In this article, we propose a Q-learning-based approach to control the stability of a seesaw system with a propeller. From the experimental results that it is possible to fully learn the balance control of a seesaw system by correctly defining the state of the system, the actions to be performed, and the reward functions. Our proposed method solves the seesaw stabilization.

• 로봇학회논문지
• /
• 제17권1호
• /
• pp.40-47
• /
• 2022

A reward function suitable for a task is required to manipulate objects through reinforcement learning. However, it is difficult to design the reward function if the ample information of the objects cannot be obtained. In this study, a demonstration-based object manipulation algorithm called stochastic exploration guided by demonstration (SEGD) is proposed to solve the design problem of the reward function. SEGD is a reinforcement learning algorithm in which a sparse reward explorer (SRE) and an interpolated policy using demonstration (IPD) are added to soft actor-critic (SAC). SRE ensures the training of the critic of SAC by collecting prior data and IPD limits the exploration space by making SEGD's action similar to the expert's action. Through these two algorithms, the SEGD can learn only with the sparse reward of the task without designing the reward function. In order to verify the SEGD, experiments were conducted for three tasks. SEGD showed its effectiveness by showing success rates of more than 96.5% in these experiments.

• 반도체디스플레이기술학회지
• /
• 제22권1호
• /
• pp.129-133
• /
• 2023

Image compression is a fundamental technique in the field of digital image processing, which will help to decrease the storage space and to transmit the files efficiently. Recently many deep learning techniques have been proposed to promise results on image compression field. Since many image compression techniques have artifact problems, this paper has compared two deep learning approaches to verify their performance experimentally to solve the problems. One of the approaches is a deep autoencoder technique, and another is a deep convolutional neural network (CNN). For those results in the performance of peak signal-to-noise and root mean square error, this paper shows that deep autoencoder method has more advantages than deep CNN approach.

• Educational Technology International
• /
• 제18권1호
• /
• pp.49-71
• /
• 2017

The purpose of the study was to identify key design features of virtual reality with head-mounted displays (HMD) and touchless interface for the hearing-impaired and hard-of-hearing learners. The virtual reality based training program was aimed to help hearing-impaired learners in machine operating learning, which requires spatial understanding to operate. We developed an immersive virtual learning environment prototype with an HMD (Oculus Rift) and a touchless natural user interface (Leap Motion) to identify the key design features required to enhance virtual reality for the hearing-impaired and hard-of-hearing learners. Two usability tests of the prototype were conducted, which revealed that several features in the system need revision and that the technology presents an enormous potential to help hearing-impaired learners by providing realistic and immersive learning experiences. After the usability tests of hearing-impaired students' exploring the 3D virtual space, interviews were conducted, which also established that further revision of the system is needed, which would take into account the learners' physical as well as cognitive characteristics.