• Title/Summary/Keyword: u-learning system

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Fuzzy iterative learning controller for dynamic plants (퍼지 반복 학습제어기를 이용한 동적 플랜트 제어)

  • 유학모;이연정
    • 제어로봇시스템학회:학술대회논문집
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    • 1996.10b
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    • pp.499-502
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    • 1996
  • In this paper, we propose a fuzzy iterative learning controller(FILC). It can control fully unknown dynamic plants through iterative learning. To design learning controllers based on the steepest descent method, it is one of the difficult problems to identify the change of plant output with respect to the change of control input(.part.e/.part.u). To solve this problem, we propose a method as follows: first, calculate .part.e/.part.u using a similarity measure and information in consecutive time steps, then adjust the fuzzy logic controller(FLC) using the sign of .part.e/.part..u. As learning process is iterated, the value of .part.e/.part.u is reinforced. Proposed FILC has the simple architecture compared with previous other controllers. Computer simulations for an inverted pendulum system were conducted to verify the performance of the proposed FILC.

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Development and Effect Verification of U-learning based Leveled Reading Education Support System (U-러닝 기반 수준별 독서교육지원 시스템 개발 및 효과검증)

  • Kim, Jeong-Rang;Ma, DaI-Sung;Cheon, Kyung-Rok;Choi, Hyun-Ho;Ko, Yoon-Mi
    • Journal of The Korean Association of Information Education
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    • v.13 no.1
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    • pp.41-49
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    • 2009
  • It's going to be ubiquitous environment which is able to use web pages independent of time and a place by recent development of mobile techniques. On this, we improved the leveled reading supporting system according to U-learning environment and make student to read on both Off-line and On-line through connecting to E-book service. So we developed the reading supporting system which can improve the interests in reading and reading skill and proved the effects. U-learning based leveled reading education support system could be helped develop the reading ability by raising the interest in the activities reading and after reading.

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Design of Mobile Learning Contents using u-smart tourist information (u-스마트 관광정보를 이용한 모바일 학습 콘텐츠 설계)

  • Sun, Su-Kyun
    • Journal of Digital Convergence
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    • v.12 no.3
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    • pp.383-390
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    • 2014
  • In recent years, the convergence of IT and IT sightseeing tour has emerged as a fusion of academic disciplines in the future. Convergence study of social data analysis, raising the heat. Social Network Services (SNS) being utilized in many areas of marketing and to apply the case study is also increasing. This study is based u-smart tourist information systems for mobile learning content design. This is the pattern of things in the template library for things to increase the effectiveness of the learning content to mobile learning content to be converted to a. Design of mobile learning content using u-smart things smart phone app (App) and XMI to go through the design process of utilizing the heat. Future through the design process by implementing a mobile learning content to meet information quality tourist information content to create mobile learning content and learning things that can be content to live it up advantage.

A study on Support System for Standard Korean Language of e-Learning Contents (e-Learning 콘텐츠의 남북한 표준언어 지원시스템 연구)

  • Choi, Sung;Chung, Ji-Moon;Yoo, Gab-Sang
    • Journal of Digital Convergence
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    • v.5 no.2
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    • pp.25-36
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    • 2007
  • In this paper, we studied on the effective structure of an e-Learning Korean Support System for foreigner based on computer systems which is to obey the rules of IMS/AICC International Standard regulations based on LCMS and SCORM. The most important task on this study is to support the function of self-study module through the review of the analysis and results of Korean learning and learning customs. We studied the effective PMS detail modules as well as the Standard Competency Module Management System, which related to LMS/LCMS, Learning an Individual Competency Management System, Competency Registry/Repository System, Knowledge Management System based on Community Competency Module, Education e-survey System and Module learning Support Service System. We suggested one of standard Effective Model of learning Korean Support System which is adopted in a various techniques for foreigner.

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Intelligent Mobile Agents in Personalized u-learning

  • Cho, Sung-Jin;Chung, Hwan-Mook
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.10 no.1
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    • pp.49-53
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    • 2010
  • e-learning and m-learning have some problems that data transmission frequently discontinuously, communication cost increases, the computation speed of mass data drops, battery limitation in the mobile learning environments. In this paper, we propose the PULIMS for u-learning systems. The proposed system intellectualize the education environment using intelligent mobile agent, supports the customized education service, and helps that learners feasible access to the education information through mobile phone. We can see the fact that the efficience of proposed method is outperformed that of the conventional methods. The PULIMS is new technology that can be used to learn whenever and wherever learners want in Ubiquitous education environment.

Mobile agent design for user in u-Learning system (u-Learning 시스템에서 사용자중심 모바일 에이전트 설계)

  • Song, Jae-Koo;Kang, Min-Gyun;Ju, Min-Seong;Kim, Seok-Soo
    • Proceedings of the KAIS Fall Conference
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    • 2006.05a
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    • pp.164-166
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    • 2006
  • 본 논문에서는 유비쿼터스 환경에서 사용자 중심의 모바일 에이전트를 설계하였다. 이는 쌍방향 통신을 기반으로 제공되는 학습 콘텐츠에 대하여 사용자의 편의 및 학습 효율을 극대화 하기위한 방안으로 모바일 기기와 센서의 기술을 결합한 디바이스로 설계하였다. 사용자 중심 모바일 에이전트는 기존 학습시스템 기반의 사용과 동시에 모바일 환경에서 보다 효과적인 교육시스템을 확장하기 위한 방안이다. 본 논문에서 제안하는 u-Learning 시스템에서 사용자중심 모바일 에이전트 설계의 결과 기존 LMS(Learning Management System)보다 적은규모로 보다 효과적인 사용자 정보 관리 및 학습 콘텐츠를 제공함으로써 차세대 교육환경에 획기적으로 기여 할 수 있을 것이다.

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Tumor Segmentation in Multimodal Brain MRI Using Deep Learning Approaches

  • Al Shehri, Waleed;Jannah, Najlaa
    • International Journal of Computer Science & Network Security
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    • v.22 no.8
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    • pp.343-351
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    • 2022
  • A brain tumor forms when some tissue becomes old or damaged but does not die when it must, preventing new tissue from being born. Manually finding such masses in the brain by analyzing MRI images is challenging and time-consuming for experts. In this study, our main objective is to detect the brain's tumorous part, allowing rapid diagnosis to treat the primary disease instantly. With image processing techniques and deep learning prediction algorithms, our research makes a system capable of finding a tumor in MRI images of a brain automatically and accurately. Our tumor segmentation adopts the U-Net deep learning segmentation on the standard MICCAI BRATS 2018 dataset, which has MRI images with different modalities. The proposed approach was evaluated and achieved Dice Coefficients of 0.9795, 0.9855, 0.9793, and 0.9950 across several test datasets. These results show that the proposed system achieves excellent segmentation of tumors in MRIs using deep learning techniques such as the U-Net algorithm.

Learning Control of a U-type Tuned Liquid Damper (U 자형 TLD 시스템의 학습제어 기법 개발)

  • Ryu, Yeong-Soon;Ga, Chun-Sik
    • Proceedings of the KSME Conference
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    • 2003.11a
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    • pp.1584-1589
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    • 2003
  • Simple and effectively developed learning control logic is used to control vibration of U type Tuned Liquid Damper system. The purpose of this paper is design optimal control system to deal with unknown errors from nonlinearity and variation that cost modeling difficulty in complex structure and is followed with the desired behavior. Finally this hybrid control method applied to U type Tuned Liquid Damper structure gives the benefit from better performance of precision and stability of the structure by reducing vibration effect. This research leads to safety design in various structure to robust unspecified foreign disturbances such as earthquake.

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Comparative Study of Deep Learning Model for Semantic Segmentation of Water System in SAR Images of KOMPSAT-5 (아리랑 5호 위성 영상에서 수계의 의미론적 분할을 위한 딥러닝 모델의 비교 연구)

  • Kim, Min-Ji;Kim, Seung Kyu;Lee, DoHoon;Gahm, Jin Kyu
    • Journal of Korea Multimedia Society
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    • v.25 no.2
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    • pp.206-214
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    • 2022
  • The way to measure the extent of damage from floods and droughts is to identify changes in the extent of water systems. In order to effectively grasp this at a glance, satellite images are used. KOMPSAT-5 uses Synthetic Aperture Radar (SAR) to capture images regardless of weather conditions such as clouds and rain. In this paper, various deep learning models are applied to perform semantic segmentation of the water system in this SAR image and the performance is compared. The models used are U-net, V-Net, U2-Net, UNet 3+, PSPNet, Deeplab-V3, Deeplab-V3+ and PAN. In addition, performance comparison was performed when the data was augmented by applying elastic deformation to the existing SAR image dataset. As a result, without data augmentation, U-Net was the best with IoU of 97.25% and pixel accuracy of 98.53%. In case of data augmentation, Deeplab-V3 showed IoU of 95.15% and V-Net showed the best pixel accuracy of 96.86%.