• Title/Summary/Keyword: supervised training

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Design of auto-tuning controller for Dynamic Systems using neural networks (신경회로망을 이용한 동적 시스템의 자기동조 제어기 설계)

  • Cho, Hyun-Seob;Oh, Myoung-Kwan
    • Proceedings of the KAIS Fall Conference
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    • 2007.05a
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    • pp.147-149
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    • 2007
  • "Dynamic Neural Unit"(DNU) based upon the topology of a reverberating circuit in a neuronal pool of the central nervous system. In this thesis, we present a genetic DNU-control scheme for unknown nonlinear systems. Our methodis different from those using supervised learning algorithms, such as the backpropagation (BP) algorithm, that needs training information in each step. The contributions of this thesis are the new approach to constructing neural network architecture and its trainin.

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A Note of A Partial Amendment of Probability and Statistics Education Curriculum in Korea

  • Lee, Sang-Bock
    • Journal of the Korean Data and Information Science Society
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    • v.18 no.4
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    • pp.1065-1071
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    • 2007
  • A partial amendment of probability and statistics education in Korea has carried out from January, 2007. We have compared between the patial amendment and 7th national mathematics curriculum. Some ideas are proposed to achieve goals of the revision; textbooks of mathematics are well supervised by well-trained statisticians and teachers are periodically trained for the statistical knowledge.

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Adapative Modular Q-Learning for Agents´ Dynamic Positioning in Robot Soccer Simulation

  • Kwon, Ki-Duk;Kim, In-Cheol
    • 제어로봇시스템학회:학술대회논문집
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    • 2001.10a
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    • pp.149.5-149
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    • 2001
  • The robot soccer simulation game is a dynamic multi-agent environment. In this paper we suggest a new reinforcement learning approach to each agent´s dynamic positioning in such dynamic environment. Reinforcement learning is the machine learning in which an agent learns from indirect, delayed reward an optimal policy to choose sequences of actions that produce the greatest cumulative reward. Therefore the reinforcement learning is different from supervised learning in the sense that there is no presentation of input-output pairs as training examples. Furthermore, model-free reinforcement learning algorithms like Q-learning do not require defining or learning any models of the surrounding environment. Nevertheless ...

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The Adaptation Controller Plan for a Transient State Efficiency Improvement (과도상태 성능 개선을 위한 적응 제어기 설계)

  • Cho, Hyun-Seob;Jun, Ho-Ik
    • Proceedings of the KAIS Fall Conference
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    • 2011.05a
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    • pp.379-381
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    • 2011
  • Dynamic Neural Unit(DNU) based upon the topology of a reverberating circuit in a neuronal pool of the central nervous system. In this thesis, we present a genetic DNU-control scheme for unknown nonlinear systems. Our methodis different from those using supervised learning algorithms, such as the backpropagation (BP) algorithm, that needs training information in each step. The contributions of this thesis are the new approach to constructing neural network architecture and its trainin.

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Unknown Nonlinear Systems Control Using Genetic Algorithms (Geneo-tic Algorithms를 이용한 비선형 시스템 제어)

  • Cho, Hyun-Seob
    • Proceedings of the KAIS Fall Conference
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    • 2009.05a
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    • pp.443-445
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    • 2009
  • Dynamic Neural Unit"(DNU) based upon the topology of a reverberating circuit in a neuronal pool of the central nervous system. In this thesis, we present a genetic DNU-control scheme for unknown nonlinear systems. Our methodis different from those using supervised learning algorithms, such as the backpropagation (BP) algorithm, that needs training information in each step. The contributions of this thesis are the new approach to constructing neural network architecture and its trainin.

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Neural Network Algorithm Application to Auto-tuning of Dynamic Systems (동적시스템의 자동동조를 위한 신경망 알고리즘 응용)

  • Cho, Hyun-Seob
    • Proceedings of the KAIS Fall Conference
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    • 2006.11a
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    • pp.186-190
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    • 2006
  • "Dynamic Neural Unit"(DNU) based upon the topology of a reverberating circuit in a neuronal pool of the central nervous system. In this thesis, we present a genetic DNU-control scheme for unknown nonlinear systems. Our methodis different from those using supervised learning algorithms, such as the backpropagation (BP) algorithm, that needs training information in each step. The contributions of this thesis are the new approach to constructing neural network architecture and its trainin.

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A Desing of position controller for manipulator using Adaptive neural network (적응 신경망을 이용한 동적 매니퓰레이터의 위치제어 설계)

  • Cho, Hyun-Seob;Ryu, In-Ho
    • Proceedings of the KIEE Conference
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    • 2007.07a
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    • pp.1574-1575
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    • 2007
  • "Dynamic Neural Unit"(DNU) based upon the topology of a reverberating circuit in a neuronal pool of the central nervous system. In this thesis, we present a genetic DNU-control scheme for unknown nonlinear systems. Our methodis different from those using supervised learning algorithms, such as the backpropagation (BP) algorithm, that needs training information in each step. The contributions of this thesis are the new approach to constructing neural network architecture and its trainin.

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Study of the UK Pharmacy Education and the Pharmacy Registration Assessment: In England and Wales (영국의 약사교육체계와 국가면허 시험제도 연구: England 와 Wales지역을 중심으로)

  • Kim, Yun Jung
    • Korean Journal of Clinical Pharmacy
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    • v.25 no.2
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    • pp.61-67
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    • 2015
  • Pharmacy education and training is continuously evolving to meet the requirement from the society in the UK. Most pharmacy schools offer the Master of pharmacy degree which is a four year undergraduate programme followed by a year of pre-registration placement spanning a year supervised by a professional pharmacist who has at least 3 years' post-registration experience; however, some universities provide either a 5-year sandwich course where the pre-registration training is split up into two periods of 6 months or a 2-year OSPAP programme for those who are already qualified as a pharmacist outside of the UK. The GPhC has announced that the format of the registration assessment is set to change in 2016. The exam questions from 2016 will be more clinical, practical and based around a patient in a real-life scenario. This article addresses important aspects of UK pharmacy education such as university curriculum, training programme, and licence exam, therefore, could potentially offer a significant contribution to the debate about raising academic standards of pharmacy education in South Korea.

Dynamic Training Algorithm for Hand Gesture Recognition System (손동작 인식 시스템을 위한 동적 학습 알고리즘)

  • Kim, Moon-Hwan;hwang, suen ki;Bae, Cheol-Soo
    • The Journal of Korea Institute of Information, Electronics, and Communication Technology
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    • v.2 no.2
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    • pp.51-56
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    • 2009
  • We developed an augmented new reality tool for vision-based hand gesture recognition in a camera-projector system. Our recognition method uses modified Fourier descriptors for the classification of static hand gestures. Hand segmentation is based on a background subtraction method, which is improved to handle background changes. Most of the recognition methods are trained and tested by the same service-person, and training phase occurs only preceding the interaction. However, there are numerous situations when several untrained users would like to use gestures for the interaction. In our new practical approach the correction of faulty detected gestures is done during the recognition itself. Our main result is the quick on-line adaptation to the gestures of a new user to achieve user-independent gesture recognition.

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Dynamic Training Algorithm for Hand Gesture Recognition System (손동작 인식 시스템을 위한 동적 학습 알고리즘)

  • Bae, Cheol-Soo
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.11 no.7
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    • pp.1348-1353
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    • 2007
  • We developed an augmented new reality tool for vision-based hand gesture recognition in a camera-projector system. Our recognition method uses modified Fourier descriptors for the classification of static hand gestures. Hand segmentation is based on a background subtraction method, which is improved to handle background changes. Most of the recognition methods are trained and tested by the same service-person, and training phase occurs only preceding the interaction. However, there are numerous situations when several untrained users would like to use gestures for the interaction. In our new practical approach the correction of faulty detected gestures is done during the recognition itself. Our main result is the quick on-line adaptation to the gestures of a new user to achieve user-independent gesture recognition.