• 제목/요약/키워드: experience-based learning algorithm

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볼과 빔 시스템의 퍼지 학습 제어 (Fuzzy Learning Control for Ball & Beam System)

  • 주해호;정병묵;이재원;이화조;이영
    • 한국정밀공학회:학술대회논문집
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    • 한국정밀공학회 1996년도 추계학술대회 논문집
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    • pp.439-443
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    • 1996
  • A fuzzy teaming controller is experimentally designed to control the ball k beam system in this paper. Although most fuzzy controllers have been built just to emulate human decision-making behavior, it is necessary to construct the rule bases by using a learning method with self-improvement when it is difficult or impossible to get them only by expert's experience. The algorithm introduces a reference model to generate a desired output and minimizes a performance index function based on the error and error-rate using the gradient-decent method. In our balancing experiment of the ball & beam system, this paper shows that the fuzzy control rules by learning are superior to the expert's experience.

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DQN 기반 비디오 스트리밍 서비스에서 세그먼트 크기가 품질 선택에 미치는 영향 (The Effect of Segment Size on Quality Selection in DQN-based Video Streaming Services)

  • 김이슬;임경식
    • 한국멀티미디어학회논문지
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    • 제21권10호
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    • pp.1182-1194
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    • 2018
  • The Dynamic Adaptive Streaming over HTTP(DASH) is envisioned to evolve to meet an increasing demand on providing seamless video streaming services in the near future. The DASH performance heavily depends on the client's adaptive quality selection algorithm that is not included in the standard. The existing conventional algorithms are basically based on a procedural algorithm that is not easy to capture and reflect all variations of dynamic network and traffic conditions in a variety of network environments. To solve this problem, this paper proposes a novel quality selection mechanism based on the Deep Q-Network(DQN) model, the DQN-based DASH Adaptive Bitrate(ABR) mechanism. The proposed mechanism adopts a new reward calculation method based on five major performance metrics to reflect the current conditions of networks and devices in real time. In addition, the size of the consecutive video segment to be downloaded is also considered as a major learning metric to reflect a variety of video encodings. Experimental results show that the proposed mechanism quickly selects a suitable video quality even in high error rate environments, significantly reducing frequency of quality changes compared to the existing algorithm and simultaneously improving average video quality during video playback.

유전 알고리즘을 이용한 임베디드 프로세서 기반의 머신러닝 알고리즘에 관한 연구 (A Study on Machine Learning Algorithms based on Embedded Processors Using Genetic Algorithm)

  • 이소행;석경휴
    • 한국전자통신학회논문지
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    • 제19권2호
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    • pp.417-426
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    • 2024
  • 일반적으로 머신러닝을 수행하기 위해서는 딥러닝 모델에 대한 사전 지식과 경험이 필요하고, 데이터를 연산하기 위해 고성능 하드웨어와 많은 시간이 필요하게 된다. 이러한 이유로 머신러닝은 임베디드 프로세서에서 실행하기에는 많은 제약이 있다.본 논문에서는 이러한 문제를 해결하기 위해 머신러닝의 과정 중 콘볼루션 연산(Convolution operation)에 유전 알고리즘을 적용하여 선택적 콘볼루션 연산(Selective convolution operation)과 학습 방법을 제안한다. 선택적 콘볼루션 연산에서는 유전 알고리즘에 의해 추출된 픽셀에 대해서만 콘볼루션을 수행하는 방식이다. 이 방식은 유전 알고리즘에서 지정한 비율만큼 픽셀을 선택하여 연산하는 방식으로 연산량을 지정된 비율만큼 줄일 수 있다. 본 논문에서는 유전 알고리즘을 적용한 머신러닝 연산의 심화학습을 진행하여 해당 세대의 적합도가 목표치에 도달하는지 확인하고 기존 방식의 연산량과 비교한다. 적합도가 충분히 수렴할 수 있도록 세대를 반복하여 학습하고, 적합도가 높은 모델을 유전 알고리즘의 교배와 돌연변이를 통해 다음 세대의 연산에 활용한다.

상황인지기반 U-Learning 응용서비스 (A Study on the U-learning Service Application Based on the Context Awareness)

  • 이기오;이현창;신현철
    • 융합보안논문지
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    • 제8권4호
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    • pp.81-89
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    • 2008
  • 상황인지에 기반한 유비쿼터스 학습서비스 응용모형에 대하여 소개한다. 이동성과 자율성에 기반하여 피학습자에게 WPAN 환경하의 적절한 상황인지 맞춤형 컨텐츠를 제공하고, 학습관리모형을 통해 경험과 선호사항 그리고 후보추천이 서비스제공 시점에 동적으로 재구성되는 서비스 응용 매커니즘을 소개한다. 개방형모형으로써 OSGi 미들웨어를 적용하며, 상황정보 및 프로파일 관리를 위해 메타모형을 통한 동적 재구성 매카니즘이 소개된다.

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강화학습의 Q-learning을 위한 함수근사 방법 (A Function Approximation Method for Q-learning of Reinforcement Learning)

  • 이영아;정태충
    • 한국정보과학회논문지:소프트웨어및응용
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    • 제31권11호
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    • pp.1431-1438
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    • 2004
  • 강화학습(reinforcement learning)은 온라인으로 환경(environment)과 상호작용 하는 과정을 통하여 목표를 이루기 위한 전략을 학습한다. 강화학습의 기본적인 알고리즘인 Q-learning의 학습 속도를 가속하기 위해서, 거대한 상태공간 문제(curse of dimensionality)를 해결할 수 있고 강화학습의 특성에 적합한 함수 근사 방법이 필요하다. 본 논문에서는 이러한 문제점들을 개선하기 위해서, 온라인 퍼지 클러스터링(online fuzzy clustering)을 기반으로 한 Fuzzy Q-Map을 제안한다. Fuzzy Q-Map은 온라인 학습이 가능하고 환경의 불확실성을 표현할 수 있는 강화학습에 적합한 함수근사방법이다. Fuzzy Q-Map을 마운틴 카 문제에 적용하여 보았고, 학습 초기에 학습 속도가 가속됨을 보였다.

로봇 프로그래밍 학습에서 문제해결력에 영향을 미치는 오류요소 (Influential Error Factors of Robot Programming Learning on the Problem Solving Skill)

  • 문외식
    • 정보교육학회논문지
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    • 제12권2호
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    • pp.195-202
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    • 2008
  • 로봇을 이용한 프로그래밍 학습은 획일적이고 정형화된 기존 교육환경에서 벗어나 미래사회의 창의적 학습을 미리 경험할 수 있으며 수학 및 과학의 가장 기초가 되는 알고리즘을 이해하고 향상시키는데 가장 적절한 학습방법이다. 본 연구에서는 초등학생들이 로봇프로그래밍 시 나타날 수 있는 오류의 유형들을 제안하였으며 학습을 위한 교육과정을 개발한 후 초등학생 5, 6학생들을 대상으로 로봇프로그래밍 학습을 시켰다. 학습과정에서 발생한 오류들을 수집하고 분류하였으며 또한, 기존 연구된 컴퓨터기반 프로그래밍언어와 비교 분석하였다. 본 연구에서의 로봇프로그래밍 실행경험을 통해 컴퓨터기반 프로그래밍에서 창의성학습에 큰 장애요소로 평가된 오류요소들 즉, 프로그램사용 미숙으로 인한 오류, 단순한 오타, 문법오류 그리고 코딩실수 등을 전체 오류의 약 21%로 나타나 기존 컴퓨터기반 프로그래밍언어 학습에서 조사된 오류비율(약 53%)에 비해 현저하게 줄어드는 것으로 분석되었다. 이러한 오류의 감소는 초등학생들의 흥미도와 성취도 향상을 위한 주요요소로 판단된다. 따라서, 학습과정에서 보다 많은 논리 및 문제해결을 위한 요소들에 노출되어 있어 창의성 알고리즘 학습에 매우 효과적임을 알 수 있다.

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QoE 향상을 위한 Deep Q-Network 기반의 지능형 비디오 스트리밍 메커니즘 (An Intelligent Video Streaming Mechanism based on a Deep Q-Network for QoE Enhancement)

  • 김이슬;홍성준;정성욱;임경식
    • 한국멀티미디어학회논문지
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    • 제21권2호
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    • pp.188-198
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    • 2018
  • With recent development of high-speed wide-area wireless networks and wide spread of highperformance wireless devices, the demand on seamless video streaming services in Long Term Evolution (LTE) network environments is ever increasing. To meet the demand and provide enhanced Quality of Experience (QoE) with mobile users, the Dynamic Adaptive Streaming over HTTP (DASH) has been actively studied to achieve QoE enhanced video streaming service in dynamic network environments. However, the existing DASH algorithm to select the quality of requesting video segments is based on a procedural algorithm so that it reveals a limitation to adapt its performance to dynamic network situations. To overcome this limitation this paper proposes a novel quality selection mechanism based on a Deep Q-Network (DQN) model, the DQN-based DASH ABR($DQN_{ABR}$) mechanism. The $DQN_{ABR}$ mechanism replaces the existing DASH ABR algorithm with an intelligent deep learning model which optimizes service quality to mobile users through reinforcement learning. Compared to the existing approaches, the experimental analysis shows that the proposed solution outperforms in terms of adapting to dynamic wireless network situations and improving QoE experience of end users.

Applying Deep Reinforcement Learning to Improve Throughput and Reduce Collision Rate in IEEE 802.11 Networks

  • Ke, Chih-Heng;Astuti, Lia
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제16권1호
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    • pp.334-349
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    • 2022
  • The effectiveness of Wi-Fi networks is greatly influenced by the optimization of contention window (CW) parameters. Unfortunately, the conventional approach employed by IEEE 802.11 wireless networks is not scalable enough to sustain consistent performance for the increasing number of stations. Yet, it is still the default when accessing channels for single-users of 802.11 transmissions. Recently, there has been a spike in attempts to enhance network performance using a machine learning (ML) technique known as reinforcement learning (RL). Its advantage is interacting with the surrounding environment and making decisions based on its own experience. Deep RL (DRL) uses deep neural networks (DNN) to deal with more complex environments (such as continuous state spaces or actions spaces) and to get optimum rewards. As a result, we present a new approach of CW control mechanism, which is termed as contention window threshold (CWThreshold). It uses the DRL principle to define the threshold value and learn optimal settings under various network scenarios. We demonstrate our proposed method, known as a smart exponential-threshold-linear backoff algorithm with a deep Q-learning network (SETL-DQN). The simulation results show that our proposed SETL-DQN algorithm can effectively improve the throughput and reduce the collision rates.

머신러닝 기반 골프 퍼팅 방향 예측 모델을 활용한 중요 변수 분석 방법론 (Method of Analyzing Important Variables using Machine Learning-based Golf Putting Direction Prediction Model)

  • Kim, Yeon Ho;Cho, Seung Hyun;Jung, Hae Ryun;Lee, Ki Kwang
    • 한국운동역학회지
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    • 제32권1호
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    • pp.1-8
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    • 2022
  • Objective: This study proposes a methodology to analyze important variables that have a significant impact on the putting direction prediction using a machine learning-based putting direction prediction model trained with IMU sensor data. Method: Putting data were collected using an IMU sensor measuring 12 variables from 6 adult males in their 20s at K University who had no golf experience. The data was preprocessed so that it could be applied to machine learning, and a model was built using five machine learning algorithms. Finally, by comparing the performance of the built models, the model with the highest performance was selected as the proposed model, and then 12 variables of the IMU sensor were applied one by one to analyze important variables affecting the learning performance. Results: As a result of comparing the performance of five machine learning algorithms (K-NN, Naive Bayes, Decision Tree, Random Forest, and Light GBM), the prediction accuracy of the Light GBM-based prediction model was higher than that of other algorithms. Using the Light GBM algorithm, which had excellent performance, an experiment was performed to rank the importance of variables that affect the direction prediction of the model. Conclusion: Among the five machine learning algorithms, the algorithm that best predicts the putting direction was the Light GBM algorithm. When the model predicted the putting direction, the variable that had the greatest influence was the left-right inclination (Roll).

Neuro-Fuzzy Algorithm for Nuclear Reactor Power Control : Part I

  • Chio, Jung-In;Hah, Yung-Joon
    • 한국지능시스템학회논문지
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    • 제5권3호
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    • pp.52-63
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    • 1995
  • A neuro-fuzzy algorithm is presented for nuclear reactor power control in a pressurized water reactor. Automatic reacotr power control is complicated by the use of control rods because of highly nonlinear dynamics in the axial power shape. Thus, manual shaped controls are usually employed even for the limited capability during the power maneuvers. In an attempt to achieve automatic shape control, a neuro-fuzzy approach is considered because fuzzy algorithms are good at various aspects of operator's knowledge representation while neural networks are efficinet structures capable of learning from experience and adaptation to a changing nuclear core state. In the proposed neuro-fuzzy control scheme, the rule base is formulated based ona multi-input multi-output system and the dynamic back-propagation is used for learning. The neuro-fuzzy powere control algorithm has been tested using simulation fesponses of a Korean standard pressurized water reactor. The results illustrate that the proposed control algorithm would be a parctical strategy for automatic nuclear reactor power control.

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