• 제목/요약/키워드: reinforcement methods

검색결과 991건 처리시간 0.025초

족궐음간경(足厥陰肝經)의 화혈(火穴)과 수혈(水穴)의 침보사(針補瀉)가 자율신경계와 뇌파에 미치는 영향 (The effects of Fire point(LR2).Water point(LR8) through Reinforcement-Reduction acupuncture stimulation on ANS & EEG)

  • 강희철;이승기
    • 동의신경정신과학회지
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    • 제21권2호
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    • pp.87-101
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    • 2010
  • Objectives : The aim of this experiment was to investigate the influences of Autonomic Nervous System and EEG by conducting Reinforcement-Reduction(補瀉) acupuncture stimulation to compare the changes in acupoints on the body before and after treatment of acupuncture at Xingjian(LR2) being referred as Fire-point(火穴) and Ququan(LR8) being referred as Water-point(水穴) of Yin Liver Meridian(足厥陰肝經). Methods : This study was carried out on 30 healthy female volunteers in their 20's. There were four tests conducted throughout and the period for each test was between 2 to 5days. HRV and EEG were measured for 5 minutes before acupuncture stimulation was conducted on LR2-Reinforcement, LR2-Reduction, LR8-Reinforcement and LR8-Reduction at random. During the 20 minutes of acupuncture treatment, same subjects were measured simultaneously to observe any significant changes in acupoints. Again, the same subjects were measured for 5 minutes after removing the acupuncture in order to perform a comparative analysis. Results : The measurement of HRV showed that LF, LFnorm and LF/HF ratio increased significantly(p<0.05) while HF, HF norm decreased significantly in case of LR2-Reinforcement & LR8-Reduction. Both LR2-Reduction and LR8-Reinforcement induced a significant increase in HFnorm. EEG measurement indicated low $\alpha$ wave decreased and high $\beta$ wave increased significantly at LR2-Reinforcement during post-acupuncture period compared with acupuncture stimulation period. Both LR2-Reduction and LR8-Reinforcement developed significantly low $\alpha$ wave and high $\alpha$ wave. High $\beta$ wave increased significantly at LR8-Reduction during the acupuncture stimulation in comparison with pre-acupuncture period. Conclusions : The manipulation of acupuncture stimulation at LR2-Reinforcement and LR8-Reduction enhanced the activity of sympathetic nerves and the state of arousal while that of para sympathetic nerves declined. On the other hand, LR2-Reduction and LR8-Reinforcement developed the levels of para sympathetic nerves and relaxation.

3차원 스트럿-타이 모델을 이용한 파일캡의 강도예측 (Strength Prediction of Concrete Pile Caps Using 3-D Strut-Tie Models)

  • 박정웅;윤영묵
    • 한국콘크리트학회:학술대회논문집
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    • 한국콘크리트학회 2003년도 가을 학술발표회 논문집
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    • pp.380-383
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    • 2003
  • Deep pile caps usually contain no transverse shear reinforcement and only small percentages of longitudinal reinforcement. The current design procedures including ACI 318-02 for the pile caps do not provide engineers with a clear understanding of the physical behavior of deep pile caps. In this study, the failure strengths of nine pile cap specimens tested to failure were evaluated using 3-dimensional strut-tie models. The analysis results obtained from the present study were compared with those obtained from several design methods, and the validity of the present method implementing 3-dimensional strut-tie models was examined.

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Effectiveness of CFRP jackets in post-earthquake and pre-earthquake retrofitting of beam-column subassemblages

  • Tsonos, Alexander G.
    • Structural Engineering and Mechanics
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    • 제27권4호
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    • pp.393-408
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    • 2007
  • This paper presents the findings of an experimental study to evaluate retrofit methods which address particular weaknesses that are often found in reinforced concrete structures, especially older structures, namely the lack of the required flexural and shear reinforcement within the columns and the lack of the required shear reinforcement within the joints. Thus, the use of a high-strength fiber jacket for cases of post-earthquake and pre-earthquake retrofitting of columns and beam-column joints was investigated experimentally. In this paper, the effectiveness of the two jacket styles was also compared.

분포 기여도를 이용한 퍼지 Q-learning (Fuzzy Q-learning using Distributed Eligibility)

  • 정석일;이연정
    • 한국지능시스템학회논문지
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    • 제11권5호
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    • pp.388-394
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    • 2001
  • 강화학습은 에이전트가 환경과의 상호작용을 통해 획득한 경험으로부터 제어 규칙을 학습하는 방법이다. 강화학습의 중요한 문제 중의 하나인 신뢰 할당 문제를 해결하기 위해 기여도가 사용되는데, 누적 기여도나 대체 기여도와 같은 기존의 기여도를 이용한 방법은 방문한 상태에서 수행된 행위만을 학습시키기 때문에 학습 자정에서 획득된 보답 신호를 효과적으로 사용하지 못한다. 본 논문에서는 방문한 상태에서 수행된 행위뿐만 아니라 인접 행위들도 학습될 수 있도록 하는 새로운 기여도로써 분포 기여도를 제안한다. 제안된 기여도를 이용한 퍼지 Q-learning 알고리즘을 역진자 시스템에 적용하여 학습 속도면에서 기존의 방법에 비해 우수함을 보인다.

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Reinforced concrete beams under drop-weight impact loads

  • May, Ian M.;Chen, Yi;Owen, D. Roger J.;Feng, Y.T.;Thiele, Philip J.
    • Computers and Concrete
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    • 제3권2_3호
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    • pp.79-90
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    • 2006
  • This paper describes the results of an investigation into high mass-low velocity impact behaviour of reinforced concrete beams. Tests have been conducted on fifteen 2.7 m or 1.5 m span beams under drop-weight loads. A high-speed video camera has been used at rates of up to 4,500 frames per second in order to record the crack formation, propagation, particle spallation and scabbing. In some tests the strain in the reinforcement has been recorded using "Durham" strain gauged bars, a technique developed by Scott and Marchand (2000) in which the strain gauges are embedded in the bars, so that the strains in the reinforcement can be recorded without affecting the bond between the concrete and the reinforcement. The impact force acting on the beams has been measured using a load cell placed within the impactor. A high-speed data logging system has been used to record the impact load, strains, accelerations, etc., so that time histories can be obtained. This research has led to the development of computational techniques based on combined continuum/discontinuum methods (finite/discrete element methods) to permit the simulation of impact loaded reinforced concrete beams. The implementation has been within the software package ELFEN (2004). Beams, similar to those tested, have been analysed using ELFEN a good agreement has been obtained for both the load-time histories and the crack patterns.

픽셀 데이터를 이용한 강화 학습 알고리즘 적용에 관한 연구 (A Study on Application of Reinforcement Learning Algorithm Using Pixel Data)

  • 문새마로;최용락
    • 한국IT서비스학회지
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    • 제15권4호
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    • pp.85-95
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    • 2016
  • Recently, deep learning and machine learning have attracted considerable attention and many supporting frameworks appeared. In artificial intelligence field, a large body of research is underway to apply the relevant knowledge for complex problem-solving, necessitating the application of various learning algorithms and training methods to artificial intelligence systems. In addition, there is a dearth of performance evaluation of decision making agents. The decision making agent that can find optimal solutions by using reinforcement learning methods designed through this research can collect raw pixel data observed from dynamic environments and make decisions by itself based on the data. The decision making agent uses convolutional neural networks to classify situations it confronts, and the data observed from the environment undergoes preprocessing before being used. This research represents how the convolutional neural networks and the decision making agent are configured, analyzes learning performance through a value-based algorithm and a policy-based algorithm : a Deep Q-Networks and a Policy Gradient, sets forth their differences and demonstrates how the convolutional neural networks affect entire learning performance when using pixel data. This research is expected to contribute to the improvement of artificial intelligence systems which can efficiently find optimal solutions by using features extracted from raw pixel data.

Application of Reinforcement Learning in Detecting Fraudulent Insurance Claims

  • Choi, Jung-Moon;Kim, Ji-Hyeok;Kim, Sung-Jun
    • International Journal of Computer Science & Network Security
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    • 제21권9호
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    • pp.125-131
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    • 2021
  • Detecting fraudulent insurance claims is difficult due to small and unbalanced data. Some research has been carried out to better cope with various types of fraudulent claims. Nowadays, technology for detecting fraudulent insurance claims has been increasingly utilized in insurance and technology fields, thanks to the use of artificial intelligence (AI) methods in addition to traditional statistical detection and rule-based methods. This study obtained meaningful results for a fraudulent insurance claim detection model based on machine learning (ML) and deep learning (DL) technologies, using fraudulent insurance claim data from previous research. In our search for a method to enhance the detection of fraudulent insurance claims, we investigated the reinforcement learning (RL) method. We examined how we could apply the RL method to the detection of fraudulent insurance claims. There are limited previous cases of applying the RL method. Thus, we first had to define the RL essential elements based on previous research on detecting anomalies. We applied the deep Q-network (DQN) and double deep Q-network (DDQN) in the learning fraudulent insurance claim detection model. By doing so, we confirmed that our model demonstrated better performance than previous machine learning models.

백스터 로봇의 시각기반 로봇 팔 조작 딥러닝을 위한 강화학습 알고리즘 구현 (Implementation of End-to-End Training of Deep Visuomotor Policies for Manipulation of a Robotic Arm of Baxter Research Robot)

  • 김성운;김솔아;하파엘 리마;최재식
    • 로봇학회논문지
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    • 제14권1호
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    • pp.40-49
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    • 2019
  • Reinforcement learning has been applied to various problems in robotics. However, it was still hard to train complex robotic manipulation tasks since there is a few models which can be applicable to general tasks. Such general models require a lot of training episodes. In these reasons, deep neural networks which have shown to be good function approximators have not been actively used for robot manipulation task. Recently, some of these challenges are solved by a set of methods, such as Guided Policy Search, which guide or limit search directions while training of a deep neural network based policy model. These frameworks are already applied to a humanoid robot, PR2. However, in robotics, it is not trivial to adjust existing algorithms designed for one robot to another robot. In this paper, we present our implementation of Guided Policy Search to the robotic arms of the Baxter Research Robot. To meet the goals and needs of the project, we build on an existing implementation of Baxter Agent class for the Guided Policy Search algorithm code using the built-in Python interface. This work is expected to play an important role in popularizing robot manipulation reinforcement learning methods on cost-effective robot platforms.

A hidden anti-jamming method based on deep reinforcement learning

  • Wang, Yifan;Liu, Xin;Wang, Mei;Yu, Yu
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제15권9호
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    • pp.3444-3457
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    • 2021
  • In the field of anti-jamming based on dynamic spectrum, most methods try to improve the ability to avoid jamming and seldom consider whether the jammer would perceive the user's signal. Although these existing methods work in some anti-jamming scenarios, their long-term performance may be depressed when intelligent jammers can learn user's waveform or decision information from user's historical activities. Hence, we proposed a hidden anti-jamming method to address this problem by reducing the jammer's sense probability. In the proposed method, the action correlation between the user and the jammer is used to evaluate the hiding effect of the user's actions. And a deep reinforcement learning framework, including specific action correlation calculation and iteration learning algorithm, is designed to maximize the hiding and communication performance of the user synchronously. The simulation result shows that the algorithm proposed reduces the jammer's sense probability significantly and improves the user's anti-jamming performance slightly compared to the existing algorithms based on jamming avoidance.