• 제목/요약/키워드: RCT scheme

검색결과 7건 처리시간 0.022초

OFDMA 상향 시스템에서의 협동 전송 기법 (Cooperative Transmission Scheme in OFDMA Uplink System)

  • 윤재선;송형규
    • 한국통신학회논문지
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    • 제32권5A호
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    • pp.475-483
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    • 2007
  • OFDMA 상향 시스템은 DVB-RCT를 위해 채택되었는데, OFDM과 동일하게 다중 경로 페이딩 영향에 강하고 성능 열화 없이 수신 가능하다는 이점을 갖는다. 이 시스템의 QoS를 향상시키기 위해 기존의 MIMO시스템이 많이 연구되었으나 다중송수신 안테나를 이용하는 이들 STC, MRC 등은 다중안테나를 사용하는 데에 따른 하드웨어 복잡도 증가, 고비용, 공간상의 제약 등 여러 가지 문제점을 안고 있다. 따라서 이들을 극복하면서 QoS도 만족시킬 수 있는 새로운 기법이 필요하다. 따라서 이 논문에서는 단일 안테나를 사용하는 OFDMA 기반의 DVB-RCT 시스템을 위하여 기존의 다중 송수신 기법들의 단점을 극복하고 현존 시스템을 이용할 수 있는 협동 전송 기법을 제안한다. OFDMA 상향 시스템 기반의 단일 안테나 DVB-RCT 시스템에 제안된 협동 전송 기법을 적용함으로써, 위에서 언급한 기존 기법들의 단점들을 극복하고 MIMO 시스템과 같이 성능을 향상 시킬 수 있다.

무모형 로봇을 위한 신경 회로망 제어 방식 (A non-model based robot manipulator control using neural networks)

  • 정슬
    • 제어로봇시스템학회:학술대회논문집
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    • 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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    • pp.698-701
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    • 1996
  • A novel neural network control scheme is proposed to identify the inverse dynamic model of robot manipulator and to compensate for uncertainties in robot dynamics. The proposed controller is called reference compensation technique(RCT) by compensating at reference input trajectory. The proposed RCT scheme has many benefits due to the differences in compensating position and learning algorithm. Since the compensation is done outside the plant it can be applied to many control systems without modifying the inside controller. It performs well with low controller gain because the operating range of input values is small and the output of the neural network controller is amplified through the controller gain. The back-propagation algorithm is used to train and simulations of three link robot manipulator are carried out to prove the proposed controller's performances.

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ATM망을 위한 실시간 트래픽 제어 기법:RCT (A Real-time Traffic Control Scheme for ATM network:RCT)

  • 이준연;이재완;권혁인
    • 한국정보처리학회논문지
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    • 제4권11호
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    • pp.2822-2831
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    • 1997
  • ATM을 기반으로 하는 B-ISDN 망은 다양한 트래픽 특성과 서비스 요구사항을 가진 여러가지 종류의 서비스를 지원해야한다. ATM 계층에는 link 계층간의 흐름 제어나 에러 제어가 없다. 그러나 다양한 종류의 각종 서비스를 위해 각기 다른 다른 흐름/에러 제어 기법이 AAL 계층이나 상위 계층(예를 들어 OSI 7 계층의 4번 전송 계층)에서 수행될 수 있다. 전통적인 데이타망에서는 에러 제어 기법이 결합된 윈도우 흐름 제어 기법이 널리 사용되었다. 그러나 윈도우 흐름 제어 기법은 전파 전달 지연(Propagation delay)이 전송률(Transmission rate)에 비해 상대적으로 너무 크기 때문에 ATM 망에서 유용하지 않을 수 있다. 본 논문에서는 양단간(end-to-end) 자료 전송을 위한 매우 간단한 흐름제어 기법인 RCT (Rate Control for end-to-end Transport)를 제안한다. RCT는 평균 과부하(Overload) 기간의 분포가 특정 시간대에 편중되어 있을 때에 높은 성능을 보인다.

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Regulatory innovation for expansion of indications and pediatric drug development

  • Park, Min Soo
    • Translational and Clinical Pharmacology
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    • 제26권4호
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    • pp.155-159
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    • 2018
  • For regulatory approval of a new drug, the most preferred and reliable source of evidence would be randomized controlled trials (RCT). However, a great number of drugs, being developed as well as already marketed and being used, usually lack proper indications for children. It is imperative to develop properly evaluated drugs for children. And expanding the use of already approved drugs for other indications will benefit patients and the society. Nevertheless, to get an approval for expansion of indications, most often with off-label experiences, for drugs that have been approved or for the development of pediatric indications, either during or after completing the main drug development, conducting RCTs may not be the only, if not right, way to take. Extrapolation strategies and modelling & simulation for pediatric drug development are paving the road to the better approval scheme. Making the use of data sources other than RCT such as EHR and claims data in ways that improve the efficiency and validity of the results (e.g., randomized pragmatic trial and randomized registry trial) has been the topic of great interest all around the world. Regulatory authorities should adopt new methodologies for regulatory approval processes to adapt to the changes brought by increasing availability of big and real world data utilizing new tools of technological advancement.

가상링크 기반의 ROBOKER 머리의 실시간 대상체 추종 성능 향상을 위한 신경망 제어 (Neural Network Compensation for Improvement of Real-Time Moving Object Tracking Performance of the ROBOKER Head with a Virtual Link)

  • 김동민;최호진;이근형;정슬
    • 제어로봇시스템학회논문지
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    • 제15권7호
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    • pp.694-699
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    • 2009
  • This paper presents the implementation of the real-time object tracking control of the ROBOKER head. The visual servoing technique is used to track the moving object, but suffers from ill-estimated Jacobian of the virtual link design. To improve the tracking performance, the RBF(Radial Basis Function) network is used to compensate for uncertainties in the kinematics of the robot head in on-line fashion. The reference compensation technique is employed as a neural network control scheme. Performances of three schemes, the kinematic based scheme, the Jacobian based scheme, and the neural network compensation scheme are verified by experimental studies. The neural compensation scheme performs best.

Neural Network Compensation Technique for Standard PD-Like Fuzzy Controlled Nonlinear Systems

  • Song, Deok-Hee;Lee, Geun-Hyeong;Jung, Seul
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • 제8권1호
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    • pp.68-74
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    • 2008
  • In this paper, a novel neural fuzzy control method is proposed to control nonlinear systems. A standard PD-like fuzzy controller is designed and used as a main controller for the system. Then a neural network controller is added to the reference trajectories to form a neural-fuzzy control structure and used to compensate for nonlinear effects. Two neural-fuzzy control schemes based on two well-known neural network control schemes, the feedback error learning scheme and the reference compensation technique scheme as well as the standard PD-like fuzzy control are studied. Those schemes are tested to control the angle and the position of the inverted pendulum and their performances are compared.

PD 기반의 퍼지제어기로 제어된 로봇의 새로운 신경회로망 보상 제어 기술 (A Novel Neural Network Compensation Technique for PD-Like Fuzzy Controlled Robot Manipulators)

  • 송덕희;정슬
    • 제어로봇시스템학회논문지
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    • 제11권6호
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    • pp.524-529
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    • 2005
  • In this paper, a novel neural network compensation technique for PD like fuzzy controlled robot manipulators is presented. A standard PD-like fuzzy controller is designed and used as a main controller for controlling robot manipulators. A neural network controller is added to the reference trajectories to modify input error space so that the system is robust to any change in system parameter variations. It forms a neural-fuzzy control structure and used to compensate for nonlinear effects. The ultimate goal is same as that of the neuro-fuzzy control structure, but this proposed technique modifies the input error not the fuzzy rules. The proposed scheme is tested to control the position of the 3 degrees-of-freedom rotary robot manipulator. Performances are compared with that of other neural network control structure known as the feedback error learning structure that compensates at the control input level.