• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.5A
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• pp.475-483
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• 2007

Recently, consumers demand high-quality wireless multimedia services via terrestrial and satellite network. And the interest for new services to sustain its successful commercial deployment grows tremendously. So, the MIMO schemes, such as STCs, MRC, has been used for realizing high reliability. However, MIMO schemes has some limitations. MIMO scheme needs more size, cost, and hardware complexity to employ additional antennas. Moreover, sufficient spaces between antennas are required to guarantee the independence of each channel and the devices which use multiple antennas should be enlarged. A cooperative transmission technique which is detect and forward type applying virtual MIMO with STBC matrix in DVB-RCT(Digital Video Broadcasting with Return Channel via Terrestrial) system based on OFDMA is also proposed.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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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.

• The Transactions of the Korea Information Processing Society
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• v.4 no.11
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• pp.2822-2831
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• 1997

A B-ISDN network based on ATM must support several kinds of transport services with different traffic characteristics and service requirements. There is neither link-by-link flow control nor error control in the ATM layer. For different services, different flow/error controls could be performed at the AAL layer or at a higher Iayer(e.g. transport layer). In traditional data networks, the window now control mechanism combined with error control was used prevalently. But, the window flow control mechanism might be useless in ATM networks because the propagation delay is too large compared with the transmission rate. In this paper, we propose a simple flow control mechanism, called RCT(Rate Control for end-to-end Transport), for end-to-end data transport. The RCT shows acceptable performance when the average overload period is bounded by a certain time.

• Translational and Clinical Pharmacology
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• v.26 no.4
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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.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.7
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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.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.1
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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.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.6
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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.