• Journal of Communications and Networks
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• v.11 no.6
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• pp.564-573
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• 2009

In this paper, we consider a wireless multiple path network in which a transmitting node would like to send a message to the receiving node with a certain probability of success. These two nodes are separated by N erasure paths, and we devise two algorithms to determine minimum redundancy and optimal symbol allocation for this setup. We discuss the case with N = 3 and then extend the case to an arbitrary number of paths. One of the algorithms minimum redundancy algorithm in exponential time is shown to be optimal in several cases, but has exponential running time. The other algorithm, minimum redundancy algorithm in polynomial time, is sub-optimal but has polynomial worstcase running time. These algorithms are based off the theory of maximum-distance separable codes. We apply the MRAET algorithm on maximum-distance separable, Luby transform, and Raptor codes and compare their performance.

• Journal of Advanced Marine Engineering and Technology
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• v.11 no.4
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• pp.41-49
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• 1987

Recently microcomputers have come into wide use in the field of control. They are used not only as monitors and or controllers in various plant control systems but also for Computer Aided Design of control systems. In this paper, authors propose a method to design the reduced order observers for the higher order systems and have digital simulation of time responses of the optimal state feedback control system using the maximum principle. And the real time optimal state feedback control system for the third order plant which is realized by an anolog computer is constructed by means of a microcomputer, A/D converter and D/A converter. Time responses of the real time control system are compared with those obtained by the digital simulation and their well coincedence is confirmed.

• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.8
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• pp.1161-1166
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• 1989

In this paper, a hierarchical state feedback control method is proposed for the optimal tracking of large-scale discrete-time systems with time-delays. The state feedback gain matrix and the compensation vector are computed from the optimal trajectories of the state variables and control inputs obtained hierarchically by the open-loop control method based on the interaction prediction method. The resulting feedback gain matrix and the compensation vector are optimal for the given initial condition. Computer simulation results show that the proposed method has better control performance and fewer second level iterations than the Tamura method.

• Journal of Applied Reliability
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• v.11 no.2
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• pp.167-176
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• 2011

This paper introduces models for preventive maintenance policies and considers periodic preventive maintenance policy with minimal repair when the failure of system occurs. It is assumed that minimal repairs do not change the failure rate of the system. The failure rate under prevention maintenance received an effect by a previously prevention maintenance and the slope of failure rate increases the model where it considered. Also the start point of failure rate under prevention maintenance considers the degradation of system and that it increases quotient, it assumed. Per unit time it bought an expectation cost from under this prevention maintenance policy. We obtain the optimal periodic time and the number for the periodic preventive maintenance by using Nakagawa's Algorithm, which minimizes the expected cost per unit time.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2003.09a
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• pp.217-221
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• 2003

Variation of decision variables for optimal remediation using the pump-and-treat method is examined to estimate the effect of the degree of concentration constraint. Simulation-optimization method using genetic algorithm is applied to minimize the total pumping volume. In total volume minimization strategy, the remediation time increases rapidly prior to significant increase in pumping rates. When the concentration constraint is set severer, the more wells are required and the well on the down-gradient direction from the plume hot-spot gives more efficient remediation performance than that on the hot-spot position. These results show that the more profitable strategy for remediation can be achieved by increasing the required remediation time than raising the pumping rate until the time reaches a certain limitation level. So, the remediation time has to be considered as one of the essential decision variables fer optimal remediation design.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.12
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• pp.1183-1187
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• 2011

In this paper, an optimal FIR (Finite-Impulse-Response) filter is proposed for discrete time-varying state-space models. The proposed filter estimates the current state using measured output samples on the recent time horizon so that the variance of the estimation error is minimized. It is designed to be linear, unbiased, with an FIR structure, and is independent of any state information. Due to its FIR structure, the proposed filter is believed to be robust for modeling uncertainty or numerical errors than other IIR filters, such as the Kalman filter. For a general system with system and measurement noise, the proposed filter is derived without any artificial assumptions such as the nonsingular assumption of the system matrix A and any infinite covariance of the initial state. A numerical example show that the proposed FIR filter has better performance than the Kalman filter based on the IIR (Infinite- Impulse-Response) structure when modeling uncertainties exist.

• Journal of Electrical Engineering and Technology
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• v.10 no.3
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• pp.795-803
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• 2015

Autoreclosure provides a means of improving power transmitting ability and system stability. Conventional reclosure adopts the fixed dead time interval strategy, where the reclosure is activated after a time delay to restore the system to normal as quickly as possible without regard to the system conditions. However, these simple techniques cannot provide optimal operating performance. This paper presents an adaptive autoreclosure algorithm including variable dead time, optimal reclosure, phase-by-phase reclosure and emergency extended equal-area criterion (EEEAC) algorithm in order to improve system stability. The reclosure algorithm performs the operations that are attuned to the power system conditions. The proposed adaptive reclosure algorithm is verified and tested using ATP/EMTP MODELS, and the simulation results show that the system oscillations are reduced and the transient stability is enhanced by employing the proposed adaptive reclosure algorithm.

• Management Science and Financial Engineering
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• v.20 no.2
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• pp.13-25
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• 2014

We investigate an optimal retirement time and consumption/investment policy of a wage earner who expects to find a better investment opportunity after retirement by being freed from other work and participating fully in the financial market. We obtain a closed form solution to the optimization problem by using a dynamic programming method under general time-separable von Neumann-Morgenstern utility. It is optimal for the wage earner to retire from work if and only if his wealth exceeds a certain critical level which is obtained from a free boundary value problem. The wage earner consumes less and takes more risk than he would without anticipation of a better investment opportunity.

• 전기의세계
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• v.21 no.1
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• pp.13-16
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• 1972

Dynamic Programming is regarded as a very powerful tool for solving nonlinear optimization problem subject to a number of constraints of state and control variables, but has definite disadvantages that it requires much more computing time and consumes much more memory spaces than other technigues. In order to eliminate the above-mentioned demerits, this paper suggests a news technique called Multiple Dynamic Programming. The underlying principles are based on the concept of multiple passes that, instead of forming fin lattices in time-state plane as adopted in the conventional Dynamic Programming, the Multiple Dynamic Programming constitutes, at the first pass, coarse lattices in the feasible domain of time-state plane and determines the optimal state trajectory by the usual method of Dynamic Programming, and at the second pass again constitutes finer lattices in the narrower domain surrounded by both the upperand lower edges next to the lattice edges through which the first pass optimal trajectory passes and determines the more accurate optimal trajectory of state, and then at the third pass repeats the same processes, and so on. The suggested technique insures remarkable curtailment in amounts of computer memory spaces and conputing time, and its applicability has been demonstrated by a case study on the hydro-thermal power coordination in Korean power system.

• Journal of Drive and Control
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• v.15 no.3
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• pp.1-7
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• 2018

Humanoid robots have fascinated many researchers since they appeared decades ago. For the requirement of both accurate tracking control and the safety of physical human-robot interaction, torque control is basically desirable for humanoid robots. Humanoid robots are highly nonlinear, coupled, complex systems, accordingly the calculation of robot model is difficult and even impossible if precise model of the humanoid robots are unknown. Therefore, it is difficult to control using traditional model-based techniques. To realize model-free torque control, time-delay control (TDC) for humanoid robot was proposed with time-delay estimation technique. Using optimal walking trajectory obtained by particle swarm optimization, TDC with proposed scheme is implemented on whole body of a humanoid, not on biped legs even though it is performed by a virtual humanoid robot. The simulation results show the validity of the proposed TDC for humanoid robots.