• Journal of Power Electronics
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• 제16권2호
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• pp.598-609
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• 2016

The good control performance of permanent magnet linear synchronous motor (LSM) drive systems is difficult to achieve using linear controllers because of uncertainty effects, such as fictitious forces. A backstepping control system using adaptive modified recurrent Laguerre orthogonal polynomial neural network uncertainty observer (OPNNUO) is proposed to increase the robustness of LSM drive systems. First, a field-oriented mechanism is applied to formulate a dynamic equation for an LSM drive system. Second, a backstepping approach is proposed to control the motion of the LSM drive system. With the proposed backstepping control system, the mover position of the LSM drive achieves good transient control performance and robustness. As the LSM drive system is prone to nonlinear and time-varying uncertainties, an adaptive modified recurrent Laguerre OPNNUO is proposed to estimate lumped uncertainties and thereby enhance the robustness of the LSM drive system. The on-line parameter training methodology of the modified recurrent Laguerre OPNN is based on the Lyapunov stability theorem. Furthermore, two optimal learning rates of the modified recurrent Laguerre OPNN are derived to accelerate parameter convergence. Finally, the effectiveness of the proposed control system is verified by experimental results.

• 조명전기설비학회논문지
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• 제12권4호
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• pp.122-127
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• 1998

산업 자동화의 고정밀도에 따라 직류 전통기는 강인제어가 요구되고 었다. 하지만 전동기 제어 시스템이 부하 외란의 영향을 받게되면 강인제어는 어렵게 된다. 슬라이딩 모드 제어는 강인성올 갖지만, 강인성을 갖는 슬라이딩 모드 제어에서의 불연속 제어법칙은 원하지 않는 떨림 현상이 발생한다. 이를 해결하기 위한 한 방법으 로 본 논문에서는 전동기 제어 시스템을 위한 신경망 슬라이딩 모드 제어기법올 제시하였다. 제의된 제어기는 떨립 현상 없이 부하 외란을 효과적으로 제거할 수 있었다. 제어기법의 효과는 시뮬레이션에 의해 확인하였다.

• 제어로봇시스템학회논문지
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• 제6권8호
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• pp.617-622
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• 2000

In this paper proposed is an $H_{\ifty}$ based flow controller for the ATM networks. The round trip time-delay uncertainty is taken into account and robustness of the proposed controller is analyzed. Maximum allowable time-delay uncertainties are computed with different weightings on performance and robustness. And discussed is a time-domain implementation method of the proposed controller. Time domain simulation with realistic environment demonstrates that the performance of the proposed controller is much better than that of conventional one.

• 한국시뮬레이션학회논문지
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• 제1권1호
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• pp.48-54
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• 1992

This study discusses the performance evaluation of queueing network methodologies as used for the planning of FMS production systems. The possibility of applications and utilities of queueing network models is investigated for FMS producton plans. Experimental results by queueing network models such as CAN-Q, MVAQ and results by detailed simulation models written in SIMAN are compared and some propositions are presented based on the results of the experiments.

• ETRI Journal
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• 제31권4호
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• pp.399-407
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• 2009

In this paper, we present a robust $H_{\infty}$ distributed power control scheme for wireless CDMA communication systems. The proposed scheme is obtained by optimizing an objective function consisting of the user's performance degradation and the network interference, and it enables a user to address various user-centric and network-centric objectives by updating power in either a greedy or energy efficient manner. The control law is fully distributed in the sense that only its own channel variation needs to be estimated for each user. The proposed scheme is robust to channel fading due to the immediate decision of the power allocation of the next time step based on the estimations from the $H_{\infty}$ filter. Simulation results demonstrate the robustness of the scheme to the uncertainties of the channel and the excellent performance and versatility of the scheme with users adapting transmit power either in a user-centric or a network-centric efficient manner.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2023년도 학술발표회
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• pp.171-171
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• 2023

This study investigates the capability of Physics-Informed Neural Networks (PINNs) for solving the solution of partial differential equations. Particularly, the 1D Saint-Venant Equations (SVEs) were considered, which describe the movement of water in a domain with shallow depth compared to its horizontal extent, and are widely adopted in hydrodynamics, river, and coastal engineering. The core contribution of this work is to combine the robustness of neural networks with the physical constraints of the SVEs. The PINNs method utilized a neural network to approximate the solutions of SVEs, while also enforcing the underlying physical principles of the equations. This allows for a more effective and reliable solution, especially in areas with complex geometry and varying bathymetry. To validate the robustness of the PINNs method, numerical experiments were conducted on several benchmark problems. The results show that the PINNs could be achieved high accuracy when compared with the solution from the numerical solution. Overall, this study demonstrates the potential of using PINNs and highlights the benefits of integrating neural network and physics information for improved efficiency and accuracy in solving SVEs.

• 한국정보과학회논문지:정보통신
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• 제35권6호
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• pp.510-520
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• 2008

오늘날 모바일 디바이스(Mobile Device)는 하나 이상의 네트워크 인터페이스를 가지고 있으며, 이를 효과적으로 활용하기 위한 네트워크 융합(Network Convergence) 기술이 활발히 연구되고 있다. 하지만, 이러한 네트워크 융합 환경을 효과적으로 활용하기 위해서는 물리적 네트워크 인터페이스의 특성뿐 아니라 비디오 부호화 기술에 대한 이해를 바탕으로 한 전송이 필수적이다. 따라서, 본 논문은 전송하려는 비디오 데이타의 특성 및 채널 환경을 이해하고 이에 따라 서로 다른 네트워크 경로로 전송하는 최적의 방법론을 밝힌다. 본 연구는 스케일러블 부호화(Scalable Coded)된 비디오를 계층적 중요성, 스트림 정보의 중요성, 그리고 비디오 디코더의 강인성(Robustness)을 고려한 중요성으로 나누어 다중 채널로 차별적 전송 한다. 실험 결과는 화질기준(PSNR)으로 평균 1dB 이상의 효과를 가졌다. 본 연구 결과는 모바일 디바이스가 하나 이상의 네트워크 인터페이스를 가지는 차세대 네트워크 컨버젼스 환경에 최적인 비디오 전송 기법이 될 것이다.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 1997년도 추계학술대회발표논문집; 홍익대학교, 서울; 1 Nov. 1997
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• pp.181-184
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• 1997

Practical applications of learning systems usually involve complex domains exhibiting nonlinear behavior and dilution by noise. Consequently, an intelligent system must be able to adapt to nonlinear processes as well as probabilistic phenomena. An important class of application for a knowledge based systems in prediction: forecasting the future trajectory of a process as well as the consequences of any decision made by e system. This paper examines the robustness of data mining tools under varying levels of noise while predicting nonlinear processes in the form of chaotic behavior. The evaluated models include the perceptron neural network using backpropagation (BPN), the recurrent neural network (RNN) and case based reasoning (CBR). The concepts are crystallized through a case study in predicting a Henon process in the presence of various patterns of noise.

• 한국경영과학회지
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• 제23권1호
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• pp.109-141
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• 1998

Many processes in the industrial realm exhibit sstochastic and nonlinear behavior. Consequently, an intelligent system must be able to nonlinear production processes as well as probabilistic phenomena. In order for a knowledge based system to control a manufacturing processes as well as probabilistic phenomena. In order for a knowledge based system to control manufacturing process, an important capability is that of prediction : forecasting the future trajectory of a process as well as the consequences of the control action. This paper examines the robustness of data mining tools under varying levels of noise while predicting nonlinear processes, includinb chaotic behavior. The evaluated models include the perceptron neural network using backpropagation (BPN), the recurrent neural network (RNN) and case based reasoning (CBR). The concepts are crystallized through a case study in predicting a chaotic process in the presence of various patterns of noise.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 C
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• pp.1177-1179
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• 1999

This paper presents a design of artificial neural network power system stabilizer(ANNPSS) using adaptive evolutionary algorithm(AEA). We have proposed an adaptive evolutionary algorithm which uses both a genetic algorithm(GA) and an evolution strategy(ES), useing the merits of two different evolutionary computations. ANNPSS shows better control performances than conventional power system stabilizer(CPSS) in three-phase fault with heavy load which is used when tuning ANNPSS. To show the robustness of the proposed ANNPSS, it is applied to damp the low frequency oscillation caused by disturbances such as three-phase fault with normal and light load. the proposed ANNPSS shows better robustness than CPSS.