• 전자공학회논문지SC
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• 제44권5호
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• pp.18-29
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• 2007

본 논문에서는 다중 패킷을 전송하는 네트워크 제어시스템을 대상으로, 전체 시스템의 안정성 변화를 분석할 수 있는 새로운 방법을 제안하였다. 먼저, 메시지의 최대 시간 지연을 보장할 수 있는 스케쥴링 방식과 데이터 손실을 나타낼 수 있는 이산 스위치 상태방정식을 새롭게 제시하였으며, 이를 이용하여 네트워크 제어시스템과 등가인 비동기 시스템(asynchronous dynamical system)을 유도하였다. 이후, 제어기의 샘플링 주기, 시간 지연, 데이터 전송 성공률, 전송 패킷의 개수에 따라 변화하는 네트워크 제어시스템의 안정성을 판별할 수 있도록 새로운 정리들을 제안하였다. 마지막으로, Batch Reactor 시스템을 대상으로 제안한 정리들을 적용하여 시뮬레이션하고 그 결과를 분석함으로써 본 논문에서 제안한 안정성 분석 방법의 타당성을 입증하였다.

• 한국지능시스템학회논문지
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• 제10권6호
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• pp.616-628
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• 2000

In this paper, we present the modeling of the ozone prediction system using Neuro-Fuzzy approaches. The mechanism of ozone concentration is highly complex, nonlinear, and nonstationary, the modeling of ozone prediction system has many problems and the results of prediction is not a good performance so far. The Dynamic Polynomial Neural Network(DPNN) which employs a typical algorithm of GMDH(Group Method of Data Handling) is a useful method for data analysis, identification of nonlinear complex system, and prediction of a dynamical system. The structure of the final model is compact and the computation speed to produce an output is faster than other modeling methods. In addition to DPNN, this paper also includes a Fuzzy Logic Method for modeling of ozone prediction system. The results of each modeling method and the performance of ozone prediction are presented. The proposed method shows that the prediction to the ozone concentration based upon Neuro-Fuzzy approaches gives us a good performance for ozone prediction in high and low ozone concentration with the ability of superior data approximation and self organization.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.597-600
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• 1997

In this paper, the authors propose a new method for linearizing a nonlinear dynamical system by use of polynomial compensation. In this method, an M-sequence is applied to the nonlinear system and the crosscorrelation function between the input and the output gives us every crosssections of Volterra kernels of the nonlinear system up to 3rd order. We construct a polynomial compensation function from comparison between lst order Volterra kernel and high order kernels. The polynomial compensation function is, in this case, of third order whose coefficients are variable depending on the amplitude of the input signal. Once we can get compensation function of nonlinear system, we can construct a linearization scheme of the nonlinear system. That is. the effect of second and third order Volterra kernels are subtracted from the output, thus we obtain a sort of linearized output. The authors applied this method to a saturation-type nonlinear system by simulation, and the results show good agreement with the theoretical considerations.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2004년도 추계학술대회
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• pp.1448-1453
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• 2004

The major parameters governing the fluid dynamical and thermo-dynamical behavior in the large pipeline network system are friction loss and the pipeline length. But in local pipeline networks and relatively short distance pipeline system, secondary loss and the considerations of the moving states of the fluid machine are also important. One of the major element in local pressure control system is pressure regulator. It causes the variations of the physical properties in that pipeline system. Especially, as there is not enough information to obtain reliable physical property values such as density, temperature etc. at the downstream of the pressure regulator, It is hard to calculate accurate solution in the pipeline network analysis. In this study, some numerical approaches to investigate the critical-flow-characteristics of the pressure regulator have been done and the detail examinations and considerations of the pressure regulator as a pipeline network elements according to the variations of the inlet-outlet pressure ratio have been carried. Finally the flow-flied distributions, relations and critical-flow-characteristics have been studied. in detail and the 1D analytic method to analyze critical pipe flow have been investigated

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 Proceedings of the Korea Automatic Control Conference, 11th (KACC); Pohang, Korea; 24-26 Oct. 1996
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• pp.149-152
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• 1996

In this paper, the authors propose a new method for linearizing a nonlinear dynamical system by use of Volterra kernel of the nonlinear system. The authors have recently obtained a new method for measuring Volterra kernels of nonlinear control systems by use of a pseudo-random M-sequence and correlation technique. In this method, an M-sequence is applied to the nonlinear system and the crosscorrelation function between the input and the output gives us every crosssection of Volterra kernels up to 3rd order. Once we can get Volterra kernels of nonlinear system, we can construct a linearization method of the nonlinear system. Simulation results show good agreement between the observed results and the theoretical considerations.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.469-469
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• 2000

The use of orthogonal functions with the aim of adapting the system and signal representation to the specific properties of the systems and signals has a long history. A least-squares identification method is studied that estimates a finite number of expansion coefficients in the series expansion of a transfer function, where the expansion is in terms of recently introduced generalized orthogonal functions. It is shown that there exist orthogonal functions that are generated by stable linear dynamical systems.

• Steel and Composite Structures
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• 제32권5호
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• pp.643-655
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• 2019

Perforation and cutouts of structures are compulsory in some modern applications such as in heat exchangers, nuclear power plants, filtration and microeletromicanical system (MEMS). This perforation complicates dynamic analyses of these structures. Thus, this work tends to introduce semi-analytical model capable of investigating the dynamic performance of perforated beam structure under free and forced conditions, for the first time. Closed forms for the equivalent geometrical and material characteristics of the regular square perforated beam regular square, are presented. The governing dynamical equation of motion is derived based on Euler-Bernoulli kinematic displacement. Closed forms for resonant frequencies, corresponding Eigen-mode functions and forced vibration time responses are derived. The proposed analytical procedure is proved and compared with both analytical and numerical analyses and good agreement is noticed. Parametric studies are conducted to illustrate effects of filling ratio and the number of holes on the free vibration characteristic, and forced vibration response of perforated beams. The obtained results are supportive in mechanical design of large devices and small systems (MEMS) based on perforated structure.

• Journal of information and communication convergence engineering
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• 제2권1호
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• pp.46-51
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• 2004

The problem of reducing the effect of an unknown disturbance on a dynamical system is one of the most fundamental issues in control design. We propose a robust PID (Proportional Integral Derivative) control method with neural network for improving the performance due to the rejection of an unknown disturbance. The proposed system consists of a model of the plant, a conventional PID controller and a multi-layer neural network, and is composed of two loop; the first loop enables the system to achieve stability of system, the second loop rejects an unknown disturbance. Simulation and experiment results show that the proposed method improves considerably on the performance of the conventional PID control method and the typical IMC method using neural network.

• Management Science and Financial Engineering
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• 제18권1호
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• pp.49-53
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• 2012

A novel method of transient stability analysis is presented in this paper. The proposed method extracts data points near the basin-of-attraction boundary and then builds a support vector machine (SVM) model learned from the generated data. The constructed SVM classifier has been shown to reduce dramatically the conservativeness of the estimated basin of attraction.

• International Journal of Contents
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• 제9권4호
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• pp.1-10
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• 2013

The electroencephalogram (EEG) time series is a measure of electrical activity received from multiple electrodes placed on the scalp of a human brain. It provides a direct measurement for characterizing the dynamic aspects of brain activities. These EEG signals are formed from a series of spatial and temporal data with multiple dimensions. Missing data could occur due to fault electrodes. These missing data can cause distortion, repudiation, and further, reduce the effectiveness of analyzing algorithms. Current methodologies for EEG analysis require a complete set of EEG data matrix as input. Therefore, an accurate and reliable imputation approach for missing values is necessary to avoid incomplete data sets for analyses and further improve the usage of performance techniques. This research proposes a new method to automatically recover random consecutive missing data from real world EEG data based on Linear Dynamical System. The proposed method aims to capture the optimal patterns based on two main characteristics in the coevolving EEG time series: namely, (i) dynamics via discovering temporal evolving behaviors, and (ii) correlations by identifying the relationships between multiple brain signals. From these exploits, the proposed method successfully identifies a few hidden variables and discovers their dynamics to impute missing values. The proposed method offers a robust and scalable approach with linear computation time over the size of sequences. A comparative study has been performed to assess the effectiveness of the proposed method against interpolation and missing values via Singular Value Decomposition (MSVD). The experimental simulations demonstrate that the proposed method provides better reconstruction performance up to 49% and 67% improvements over MSVD and interpolation approaches, respectively.