• Title/Summary/Keyword: State Space Model

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Steady-State Solution for Solar Wind Electrons by Spontaneous Emissions

  • Kim, Sunjung;Yoon, Peter H.;Choe, G.S.
    • The Bulletin of The Korean Astronomical Society
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    • v.41 no.1
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    • pp.44.2-44.2
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    • 2016
  • The solar wind electrons are made of three or four distinct components, which are core Maxwellian background, isotropic halo, and super-halo (and sometimes, highly field-aligned strahl component which can be considered as a fourth element). We put forth a steady-state model for the solar wind electrons by considering both the steady-state particle and wave kinetic equations. Since the steady-state solar wind electron VDFs and the steady-state wave fluctuation spectrum are related to each other, we also investigate the complete fluctuation spectra in the whistler and Langmuir frequency ranges by considering halo- and superhalo-like model electron VDFs. It is found that the energetic electrons make important contributions to the total emission spectrum. Based on this, we complete the steady-state model by considering both the whistler and Langmuir fluctuations. In particular, the Langmuir fluctuation plays an important role in the formation and maintenance of nonthermal electrons.

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Descriptor Type Linear Parameter Dependent System Modeling And Control of Lagrange Dynamics

  • Kang, Jin-Shik
    • 제어로봇시스템학회:학술대회논문집
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    • 2003.10a
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    • pp.444-448
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    • 2003
  • In this paper, the Lagrange dynamics is studied. A state space representation of Lagrange dynamics and control algorithm based on the state feedback pole placement are presented. The state space model presented is descriptor type linear parameter dependent system. It is shown that the control algorithms based on the linear system theory can be applicable to the state space representation of Lagrange dynamics. To show that the linear system theory can be applicable to the state space representation of Lagrange dynamics, the LMI based regional pole-placement design algorithm is developed and present two examples.

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Bayesian Estimation of State-Space Model Using the Hybrid Monte Carlo within Gibbs Sampler

  • Park, Ilsu
    • Communications for Statistical Applications and Methods
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    • v.10 no.1
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    • pp.203-210
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    • 2003
  • In a standard Metropolis-type Monte Carlo simulation, the proposal distribution cannot be easily adapted to "local dynamics" of the target distribution. To overcome some of these difficulties, Duane et al. (1987) introduced the method of hybrid Monte Carlo(HMC) which combines the basic idea of molecular dynamics and the Metropolis acceptance-rejection rule to produce Monte Carlo samples from a given target distribution. In this paper, using the HMC within Gibbs sampler, an asymptotical estimate of the smoothing mean and a general solution to state space modeling in Bayesian framework is obtaineds obtained.

A Study on the Thermal Behavior of Bearing Surroundings using State-Space in Machine Tool Spindle System (공작기계 스핀들시스템에서 상태공간을 이용한 베어링 주변의 열거동에 대한 연구)

  • 신동수;정성종
    • Proceedings of the Korean Society of Precision Engineering Conference
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    • 1995.10a
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    • pp.1045-1049
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    • 1995
  • This paper proposes the state-space model of the thermal behavior of the spindle system to establish dynamic mathematical model of thermal characteristics in machine tool spindle system. the model is derived form physical law of heat transfer and thermoelasticity and represents the thermal behavior induced by uneven thermal expansions whitin a bearing. The model, which is sucessfully validated for two typical configurations of high speed spindle assembles, provides a tool for understanding the basis mechanics of induced thermal expansion as a function of initial preload, spindle speed and housing cooling conditions.

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Identification of MIMO State Space Model based on MISO High-order ARX Model: Design and Application (MISO 고차 ARX 모델 기반의 MIMO 상태공간 모델의 모델인식: 설계와 적용)

  • Won, Wangyun;Yoon, Jieun;Lee, Kwang Soon;Lee, Bongkook
    • Korean Chemical Engineering Research
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    • v.45 no.1
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    • pp.67-72
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    • 2007
  • An efficient method for identification of MIMO state space model has been developed by combining partial least squares (PLS) regression, balanced realization, and balanced truncation. In the developed method, a MIMO system is decomposed into multiple MISO systems each of which is represented by a high-order ARX model and the parameters of the ARX models are estimated by PLS. Then, MISO state space models for respective MISO ARX transfer function are found through realization and combined to a MIMO state space model. Finally, a minimal balanced MIMO state space model is obtained through balanced realization and truncation. The proposed method was applied to the design of model predictive control for temperature control of a high pressure $CO_2$ solubility measurement system.

Modeling and State Observer Design for Roll Slip in Cold Cluster Mills (냉간압연 다단 압연기의 롤 슬립 모델링 및 상태 관측기 설계)

  • Kang, Hyun Seok;Hong, Wan Kee;Hwang, I Cheol
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.36 no.12
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    • pp.1543-1549
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    • 2012
  • This study focuses on the state space model and the design of a state observer for the slip dynamics between rolls in STS cold cluster mills. First, a mathematical model of the roll slip is given as a nonlinear differential equation. Then, by using a Taylor series expansion, it is linearized as a state space model. Next, by using Gopinath's algorithm, a minimal-order state observer based on the state space model is designed to estimate the angular speed of all idle rolls except for an actuated roll that is measureable. Finally, a computer simulation is used to validate that the proposed state space model very well describes slip dynamics between, and moreover, the state observer very well estimates the angular speed of the idle roll.

Stabilizable Predictiye Control with $H_{\infty}$ performance : The State-space approach ($H_{\infty}$ 성능을 가지는 안정화 예측제어 : 상태공간 접근법)

  • 정종남;조상현;전재완;박흥배
    • 제어로봇시스템학회:학술대회논문집
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    • 2000.10a
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    • pp.269-269
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    • 2000
  • This paper presents a predictive control with H$_{\infty}$ suboptimal performance which is robust to disturbances and has a guaranteed stability. In order to derive the control law conveniently, state-space based approach, where the state variable is involved explicitly in the controller design and implementation is allowed. So an input-output model is converted to an equivalent observable canonical state-space form. The suggested control guarantees the norm bounded system output values from disturbances. A systematic way using the LMI method is presented to obtain appropriate parameters for Quadratic stability condition and optimization problem.

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A Study on State-Space Model Identification of AC Servo Motor System (AC 서보 전동기 시스템의 상태공간 모델 식별에 관한 연구)

  • 이태훈;김상환;송봉철;원충연;이상석
    • Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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    • 2000.11a
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    • pp.199-204
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    • 2000
  • Generally, The systems are so complex that it not possible to obtain reasonable model using physical insight. Also a model based on physical insight contains a number of unknown parameters even if the structure is derived from physical laws. To solve these problems, the systems identification is described in this paper. So, AC servo motor system which has both open loop and closed loop is selected as an example for identification. A state-space model of AC servo motor system is identified through open loop experiment and identified through closed loop experiment and using pole placement integral controller to open loop system. As the results, From ARMA model, We have obtained continuous-time state space model.

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A Design of One-Stage Dynamic Prediction Model with State Space Model (상태공간 모형을 이용한 동적 예측 모형 설계)

  • 고명훈;윤상원;신용백
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.18 no.34
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    • pp.107-114
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    • 1995
  • The objective of this study is to design a one-stage dynamic prediction model with Kalman state space model. For a model verification, it is compared with EWMA(Exponentially Weighed Moving Average) model. The model designed in this research can be extended to process prevention control and quality monitoring.

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Cointegration Analysis with Mixed-Frequency Data of Quarterly GDP and Monthly Coincident Indicators

  • Seong, Byeongchan
    • The Korean Journal of Applied Statistics
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    • v.25 no.6
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    • pp.925-932
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    • 2012
  • The article introduces a method to estimate a cointegrated vector autoregressive model, using mixed-frequency data, in terms of a state-space representation of the vector error correction(VECM) of the model. The method directly estimates the parameters of the model, in a state-space form of its VECM representation, using the available data in its mixed-frequency form. Then it allows one to compute in-sample smoothed estimates and out-of-sample forecasts at their high-frequency intervals using the estimated model. The method is applied to a mixed-frequency data set that consists of the quarterly real gross domestic product and three monthly coincident indicators. The result shows that the method produces accurate smoothed and forecasted estimates in comparison to a method based on single-frequency data.