• Title/Summary/Keyword: three-state model

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Prognostic Model Built on Blood-based Biomarkers in Patients with Metastatic Colorectal Cancer

  • He, Wen-Zhuo;Jiang, Chang;Yin, Chen-Xi;Guo, Gui-Fang;Rong, Ru-Ming;Qiu, Hui-Juan;Chen, Xu-Xian;Zhang, Bei;Xia, Liang-Ping
    • Asian Pacific Journal of Cancer Prevention
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    • v.15 no.17
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    • pp.7327-7331
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    • 2014
  • Background: We had previously showed that the neutrophil lymphocyte ratio (NLR), ${\gamma}$-glutamyl transpeptidase (GGT) and carcinoembryonic antigen (CEA) are prognostic factors for metastatic colorectal cancer (mCRC) patients. In this study we developed a prognostic model based on these three indices. Materials and Methods: A total of 243 patients who were initially diagnosed as mCRC between 2005 and 2010 in the Sun Yat-sen University Cancer Center were studied. The endpoint was overall survival (OS). Results: NLR>3, elevated GGT and elevated CEA were confirmed as independent risk factors which could predict poor prognosis. Patients could be divided into three groups according to the number of risk factors they had. Those with two or three were defined as the high risk group, individuals with one risk factor as the modest risk group and patients without risk factor as the low risk group. The OS values for these three groups were 16.2 months (2.80~68.8), 24.2 months (4.07~79.0), and 37.2 months (12.6~87.8), respectively (p<0.001). Conclusions: We developed a simple but useful model based on NLR, GGT and CEA to provide prognostic information to clinical practice in highly selected mCRC patients. Further prospective and multi-center studies are warranted to test our model.

State-of-charge Estimation for Lithium-ion Batteries Using a Multi-state Closed-loop Observer

  • Zhao, Yulan;Yun, Haitao;Liu, Shude;Jiao, Huirong;Wang, Chengzhen
    • Journal of Power Electronics
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    • v.14 no.5
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    • pp.1038-1046
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    • 2014
  • Lithium-ion batteries are widely used in hybrid and pure electric vehicles. State-of-charge (SOC) estimation is a fundamental issue in vehicle power train control and battery management systems. This study proposes a novel model-based SOC estimation method that applies closed-loop state observer theory and a comprehensive battery model. The state-space model of lithium-ion battery is developed based on a three-order resistor-capacitor equivalent circuit model. The least square algorithm is used to identify model parameters. A multi-state closed-loop state observer is designed to predict the open-circuit voltage (OCV) of a battery based on the battery state-space model. Battery SOC can then be estimated based on the corresponding relationship between battery OCV and SOC. Finally, practical driving tests that use two types of typical driving cycle are performed to verify the proposed SOC estimation method. Test results prove that the proposed estimation method is reasonably accurate and exhibits accuracy in estimating SOC within 2% under different driving cycles.

A Comparison Study of MIMO Water Wall Model with Linear, MFNN and ESN Models

  • Moon, Un-Chul;Lim, Jaewoo;Lee, Kwang Y.
    • Journal of Electrical Engineering and Technology
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    • v.11 no.2
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    • pp.265-273
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    • 2016
  • A water wall system is one of the most important components of a boiler in a thermal power plant, and it is a nonlinear Multi-Input and Multi-Output (MIMO) system, with 6 inputs and 3 outputs. Three models are developed and comp for the controller design, including a linear model, a multilayer feed-forward neural network (MFNN) model and an Echo State Network (ESN) model. First, the linear model is developed by linearizing a given nonlinear model and is analyzed as a function of the operating point. Second, the MFNN and the ESN are developed by using training data from the nonlinear model. The three models are validated using Matlab with nonlinear input-output data that was not used during training.

A combined stochastic diffusion and mean-field model for grain growth

  • Zheng, Y.G.;Zhang, H.W.;Chen, Z.
    • Interaction and multiscale mechanics
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    • v.1 no.3
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    • pp.369-379
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    • 2008
  • A combined stochastic diffusion and mean-field model is developed for a systematic study of the grain growth in a pure single-phase polycrystalline material. A corresponding Fokker-Planck continuity equation is formulated, and the interplay/competition of stochastic and curvature-driven mechanisms is investigated. Finite difference results show that the stochastic diffusion coefficient has a strong effect on the growth of small grains in the early stage in both two-dimensional columnar and three-dimensional grain systems, and the corresponding growth exponents are ~0.33 and ~0.25, respectively. With the increase in grain size, the deterministic curvature-driven mechanism becomes dominant and the growth exponent is close to 0.5. The transition ranges between these two mechanisms are about 2-26 and 2-15 nm with boundary energy of 0.01-1 J $m^{-2}$ in two- and three-dimensional systems, respectively. The grain size distribution of a three-dimensional system changes dramatically with increasing time, while it changes a little in a two-dimensional system. The grain size distribution from the combined model is consistent with experimental data available.

Invariant Set Based Model Predictive Control of a Three-Phase Inverter System (불변집합에 기반한 삼상 인버터 시스템의 모델예측제어)

  • Lim, Jae-Sik;Park, Hyo-Seong;Lee, Young-Il
    • Journal of Institute of Control, Robotics and Systems
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    • v.18 no.2
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    • pp.149-155
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    • 2012
  • This paper provides an efficient model predictive control for the output voltage control of three-phase inverter system which includes output LC filters. Use of SVPWM (Space Vector Pulse-Width-Modulation) and the rotating d-q frame is made to obtain an input constrained dynamic model of the inverter system. From the measured/estimated output current and reference output voltage, corresponding equilibrium values of the inductor current and the control input are computed. Derivation of a feasible and invariant set around the equilibrium state is made and then a receding horizon strategy which steers the current state deep into the invariant set is proposed. In order to remove offset error, use of disturbance observer is made in the form of state estimator. The efficacy of the proposed method is verified through simulations.

Motional Properties in the Structure of GlcNAc(β1,3)Gal(β)OMe Studied by NMR Spectroscopy and Molecular Modeling

  • 심규창;이상원;김양미
    • Bulletin of the Korean Chemical Society
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    • v.18 no.4
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    • pp.415-424
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    • 1997
  • Conformational flexibilities of the GlcNAc(β1,3)Gal(β)OMe are investigated through NMR spectroscopy and molecular modeling. Adiabatic energy map generated with a dielectric constant of 50 contains three local minima. All of the molecular dynamics simulations on three local minimum energy structures show fluctuations between two low energy structures, N2 at φ=80° and ψ=60° and N3 at φ=60° and ψ=-40°. We have presented adequate evidences to state that GlcNAc(β1,3)Gal(β)OMe exists in two conformationally discrete forms. Two state model of N2 and N3 conformers with a population ratio of 40:60 is used to calculate the effective cross relaxation rate and reproduces the experimental NOEs very well. Molecular dynamics simulation in conjunction with two state model proves successfully the dynamic equilibrium existed in GlcNAc(β1,3)Gal(β)OMe and can be considered as a powerful method to analyze the motional properties in the structure of carbohydrate. This observation also cautions against the indiscriminate use of a rigid model to analyze NMR data.

Numerical simulation of non-isothermal flow in oil reservoirs using a two-equation model

  • dos Santos Heringer, Juan Diego;de Souza Debossam, Joao Gabriel;de Souza, Grazione;Souto, Helio Pedro Amaral
    • Coupled systems mechanics
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    • v.8 no.2
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    • pp.147-168
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    • 2019
  • This work aims to simulate three-dimensional heavy oil flow in a reservoir with heater-wells. Mass, momentum and energy balances, as well as correlations for rock and fluid properties, are used to obtain non-linear partial differential equations for the fluid pressure and temperature, and for the rock temperature. Heat transfer is simulated using a two-equation model that is more appropriate when fluid and rock have very different thermal properties, and we also perform comparisons between one- and two-equation models. The governing equations are discretized using the Finite Volume Method. For the numerical solution, we apply a linearization and an operator splitting. As a consequence, three algebraic subsystems of linearized equations are solved using the Conjugate Gradient Method. The results obtained show the suitability of the numerical method and the technical feasibility of heating the reservoir with static equipment.

A Study on the Effect of the Machine State Considering Human Skillfulness (Kalman Filtering Approach) (작업자의 숙련도가 기계상태에 미치는 영향에 관한 연구 (최적 제어 이론(Kalman Filtering) 적용 중심으로))

  • 윤상원;갈원모;신용백
    • Journal of the Korean Society of Safety
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    • v.9 no.4
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    • pp.125-131
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    • 1994
  • This paper proposes a dynamic recursive model with the effect analysis of machine state considering human factor(human skillfulness) In a single lot man-machine production system. This model obtained using Kalman Filtering Algorithm Is based on input state, output state, machine state. For sensitivity analysis, this model constructed is examined according to the impact of human skillfulness with computer simulation. The model studied in this paper has a great advance from the point of view a combination of three factors( human engineering, dynamic control theory, quality control ) and can also be extended in several applications.

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EEG Signal Prediction by using State Feedback Real-Time Recurrent Neural Network (상태피드백 실시간 회귀 신경회망을 이용한 EEG 신호 예측)

  • Kim, Taek-Soo
    • The Transactions of the Korean Institute of Electrical Engineers D
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    • v.51 no.1
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    • pp.39-42
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    • 2002
  • For the purpose of modeling EEG signal which has nonstationary and nonlinear dynamic characteristics, this paper propose a state feedback real time recurrent neural network model. The state feedback real time recurrent neural network is structured to have memory structure in the state of hidden layers so that it has arbitrary dynamics and ability to deal with time-varying input through its own temporal operation. For the model test, Mackey-Glass time series is used as a nonlinear dynamic system and the model is applied to the prediction of three types of EEG, alpha wave, beta wave and epileptic EEG. Experimental results show that the performance of the proposed model is better than that of other neural network models which are compared in this paper in some view points of the converging speed in learning stage and normalized mean square error for the test data set.

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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