电渗微泵的生理溶液渗透特性研究

介绍了一种电渗微泵的结构及其驱动原理,研究了其对不同生理溶液的渗透特性。模拟眼内房水引流装置,利用电渗微泵对三种生理溶液进行测试,通过改变电压,测试定量体积的溶液,记录时间得出相应流速,用Origin Proporable软件对数据拟合得出压电流速曲线,结果表明电压对液体流动速度的影响成正比。人眼可承受最大5 V电压,此时该测试微泵传输液体流量每分钟可达到十几微升,通过分析5 V以内微泵对溶液的渗透性能,结合正常眼内房水引流速度,确定合理的驱动电压设计值,这将对未来制作实际可用的房水引流装置和器件产生指导意义。     

Effect of finite reservoir size on electroosmotic flow in microchannels

In electrokinetically driven microfluidic applications, reservoirs are indispensable and have finite sizes. During operation processes, as the liquid level in reservoirs keeps changing as time elapses, a backpressure is generated. Thus, the flow in microfluidic channels actually exhibits a combination of the electroosmotic flow and the time-dependent induced backpressure-driven flow. In this paper, a model is presented to describe the effect of the finite reservoir size on electroosmotic flow in a rectangular microchannel. Important parameters that describe the effect of finite reservoir size on flow characteristics are discussed. A new concept termed as “effective pumping period” is introduced to characterize the reservoir size effect. The proposed model identifies the mechanisms of the finite-reservoir size effects and is verified by experiment using the micro-PIV technique. The results reported in this study can be used for facilitating the design of microfluidic devices.     

Robust, reduced-order, nonstrictly proper state estimation via the optimal projection equations with Petersen—Hollot bounds

A state-estimation design problem involving parametric plant uncertainties is considered. An error bound suggested by recent work of Petersen and Hollot is utilized for guaranteeing robust estimation. Necessary conditions which generalize the optimal projection equations for reduced-order state estimation are used to characterize the estimator which minimizes the error bound. The design equations thus effectively serve as sufficient conditions for synthesizing robust estimators. An additional feature is the presence of a static estimation gain in conjunction with the dynamic (Kalman) estimator, i. e., a nonstrictly proper estimator.     

Effects of molecular level surface roughness on electroosmotic flow

Electroosmotic flow is widely used to transport and mix fluids in micro- and nanofluidic systems. Though essentially all surfaces exhibit certain degrees of roughness, the effects of surface roughness on electroosmotic flow is not well-understood. In this paper, we investigate how the electrical double layer and electroosmotic flow are affected by molecular level surface roughness by using molecular dynamics simulations. The simulation results indicate that, when the thickness of the electrical double layer is comparable to the height of surface roughness, presence of sub-nanometer deep concave regions on a rough surface can alter the electrical double layer near the surface, and reduce the electroosmotic flow significantly.     

Analysis of combined pressure-driven electroosmotic flow through square microchannels

In this paper three-dimensional single-phase liquid flow through microchannels with a square-shaped cross-section driven by simultaneous application of pressure gradient and electroosmotic pumping mechanism is studied. The governing system of equations consists of the electric potential field and flow field equations. The solution procedure involves three steps. First, the net charge distribution on the cross-section of the microchannel is computed by solving two-dimensional Poisson–Boltzmann equation using the finite element method. Then, using the computed fluid’s charge distribution, the magnitude of the resulting body force due to interaction of an external electric field with the charged fluid particles is calculated along the microchannel. In the third step, the flow equations are solved by considering three-dimensional Navier–Stokes equations with an electrokinetic body force. The computations reveal that the flow pattern in the microchannel is significantly different from the parabolic velocity profile of the laminar pressure-driven flow. The effect of the liquid bulk ionic concentration and the external electric field strength on flow patterns through the square-shaped microchannels is also investigated.     

Mesoscopic simulations of electroosmotic flow and electrophoresis in nanochannels

We review recent dissipative particle dynamics (DPD) simulations of electrolyte flow in nanochannels. A method is presented by which the slip length δB at the channel boundaries can be tuned systematically from negative to infinity by introducing suitably adjusted wall-fluid friction forces. Using this method, we study electroosmotic flow (EOF) in nanochannels for varying surface slip conditions and fluids of different ionic strength. Analytic expressions for the flow profiles are derived from the Stokes equation, which are in good agreement with the numerical results. Finally, we investigate the influence of EOF on the effective mobility of polyelectrolytes in nanochannels. The relevant quantity characterizing the effect of slippage is found to be the dimensionless quantity κδB, where 1/κ is an effective electrostatic screening length at the channel boundaries.     

Experimental characterization of the temperature dependence of zeta potential and its effect on electroosmotic flow velocity in microchannels

This study attempts to characterize the influence of temperature on zeta potential for a number of commonly used buffers in both poly(dimethylsiloxane) (PDMS):glass and PDMS:PDMS microchannels. The study is motivated by the apparent inability of the Smoluchowski equation for electroosmotic flow (EOF) velocity, U = [ε ₀ ε _r ζ/μ]E _z , to accurately predict EOF velocities at elevated temperatures. Error can result if zeta potential (ζ) is taken to be constant, even if permittivity (ε) and viscosity (μ) are treated as temperature-dependent variables. In some cases, velocity may be underestimated by more than 30%. In this study, the time-interval current-monitoring method was used to measure zeta potential. A hotplate maintained precise channel temperatures and applied electric field strengths were selected so that Joule heating was negligible. Results show that in some solutions (e.g., KCl, TBE), the zeta potential can exhibit a strong dependence on temperature, changing by as much as 50% over a span of 60°C. This influence was found to increase with solution concentration. Other buffers (e.g., TE, Na₂CO₃/NaHCO₃) were stable over all measured temperatures.     

A class of single-input single-output systems stabilizable by reduced-order controllers

Let the Euclidian algorithm be applied to the numerator and denominator polynomial of an n-th order system transfer function. It isshown that there exists an (n − 1 − k)-nt order stabilizing controller provided one of the remainder polynomials occuring in the algorithm is a k-th order Hurwitz polunomial. The controller is constructed using almost disturbance decoupling techniques. The result specifies an algebraic condition under which a reduced-order controller can be determined using a reduced-order model obtained by minimal partial realization or a certain continued-fraction expansion.     

A pneumatic micropump incorporated with a normally closed valve capable of generating a high pumping rate and a high back pressure

This study reports on a new pneumatic micropump integrated with a normally closed valve that is capable of generating a high pumping rate and a high back pressure. The micropump consists of a sample flow microchannel, three underlying pneumatic air chambers, resilient polydimethylsiloxane (PDMS) membrane structures and a normally closed valve. The normally closed valve of the micropump is a PDMS-based floating block structure located inside the sample flow microchannel, which is activated by hydraulic pressure created by the peristaltic motion of the PDMS membranes. The valve is used to effectively increase pumping rates and back pressures since it is utilized to prevent backflow. Experimental results indicate that a pumping rate as high as 900 μL/min at a driving frequency of 90 Hz and at an applied pressure of 20 psi (1.378 × 10⁵ Nt/m²) can be obtained. The back pressure on the micropump can be as high as 85 cm-H₂O (8,610.5 Nt/m²) at the same operation conditions. The micropump is fabricated by soft lithography processes and can be easily integrated with other microfluidic devices. To demonstrate its capability to prevent cross contamination during chemical analysis applications, two micropumps and a V-shape channel are integrated to perform a titration of two chemical solutions, specifically sodium hydroxide (NaOH) and benzoic acid (C₆H₅COOH). Experimental data show that mixing with a pH value ranging from 2.8 to 12.3 can be successfully titrated. The development of this micropump can be a promising approach for further biomedical and chemical analysis applications.     

Valuation of option price in commodity markets described by a Markov-switching model: A case study of WTI crude oil market

This paper suggests a Markov-switching model to evaluate commodity futures and spot dynamics, such that the diffusion coefficients and jump size parameter are associated with a hidden Markov chain. We improve the current models in the literature of the commodity markets by modeling the sudden jumps in the commodity prices through the hidden Markov chain. From the crude oil spot price in West Texas Intermediate, we estimate the parameters of proposed Markov-switching model based on expectation–maximization algorithm. To perform this task, we apply this estimation algorithm to the model discretized by Euler scheme and provide some convergence analysis for this discretization method. There are options, such as European options, which are written on the commodity futures. In this study, we evaluate them under the regime-switching model with various economic states. In the following, we calibrate the option prices resulting from the proposed commodity model to a set of observed European call options written on crude oil futures. For this purpose, we first apply an inverse Fourier transform and obtain a semi-analytical option pricing formula. Then, we use the fast Fourier transform method to compute option prices. Since the investors need to calculate Greeks in order to understand the risk involved in option investments, the Greek formulas of Delta, Rho, Theta, and Gamma are derived.     

Stochastic gradient algorithm for a dual-rate Box-Jenkins model based on auxiliary model and FIR model

Based on the work in Ding and Ding(2008),we develop a modifed stochastic gradient(SG)parameter estimation algorithm for a dual-rate Box-Jenkins model by using an auxiliary model.We simplify the complex dual-rate Box-Jenkins model to two fnite impulse response(FIR)models,present an auxiliary model to estimate the missing outputs and the unknown noise variables,and compute all the unknown parameters of the system with colored noises.Simulation results indicate that the proposed method is efective.     

两相流流速测量的参数估计法研究

为了解决相关法流速测量分辨率较低、实时性较差的问题，介绍利用极性导向式自适应算法测量两相流流速的参数估计新方法。文中详细论述实现这种算法的硬件电路结构及其工作过程，并给出气水两相流垂直管段与极性相关法进行对比实验的结果。新仪器测量结果的分辨率和实时性优于相关法。     

A modified hidden Markov model

This paper considers two discrete time, finite state processes X$X$ and Y$Y$. In the usual hidden Markov model X$X$ modulates the values of Y$Y$. However, the values of Y$Y$ are then i.i.d. given X$X$. In this paper a new model is considered where the Markov chain X$X$ modulates the transition probabilities of the second, observed chain Y$Y$. This more realistically can represent problems arising in DNA sequencing. Algorithms for all related filters, smoothers and parameter estimations are derived. Versions of the Viterbi algorithms are obtained.     

Finding the optimal parameters for a software reliability model

Failure rate parameters exert a great deal of influence on the accuracy of software reliability predictions. Thus, it behooves us to examine the methods that are used to estimate the parameters. The value of parameters is determined by the pattern of historical failure data and the amount of data used in the estimates. Conventional wisdom suggests that all the data be used. However, this could be counterproductive if all the data are not representative of the current and future failure process. Therefore, we can analyze these factors to obtain the optimal parameter estimates, where “optimal” means parameter values that will result in minimum prediction error. We examine one software reliability model that does not use all the failure data if a priori analysis indicates that optimal parameter values could not be obtained. Historically, this model has used a mean square error criterion (MSE) of the difference between actual and predicted reliability in the parameter estimation range. However, it has been observed that this policy does not always result in minimum error predictions in the prediction range. Therefore, we investigated the linear interpolation estimation method as an alternative to MSE for three NASA shuttle flight software releases comprising forty-two reliability predictions. Interestingly, linear interpolation provided better prediction accuracy, overall. We believe our analysis can be applied to other software and failure data. In order to systematize our thinking on developing new models using linear interpolation, we employed object-oriented design concepts as guides to implementing C++ programs for evaluating linear interpolation with respect to MSE. The author is a fellow of the IEEE, an IEEE Congressional Fellow, and a member of the US Senate 2005.     

基于简化模型的动态矩阵控制算法

提出了一种改进的动态矩阵控制算法。通过将模型参数转化为控制参数,过程模型的辨训僦转化为控制器参数整定的过程,本算法特别适用于智能化数字调节仪表。     

Estimation of an N-L-N Hammerstein-Wiener model

Estimation of a single-input single-output block-oriented model is studied. The model consists of a linear block embedded between two static nonlinear gains. Hence, it is called N-L-N Hammerstein-Wiener model. First, the model structure is motivated and the disturbance model is discussed. The paper then concentrates on parameter estimation. A relaxation iteration scheme is proposed by making use of a model structure in which the error is bilinear-in-parameters. This leads to a simple algorithm which minimizes the original loss function. The convergence and consistency of the algorithm are studied. In order to reduce the variance error, the obtained linear model is further reduced using frequency weighted model reduction. Simulation study will be used to illustrate the method.     

核密度估计与高斯模型联级运动目标检测

高斯模型与核密度估计模型是两种有效的背景建模及目标检测方法。高斯模型运算简单,但对复杂背景的描述能力差;核密度估计模型对背景描述能力强,但运算复杂,难以实现实时性检测。提出了一种分层联级检测机制,由高斯模型对大部分相对稳定的像素进行分割与检测,对于高斯模型无法精确描述的小部分像素通过核密度估计模型完成分割与检测。实验证实了该方法在适应动态背景扰动与运行效率方面的有效性。     

Parameter estimation for crop growth model using evolutionary and bio-inspired algorithms

All dynamic crop models for growth and development have several parameters whose values are usually determined by using measurements coming from the real system. The parameter estimation problem is raised as an optimization problem and optimization algorithms are used to solve it. However, because the model generally is nonlinear the optimization problem likely is multimodal and therefore classical local search methods fail in locating the global minimum and as a consequence the model parameters could be inaccurate estimated. This paper presents a comparison of several evolutionary (EAs) and bio-inspired (BIAs) algorithms, considered as global optimization methods, such as Differential Evolution (DE), Covariance Matrix Adaptation Evolution Strategy (CMA-ES), Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) on parameter estimation of crop growth SUCROS (a Simple and Universal CROp Growth Simulator) model. Subsequently, the SUCROS model for potential growth was applied to a husk tomato crop (Physalis ixocarpa Brot. ex Horm.) using data coming from an experiment carried out in Chapingo, Mexico. The objective was to determine which algorithm generates parameter values that give the best prediction of the model. An analysis of variance (ANOVA) was carried out to statistically evaluate the efficiency and effectiveness of the studied algorithms. Algorithm's efficiency was evaluated by counting the number of times the objective function was required to approximate an optimum. On the other hand, the effectiveness was evaluated by counting the number of times that the algorithm converged to an optimum. Simulation results showed that standard DE/rand/1/bin got the best result.     

A passive movement method for parameter estimation of a musculo-skeletal arm model incorporating a modified hill muscle model

In this paper we present an experimental method of parameterising the passive mechanical characteristics of the bicep and tricep muscles in vivo, by fitting the dynamics of a two muscle arm model incorporating anatomically meaningful and structurally identifiable modified Hill muscle models to measured elbow movements. Measurements of the passive flexion and extension of the elbow joint were obtained using 3D motion capture, from which the elbow angle trajectories were determined and used to obtain the spring constants and damping coefficients in the model through parameter estimation. Four healthy subjects were used in the experiments. Anatomical lengths and moment of inertia values of the subjects were determined by direct measurement and calculation. There was good reproducibility in the measured arm movement between trials, and similar joint angle trajectory characteristics were seen between subjects. Each subject had their own set of fitted parameter values determined and the results showed good agreement between measured and simulated data. The average fitted muscle parallel spring constant across all subjects was 143 N/m and the average fitted muscle parallel damping constant was 1.73 Ns/m. The passive movement method was proven to be successful, and can be applied to other joints in the human body, where muscles with similar actions are grouped together.     

Quantifying the accuracy of Hammerstein model estimation

This paper investigates the accuracy of the linear component that forms part of an overall Hammerstein model-structure estimate, and a key finding is that the process of estimating the non-linear element can have a strong effect on the associated estimate of the linear dynamics. Furthermore, this effect is not explained simply by way of considering how the input spectrum is changed by the non-linearity. Instead, it arises that the linear model-estimate variability may be dominated by a term that depends on the frequency response of the linear system itself. Amongst other things, the main results derived here have experiment design implications for Hammerstein system estimation.