Tuning of observer-based estimators: theory and application to the on-line estimation of kinetic parameters

This paper deals with the tuning of observer-based estimators. Initially, these algorithms were designed for estimating on-line kinetic parameters, like specific growth rates, in bioprocesses, and have proved to be very successful in practical applications. Here a systematic tuning approach that allows a decoupled estimation of each parameter and the assignment of the estimator dynamics independently of the process dynamics is proposed. The presented approach is illustrated on an animal cell culture example in numerical simulation and with real-life data.     

Smooth sliding-mode observers for specific growth rate and substrate from biomass measurement

This paper addresses the estimation of specific growth rate and substrate concentration from biomass measurements in fermentation processes. Specifically, sliding-mode observers are proposed, for which finite-time global convergence is demonstrated using Lyapunov stability theory and concepts of variable structure systems. Two observers are developed for specific growth rate estimation, one producing a discontinuous estimation which is used afterwards for substrate estimation, and the other one – based on high-gain observers – that generates a smooth estimation with first-order dynamics and finite-time bounded convergence error. In the case of substrate estimation, an observer that increases the convergence rate to a vicinity of the real substrate concentration while achieving asymptotic convergence despite kinetic model uncertainties in properly excited processes is designed. This observer also exhibits first-order dynamics.     

Statistical identifiability and convergence evaluation for nonlinear pharmacokinetic models with particle swarm optimization

The statistical identifiability of nonlinear pharmacokinetic (PK) models with the Michaelis–Menten (MM) kinetic equation is considered using a global optimization approach, which is particle swarm optimization (PSO). If a model is statistically non-identifiable, the conventional derivative-based estimation approach is often terminated earlier without converging, due to the singularity. To circumvent this difficulty, we develop a derivative-free global optimization algorithm by combining PSO with a derivative-free local optimization algorithm to improve the rate of convergence of PSO. We further propose an efficient approach to not only checking the convergence of estimation but also detecting the identifiability of nonlinear PK models. PK simulation studies demonstrate that the convergence and identifiability of the PK model can be detected efficiently through the proposed approach. The proposed approach is then applied to clinical PK data along with a two-compartmental model.     

外极面图像的运动遮挡模型和运动纹理方向检测算法

运动遮挡边界处的运动估计是一种困难的问题,外极面图像方法将运动估计转化为转迹线的检测,人造物体的轨迹线容易通过边缘跟踪的方法获得,但对于纹理复杂的自然景物,轨迹跟踪较为困难。     

Three-dimensional motion and reconstruction of coronary arteries from biplane cineangiography

A new approach is described for reconstructing coronary arteries from two sequences of projection images. The estimation of motion is performed on three-dimensional line segments (or centrelines), and is based on a ‘predictionprojection-optimization’ loop. The method copes with time varying properties, deformations and superpositions of vessels. Experiments using simulated and real data have been carried out. and the results found to be robust over a full cycle of a human heart. Local and global kinetic features can then be derived to obtain a greater insight on the cardiac functional state     

应用Excel处理生化过程数据(Ⅱ)动力学方程参数估值

对Ｅｘｃｅｌ在生化过程动力学参数估值中的应用。提出应采用相对误差平方和最小作为动力学参数优化的目标,将ＶＢＡ编程与规划求解结合可对复杂的多变量不可积动力学方程进行参数估值。     

Modelling precipitation kinetics: Evaluation of the thermodynamics of nucleation and growth

Modelling of (solid-state) precipitation kinetics in terms of particle nucleation and particle growth requires evaluation of the thermodynamic relations pertaining to these mechanisms, i.e. evaluation of the nucleation barrier and of the Gibbs–Thomson effect. In the present work, frequently occurring problems and misconceptions of the thermodynamic evaluation are identified and a practical approach with regard to kinetic modelling is proposed for combined and unified analysis of the thermodynamics of nucleation and growth, based on the fundamental thermodynamic equilibrium consideration in a particle–matrix system. A computationally efficient method for numerical determination of the thermodynamic relations is presented which allows an easy and flexible implementation into kinetic modelling.     

Getting the most out of it: Optimal experiments for parameter estimation of microalgae growth models

Mathematical models are expected to play a pivotal role for driving microalgal production towards a profitable process of renewable energy generation. To render models of microalgae growth useful tools for prediction and process optimization, reliable parameters need to be provided. This reliability implies a careful design of experiments that can be exploited for parameter estimation. In this paper, we provide guidelines for the design of experiments with high informative content based on optimal experiment techniques to attain an accurate parameter estimation. We study a real experimental device devoted to evaluate the effect of temperature and light on microalgae growth. On the basis of a mathematical model of the experimental system, the optimal experiment design problem was formulated and solved with both static (constant light and temperature) and dynamic (time varying light and temperature) approaches. Simulation results indicated that the optimal experiment design allows for a more accurate parameter estimation than that provided by the existing experimental protocol. For its efficacy in terms of the maximum likelihood properties and its practical aspects of implementation, the dynamic approach is recommended over the static approach.     

一种基于人脸图像的年龄估计方法

有关年龄估计的研究在人机交互领域有着非常重要的意义。该文提出一种基于人脸图像的年龄估计方法,该方法首先基于颅面成长模式理论建立人脸测量模板,在此模板上计算面部几何比例特征,然后运用分数阶微分提取人脸局部区域的纹理特征,结合这两类特征构成个体年龄特征向量;通过聚类学习的方法训练年龄特征向量获得年龄-特征映射矩阵,最后由此矩阵表决出输入人脸的估计年龄。实验结果表明,基于这两种特征构建的年龄估计模型可以获得较好的年龄估计结果,年龄误差较小,分类准确率接近人的主观判断结果。     

Monitoring the physiological state of mammalian cell perfusion processes by on-line estimation of intracellular fluxes

Rapid assessment of the cells physiological state during a culture is essential for bioprocess optimization and the design of effective control strategies. In this work, an approach was developed to provide an on-line estimation of the intracellular flux distribution of cells grown in perfusion cultures, based on a comprehensive metabolic network involving 40 biochemical reactions and 46 components. The specific uptake and production rates were evaluated from daily nutrient and metabolite concentration measurements, using an extended Kalman-filter for noise reduction and rate estimation. The biosynthetic rates were determined from an on-line estimate of the specific growth rate using a recursive least-squares method. Our results demonstrate that this approach allows monitoring of the cells metabolic activity and can be a useful tool to facilitate process development.     

An extremum seeking approach via variable-structure control for fed-batch bioreactors with uncertain growth rate

In this paper, we present an extremum-seeking scheme based on an approach to variable structure control for fed-batch bioreactors. The proposed scheme deals with uncertainty on the specific growth rate without assuming an explicit mathematical expression. The control approach exploits the inhibitory effect of the substrate concentration on the growth rate, in such a manner that the closed-loop system reaches the sliding regime on an optimal switching manifold, which is defined by maximizing biomass production. The control scheme comprises an estimation scheme consisting of a high-gain observer and a discrete gradient estimator which computes the unknown terms. The practical stabilizability for the closed-loop system around an unknown optimal set-point is analyzed. Numerical experiments illustrate the effectiveness of the proposed approach.     

基于状态验证覆盖的Bayes软件可靠性评估

传统的可靠性评估方法都是基于系统软件运行期间的失效,对于武器系统软件,由于其使用试验耗费巨资且周期很长,不可能对系统进行过多的使用试验,导致难以采集到高质量的失效数据。提出一种基于系统状态验证覆盖的Bayes软件可靠性评估方法,该方法以Bayes可靠性模型为评估准则,通过状态覆盖率来保证充分性,通过状态测试验证来保证可靠性,提倡可信性与可靠性并行增长。     

Diagnosis of inhomogeneous insulation degradation in electric cables by distributed shunt conductance estimation

For the diagnosis of inhomogeneous insulation degradation in electric cables, the estimation of distributed shunt conductance is studied in this paper. Gradual growth of the shunt conductance is a consequence of degradation of the dielectric properties of the insulator. The proposed estimation method is based on voltage and current measurements at a single end of the cable. After the linearization of the bilinear term of the telegrapher's equations through a perturbation approach, the Kalman filter is applied to transform the problem of dynamic system parameter estimation to a simple linear regression problem. Results of numerical simulations are presented to demonstrate the feasibility of the proposed method. In particular, it is shown that the weak sensitivity of the available measurements to the shunt conductance can be compensated by long time data samples.     

A hybrid variable neighborhood search for solving the hybrid flow shop scheduling problem

This paper proposes a hybrid variable neighborhood search (HVNS) algorithm that combines the chemical-reaction optimization (CRO) and the estimation of distribution (EDA), for solving the hybrid flow shop (HFS) scheduling problems. The objective is to minimize the maximum completion time. In the proposed algorithm, a well-designed decoding mechanism is presented to schedule jobs with more flexibility. Meanwhile, considering the problem structure, eight neighborhood structures are developed. A kinetic energy sensitive neighborhood change approach is proposed to extract global information and avoid being stuck at the local optima. In addition, contrary to the fixed neighborhood set in traditional VNS, a dynamic neighborhood set update mechanism is utilized to exploit the potential search space. Finally, for the population of local optima solutions, an effective EDA-based global search approach is investigated to direct the search process to promising regions. The proposed algorithm is tested on sets of well-known benchmark instances. Through the analysis of experimental results, the high performance of the proposed HVNS algorithm is shown in comparison with four efficient algorithms from the literature.     

Some edge correction methods for marked spatio-temporal point process models

Three edge correction methods for (marked) spatio-temporal point processes are proposed. They are all based on the idea of placing an approximated expected behaviour of the process at hand (simulated realisations) outside the study region which interacts with the data during the estimation. These methods are applied to the so-called growth-interaction model. The specific choices of growth function and interaction function made are purely motivated by the forestry applications considered. The parameters of the growth and interaction functions, i.e. the parameters related to the development of the marks, are estimated using the least-squares approach together with the proposed edge corrections. Finally, the edge corrected estimation methods are applied to a data set of Swedish Scots pine.     

A general modeling and analysis framework for software fault detection and correction process

Software reliability growth modeling plays an important role in software reliability evaluation. To incorporate more information and provide more accurate analysis, modeling software fault detection and correction processes has attracted widespread research attention recently. In modeling software correction processes, the assumption of fault correction time is relaxed from constant delay to random delay. However, stochastic distribution of fault correction time brings more difficulties in modeling and corresponding parameter estimation. In this paper, a framework of software reliability models containing both information from software fault detection process and correction process is studied. Different from previous extensions on software reliability growth modeling, the proposed approach is based on Markov model other than a nonhomogeneous Poisson process model. Also, parameter estimation is carried out with weighted least‐square estimation method, which emphasizes the influence of later data on the prediction. Two data sets from practical software development projects are applied with the proposed framework, which shows satisfactory performance with the results. Copyright © 2016 John Wiley & Sons, Ltd.     

PSO-based back-propagation artificial neural network for product and mold cost estimation of plastic injection molding

To simplify complicated traditional cost estimation flow, this study emphasizes the cost estimation approach for plastic injection products and molds. It is expected designers and R&D specialists can consider the competitiveness of product cost in the early stage of product design to reduce product development time and cost resulting from repetitive modification. Therefore, the proposed cost estimation approach combines factor analysis (FA), particle swarm optimization (PSO) and artificial neural network with two back-propagation networks, called FAPSO-TBP. In addition, another artificial neural network estimation approach with a single back-propagation network, called FAPSO-SBP, is also established. To verify the proposed FAPSO-TBP approach, comparisons with the FAPSO-SBP and general back-propagation artificial neural network (GBP) are made. The computational results show the proposed FAPSO-TBP approach is very competitive for the product and mold cost estimation problems of plastic injection molding.     

Microbial specific growth rate control via MRAC method

A problematic feature in most fermentation processes is that on-line measurement of the most important biological process variables, the concentration of the bio-mass and its specific growth rate, cannot be directly measured. An approach for estimating these biological variables and for controlling the specific growth rate in a continuous flow bioprocess of a stirred tank reactor is presented and analysed. The goal in the control is to get the specific growth rate to track the rate of a given reference model, A dynamic model for the specific growth rate is obtained from the general growth and substrate consumption model. The structure of the rate model is then applied in the reference model structure. The methodology studied is based on two issues, joint state and parameter estimation and direct adaptive control. From the theoretical viewpoint, this forms a combination of indirect and direct adaptive control. The structure of the control law is justified by the structure of the law obtained for known fixed parameters. Convergence of the estimation scheme and of the adaptive control scheme are demonstrated via Lyapunov's method, and separately. The choice of the appropriate Lyapunov function results in a Pl-type controller with the gains as the parameters to be adjusted by the user. A simulation study based on Monod's model and time-varying model parameters and subject to measurement noise shows the feasibility and robustness of the adaptive control methodology developed for this non-linear process.     

Bayesian更新与EM算法协作下退化数据驱动的剩余寿命估计方法

设备的剩余寿命(RUL)估计是对设备进行视情维护、预测与健康管理的关键问题之一.为实现对于单个服役设备退化过程的建模以及RUL的估计,文中提出一种Bayesian更新与期望最大化算法协作下退化数据驱动的RUL估计方法.首先利用指数退化模型来描述设备的退化过程,基于监测的退化数据,利用Bayesian方法对模型的随机参数进行更新,进而得到RUL的概率分布函数和点估计.其次,利用运行设备到当前时刻的监测数据,基于EM算法给出退化模型中非随机未知参数的估计方法,并证明参数迭代估计中每步得到的结果是唯一最优解.最后通过数值仿真和实际数据应用研究,表明文中方法可对单个设备退化过程进行建模,有效估计退化模型中的未知参数,进而得到更好的RUL估计结果.     

A comparison among stochastic optimization algorithms for parameter estimation of biochemical kinetic models

Mathematical models in biochemical engineering field are usually composed by nonlinear kinetic equations, where the number of parameters that must be estimated from a set of experimental measurements is usually very high. In these cases, the estimation of the model parameters comprises numerical iterative methods for minimization of the objective function. Classical methods for minimization of the objective function, like the Newton method, requires a good initial guess for all parameters and differentiation of the objective function and/or model equations with respect to the model parameters. Besides, the use of stochastic optimization methods for parameter estimation has gained attention, since these methods do not require a good initial guesses of all model parameters and neither the evaluation of derivatives. In this work, some stochastic optimization methods (Artificial Bee Colony, Differential Evolution, Particle Swarm Optimization and Simulated Annealing) were used in the estimation of kinetic parameters of a biochemical model for an alcoholic fermentation of cassava hydrolyzed. The results indicated that Differential Evolution provides better results among the stochastic optimization methods evaluated.