磁致伸缩换能器电气模拟中阻抗圆图的应用研究

分析了磁致伸缩换能器的等效电路模型，并且提出了用阻抗圆图确定换能器电气模拟网络的电气元件参数的计算方法。最后通过对试验数据的分析和采用曲线拟合的方法进一步对阻抗圆图的应用进行说明。     

Multi-objective parameter estimation of induction motor using particle swarm optimization

In order to simplify the offline parameter estimation of induction motor, a method based on optimization using a particle swarm optimization (PSO) technique is presented. Three different induction motor models such as approximate, exact and deep bar circuit models are considered. The parameter estimation methodology describes a method for estimating the steady-state equivalent circuit parameters from the motor performance characteristics, which is normally available from the manufacturer data or from tests. The optimization problem is formulated as multi-objective function to minimize the error between the estimated and the manufacturer data. The sensitivity analysis is also performed to identify parameters, which have the most impact on motor performance. The feasibility of the proposed method is demonstrated for two different motors and it is compared with the genetic algorithm and the classical parameter estimation method. Simulation results show that the proposed PSO method was indeed capable of estimating the parameters over a wide operating range of the motor.     

Measurement of three independent components in impedance sensors using a single square wave

Two-wire impedance-based sensors involving electrolytes add the impedance of the electrodes to the electrical impedance of the medium to measure. An equivalent circuit for the measured impedance is a resistance in series with the parallel combination of another resistance and a capacitance. If the two electrodes are modelled by equal impedances, the equivalent circuit for the complete set up consists of three impedance components, which can be determined from three independent measurements. This paper describes a novel method to obtain those three components using a single square wave voltage (period 2T) instead of several sine waves and provides the equations to calculate their value from the three current intensity amplitudes measured at T/8, 3T/8 and 5T/8. Other measurement times would need different equations, but the same procedure applies. Anyway, the proposed method keeps the advantages of synchronous detection and relies on analytical solutions instead of the customary curve fitting procedures. Computer simulation and experimental results obtained by measuring the conductivity of known electrolyte samples validate the proposed method.     

On the thru‐reflect‐line (TRL) numerical calibration and error analysis for parameter extraction of circuit model

In this work, a resistor standard is introduced into our previously proposed numerical thru‐reflect‐line (TRL) calibration procedure in order to determine the characteristic impedance of the line standard of calibration on the basis of a deterministic method of moments (MoM) algorithm. A comprehensive analytical derivation is presented with regards to electrical properties of such a resistor standard in comparison to other standards. In addition, an error analysis is detailed, which reveals correlations of characteristic parameters in connection with equivalent circuit model development from the conversions from field‐based S‐parameters to circuit‐based Y‐ or Z‐parameters. Interesting properties and criteria are derived, allowing accurate parameter extractions. To validate the proposed numerical TRL calibration procedure with this new resistor standard concept and the developed error analysis, the characteristic impedance of a microstrip line is extracted from a commercial software. In addition, a further example with microstrip discontinuity is shown and the effectiveness of the proposed technique is verified. © 2006 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2006.     

A dental application of neural network computing: Classification of complex electrical impedance measurements to aid root canal treatment

Electrical impedance measurements provide an alternative diagnostic technique to the use of radiographs for aiding dental root canal treatment. Analysis of impedance data was based on Complex NonLinear Least Squares (CNLS) regression with electrical circuits as models. Different equivalent circuits were required to model the data at various depths within root canals. Therefore, it was not valid to compare directly the parameter values obtained for the same electrical components when different circuits were used. This problem was solved with a neural computing approach based on supervised training of the backpropagation algorithm to classify the data. Two strategies were investigated. The first produced a network output which indicated the electrode depth within the canal. The second approach employed the neural network as a preprocessor to establish which equivalent circuit was appropriate for the CNLS. Tests were also carried out to determine the minimum number of input nodes required by a neural network for this dental application.     

Hybrid genetic algorithm for the identification of high-order synchronous machine two-axis equivalent circuits

A hybrid genetic algorithm is used to find high-order equivalent circuits (ECs) of synchronous machines using standstill frequency response (SSFR) data. The algorithm performs satisfactorily despite the great deal of local minima surrounding the optimal solution of high-order ECs. It gives circuit parameters that simultaneously fit the three independent transfer functions given by the d-axis two-port network of the synchronous machine. It is found that as the order of the EC is increased, the optimization index used in the identification procedure is enhanced in a clear fashion. This leads to a new way for determining the right number of rotor branches required to correctly reproduce the SSFR data. The q-axis network is also analyzed with the hybrid algorithm. The so-called Canay's inductances are included in this one-port network to test if the fitting properties of the q-axis EC can be improved. The SSFR data used in this work is generated by a finite element model of a turbine generator, but actual data can also be readily handled.     

Parameter estimation of three-winding transformers using genetic algorithm

The paper presents an in situ parameter estimation method to determine the equivalent circuit parameters of the three-winding transformer (TWT). The suggested method also estimates geometrically a complex parameter that is mutual leakage between secondary and tertiary windings, which would be useful in transient studies. Beside the saturation effect of the transformer is taken into account by estimating a highly nonlinear parameter that is magnetizing circuit reactance. Different search based optimization tools are applied for parameter estimation among which the results obtained using genetic algorithm is found to be encouraging. Load test data at one particular operating point namely supply voltage, load currents, input power, and load impedance is sufficient to estimate the parameters. The results are experimentally validated against the directly measured performance of TWT. The method is applicable for performance determination, power system load flow studies and design of protection circuits of the TWT.     

Piezoresistive device optimization using topological derivative concepts

A piezoresistive sensor is composed of a piezoresistive membrane attached to a flexible plate. The piezoresistive material is anisotropic, and its electrical properties change when subjected to mechanical stresses. In this work, the topology design of a piezoresistive pressure sensor is addressed. More specifically, an optimization technique based on topological sensitivity analysis is proposed in order to obtain the optimized distribution of piezoresistive material over the plate. In most of the works regarding topological sensitivity analysis, isotropic materials are considered. However, the problem of conductivity in an anisotropic non-homogeneous domain has been recently addressed, and a closed form for the topological derivative associated to the energy shape functional has been presented. In this work, on the other hand, a closed form for the topological derivative associated with a multi-objective shape functional related to the steady-state anisotropic current density diffusion problem is presented. To illustrate the applicability of the closed formula and the proposed optimization procedure, numerical examples regarding the conceptual design of piezoresistive sensors, considering distinct optimization parameters and boundary conditions in the conductivity problem, are presented.     

Surrogate‐assisted EM‐driven miniaturization of wideband microwave couplers by means of co‐simulation low‐fidelity models

This article proposes a methodology for rapid design optimization of miniaturized wideband couplers. More specifically, a class of circuits is considered, in which conventional transmission lines are replaced by their abbreviated counterparts referred to as slow‐wave compact cells. Our focus is on explicit reduction of the structure size as well as on reducing the CPU cost of the design process. For the sake of computational feasibility, a surrogate‐based optimization paradigm involving a co‐simulation low‐fidelity model is used. The latter is a fundamental component of the proposed technique. The low‐fidelity model represents cascaded slow‐wave cells replacing the low‐impedance lines of the original coupler circuit. It is implemented in a circuit simulator (here, ADS) and consists of duplicated compact cell EM simulation data as well as circuit theory‐based feeding line models. Our primary optimization routine is a trust‐region‐embedded gradient search algorithm. To further reduce the design cost, the system response Jacobian is estimated at the level of the low‐fidelity model, which is sufficient due to good correlation between the low‐ and high‐fidelity models. The coupler is explicitly optimized for size reduction, whereas electrical performance parameters are controlled using a penalty function approach. The presented methodology is demonstrated through the design of a 1‐GHz wideband microstrip branch‐line coupler. Numerical results are supported by experimental validation of the fabricated coupler prototype.     

基于免疫粒子群算法的参数估计方法

针对污水生化反应模型参数估计问题,提出一种基于免疫粒子群算法的估计方法。该方法采用免疫算法保持粒子群的多样性,避免粒子群算法的过早收敛而降低寻优能力。利用估计的参数值对实验数据进行拟合,仿真结果表明,拟合误差率低于标准的粒子群和遗传算法,进一步提高了污水生化反应模型参数估计精度。     

Modeling the hollow cylindrical piezo-ceramics with axial polarization using equivalent electro-mechanical admittance matrix

Modeling a piezo-ceramics as an electro-mechanical element is usually done using the laws of electricity. Due to complexity of the derived circuits, this kind of modeling usually results in some simplifications. In this paper using the force–velocity boundary conditions and equalizing the mechanical elements to the electrical ones, the governing equations of a hollow cylindrical piezo-ceramics with axial polarization are solved. Then the equivalent electro-mechanical impedance is represented via matrix notation. Using the matrix algebra the resonance and anti-resonance frequencies are calculated and then compared to the experimental results. The proposed method does not need to any simplification in circuit analysis, and provides the capability of modeling the various layers of piezo-ceramics and metals together in electro-acoustical transducers.     

Tabu search based circuit optimization

In this paper we address the problem of optimizing mixed CMOS/BiCMOS circuits. The problem, formulated as a constrained combinatorial optimization problem is addressed using a tabu search algorithm. Initially a random approach is adopted for selecting among available solutions. Further, as an alternative competing solution the concepts of simulated evolution are applied to classical tabu search (CTS). This allows for a stochastic criterion for selecting among available solutions as compared to the random approach of CTS. Only gates on the critical sensitizable paths are considered for optimization. Such a strategy leads to sizeable circuit speed improvement with minimum increase in the overall circuit capacitance. Compared to earlier approaches, the presented techniques produce circuits with remarkable increase in speed (greater than 20%) for very small increase in overall circuit capacitance (less than 3%).     

基于移动数据窗的传递函数多新息随机梯度辨识方法

一些工业过程可以近似用一个传递函数描述,结合统计辨识方法和非线性优化策略提出传递函数参数辨识方法.该方法采用动态数据方案,使用系统观测数据获得系统更多的模态信息.基于动态观测数据,提出传递函数随机梯度参数辨识方法.为进一步提高辨识精度,利用动态窗数据将随机梯度参数辨识方法中的标量新息扩展为新息向量,提出传递函数多新息随机梯度参数估计方法.最后通过仿真例子对所提出的方法进行了性能分析和模型验证.     

Multi-level and multi-objective design optimisation of a MEMS bandpass filter

Microelectromechanical system (MEMS) design is often complex, containing multiple disciplines but also conflicting objectives. Designers are often faced with the problem of balancing what objectives to focus upon and how to incorporate modeling and simulation tools across multiple levels of abstraction in the design optimization process. In particular due to the computational expense of some of these simulation methods there are restrictions on how much optimization can occur. In this paper we aim to demonstrate the application of multi-objective and multi-level design optimisation strategies to a MEMS bandpass filter. This provides for designers the ability to evolve solutions that can match multiple objectives. In order to address the problem of a computationally expensive design process a novel multi-level evaluation strategy is developed. In addition a new approach for bandpass filter modeling and optimization is presented based up the electrical equivalent circuit method. In order to demonstrate this approach a comparison is made to previous attempts to design similar bandpass filters. Results are comparable in design but at a significant reduction in functional evaluations, needing only 10,000 functional evaluations in comparison to 2.6 million with the previous work.     

Synchronous generator modelling and parameters estimation using least squares method

In this paper, a technique for estimating the synchronous machine’s parameters using sudden short circuit test, is proposed. Before implementing estimation algorithms, a special method of the machine modelling is given. This last one allows to perform tests such as short-circuit, load impact and shedding test, in an easier way than the models usually developed in the literature. Thanks to the well known electrical equivalent circuit of the generator, the relationships between parameters generally used in the industry (i.e., reactances and time constants) and those used in researcher’s domains will be given. Finally, simulation results of the proposed method, allows to show that the algorithm is capable of providing very good estimated parameters fitting with the actual parameters.     

PSO在非线性电路及器件参数优化设计中的应用

为了解决非线性电路的一个多参数组合优化问题,使用了一种全局优化搜索方法-粒子群优化方法(PSO)来获取非线性电路及其中非线性器件参数的最优组合.首先对非线性电路建立线性化的数学模型,分析关键参数的基本特性,然后利用粒子群优化方法良好的全局搜索特性以及快速的收敛速度,在整个参数空间进行高效并行搜索来获得参数组合的最优化,以有效降低电路功耗.对典型的非线性分压式偏置电路以及其中BJT参数提取的设计实例证明了该方法的有效性以及其在搜索速度上的优越性.     

数字系统单加权集下随机测试生成方法

为在不引入额外的硬件开销以下较短的测试序列获得较高的故障覆盖率，提出一种基于细胞自动机（CA）的数字集成电路加权随机测试方法。该方法利用可测性测度建立反映故障侦查代价的可测性代价函数，对此函数的寻优得到被测电路主输入处的权值，再由一维混合型CA实现了该权值下的随机序列。对标准电路的实验验证了该方法是一种有效的、且便于BIST的应用的测试生成算法。     

Probabilistic Modeling and Recognition of 3-D Objects

This paper introduces a uniform statistical framework for both 3-D and 2-D object recognition using intensity images as input data. The theoretical part provides a mathematical tool for stochastic modeling. The algorithmic part introduces methods for automatic model generation, localization, and recognition of objects. 2-D images are used for learning the statistical appearance of 3-D objects; both the depth information and the matching between image and model features are missing for model generation. The implied incomplete data estimation problem is solved by the Expectation Maximization algorithm. This leads to a novel class of algorithms for automatic model generation from projections. The estimation of pose parameters corresponds to a non-linear maximum likelihood estimation problem which is solved by a global optimization procedure. Classification is done by the Bayesian decision rule. This work includes the experimental evaluation of the various facets of the presented approach. An empirical evaluation of learning algorithms and the comparison of different pose estimation algorithms show the feasibility of the proposed probabilistic framework.     

Real time recursive prediction of river flows

A method is presented for predicting flows in a river. A stochastic model with a set of undetermined parameters is postulated, and these parameters are estimated in real time using the available flow data, and the model is used for prediction. The predictor is recursive and yields the predicted values of flows at a future time instant based upon all presently available information.In addition, a method is given for comparing optimal predictors obtained from different stochastic models. We use the prediction schemes to obtain one-day-ahead forecasts of the Wabash River in the U.S.A. by using the daily riverflow data, paying particular attention to the choice of the order of the stochastic difference equation.