基于模板的骨骼参数自动测量方法

为快速测量骨骼形态参数,提出基于模板的自动测量方法。首先通过在骨骼模型上构建参考实体、设置层次化语义参数,生成测量模板;然后先对待测量骨骼向测量模板做刚体配准,再对测量模板向待测量骨骼做非刚体配准;最后将变形后的测量模板作为待测量骨骼的替代模型,同时根据测量模板上标定的特征点实现骨骼参数的自动计算。以股骨为例,实验结果表明,利用基于模板的测量方法可以快速测量出股骨的一整套形态参数,且测量结果与现有软件测量结果基本一致;部分骨骼参数具有较强相关性,且大多数参数分布形态基本对称,接近正态分布。这为后期对股骨形态参数作进一步分析以及接骨板的参数设计提了供科学的理论依据。     

Anomaly detection/detectability for a linear model with a bounded nuisance parameter

Anomaly detection is addressed within a statistical framework. Often the statistical model is composed of two types of parameters: the informative parameters and the nuisance ones. The nuisance parameters are of no interest for detection but they are necessary to complete the model. In the case of unknown, non-random and non-bounded nuisance parameters, their elimination is unavoidable. Some approaches based on the assumption that the nuisance parameters belonging to a subspace interfere with the informative ones in a linear manner, use the theory of invariance to reject the nuisance. Unfortunately, this can lead to a serious degradation of the detector capacity because some anomalies are masked by nuisance parameters. Nevertheless, in many cases the physical nature of nuisance parameters is (partially) known, and this a priori knowledge permits to define lower and upper bounds for the nuisance parameters. The goal of this paper is to study the statistical performances of the constrained generalized likelihood ratio test used to detect an additive anomaly in the case of bounded nuisance parameters. An example of the integrity monitoring of GNSS train positioning illustrates the relevance of the proposed method.     

元模型输入参数筛选方法研究

针对传统的灵敏度分析方法应用于元模型输入参数筛选时存在的突出问题,基于信息熵理论提出了元模型输入参数筛选方法。以条件熵为基础,提出了用于元模型输入参数筛选的参数体系,包括不确定度、不确定度浮动、不确定度累积浮动,以及元模型输入参数筛选分析过程;采用贝叶斯网络技术研究了多个子模型构成的组合模型描述问题,以及相应的条件熵计算问题;针对航空兵轰炸对于装备的损伤仿真问题,对单一模型和组合模型的输入参数筛选问题开展了案例研究。     

Application of game theory to the sensitivity design of optimal systems

The theory of games is applied to the design of systems with unknown plant parameters. It is assumed that a controller structure is known and furthermore that this controller is optimal when the controller parameters are equal to the plant parameters. The performance index then becomes a function of plant and controller parameters. This function is treated as a pay-off function with the antagonists represented by the controller and plant parameters. The theory is illustrated with some simple samples.     

ARMA model parameter estimation based on the equivalent MA approach

The paper investigates the relation between the parameters of an autoregressive moving average (ARMA) model and its equivalent moving average (EMA) model. On the basis of this relation, a new method is proposed for determining the ARMA model parameters from the coefficients of a finite-order EMA model. This method is a three-step approach: in the first step, a simple recursion relating the EMA model parameters and the cepstral coefficients of an ARMA process is derived to estimate the EMA model parameters; in the second step, the AR parameters are estimated by solving the linear equation set composed of EMA parameters; then, the MA parameters are obtained via simple computations using the estimated EMA and AR parameters. Simulations including both low- and high-order ARMA processes are given to demonstrate the performance of the new method. The end results are compared with the existing method in the literature over some performance criteria. It is observed from the simulations that our new algorithm produces the satisfactory and acceptable results.     

Analysis of convergence of an evolutionary algorithm with self-adaptation using a stochastic Lyapunov function

This paper analyses the convergence of evolutionary algorithms using a technique which is based on a stochastic Lyapunov function and developed within the martingale theory. This technique is used to investigate the convergence of a simple evolutionary algorithm with self-adaptation, which contains two types of parameters: fitness parameters, belonging to the domain of the objective function; and control parameters, responsible for the variation of fitness parameters. Although both parameters mutate randomly and independently, they converge to the "optimum" due to the direct (for fitness parameters) and indirect (for control parameters) selection. We show that the convergence velocity of the evolutionary algorithm with self-adaptation is asymptotically exponential, similar to the velocity of the optimal deterministic algorithm on the class of unimodal functions. Although some martingale inequalities have not be proved analytically, they have been numerically validated with 0.999 confidence using Monte-Carlo simulations.     

带有干扰的线性时变系统的非线性鲁棒控制

研究了含有未知时变参数和有界干扰的单输入单输出线性时变系统的鲁棒控制问题.系统时变参数只要求光滑有界而不限制为慢时变或参数上界已知.利用时变的状态变换得到新的动态系统,基于Backstepping方法,设计出一种非线性鲁棒控制器.通过适当选择控制器参数,可以保证闭环系统是全局渐近稳定的.仿真例子表明了算法的有效性.     

含有非线性不确定参数的电液系统滑模自适应控制

针对含有非线性不确定参数的电液控制系统, 提出了一种滑模自适应控制方法. 该控制方法主要是为了解决由于初始控制容积的不确定性而引起的, 非线性不确定参数自适应律设计的难题. 其主要特点为, 通过定义一个新型的特Lyapunov 函数, 进而构建系统的自适应控制器及参数自适应律, 并结合滑模控制方法及一种简单的鲁棒设计方法, 给出整个电液系统的滑模自适应控制器, 及所有不确定参数的自适应律. 试验结果表明, 采用该控制方法能够取得良好的性能, 尤其可以补偿非线性不确定参数对系统的影响.     

基于改进粒子群优化算法的预测控制

在实际工业过程中预测控制算法应用广泛,但是对于多变量预测控制算法其参数较多,且各个参数之间相互耦合,故整定其参数比较复杂,鉴于此提出一种基于改进粒子群算法的预测控制参数优化算法。该算法的基本思想是将生物寄生行为机制引入到粒子群优化算法中,形成双种群粒子群优化算法,使用该改进粒子群算法对多变量预测控制算法的参数进行离线优化,从而确定预测控制算法参数的最优取值。最后,将本文算法用于冷热水系统液位和温度的控制,并通过仿真将该算法与标准粒子群优化算法相比较,仿真结果表明使用该算法对多变量预测控制的参数进行优化整定时,系统的阶跃响应具有抗干扰性能好、超调量小、调节时间短等优点。     

Identification of ‘linear’ systems having signal-dependent parameters †

The paper deals with the identification of such ‘linear’ dynamic systems, whose parameters are working point-, signal-, and/or direction-dependent. Parameter estimation methods are presented for both cases: (i) the structure and how the parameters depend on a given signal is a priori not known; and (ii) the structure is known, only the parameters are unknown. In the latter case the structure to be identified is generally not linear in the parameters. Applying, however, a proper transformation, the continuous form can be approximated by a difference equation which becomes linear in the parameters but also includes redundant parameters.     

Optimization of coupled thermal-structural problems of laminated plates with lamination parameters

The behaviour of a laminated plate with given boundary temperatures and displacement constraints is optimized and the optimization problem is expressed in terms of lamination parameters. Because the thermal conductivity and structural properties of a laminate depend on the lamination parameters of the laminate, the analysis of the plate consists of solving a coupled-field problem. The strain energy, or certain displacements of the laminated plate due to given boundary temperatures and displacement boundary conditions, is optimized with respect to in-plane lamination parameters, and also buckling of the plate is considered. The buckling factors for thermal loading are expressed as a function of four in-plane and four bending lamination parameters, and the smallest factor is maximized with respect to these parameters. In addition to these thermal problems, the natural frequencies of the laminated plate are studied. Since transverse shear deformations are taken into account,the natural frequencies can be expressed as functions of two in-plane and four bending lamination parameters, with respect to which the lowest natural frequency of the plate is maximized. The lay-up for the laminate, corresponding to four optimal in-plane or bending lamination parameters, consists of three layers at most and can be determined using explicit equations. Explicit equations are derived for creating a lay-up having optimal bending lamination parameters. Received May 12, 1999     

基于PCA与支持向量回归的储层渗透率预测

在测井技术与储层基本特征研究的基础上,对与渗透率相关的测井参数和岩心参数进行了分析,根据传统的储层渗透率预测方法,提出了一种基于主成分分析与支持向量回归的储层渗透率预测方法。应用主成分分析对测井参数和岩心参数进行数据降维,优选出与渗透率最相关的参数,将优选出的测井参数和岩心参数作为支持向量回归模型的输入参数进行渗透率预测。实验结果表明,利用主成分分析算法提取的特征参数与渗透率有较好的相关性,且支持向量回归具有较高的预测精度,显示出主成分分析和支持向量回归在储层渗透率预测中的优势与实际应用价值。     

Precomputation of generalized predictive self-tuning controllers

A method for implementing generalized predictive self-tuning controllers is presented which avoids heavy computational requirements. The method makes use of the fact that a general predictive controller using a quadratic function results in a linear control law that can be described by a few parameters. These parameters are computed over the range of interest of the process parameters and a function is used to obtain an approximation to the real controller parameters. The controllers' parameters are given for processes which can be modeled by a static gain, a time constant, and an effective dead time, that is, for the majority of processes in industry     

网格调度中的QoS参数容错

基于QoS参数的资源调度在执行中由于参数不满足要求导致调度失败,可靠性不强,而部分QoS参数具有累加属性,可实现不同任务之间的参数容错。基于此,在考虑用户偏好的基础上,分析参数特性,提出一个在时间和成本上实现多任务之间容错的调度算法。该算法在不增加预算的基础上可以提高任务执行的成功率,并能够根据用户的偏好更好地满足用户的需求。实验结果表明,在QoS参数上的调度容错能够较大地提高调度的可靠性。     

Process Identification and Adaptation by High Frequency Sinusoidal Test Signal†

The problem of selecting a suitable high frequency sinusoidal test signal for identification of the parameters in a two-parameter adaptive system is considered. With a sinusoidal test signal it is not possible in general to have a one-to-one independent linear relationship between closed-loop gain and phase shift in terms of the individual parameters. This is due to the large variations in parameters in an adaptive system and due to the practical upper limit on the frequency of the test signal. Retaining the dependence of each of the parameters to both gain and phase shift, a logical method is developed via sensitivity considerations aiming at a. reduction in mathematical computations and obtaining a significant change in phase shift over the entire range of parameters. To illustrate the method two third-order plants each with two varying parameters are considered. Results of analogue computer simulation are given for one of the examples.     

Reinforcement learning for parameter estimation in statistical spoken dialogue systems

Reinforcement techniques have been successfully used to maximise the expected cumulative reward of statistical dialogue systems. Typically, reinforcement learning is used to estimate the parameters of a dialogue policy which selects the system's responses based on the inferred dialogue state. However, the inference of the dialogue state itself depends on a dialogue model which describes the expected behaviour of a user when interacting with the system. Ideally the parameters of this dialogue model should be also optimised to maximise the expected cumulative reward.This article presents two novel reinforcement algorithms for learning the parameters of a dialogue model. First, the Natural Belief Critic algorithm is designed to optimise the model parameters while the policy is kept fixed. This algorithm is suitable, for example, in systems using a handcrafted policy, perhaps prescribed by other design considerations. Second, the Natural Actor and Belief Critic algorithm jointly optimises both the model and the policy parameters. The algorithms are evaluated on a statistical dialogue system modelled as a Partially Observable Markov Decision Process in a tourist information domain. The evaluation is performed with a user simulator and with real users. The experiments indicate that model parameters estimated to maximise the expected reward function provide improved performance compared to the baseline handcrafted parameters.     

A method for the identification of time delays in linear systems

An automatic method is presented for the identification of time delays and parameters in linear systems. It is assumed that processes to be identified can be described by linear differential-difference equations. Unknown parameters in these equations are determined by a continuous steepest descent minimization of an error function. If the process parameters are constant, a region exists in the parameter space in which the tracking paramters converge to the process parameters. If all process time delays are known as well, the tracking parameters will always converge. Two examples are presented which show that the method may be used to identify slowly varying parameters. A third example indicates techniques leading to a more economical implementation of the method.     

IMC based Robust PID design: Tuning guidelines and automatic tuning

This communication addresses the problem of tuning a PID controller for step response. The tuning is based upon a First Order Plus Time Delay (FOPTD) model and aims to achieve a step response specification while taking into account robustness considerations. The industrial ISA-PID formulation is chosen. A tuning rule is derived first where the four parameters of the ISA-PID are determined by means of two new parameters: one parameter is related to the desired closed-loop time constant and the other one to the robustness level. On a second step, these two parameters are set to a fixed value in order to get a simple and automatic rule that directly gives the controller parameters in terms of the process model parameters. The proposed automatic tuning rule is compared with other known tunings.     

基于模糊操作模式的砷盐除钴过程操作参数协同优化

针对湿法炼锌砷盐除钴过程工况变化频繁和操作参数之间具有强耦合关系,导致操作参数优化困难的问题,提出了一种基于模糊操作模式的操作参数协同优化方法.根据大量的砷盐除钴工业运行数据,提炼初始操作模式库,根据入口工况参数,采用模糊匹配方法检索出相似操作模式,在操作模式重用时综合考虑系统参数缓慢变化和资源消耗的特点,然后采用灰色模糊最小二乘支持向量机（Least squares support vector machine,LSSVM）评估操作模式重用后的操作参数的可行性,并根据评估结果采用模糊专家规则修正操作参数.在工况发生变化时,系统能自动优化设定操作参数.工业验证结果表明,本文提出的操作参数协同优化方法保证了生产稳定,可有效提高净化后溶液中钴离子浓度的合格率和降低锌粉的消耗.     

An approach to the identifiable parameters of a manipulator

This article deals with the minimal parameters of a manipulator in the least squares sense, so that the minimal parameters are equivalent to the identifiable parameters. The least squares concept is used to introduce terminology for the minimal linear combinations (MLCs) of the system parameters that define a set of linear combinations of the system parameters. The number of elements of the set is minimal, yet the set still completely determines the system. Furthermore, it is shown that the problem of finding a set of MLCs of a manipulator can be simplified to that of finding two individual sets of MLCs that determine the entries of the inertia matrix and the gravity load. Although the approach is applied to the inertia constants of composite bodies to obtain a set of MLCs identical to an earlier one, the result is newly interpreted in the least squares sense. The approach itself is a new method for finding the identifiable parameters of a manipulator, and it yields some new insight into the manipulator dynamics. The crucial feature is that a set of MLCs found by using the present approach is guaranteed to be identifiable. The earlier approaches always require an identification method to verify the results. An equivalence theorem is also presented that rigorously states the equivalence between the different sets of minimal parameters. © 1994 John Wiley & Sons, Inc.