An error analysis and mesh adaptation method for shape design of structural components

A finite element error analysis and mesh adaptation method that can be used for improving analysis accuracy in carrying out shape design of structural components is presented in this paper. The simple error estimator developed by Zienkiewicz is adopted in this study for finite element error analysis, using only post-processing finite element data. The mesh adaptation algorithm implemented in ANSYS is investigated and the difficulties found are discussed. An improved algorithm that utilizes ANSYS POST1 capabilities is proposed and found to be more efficient than the ANSYS algorithm. An example is given to show the efficiency. An interactive mesh adaptation method that utilizes PATRAN meshing and result-displaying capabilities is proposed. This proposed method displays error distribution and stress contour of analysis results using color plots, to help the designer in identifying the critical regions for mesh refinement. Also, it provides guidance for mesh refinement by computing and displaying the desired element size information, based on error estimate and a mesh refinement criterion defined by the designer. This method is more efficient and effective than the semi-automatic algorithm implemented in ANSYS, and is suitable for structural shape design. This method can be applied not only to set-up a finite element mesh of the structure at initial design but to ensure analysis accuracy in the design process. Examples are given to demonstrate feasibility of the proposed method.     

基于自适应Nyström采样的大数据谱聚类算法

面对结构复杂的数据集,谱聚类是一种灵活而有效的聚类方法,它基于谱图理论,通过将数据点映射到一个由特征向量构成的低维空间,优化数据的结构,得到令人满意的聚类结果.但在谱聚类的过程中,特征分解的计算复杂度通常为O(n³),限制了谱聚类算法在大数据中的应用.Nyström扩展方法利用数据集中的部分抽样点,进行近似计算,逼近真实的特征空间,可以有效降低计算复杂度,为大数据谱聚类算法提供了新思路.抽样策略的选择对Nyström扩展技术至关重要,设计了一种自适应的Nyström采样方法,每个数据点的抽样概率都会在一次采样完成后及时更新,而且从理论上证明了抽样误差会随着采样次数的增加呈指数下降.基于自适应的Nyström采样方法,提出一种适用于大数据的谱聚类算法,并对该算法的可行性和有效性进行了实验验证.     

A methodology for control-relevant nonlinear system identification using restricted complexity models

A broadly-applicable, control-relevant system identification methodology for nonlinear restricted complexity models (RCMs) is presented. Control design based on RCMs often leads to controllers which are easy to interpret and implement in real-time. A control-relevant identification method is developed to minimize the degradation in closed-loop performance as a result of RCM approximation error. A two-stage identification procedure is presented. First, a nonlinear ARX model is estimated from plant data using an orthogonal least squares algorithm; a Volterra series model is then generated from the nonlinear ARX model. In the second stage, a RCM with the desired structure is estimated from the Volterra series model through a model reduction algorithm that takes into account closed-loop performance requirements. The effectiveness of the proposed method is illustrated using two chemical reactor examples.     

Towards an adaptive POD/SVD surrogate model for aeronautic design

A computational methodology is presented to obtain a model reduction of the steady compressible Reynolds-Averaged Navier–Stokes equations with a high-dimensional parameter space. It combines both a reduced-basis method and an adaptive sequential sampling technique. The reduced basis is made of the leading eigenvectors computed by a Singular Value Decomposition of the snapshot span basis. The sampling method uses an a posteriori error estimator combined with a leave-one-out algorithm. This methodology allows a dynamic building of surrogate models for aerodynamic flight data generation and for multidisciplinary design optimization with a small number of full numerical flow simulations. The efficiency of the method is assessed on an analytic test case and a classical aerodynamic transonic flow around a 2D profile.     

针对非平衡警情数据改进的K-Means-Boosting-BP模型

目的 掌握警情的时空分布规律,通过机器学习算法建立警情时空预测模型,制定科学的警务防控方案,有效抑制犯罪的发生,是犯罪地理研究的重点。已有研究表明,警情时空分布多集中在中心城区或居民密集区,在时空上属于非平衡数据,这种数据的非平衡性通常导致在该数据上训练的模型成为弱学习器,预测精度较低。为解决这种非平衡数据的回归问题,提出一种基于KMeans均值聚类的Boosting算法。方法 该算法以Boosting集成学习算法为基础,应用GA-BP神经网络生成基分类器,借助KMeans均值聚类算法进行基分类器的集成,从而实现将弱学习器提升为强学习器的目标。结果 与常用的解决非平衡数据回归问题的Synthetic Minority Oversampling Technique Boosting算法,简称SMOTEBoosting算法相比,该算法具有两方面的优势：1）在降低非平衡数据中少数类均方误差的同时也降低了数据的整体均方误差,SMOTEBoosting算法的整体均方误差为2.14E-04,KMeans-Boosting算法的整体均方误差达到9.85E-05;2）更好地平衡了少数类样本识别的准确率和召回率,KMeans-Boosting算法的召回率约等于52%,SMOTEBoosting算法的召回率约等于91%;但KMeans-Boosting算法的准确率等于85%,远高于SMOTEBoosting算法的19%。结论 KMeans-Boosting算法能够显著的降低非平衡数据的整体均方误差,提高少数类样本识别的准确率和召回率,是一种有效地解决非平衡数据回归问题和分类问题的算法,可以推广至其他需要处理非平衡数据的领域中。     

A sequential initialization technique for vector quantizer design

Vector quantization has been used in compressing both speech and image data. In theory, better performance can always be achieved by coding vectors instead of scalars. However, actual results depend upon the proper design of the quantizer. Vector quantizer design typically employs an algorithm such as the K-means algorithm or the Linde Buzo Gray algorithm in which the initialization affects the design cost (convergence rate) and the achievable performance (quantization error). After reviewing several current initialization techniques, a sequential initialization method called Error Function Initialization is presented. In this method, the seeds are chosen one at a time by attempting to maximize the step-wise reduction in the quantization error. Experimental results show that this technique yields faster convergence and smaller quantization errors. For real time applications, the technique could be used to design sub-optimal vector quantizers.     

Two design methods of hyperparameters in variational Bayes learning for Bernoulli mixtures

Variational Bayes learning or mean field approximation is widely used in statistical models which are made of mixtures of exponential distributions, for example, normal mixtures, binomial mixtures, and hidden Markov models. To derive variational Bayes learning algorithm, we need to determine the hyperparameters in the a priori distribution; however, the design method of hyperparameters has not yet been established. In the present paper, we propose two different design methods of hyperparameters which are applied to the different purposes. In the former method, the hyperparameter is determined for minimization of the generalization error. In the latter method, it is chosen so that candidates of hidden structure in training data are extracted. It is experimentally shown that the optimal hyperparameters for two purposes are different from each other.     

Missing data imputation in multivariate data by evolutionary algorithms

This paper presents a proposal based on an evolutionary algorithm to impute missing observations in multivariate data. A genetic algorithm based on the minimization of an error function derived from their covariance matrix and vector of means is presented.All methodological aspects of the genetic structure are presented. An extended explanation of the design of the fitness function is provided. An application example is solved by the proposed method.     

Feedback nonlinear discrete-time systems

In this paper, we design an adaptive iterative learning control method for a class of high-order nonlinear output feedback discrete-time systems with random initial conditions and iteration-varying desired trajectories. An n-step ahead predictor approach is employed to estimate future outputs. The discrete Nussbaum gain method is incorporated into the control design to deal with unknown control directions. The proposed control algorithm ensures that the tracking error converges to zero asymptotically along the iterative learning axis except for the beginning outputs affected by random initial conditions. A numerical simulation is carried out to demonstrate the efficacy of the presented control laws.     

Constrained multifidelity optimization using model calibration

Multifidelity optimization approaches seek to bring higher-fidelity analyses earlier into the design process by using performance estimates from lower-fidelity models to accelerate convergence towards the optimum of a high-fidelity design problem. Current multifidelity optimization methods generally fall into two broad categories: provably convergent methods that use either the high-fidelity gradient or a high-fidelity pattern-search, and heuristic model calibration approaches, such as interpolating high-fidelity data or adding a Kriging error model to a lower-fidelity function. This paper presents a multifidelity optimization method that bridges these two ideas; our method iteratively calibrates lower-fidelity information to the high-fidelity function in order to find an optimum of the high-fidelity design problem. The algorithm developed minimizes a high-fidelity objective function subject to a high-fidelity constraint and other simple constraints. The algorithm never computes the gradient of a high-fidelity function; however, it achieves first-order optimality using sensitivity information from the calibrated low-fidelity models, which are constructed to have negligible error in a neighborhood around the solution. The method is demonstrated for aerodynamic shape optimization and shows at least an 80% reduction in the number of high-fidelity analyses compared other single-fidelity derivative-free and sequential quadratic programming methods. The method uses approximately the same number of high-fidelity analyses as a multifidelity trust-region algorithm that estimates the high-fidelity gradient using finite differences.     

Bayesian estimation and variable selection for single index models

We develop a fully Bayesian method to analyze the single index models, including variable selection, the index vector estimation and the link function fitting with free-knot splines. The proposed method is implemented by means of the reversible jump Markov chain Monte Carlo technique. We treat the marginal posterior of all the unknown quantities except the spline coefficients and error variance as the target distribution to reduce the dimension of the parameters and to obtain a rapid algorithm. We design a new random walk Metropolis sampler to sample from the conditional posterior distribution of the index vector. The proposed method is verified by simulation studies, and is applied to analyze two real data sets.     

具有多峰正态分布属性的视频语义分类研究

视频语义分类中常遇到多峰正态分布属性,如采用单峰值正态分布设计的贝叶斯分类模型会造成较大分类误差。本文采用定步长组合划分算（FLCPA）对多峰分布属性值域按类进行划分,以留一校验法（LOOCV）估算分类错误,找出给定步长下属性的多峰分布边界点,并用监督参数估计推断出每个分段区间上的概率分布函数,从而得到整个值域上的总体分布。此外,文中给出了涉及多峰分布属性的视频语义分类器设计步骤。实验数据表明,该方法能明显降低分类错误,有效提高分类性能。     

Linear mixed models with skew-elliptical distributions: A Bayesian approach

Normality of random effects and error terms is a routine assumption for linear mixed models. However, such an assumption may be unrealistic, obscuring important features of within- and among-unit variation. A simple and robust Bayesian parametric approach that relaxes this assumption by using a multivariate skew-elliptical distribution, which includes the Skew-t, Skew-normal, t-Student, and Normal distributions as special cases and provides flexibility in capturing a broad range of non-normal and asymmetric behavior is presented. An appropriate posterior simulation scheme is developed and the methods are illustrated with an application to a longitudinal data example.     

Perfect load balancing on the star interconnection network

In this paper, we use the regular distribution method to design a perfect load balancing algorithm for an n-star with a maximum error of 1 and a time complexity of 3n(n+1). This algorithm is based on the novel notion of leader trees. A second algorithm proposed in this paper as an enhancement to our first algorithm and uses an arbitrary spanning tree as the leader tree and has a worst time complexity of 2.25n ²−3n+0.75. We also discuss the issue of dynamically selecting the leader tree and hybrid load balancing algorithms in general. Furthermore, we present a hybrid algorithm for load balancing on the star interconnection network which benefits from a diffusion load balancing preprocessing phase and shows a smaller mean time complexity than our two first algorithms.     

一种改进的基于三角形折叠的网格简化算法

在已有的基于三角形折叠网格简化算法的基础之上，提出了一种改进的算法。对原算法的误差矩阵的计算进行了改进，提出了一种简单的误差控制方法。该改进的简化算法不仅能减少模型中的三角形数目和保持模型拓扑结构，而且实现简单、速度快。     

A Local Deviation Constraint Based Non-Rigid Structure From Motion Approach

In many traditional non-rigid structure from motion (NRSFM) approaches, the estimation results of part feature points may significantly deviate from their true values because only the overall estimation error is considered in their models. Aimed at solving this issue, a local deviation-constrained-based column-space-fitting approach is proposed in this paper to alleviate estimation deviation. In our work, an effective model is first constructed with two terms: the overall estimation error, which is computed by a linear subspace representation, and a constraint term, which is based on the variance of the reconstruction error for each frame. Furthermore, an augmented Lagrange multipliers (ALM) iterative algorithm is presented to optimize the proposed model. Moreover, a convergence analysis is performed with three steps for the optimization process. As both the overall estimation error and the local deviation are utilized, the proposed method can achieve a good estimation performance and a relatively uniform estimation error distribution for different feature points. Experimental results on several widely used synthetic sequences and real sequences demonstrate the effectiveness and feasibility of the proposed algorithm.      

State analytical predictor for systems with multiple time delays

An approach for the design of a dead-time compensator for processes with time delays is presented. The proposed algorithm deals with multivariable state-space models instead of input-output models and utilizes not only a single delay but also distributed delays in the feedback loop. Both the continuous-time and discrete-time versions of the algorithm are derived. The design strategy of the controller is based on the approach of LQG design. It is incorporated into the dead-time compensation technique of the analytical predictor, which uses the process model directly to predict the effect of input variables on the process outputs. By introducing an integral action into the observation system, the steady-state observation error of the inaccessible load inputs converges to zero. This permits us to perform disturbance prediction and compensation within a single design. Theoretical analysis shows that this control strategy fully removes time delay elements from a system characteristic equation in the ideal situation of a system without model-plant mismatch and/or noise measurements. By the estimation and prediction of the unknown plant-input disturbances, the capability for rejecting input disturbances has also been improved. The control structure developed is shown to have the same closed-loop relationships as the linear quadratic feedback structure for a system without lime delays. The potential benefits of the proposed controller are demonstrated by the control problem of an industrial grinding system studied previously by Niemi et al. (1982) and Ylinen et al. (1987), which is a challenging problem for MPC-type algorithms.     

基于区域分解的并行动态LOD构建算法

面向大规模可视数据的高速绘制问题,提出了一种基于区域分解的并行动态LOD（level-of-detail,层次细节模型）构建算法。算法首先改进了传统的渐进网格方法,实现了基于二次误差测度网格简化算法的渐进网格方法;接着提出了一种基于模型包围盒的区域分解算法,实现了原始模型的自适应区域分解;在每个子区域上,并行地执行渐进网格方法,实现了模型的并行动态LOD构建。实验结果表明,该算法可生成高质量的LOD模型,具备理想的加速比和可扩放性;与串行算法相比,该算法有效地提高了算法的执行效率。     

Robust fault diagnosis and fault‐tolerant control for non‐Gaussian uncertain stochastic distribution control systems

The purpose of fault diagnosis of stochastic distribution control systems is to use the measured input and the system output probability density function to obtain the fault estimation information. A fault diagnosis and sliding mode fault‐tolerant control algorithms are proposed for non‐Gaussian uncertain stochastic distribution control systems with probability density function approximation error. The unknown input caused by model uncertainty can be considered as an exogenous disturbance, and the augmented observation error dynamic system is constructed using the thought of unknown input observer. Stability analysis is performed for the observation error dynamic system, and the H_∞ performance is guaranteed. Based on the information of fault estimation and the desired output probability density function, the sliding mode fault‐tolerant controller is designed to make the post‐fault output probability density function still track the desired distribution. This method avoids the difficulties of design of fault diagnosis observer caused by the uncertain input, and fault diagnosis and fault‐tolerant control are integrated. Two different illustrated examples are given to demonstrate the effectiveness of the proposed algorithm. Copyright © 2016 John Wiley & Sons, Ltd.     

数据检测系统中de粗差数据综合处理算法

本文以数据检测系统中经常使用的对检测过程中所得数据做粗差判断处理为主要研究对象,在过去常用的差分法和分位数方法分布图法的基础上提出了一种综合处理方法--基于差分法的分位数方法,使数据采集系统中的粗差数据处理更趋于完善.