Viscosity estimation for slags containing calcium fluoride

Based on recently published experimental data, the Riboud model was modified for viscosity estimation of the slags containing calcium fluoride. The estimated values were in good agreement with measured data. Reasonable estimation can be achieved using the modified Riboud model for mould fluxes and ESR (eletro slag remelting) slags. Especially for ESR slags, the modified Riboud model can provide much more precise values than the original Riboud model.     

用于机器人轨迹定位纠正系统的回声状态神经网络研究

循环神经网络(RecurrentNeuralNetworks)是人工神经网络(ArtificialNeuralNetworks)中重要的分支,与前馈神经网络(ForwardNeuralNetworks)相比具有更好的时间序列学习能力。但长期以来其学习法一直不能脱离前馈神经网络而自成一体,回声状态神经网络(EchoStateNetworks(ESN))是打破这一局面的全新学习方法。其独特的结构,良好的短期记忆能力,方便的学习方法,不俗的非线性特性是以前循环神经网络所不可比的。本文在介绍了回声状态神经网络之后将其用于四轮机器人的位置测量系统中,有良好的表现。     

Simulation of Strength Distribution in Ground Ceramics by Incorporating Residual Stress Effect

Strength of ground ceramics may be affected by residual stress as well as surface flaws induced by grinding. Strength prediction for ground ceramics is convenient for mechanical design of ceramic components. In this article, a numerical procedure based on fracture mechanics was proposed to estimate strength distribution of ground ceramics by considering grinding-induced residual stress. Bending strength and residual stress of ground ceramics were measured for three grinding-conditions. By comparison of simulated results with experimental ones, it was revealed that strength characteristics in experiments were well simulated by using the proposed procedure.     

Semiparametric Estimation in Time‐Series Regression with Long‐Range Dependence

Abstract. We consider semiparametric estimation in time‐series regression in the presence of long‐range dependence in both the errors and the stochastic regressors. A central limit theorem is established for a class of semiparametric frequency domain‐weighted least squares estimates, which includes both narrow‐band ordinary least squares and narrow‐band generalized least squares as special cases. The estimates are semiparametric in the sense that focus is on the neighbourhood of the origin, and only periodogram ordinates in a degenerating band around the origin are used. This setting differs from earlier studies on time‐series regression with long‐range dependence, where a fully parametric approach has been employed. The generalized least squares estimate is infeasible when the degree of long‐range dependence is unknown and must be estimated in an initial step. In that case, we show that a feasible estimate which has the same asymptotic properties as the infeasible estimate, exists. By Monte Carlo simulation, we evaluate the finite‐sample performance of the generalized least squares estimate and the feasible estimate.     

Motion and structure interpretation of an optical switch

This paper focuses on tracking, reconstruction and motion estimation of a well-defined MEMS optical switch from a microscopic view. For out-of-view reconstruction, a homography capable of transforming feature points and feature lines between a microscopic image and a CAD model of the switch is implemented. The homography between two sequential microscopic images is decomposed and factorized for motion estimation. Optical flow has also been explored to provide rough estimations of rotation centre and angle. The paper also illustrates motion parameter optimization principles to deal with uncertainty inherent in micro world. After non-linear optimization, estimation accuracy for rotation angle and rotation centre can reach 0.06° and pixel level, respectively.     

Florida DOT Project-Level Bridge Management Models

The Florida Department of Transportation (FDOT) is in the process of implementing Pontis, a Bridge Management System, to provide decision support to engineers in the headquarters and district offices as they make routine policy, programming, and budgeting decisions regarding the preservation and improvement of the state’s bridges. As part of this effort, an ongoing research program is underway to adapt the system to FDOT needs as well as to advance the state of the art in several areas important to the Department. Most of the research results are organized around a new project-level decision support framework that complements and builds on Pontis’ existing network-level analysis. Specific new models include accident risk and user cost due to roadway width and alignment deficiencies; user cost of load capacity and vertical clearance restrictions, and moveable bridge openings; project-level prediction models for bridge element condition and costs; and prediction of economics of scale and scoping possibilities. The new models are built into a highly graphical spreadsheet model for decision support use.     

Robustness of classification rules that incorporate additional information

The discrimination problem for two normal populations with the same covariance matrix when additional information on the population is available is considered. A study of the robustness properties against training sample contamination of classification rules that incorporate this additional information is performed. These rules have received recently attention where their total misclassification probability (TMP) is proved to be lower than Fisher's linear discriminant rule. The results of a simulation study on the TMP which compares the behaviour of the new rules against Fisher's rule and some of its robustified versions under different types of contamination are presented. These results show that the rules that incorporate the additional information not only have lower TMP, but they also prevent against some types of contamination. In order to achieve prevention from all types of contamination a robustifed version of these rules is recommended.     

RTK技术在控制测量中的应用研究

GPS RTK(Real Time Kinematic)技术的快速定位功能为其应用创造了良好的条件,但其定位精度验后估计的欠缺又使其应用受到一定的限制。本文针对此问题进行了研究,给出了GPS RTK测量成果的精度估计方法,并对GPS RTK测量技术用于控制测量的可行性进行了探讨,导出了相应的测量限差,为CPS RTK测量技术用于控制测量提供了理论依据和技术规定。     

Bootstrapping the Local Periodogram of Locally Stationary Processes

Abstract. Locally stationary processes are non‐stationary stochastic processes the second‐order structure of which varies smoothly over time. In this paper, we develop a method to bootstrap the local periodogram of a locally stationary process. Our method generates pseudo local periodogram ordinates by combining a parametric time and non‐parametric frequency domain bootstrap approach. We first fit locally a time varying autoregressive model so as to capture the essential characteristics of the underlying process. A locally calculated non‐parametric correction in the frequency domain is then used so as to improve upon the locally parametric autoregressive fit. As an application, we investigate theoretically the asymptotic properties of the bootstrap method proposed applied to the class of local spectral means, local ratio statistics and local spectral density estimators. Some simulations demonstrate the ability of our method to give accurate estimates of the quantities of interest in finite sample situations and an application to a real‐life data‐set is presented.