热谱重建法及其在热动力学研究中的应用

在热动力学研究中,大多数实验是在热导式热量计中进行的。由于量热系统的热惯性,记录得到的热谱会出现“失真”,为了正确方便地分析被研究过程的动力学性质,必须对所得热谱进行改造或重建。本文系统地提出了一种新的重建法,即热谱重建法。实验结果表明,该法可广泛应用于热动力学研究。     

XPS,AFM, ATR and TPD evidence for terraced,dihydrogen terminated, 1×1 (100) silicon

Heating (100) silicon at high temperature (say, higher than 850 °C) in H₂, cooling to 670–700 °C in the same ambient, and quenching to room temperature in N₂ results in environmentally robust, terraced 1 × 1 (100) SiH₂. Evidence for this conclusion is based on angle‐resolved x‐ray photoelectron spectroscopy, atomic force microscopy, infrared absorption spectroscopy in the attenuated total reflection mode, thermal programmed desorption, and reflection high‐energy electron diffraction. Copyright © 2005 John Wiley & Sons, Ltd.     

Quantitative High Resolution Electron Microscopy

 With the resolution becoming sufficient to reveal individual atoms, HREM is now entering the stage where it can compete with X-ray methods to quantitatively determine atomic structures of materials without much prior knowledge, but with the advantage of being applicable to aperiodic objects such as crystal defects. In our view the future electron microscope will be characterised by a large versatility in experimental settings under computer control such as the illumination conditions (TEM-STEM), CBED, detecting conditions (diffraction, image, ptychography) and many other tunable parameters such as focus (g), voltage, spherical aberration (C _s ), beam tilt, etc. Since modern detectors can detect single electrons, also the counting statistics is known. The only limiting factor in the experiment will be the total number of electrons that interact with the object during the experiment due to the limitations in the exposure time or in the object damage. However, instrumental potentialities will never be exploited fully if not guided by an experimental strategy. Here intuitive guidelines can be very deceptive. For instance an image made with the best electron microscope (C _s  = 0) at the best focus (g = 0) from the best object (phase object) would show no contrast at all. Hence, questions such as what is the best C _s , focus, object thickness, etc. can only be answered properly if done using a method of experiment design.     

Extrapolated Smoothing Descent Algorithm for Constrained Nonconvex and Nonsmooth Composite Problems*

In this paper, the authors propose a novel smoothing descent type algorithm with extrapolation for solving a class of constrained nonsmooth and nonconvex problems,where the nonconvex term is possibly nonsmooth. Their algorithm adopts the proximal gradient algorithm with extrapolation and a safe-guarding policy to minimize the smoothed objective function for better practical and theoretical performance. Moreover, the algorithm uses a easily checking rule to update the smoothing parameter to ensure that any accumulation point of the generated sequence is an (affine-scaled) Clarke stationary point of the original nonsmooth and nonconvex problem. Their experimental results indicate the effectiveness of the proposed algorithm.     

Elliptic Reconstruction and a Posteriori Error Estimates for Fully Discrete Semilinear Parabolic Optimal Control Problems

This article studies a posteriori error analysis of fully discrete finite element approximations for semilinear parabolic optimal control problems. Based on elliptic reconstruction approach introduced earlier by Makridakis and Nochetto [25], a residual based a posteriori error estimators for the state, co-state and control variables are derived. The space discretization of the state and co-state variables is done by using the piecewise linear and continuous finite elements, whereas the piecewise constant functions are employed for the control variable. The temporal discretization is based on the backward Euler method. We derive a posteriori error estimates for the state, co-state and control variables in the $L^\infty(0,T;L^2(\Omega))$-norm. Finally, a numerical experiment is performed to illustrate the performance of the derived estimators.     

Trigonometric wavelets for Hermite interpolation

The aim of this paper is to investigate a multiresolution analysis of nested subspaces of trigonometric polynomials. The pair of scaling functions which span the sample spaces are fundamental functions for Hermite interpolation on a dyadic partition of nodes on the interval . Two wavelet functions that generate the corresponding orthogonal complementary subspaces are constructed so as to possess the same fundamental interpolatory properties as the scaling functions. Together with the corresponding dual functions, these interpolatory properties of the scaling functions and wavelets are used to formulate the specific decomposition and reconstruction sequences. Consequently, this trigonometric multiresolution analysis allows a completely explicit algorithmic treatment.

     

Thermal Deformation Measurement and Stress Analysis of PQFP Assembly during Power Cycling

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齿轮故障诊断中振动信号的提纯和重构

在分析了齿轮振动产生机理和振动信号频谱特征的基础上 ,提出了齿轮振动信号提纯和重构的诊断分析方法 .与传统的窄带滤波相比 ,该方法可识别出齿轮啮合副中有故障的齿轮 .由于消除了 2个齿轮间的相互干扰 ,由该方法提纯重构信号的幅值包络、相位信息和瞬态频率 ,能更准确地反映出齿轮的故障 .     

Characterization of Heterogeneous Catalysts by use of Test Reactions

The following report gives an overview on work done in the Catalysis Laboratory of the Department of Chemistry, National University of Singapore over the last 15 years (1989–2004). Much of this work can be described as “characterization of catalytically active surfaces through test reactions”. The methods, systems studied and the reactions that we evaluated will be described. The review will mostly concentrate on work from the authors’ laboratory, but other relevant work will also be cited.     

Component reconstruction in the primary space of spectra and concentrations. Alternating regression and related direct methods

A set of methods that extract the spectral components in a chromatographic run is considered. The methods do not need libraries of previously known spectra or retention times. The methods have been developed for two-dimensional spectra but they can also be used for chromatographic analyses with a single-channel detector. The methods are direct; they do not use principal components as the starting point. Alternating regression (AR) remains in the primary space of spectra and concentrations during the calculations. Random numbers are used as the starting spectra. Regression is used to solve first for the concentrations, then for the spectra. The method uses two kinds of constraints: all spectra and concentrations are forced to be positive; and all concentration profiles are forced to a unimodal shape with a single local maximum. It is assumed that all observations are a linear sum of components. Compact alternating regression (CAR) is a new variant of the basic AR. The idea is to replace multiplication of a large matrix by two multiplications of smaller matrices. This typically speeds up the iterations by a factor of ten. AR and CAR have been successfully used with combined techniques such as gas chromatography—mass spectrometry and liquid chromatography with UV—visible detection. The reliability of the solution is checked by repeatedly injecting noise and performing the analysis several times. This produces estimates of confidence intervals. AR and CAR have recently been extended to handle single-dimensional signals. Examples are single-channel detectors such as the flame ionization detector in gas or liquid chromatography with a fixed-wavelength UV detector. A batch of samples is used as the observation matrix. As a result, one obtains both the concentrations and the elution shapes of individual chromatographic peaks.