Residual stress/strain analysis in thin films by X-ray diffraction

Residual stresses are found in the majority of multilayer thin film structures used in modem technology. The measurement and modeling of such stress fields and the elucidation of their effects on structural reliability and device operation have been a “growth area” in the literature, with contributions from authors in various scientific and engineering disciplines.

In this article the measurement of the residual stresses in thin film structures with X-ray diffraction techniques is reviewed and the interpretation of such data and their relationship to mechanical reliability concerns are discussed.     

Direct analysis of thin-layer chromatographic spots of narcotics by means of diffuse reflectance Fourier transform infrared spectroscopy

The technique of diffuse reflectance Fourier transform infrared spectrometry (DRIFT) as an in situ detection method was used for the qualitative and quantitative analysis of drugs (heroin, cocaine and codeine) separated by thin layer chromatography.It was found that at a given interferometer throughput and detector sensitivity the quality of the spectrum depends strongly in the type of the chromatographic thin layer used. A detection limit of approx. 2 g was attained on a microcrystalline cellulose thin layer with a dynamically aligned Bio-Rad Digilab FTS 60A/896 type interferometer and room temperature DTGS detector. A reliable qualitative analysis can be made with as little as 10 to 15 g drug per spot.     

Quantitative determination of chloride by means of flow injection analysis with spectrophotometric detection in the UV/VIS range

Chloride is determined indirectly by Spectrophotometric flow injection analysis. Two systems are compared, both based on the principle of ion exchange of easily detectable anions versus chloride from suitable mercury salts. The first method is based on the exchange of chloride with chloranilate which is detected at 332 nm or at 306 nm in neutral or in acidic medium respectively. In the second case, chloride reacts with Hg(SCN)₂. The liberated thiocyanate forms a strongly coloured complex with Fe(III) in acidic solution with an absorption maximum at 460 nm. Both methods have a detection limit of about 5 mol Cl^–/l (175 ng/ml). In the case of the thiocyanate method, the relative standard deviation is about 2% (7 measurements) in the range of 5 to 150 mol/l and decreases significantly to a value of approximately 0.2% at higher concentrations; for the chloranilate method it is 10% for lower and about 1% for higher concentrations respectively.     

Parallelism across the steps in iterated Runge-Kutta methods for stiff initial value problems

For the parallel integration of stiff initial value problems (IVPs) three main approaches can be distinguished: approaches based on parallelism across the problem, on parallelism across the method and on parallelism across the steps. The first type of parallelism does not require special integration methods can be exploited within any available IVP solver. The methodparallel approach received some attention in the case of Runge-Kutta based methods. For these methods, the required number of processors is roughly half the order of the generating Runge-Kutta method and the speed-up with respect to a good sequential IVP solver is about a factor 2. The third type of parallelism (step-parallelism) can be achieved in any IVP solver based on predictor-corrector iteration. Most step-parallel methods proposed so far employ a large number of processors, but lack the property of robustness, due to a poor convergence behaviour in the iteration process. Hence, the effective speed-up is rather poor. The step-parallel iteraction process proposed in the present paper is less massively parallel, but turns out to be sufficiently robust to solve the four-stage Radau IIA corrector used in our experiments within a few effective iterations per step and to achieve speed-up factors up to 10 with respect to the best sequential codes.The research reported in this paper was partly supported by the Technology Foundation (STW) in the Netherlands.     

Initial data truncation for multivariate output of discrete-event simulations using the Kalman filter

Data truncation is a commonly accepted method of dealing with initialization bias in discrete-event simulation. An algorithm for determining the appropriate initial-data truncation point for multivariate output is proposed. The technique entails averaging across independent replications and estimating a steady-state output model in a state-space framework. A Bayesian technique called Multiple Model Adaptive Estimation (MMAE) is applied to compute a time varying estimate of the output's steady-state mean vector. This MMAE implementation features the use, in paralle, of a bank of Kalman filters. Each filter is constructed under a different assumption concerning the output's steady-state mean vector. One of the filters assumes that the steady-state mean vector is accurately reflected by an estimate, called the assumed steady-state mean vector, taken from the last half of the simulation data. As the filters process the output through the effective transient, this particular filter becomes more likely (in a Bayesian sense) to be the best filter to represent the data and the MMAE mean estimator is influenced increasingly towards the assumed steady-state mean vector. The estimated truncation point is selected when a norm of the MMAE mean vector estimate is within a small tolerance of the assumed steady-state mean vector. A Monte Carlo analysis using data from simulations of open and closed queueing models is used to evaluate the technique. The evaluation criteria include the ability to construct accurate and reliable confidence regions for the mean response vector based on the truncated sequences.     

Perturbation analysis of theGI/GI/1 queue

We examine a family ofGI/GI/1 queueing processes generated by a parametric family of service time distributions,F(x,), and we show that under suitable conditions the corresponding customer stationary expectation of the system time is twice continuously differentiable with respect to. Expressions for the derivatives are given which are suitable for single run derivative estimation. These results are extended to parameters of the interarrival time distribution and expressions for the corresponding second derivatives (as well as partial second derivatives involving both interarrivai and service time parameters) are also obtained. Finally, we present perturbation analysis algorithms based on these expressions along with simulation results demonstrating their performance.     

基于MELMD-ICA的光纤振动信号降噪方法

针对分布式光纤传感系统所采集含噪信号,提出一种改进集成局部均值分解(MELMD)联合独立成分分析(ICA)的降噪方法,引入排列熵判决机制提高抑制模态混叠与虚假分量能力。首先使用MELMD方法分解含噪信号得到乘积函数(PF)并进行信号重构;将含噪信号和重构信号求差得到虚拟噪声,构造虚拟通道;然后使用ICA对含噪信号和虚拟通道进行信噪分离,得到最终结果。通过实验验证,该方法与EMD-ICA,EEMD-ICA,MELMD相比,能更好地消除信号中的噪声,保留信号的特征信息。     

一种基于SPWVD-WVD的高质量时频分析方法及ISAR成像应用

交叉项干扰抑制与高时频聚集度是准确反应信号的时频分布特征的重要因素。传统的魏格纳-维尔分布(Wigner-Ville Distribution,WVD)算法虽能获得较高的时频分辨特征,但分析多成分信号时存在严重的交叉项干扰问题,限制了其实用性;而平滑伪魏格纳-维尔分布(Smoothed Pseudo Wigner-Ville Distribution,SPWVD)算法虽在一定程度上抑制交叉项干扰,但降低了时频聚集度。为了解决上述问题,提出了基于SPWVD-WVD的时频分析方法。该方法利用SPWVD与WVD之间的滤波互消效应,将SPWVD二值化结果与WVD结果进行矩阵运算,最终得到高质量的时频分析结果。实验结果表明,所提出的算法能够有效去除多分量信号的交叉项干扰,提高信号分析结果的时频聚集度,还原多分量信号的真实时频分布。最后将该算法成功应用于逆合成孔径雷达成像中。     

基于图卷积的视网膜血管轮廓及高不确定度区域细化框架

针对传统卷积神经网络(convolutional neural network, CNN)受感受野大小的限制,无法直接有效地获取空间结构及全局语义等关键信息,导致宽血管边界及毛细血管区域特征提取困难,造成视网膜血管分割表现不佳的问题,提出一种基于图卷积的视网膜血管分割细化框架。该框架通过轮廓提取及不确定分析方法,选取CNN粗分割结果中潜在的误分割区域,并结合其提取的特征信息构造出合适的图数据,送入残差图卷积网络(residual graph convolutional network, Res-GCN)二次分类,得到视网膜血管细化分割结果。该框架可以作为一个即插即用模块接入任意视网膜血管分割网络的末端,具有高移植性和易用性的特点。实验分别选用U型网络(U-neural network, U-Net)及其代表性改进网络DenseU-Net和AttU-Net作为基准网络,在DRIVE、STARE和CHASEDB1数据集上进行测试,本文框架的Sp分别为98.28%、99.10%和99.04%,Pr分别为87.97%、88.87%和90.25%,证明其具有提升基准网络分割效果的细化能力。     

基于时域频域分析结合的调谐激光吸收光谱检测参数优化

随着调谐激光吸收光谱(tunable laser absorption spectroscopy, TLAS)技术在气体检测中的应用越来越广泛,二次谐波信号的质量与检测参数紧密相关,因而分析检测参数的优化方法很有意义。本文根据谱线预处理中的滤波参数、系统采样时间、锁相放大器的时间常数对信号的影响以及参数间的联系,总结检测参数的选取规律。根据滤波原理和不同浓度下信号的均方根误差(root mean squared error,RMSE)值选择合适的滤波阶数和窗宽。选择信噪比(signal-to-noise ratio,SNR)、RMSE、信号与噪声频域幅度之比(ratio of amplitude in frequency domain of signal to noise,f_SNR)3种评价指标的曲线变化趋势进行时频分析,得到最佳采样周期数为30,结合实验系统具体参数可计算最佳采样时间。通过信号主频带与截止频率的关系和滤波效果选择合适的时间常数。综合分析3个检测参数并总结选取方法,可提高二次谐波信号的质量。本文提出的参数选取方法对提高二次谐波在实际应用中的准确度有...