A simple background elimination method for Raman spectra

In this paper, we consider a new background elimination method for Raman spectra. The proposed method is based on peak detection, smoothing, and interpolation. Since the background is usually slowly varying with respect to wavelength, we could estimate the background by eliminating significant peaks. For this purpose, we seek the peaks by inspecting the smoothed derivative of a given spectrum. After clipping out the corresponding peak regions, we estimate the background by applying a modified linear interpolation. Then the background is eliminated from the measured Raman spectrum by simple subtraction. The experimental results showed that the proposed method gave satisfactory results for real Raman spectra as well as synthetic data. As the proposed method requires no prior knowledge of spectrum, we expect that the method could be applied to other spectral data as well.     

Multivariate calibration of on-line enrichment near-infrared (NIR) spectra and determination of trace lead in water

An on-line enrichment near-infrared spectroscopy (NIRS) technique was developed in order to improve the sensitivity for the determination of trace heavy metal lead. Trace Pb²⁺ was enriched by chelating resin with on-line enrichment equipment and then the enriched lead on the resin was directly determined by near-infrared diffuse reflectance spectroscopy without elution procedure for simplicity of measurements. Partial least squares (PLS) regression was used to build models between the corrected spectra and concentrations of Pb²⁺, and segmental cross-validation was used to search for a reasonable number of PLS factors. The results showed that NIR spectra and concentrations of Pb²⁺ had a good linear relationship with the maximum of correlation coefficient of 0.9228 in the 1006–1383 nm region. The minimum RMSEP was 0.4777 mg L^− 1 when the latent variable number was 5 by using the region of 1006–1383 nm. The limit of detection (LOD) of lead was 0.2384 mg L^− 1 in this case. This study demonstrates that the prediction of trace heavy metal lead concentrations by NIRS is feasible with enrichment technique.     

The application of direct orthogonal signal correction for linear and non-linear multivariate calibration

Traditionally, the direct orthogonal signal correction (DOSC) is always used together with a latent variable method such as partial least square (PLS) or principal component regression (PCR), to build a linear calibration model. In this study, PLS and least square support vector machine (LSSVM) were used to develop the linear and non-linear relation between spectra and components, respectively. DOSC was used to preprocess the input data, and the effect of DOSC pretreatment on linear and non-linear calibration model was investigated. The experiment was performed with three data sets. The first one was the acousto-optic tunable filter near infrared (AOTF-NIR) spectra of apples, the second one was the temperature-induced spectra of a ternary mixture of ethanol, water and 2-propanol, and the third one was the NIR spectra of corn. For all of the applications, the relation between spectra and components can be clearly observed in the spectra plot or the score plot after DOSC pretreatment. DOSC improved the predictive ability of PLS model. However, DOSC removed useful non-linear information that was related to components, thus, was not able to improve the performance of LSSVM model. DOSC pretreatment seems to be not suitable for non-linear calibration.     

温度对果糖含量近红外光谱检测模型传递的影响

以果糖溶液为研究对象,采用斜率/截距(S/B)算法,通过不同温度的果糖含量检测模型在两台不同厂家的傅里叶变换近红外光谱仪间的传递,讨论了样品温度变化对模型传递的影响.主、从仪器直接建模时,预测集的均方根偏差(RMSEP)均随温度升高呈增大的趋势,但整体变化不显著.直接预测时,预测集的RMSEP均大于0.86,效果不理想;采用S/B算法传递后,预测结果得到改善,主、从仪器相同温度光谱间的传递结果优于不同温度光谱间的传递,15℃、22℃和28℃时相同温度光谱间传递后的RMSEP分别为0.317、0.389和0.416,差异显著.实验结果表明,进行模型传递时,样品温度变化会对模型传递产生较大影响;保证样品温度一致下,选择合适温度有利于取得最佳传递效果.     

Fault detection based on Gaussian process latent variable models

Gaussian processes, GPs, can be used to approximate complex non-linear functions with relative simplicity. Their regression performance is, at least, comparable to that achieved via artificial neural networks (ANN) and, in fact, both methods are intrinsically related. They are both non-parametric and, as Neal (1994) [1] has shown, when the number of nodes in the hidden layer of a neural network tends to infinity the ANN converge to a Gaussian process.In most of the cases, the GP will map a multivariate input into a univariate response. In this paper, however, we present an approach to process monitoring that combines several GPs so that multivariate responses can be appropriately modeled. We review a similar approach recently proposed in the literature and highlight some concerns related to it that needs to be taken into consideration. Additionally, we propose an alternative procedure to the way in which new observations are mapped into the non-linear model. A simulation study is provided that will help understand the method flexibility. Furthermore, results from a real example are also discussed.     

Optimisation of secondary electron detector for variable pressure SEM with Monte Carlo method

A computer programme for numerical modelling of electron flow in vacuum instruments is presented. The programme allows to simulate trajectories of charged particles in both high and low vacuum of the order of tens milibars. It combines a commercially available packet SIMION 3D v.7.0 destined for tracing trajectories of charged particles in electric and magnetic fields, and a Monte Carlo programme modelling phenomena accompanying electron collisions with gas molecules. The programme was applied for analysis and optimisation of a novel secondary electron detector for a variable-pressure scanning electron microscopy.     

Application of spectra cross-correlation for Type II outliers screening during multivariate near-infrared spectroscopic analysis of whole blood

In this study, a simple screening algorithm was developed to prevent the occurrence of Type II errors or samples with high prediction error that are not detected as outliers. The method is used to determine “good” and “bad” spectra and to prevent a false negative condition where poorly predicted samples appear to be within the calibration space, yet have inordinately large residual or prediction errors. The detection and elimination of this type of sample, which is a true outlier but not easily detected, is extremely important in medical decisions, since such erroneous data can lead to considerable mistakes in clinical analysis and medical diagnosis. The algorithm is based on a cross-correlation comparison between samples spectra measured over the region of 4160-4880 cm^− 1. The correlation values are converted using the Fisher's z-transform, while a z-test of the transformed values is performed to screen out the outlier spectra. This approach allows the use of a tuning parameter used to decrease the percentage of samples with high analytical (residual) errors. The algorithm was tested using a dataset with known reference values to determine the number of false negative and false positive samples. The cross-correlation algorithm performance was tested on several hundred blood samples prepared at different hematocrit (24 to 48%) and glucose (30 to 500 mg/dL) levels using blood component materials from thirteen healthy human volunteers. Experimental results illustrate the effectiveness of the proposed algorithm in finding and screening out Type II outliers in terms of sensitivity and specificity, and the ability to predict or estimate future or validation datasets ensuring lower error of prediction. To our knowledge this is the first paper to introduce a statistically useful screening method based on spectra cross-correlation to detect the occurrence of Type II outliers (false negative samples) for routine analysis in a clinically relevant application for medical diagnosis.     

Wavelet neural network (WNN) approach for calibration model building based on gasoline near infrared (NIR) spectra

In this paper we have compared the abilities of two types of artificial neural networks (ANN): multilayer perceptron (MLP) and wavelet neural network (WNN) — for prediction of three gasoline properties (density, benzene content and ethanol content). Three sets of near infrared (NIR) spectra (285, 285 and 375 gasoline spectra) were used for calibration models building. Cross-validation errors and structures of optimized MLP and WNN were compared for each sample set. Four different transfer functions (Morlet wavelet and Gaussian derivative – for WNN; logistic and hyperbolic tangent – for MLP) were also compared. Wavelet neural network was found to be more effective and robust than multilayer perceptron.     

Automated wavelength selection for spectroscopic fuel models by symmetrically contracting repeated unmoving window partial least squares

The need for automated quality surveillance of liquid hydrocarbon fuels has driven the development of rapid fuel property modeling from spectroscopic sensor data. The correlation of near-infrared (NIR) and Raman spectroscopic data with jet and diesel fuel properties can be improved by the deliberate selection of continuous wavelength sub-ranges. An automatic wavelength selection strategy would allow for the unsupervised construction of partial least squares (PLS) regression models of increased predictive utility when supervised model construction and maintenance is not feasible. Changeable size moving window partial least squares (CSMWPLS) is one of the most thorough operations suited for this task. Unfortunately, the necessarily large number of PLS model constructions required by an automated version of this procedure limits the evaluation of the predictive ability of the resulting models through full cross-validation results. Presented here is a novel restricted version of the CSMWPLS algorithm in which the initial spectral range selection is accomplished through multiple interval PLS (iPLS) analyses, where analysis windows for the refinement step no longer move, and size changes are limited to a series of symmetric attenuations. It is shown that the proposed algorithm can provide significant PLS model improvements during the course of a fully automated analysis of jet and diesel fuel spectra in less time than an automated CSMWPLS algorithm.     

A multivariate Poisson-lognormal regression model for prediction of crash counts by severity, using Bayesian methods

Numerous efforts have been devoted to investigating crash occurrence as related to roadway design features, environmental factors and traffic conditions. However, most of the research has relied on univariate count models; that is, traffic crash counts at different levels of severity are estimated separately, which may neglect shared information in unobserved error terms, reduce efficiency in parameter estimates, and lead to potential biases in sample databases. This paper offers a multivariate Poisson-lognormal (MVPLN) specification that simultaneously models crash counts by injury severity. The MVPLN specification allows for a more general correlation structure as well as overdispersion. This approach addresses several questions that are difficult to answer when estimating crash counts separately. Thanks to recent advances in crash modeling and Bayesian statistics, parameter estimation is done within the Bayesian paradigm, using a Gibbs Sampler and the Metropolis–Hastings (M–H) algorithms for crashes on Washington State rural two-lane highways. Estimation results from the MVPLN approach show statistically significant correlations between crash counts at different levels of injury severity. The non-zero diagonal elements suggest overdispersion in crash counts at all levels of severity. The results lend themselves to several recommendations for highway safety treatments and design policies. For example, wide lanes and shoulders are key for reducing crash frequencies, as are longer vertical curves.     

基于改进SCAM算法的结构物理参数识别贝叶斯方法

针对贝叶斯估计中逐分量自适应Metropolis(single component adaptive Metropolis,SCAM)算法易生成重复性样本,导致抽样效率低、结果误差大等问题,重新定义了提议分布方差的表达式,提出了改进的SCAM算法,使得抽样样本序列构成的马尔可夫链相对稳定.进而将贝叶斯理论与改进的SCA...     

A novel approach for development of a multivariate calibration model using a Doehlert experimental design: Application for prediction of key gasoline properties by Near-infrared Spectroscopy

Alternative methods for quality control in the petroleum industry have been obtained using Near-infrared Spectroscopy (NIRS) combined with multivariate techniques such as PLS (Partial Least-Square). The process of development and refinement of PLS models usually follows a nonsystematic and univariate procedure. The Standard Error of Cross Validation (SECV), the Standard Error of Prediction (SEP) and the determination coefficient (r²_regr.) are usually the only guides used in pursuit of the best model. In the present work, a novel approach was proposed using a Doehlert experimental design with three input variables (wavenumber range, preprocessing technique and regression/validation technique) varied at 5, 7 and 3 levels, respectively. Besides SECV, SEP and r²_regr., some additional response variables, such as the slope, r² and p_value from the external validation, as well as the number of PLS factors, were simultaneously assessed to find the optimum conditions for PLS modeling. The optimum setting for each input variable was simultaneously defined through a multivariate approach using a desirability function. With the proposed approach, the main and interaction effects could also be investigated. The methodology was successfully applied to obtain PLS models to monitor the gasoline quality through the process of product loading in trucks. To prevent product contamination or adulteration, fast prediction of key properties was obtained from FT-NIR spectra within the 7300-3900 cm^− 1 region with SECV in the range 0.04-0.63% w/w for composition (Aromatics, Saturates, Olefins and Benzene) and 0.0008 for Relative Density 20/4 °C. Each optimized PLS model was obtained with less than 40 modeling runs, demonstrating the efficiency of the proposed approach.     

A particle code for plasma technological applications

The 2D Direct Simulation Monte Carlo code LasVegas was originally developed and used for the simulation of atmospheric re-entry and the flow around plasma wind tunnel probes. This code is reviewed regarding its applicability to appropriate plasma technological applications. The main DSMC theory and the code structure, features and properties are described in order to classify the LasVegas code in regard to these applications. General restrictions, model uncertainties and geometrical considerations of the DSMC method itself as well as of the LasVegas code put the focus on the vacuum spray process. As proof of concept, results of a supersonic free stream simulation in a vacuum chamber with typical inflow properties are presented and discussed. Appropriate code extensions such as the flow-particle interaction, ionization model and parallel computation capability were identified, thus indicating future numerical works.     

Dynamic orthogonal projection. A new method to maintain the on-line robustness of multivariate calibrations. Application to NIR-based monitoring of wine fermentations

A new method is developed to maintain the on-line robustness of multivariate calibrations against unknown influence factors, especially in the case of spectroscopy. This method is based on the use of the calibration database and the periodical on-line reference measurements with their corresponding spectra to compute an orthogonal correction. The spectra that would have been obtained in the absence of influence factor occurrence are estimated from the calibration database using the reference measurements and a kernel function. The difference between the estimated and the on-line spectra is used to compute an orthogonal projection correction of the calibration database that is used for recalibration. The originality of this method is its capability to handle different kinds of perturbations (chemical, physical, and environmental) using the original calibration database. This method was applied for on-line monitoring of alcoholic wine fermentation in the presence of temperature variations as the influence factor, where it showed a good performance for temperature variation and batch effects. Also, this method provides an on-line diagnosis tool for the spectral causes of robustness problems thus providing a basis to better understand the process in order to perform adjustments, if necessary, at an earlier stage.     

基于等质量变壁厚球缺罩聚能杆式射流成型特性研究*

基于变壁厚球缺罩质量恒定假设,通过改变药型罩内表面曲率半径rx 和罩顶厚度s,调整药型罩的结构,建立了δs与rx 设计参数计算模型;采用LS-DYNA3D显式非线性动力有限元程序,对不同结构变壁厚球缺罩形成聚能杆式射流的过程进行三维数值模拟;设计出罩顶厚度改变量δs 在（-1．2～＋1．2） mm范围内的7种不同结构变壁厚球缺罩方案,研究其形成聚能杆式射流的形态和各项性能参数的变化规律,并与等壁厚药型罩形成的聚能杆式射流参数进行对比分析。研究结果表明：变壁厚球缺罩对杆式射流的形状、长度、密实度和断裂时间等性能参数均有较显著影响;当δs ＝-0．4mm时,杆式射流头部速度为3317m/s,并且速度梯度分布合理,杆式射流的综合性能较优。     

Investigation of time and weather effects on crash types using full Bayesian multivariate Poisson lognormal models

Previous research shows that various weather elements have significant effects on crash occurrence and risk; however, little is known about how these elements affect different crash types. Consequently, this study investigates the impact of weather elements and sudden extreme snow or rain weather changes on crash type. Multivariate models were used for seven crash types using five years of daily weather and crash data collected for the entire City of Edmonton. In addition, the yearly trend and random variation of parameters across the years were analyzed by using four different modeling formulations. The proposed models were estimated in a full Bayesian context via Markov Chain Monte Carlo simulation. The multivariate Poisson lognormal model with yearly varying coefficients provided the best fit for the data according to Deviance Information Criteria. Overall, results showed that temperature and snowfall were statistically significant with intuitive signs (crashes decrease with increasing temperature; crashes increase as snowfall intensity increases) for all crash types, while rainfall was mostly insignificant. Previous snow showed mixed results, being statistically significant and positively related to certain crash types, while negatively related or insignificant in other cases. Maximum wind gust speed was found mostly insignificant with a few exceptions that were positively related to crash type. Major snow or rain events following a dry weather condition were highly significant and positively related to three crash types: Follow-Too-Close, Stop-Sign-Violation, and Ran-Off-Road crashes. The day-of-the-week dummy variables were statistically significant, indicating a possible weekly variation in exposure. Transportation authorities might use the above results to improve road safety by providing drivers with information regarding the risk of certain crash types for a particular weather condition.     

基于动态滤波重采样的星载微波辐射计观测亮温方法

研究了星载微波计的亮温观测,针对目前常用的采用重采样的固定滤波方法不考虑观测几何变化的不足,开发了一种采用基于Backus-Gilbert (BG)方法的动态滤波重采样的亮温观测方法,该方法从卫星观测方式入手,考虑了观测几何沿经线方向和沿纬线方向的变化,采用线性插值的方法对滤波模板进行实时更新,实现了动态滤波重采样的目的.对该方法并进行了仿真试验和基于对地观测的先进扫描微波辐射计(AMSR-E)观测资料的真实数据试验,试验结果表明,这种方法的匹配误差小于固定滤波方法且具有较高的计算效率,具有良好的业务化应用潜力.     

Effects of geochemical composition on neutron die-away measurements: Implications for Mars Science Laboratory's Dynamic Albedo of Neutrons experiment

The Dynamic Albedo of Neutrons (DAN) experiment, part of the scientific payload of the Mars Science Laboratory (MSL) rover mission, will have the ability to assess both the abundance and the burial depth of subsurface hydrogen as the rover traverses the Martian surface. DAN will employ a method of measuring neutron fluxes called “neutron die-away” that has not been used in previous planetary exploration missions. This method requires the use of a pulsed neutron generator that supplements neutrons produced via spallation in the subsurface by the cosmic ray background. It is well established in neutron remote sensing that low-energy (thermal) neutrons are sensitive not only to hydrogen content, but also to the macroscopic absorption cross-section of near-surface materials. To better understand the results that will be forthcoming from DAN, we model the effects of varying abundances of high absorption cross-section elements that are likely to be found on the Martian surface (Cl, Fe) on neutron die-away measurements made from a rover platform. Previously, the Mars Exploration Rovers (MER) Spirit and Opportunity found that elevated abundances of these two elements are commonly associated with locales that have experienced some form of aqueous activity in the past, even though hydrogen-rich materials are not necessarily still present. By modeling a suite of H and Cl compositions, we demonstrate that (for abundance ranges reasonable for Mars) both the elements will significantly affect DAN thermal neutron count rates. Additionally, we show that the timing of thermal neutron arrivals at the detector can be used together with the thermal neutron count rates to independently determine the abundances of hydrogen and high neutron absorption cross-section elements (the most important being Cl). Epithermal neutron die-away curves may also be used to separate these two components. We model neutron scattering in actual Martian compositions that were determined by the MER Alpha Proton X-Ray Spectrometer (APXS), as examples of local geochemical anomalies that DAN would be sensitive to if they were present at the MSL landing site. These MER targets, named “Eileen Dean,” “Jack Russell,” and “Kenosha Comets,” all have unusually high or low Cl or Fe abundances as a result of geochemical interactions involving water. Using these examples we demonstrate that DAN can be used not only to assess the amount of present-day hydrogen in the near-surface but also to identify locations that may preserve a geochemical record of past aqueous processes.     

MCR-NAS: A combined hard-soft multivariate curve resolution method based on net analyte signal concept for modeling kinetic data with inert interference and baseline drift

This paper demonstrates a novel implementation of a net analyte signal (NAS) algorithm for resolving kinetic model. The evaluation of spectroscopic kinetics data by the established hard-modeling approach assumes that all the variation related to the response is linked to the species involved in the kinetic process. If effects are present in the data that cannot be explained by the model, such as baseline drift or inert interference, the estimated rate constant might be incorrect. An iterative net analyte signal procedure is shown to yield good rate constant estimates when the spectroscopic data is influenced by additional sources of variance. Simulated and real examples are used to confirm this statement.     

Stochastic finite element analysis of a cable-stayed bridge system with varying material properties

Stochastic seismic finite element analysis of a cable-stayed bridge whose material properties are described by random fields is presented in this paper. The stochastic perturbation technique and Monte Carlo simulation (MCS) method are used in the analyses. A summary of MCS and perturbation based stochastic finite element dynamic analysis formulation of structural system is given. The Jindo Bridge, constructed in South Korea, is chosen as a numerical example. The Kocaeli earthquake in 1999 is considered as a ground motion. During the stochastic analysis, displacements and internal forces of the considered bridge are obtained from perturbation based stochastic finite element method (SFEM) and MCS method by changing elastic modulus and mass density as random variable. The efficiency and accuracy of the proposed SFEM algorithm are evaluated by comparison with results of MCS method. The results imply that perturbation based SFEM method gives close results to MCS method and it can be used instead of MCS method, especially, if computational cost is taken into consideration.