Cryogenic metallic positive expulsion bellows evaluation

Cryogenic metallic positive expulsion bellows evaluatio

Comparison of partial least squares regression, least squares support vector machines, and Gaussian process regression for a near infrared calibration

This paper investigates the use of least squares support vector machines and Gaussian process regression for multivariate spectroscopic calibration. The performances of these two non-linear regression models are assessed and compared to the traditional linear regression model, partial least squares regression on an agricultural example. The non linear models, least squares support vector machines, and Gaussian process regression, showed enhanced generalization ability, especially in maintaining homogeneous prediction accuracy over the range. The two non-linear models generally have similar prediction performance, but showed different features in some situations, especially when the size of the training set varies. This is due to fundamental differences in fitting criteria between these models

A linearization method for partial least squares regression prediction uncertainty

We study a local linearization approach put forward by Romera to provide an approximate variance for predictions in partial least squares regression. We note and correct some problems with the original formulae, study the stability of the resulting approximation using some simulations, and suggest an alternative method of computation using a parametric bootstrap. The alternative method is more stable than the algebraic approximation and is faster when the number of predictors is large

Modern practical convolutional neural networks for multivariate regression: Applications to NIR calibration

In this study, we investigate the use of convolutional neural networks (CNN) for near infrared (NIR) calibration. We propose a unified CNN structure that can be used for general multivariate regression purpose. The comparison between the CNN method and the partial least squares regression (PLSR) method was done on three different NIR datasets of spectra and lab reference values. Datasets are from different sources and contain 6998, 1000 and 415 training and 618, 597 and 108 validation samples, respectively. Results indicated that compared to the PLSR models, the CNN models are more accurate and less noisy. The convolutional layer in the CNN model can automatically find the suitable spectral preprocessing filter on the dataset, which significantly saves efforts in training the model

Hierarchical Bayesian variable selection in the probit model with mixture of nominal and ordinal responses

Multi-class classification problems have been studied for pure nominal and pure ordinal responses. However, there are some cases where the multi-class responses are a mixture of nominal and ordinal. To address this problem we build a hierarchical multinomial probit model with a mixture of both types of responses using latent variables. The nominal responses are each associated to distinct latent variables whereas the ordinal responses have a single latent variable. Our approach first treats the ordinal responses as a single nominal category and then separates the ordinal responses within this category. We introduce sparsity into the model using Bayesian variable selection (BVS) within the regression in order to improve variable selection classification accuracy. Two indicator vectors (indicating presence of the covariate) are used, one for nominal and one for ordinal responses. We develop efficient posteriorsampling. Using simulated data, we compare the classification accuracy of our method to existing ones

Spectral sensitivity of the discoloration of Historical rag paper

This paper discusses the spectral sensitivity of the discoloration of historical rag paper simultaneously affected by Relative Humidity (RH) and Oxygen concentration [O2] in the ambient environment. Sacrificial samples were degraded using narrowband radiation sources centred at 450 nm, 525 nm, and 625 nm in combinations of RH and [O2] at two levels: 0% [O2] and 70% RH, 21% [O2] and 70% RH, 0% [O2] and 20% RH, and 21% [O2] and 20% RH. Diffuse reflectance was measured before and during the degradation experimental runs. Consistent qualitative results were obtained for the change in reflectance and the change in tristimulus total color change in CIELAB color space. In both cases, the increase of discoloration was modelled logarithmically over time. Among the three factors investigated in this research, wavelength of the radiation (Λ) was found to have the strongest effect. The radiation at 450 nm induced the most and fastest discoloration whereas the radiation at 625 nm induced the least and slowest discoloration. This spectral dependence was likely to be related to the photo energies at different wavelengths, but other factors were found to have played a role. Further analyses revealed that the main effects and the effects of the interactions between [O2] and Λ and between RH and Λ on the discoloration of historical rag paper were statistically significant. It suggests that managing the spectral power distribution of the radiation source can be crucial in the collection management

Insight Gained from Using Machine Learning Techniques to Predict the Discharge Capacities of Doped Spinel Cathode Materials for Lithium-Ion Batteries Applications

Abstract The electrochemical potentials of spinel lithium manganese oxide (LMO) have long been plagued by the significant Mn3+ dissolution during long cycle discharging, resulting in rapid capacity fading and short cycle life. Although the doping mechanisms are effective in suppressing these reactions, the correlations of their effects on the material properties and the improved discharging performance still remain uncovered. In this study, seven machine learning (ML) methods are applied to a manually curated dataset of 102 doped LMO spinel systems to predict the initial discharge capacities (IC) and 20th cycle end discharge capacities (EC) from fundamental system properties like material molar mass and crystal structure dimension. Gradient boosting models achieved the best prediction powers for IC and EC with their errors estimated to be 11.90 and 11.77 mAhg−1, respectively. Besides, a higher formula molar mass of doped LMO can improve both capacities and additionally, a shorter crystal lattice dimension with a dopant with smaller electronegativity can slightly improve the value of the IC and EC, respectively. This study demonstrates the great potential of using ML models to both predict the discharging performance of doped spinel cathodes and identify the governing material properties for controlling the discharging performance

Quantitative NIR spectroscopy for determination of degree of polymerisation of historical paper

This paper discusses the development of a near infrared (NIR) spectroscopic method coupled with multivariate analysis to characterise historical paper. Specifically, partial least squares (PLS) regression was used to predict one of the most important properties of paper as a condition indicator – degree of polymerisation (DP). Supported by a set of model cellulose samples, the NIR-PLS method for DP prediction was validated and the modelling approach that led to the best prediction of DP of paper was established. The coefficient of variation of the NIR-PLS models were found to be approximately 8% and 20% of the DP of model cellulose and historical paper, respectively. The variance of the reference DP, the variance of the predicted DP, and the model bias were identified as the main sources of the total expected generalisation error of prediction. For both model cellulose and historical paper, the variance of the predicted DP by the NIR-PLS models contributed the most to the total error of prediction. This suggests that improving the instrumentation and the operation procedure is essential to improve model performance. Furthermore, the effect of water content of the samples on model performance was investigated. The model for historical paper was proven to be robust to relative humidity fluctuations between 30% and 70%, indicating the applicability of the model for collection surveys in a range of environments

Factorial experimentation on photodegradation of historical paper by polychromatic visible radiation

Quantification of the degradation behaviour of heritage objects is essential to manage the rate of degradation and hence optimise their lifetime. In this research, a 23 full factorial experiment was carried out to deepen the understanding of the photodegradation of historical rag paper induced by continuous polychromatic visible radiation. Oxygen concentration, relative humidity and illuminance were investigated as the three environmental factors of primary concern. The effects of these factors on the rate constant of change in diffuse reflectance and tristimulus discolouration were investigated by analysis of variance and multiple linear regression. The three main effects were found to contribute the most to the rate of photodegradation of historical paper, among which relative humidity played the most important role whereas illuminance played the least. This observation is likely to hold when extrapolating the experimental conditions to real conditions in collection storage and display