Scientometric analysis of geostatistics using multivariate methods

Multivariate methods were successfully employed in a comprehensive scientometric analysis of geostatistics research, and the publications data for this research came from the Science Citation Index and spanned the period from 1967 to 2005. Hierarchical cluster analysis (CA) was used in publication patterns based on different types of variables. A backward discriminant analysis (DA) with appropriate statistical tests was then conducted to confirm CA results and evaluate the variations of various patterns. For authorship pattern, the 50 most productive authors were classified by CA into 4 groups representing different levels, and DA produced 92.0% correct assignment with high reliability. The discriminant parameters were mean impact factor (MIF), annual citations per publication (ACPP), and the number of publications by the first author, for country/region pattern, CA divided the top 50 most productive countries/regions into 4 groups with 95.9% correct assignments, and the discriminant parameters were MIF, ACCP, and independent publication (IP); for institute pattern, 3 groups were identified from the top 50 most productive institutes with nearly 88.0% correct assignment, and the discriminant parameters were MIF, ACCP, IP, and international collaborative publication; last, for journal pattern, the top 50 most productive journals were classified into 3 groups with nearly 98.0% correct assignment, and its discriminant parameters were total citations, impact factor and ACCP. Moreover, we also analyzed general patterns for publication document type, language, subject category, and publication growth.     

Improved methods for performing multivariate analysis and deriving background spectra in atmospheric open-path FT-IR monitoring

Open-path Fourier transform infrared spectrometry (OP/FT-IR) may improve the temporal and spatial resolution in air pollutant measurements compared to conventional sampling methods. However, a successful OP/FT-IR operation requires an experienced analyst to resolve chemical interference as well as to derive a suitable background spectrum. The present study aims at developing a systematic method of handling the OP/FT-IR derived spectra for the measurement of photochemical oxidants and volatile organic compounds (VOCs) in urban areas. A classical least-squares (CLS) method, the most frequently used regression method in OP/FT-IR, is modified to constrain all the analyzed chemical species concentrations within a physically reasonable range. This new CLS method, named constrained CLS, may save the effort of predetermining the chemical species to be analyzed. A new background spectrum generation method is also introduced to more efficiently handle chemical interferences. Finally, CLS is shown to be prone to propagating errors in the case that a few data points contain a significant amount of error. The LI-norm minimization method reduces this error propagation to considerably increase the stability compared to CLS. The presently developed analysis software based on these approaches is compared with the other conventional CLS method using an artificially made single-beam spectrum as well as a field single-beam spectrum.     

Improved multivariate image analysis for product quality monitoring

Product quality monitoring by image texture analysis underwent a tremendous growth in the last few years in several industrial sectors, due to the availability of low-cost digital imaging sensors. Multivariate image analysis (MIA) can be used within an image texture analysis technique to provide a spatial statistical characterization of an image. However, in most cases this spatial characterization is possible only for very local texture neighborhoods, due to the high computational cost of MIA. In this paper, the iMIA (improved Multivariate Image Analysis) algorithm is proposed, that improves over previous implementations of MIA by extending its range of applicability due to its reduced computational complexity and memory requirements. The proposed algorithm uses the Fourier transform and the convolution theorem to efficiently compute the MIA model, in such a way that the image texture can be characterized by taking into account also large neighborhood sizes. The proposed approach is applied to two case studies concerning the estimation of the fiber diameter distribution in nanostructured membranes, and the classification of paper surfaces. The results suggest that the optimum range of spatial statistics used for characterizing the image is related to the size of the main textural features.     

Characterization of methanol-water and acetonitrile-water association using multivariate curve resolution methods

The solvatochromic comparison method has been used to probe the interactions of solutes with binary solvent mixtures of methanol-water and acetonitrile-water. The solute spectra recorded in these mixtures are composed of the additive spectral contributions of the different solvated species of the solute, i.e., the water-solvated species, the cosolvent-solvated species, and the species solvated by water-solvent complexes. Multivariate curve resolution-alternating least squares has been used to model the solvation of the solutes as a function of the composition of the binary solvent mixture. Spectra and concentration profiles of the dye surrounded by the different solvation environments have been isolated. For the first time, solute spectra solvated exclusively by methanol-water and acetonitrile-water complexes have been obtained, and the solvatochromic parameters of dipolarity/polarizability and hydrogen-bonding acidity have been estimated for these complex species.     

Variational projection methods for gradient crystal plasticity using Lie algebras

A computational method is developed for evaluating the plastic strain gradient hardening term within a crystal plasticity formulation. While such gradient terms reproduce the size effects exhibited in experiments, incorporating derivatives of the plastic strain yields a nonlocal constitutive model. Rather than applying mixed methods, we propose an alternative method whereby the plastic deformation gradient is variationally projected from the elemental integration points onto a smoothed nodal field. Crucially, the projection utilizes the mapping between Lie groups and algebras in order to preserve essential physical properties, such as orthogonality of the plastic rotation tensor. Following the projection, the plastic strain field is directly differentiated to yield the Nye tensor. Additionally, an augmentation scheme is introduced within the global Newton iteration loop such that the computed Nye tensor field is fed back into the stress update procedure. Effectively, this method results in a fully implicit evolution of the constitutive model within a traditional displacement‐based formulation. An elemental projection method with explicit time integration of the plastic rotation tensor is compared as a reference. A series of numerical tests are performed for several element types in order to assess the robustness of the method, with emphasis placed upon polycrystalline domains and multi‐axis loading. Copyright © 2016 John Wiley & Sons, Ltd.     

Monitoring and modeling of protein processes using mass spectrometry, circular dichroism, and multivariate curve resolution methods

Mass spectrometry has recently become one of the major analytical tools to study biomolecular structure and function. Ionization techniques, such as electrospray ionization (ESI), desorb biomolecules from solution to the gas phase keeping practically intact their natural structure. ESI applied to a protein solution produces a mixture of multiply charged ions, the ion charge distribution of which depends on the oligomeric form (mass) and on the protein surface exposed (amount of accommodated charges) of the related protein conformation. ESI-MS provides an efficient way to monitor protein processes; however, the ionic contributions of the different protein conformations involved usually overlap, and the use of chemometric tools is necessary to unravel the information related to the pure conformations that the biomolecule adopts along the process. Multivariate curve resolution-alternating least squares applied to MS-monitored protein processes provides the concentration profiles associated with the different protein conformations occurring during the process and the related pure mass spectra. The concentration profiles, in this context, the ionic contributions, describe the process mechanism and the structural information derived from the pure mass spectra characterizes the involved conformations. Mass spectra can be expressed schematically through percentages of base peak intensity. This chemical transformation compresses significantly the raw spectra and allows for an easier application of natural MS-related constraints, such as the presence of only one maximum, i.e., the base peak of a particular conformation, into the resolution of the pure signals. The combination of mass spectrometry and multivariate curve resolution methods is used to elucidate the mechanism of the pH-induced conformation changes of the bovine beta-lactoglobulin. As a final step, MS data are fused with circular dichroism data and are simultaneously analyzed to ensure and confirm that all the previously detected MS conformations really exist in solution and are an artifact of neither the ionization process nor their chemometric resolution.     

Statistical process monitoring using an empirical Bayes multivariate process control chart

In this paper, we describe the theory underlying an empirical Bayesian approach to monitoring two or more process characteristics simultaneously. If the data is continuous and multivariate in nature, often the multivariate normal distribution can be used to model the process. Then, using Bayesian theory, we develop techniques to implement empirical Bayes process monitoring of the multivariable process. Lastly, an example is given to illustrate the use of our techniques. Copyright © 2001 John Wiley & Sons, Ltd.     

Cycle-based signal monitoring using a directionally variant multivariate control chart system

Cycle-based signals are generally obtained through the automatic sensing of critical process variables during each repetitive operation cycle of a manufacturing process, and they thus contain a significant amount of information about the process condition. Increasing attention has been paid recently to the problem of effectively monitoring these signals as an aid to the detection of process changes. In general, either based on process engineering knowledge or on historical data analysis, it is possible to obtain process faults and the corresponding signal patterns (the direction and magnitude of a mean shift). In order to fully utilize such fault pattern information in process monitoring, this paper proposes a directionally variant control chart obtained through the effective combination of a multivariate χ² chart and a univariate projection chart. It is shown that the addition of the univariate projection chart can improve the detection power for pre-known process faults, however, this may be at the cost of a deterioration in the detection power for unknown faults. A detailed quantitative analysis is provided to justify the application conditions of the proposed chart. A case study of cycle-based tonnage monitoring of a forging process is presented to illustrate the design procedures and the effectiveness of the proposed control chart system.     

EEG monitoring using bispectral analysis

The modern practice of administering paralysing drugs to relax muscles while maintaining shallower depths of anaesthesia has led to the possibility of patients being fully aware of surgery without being able to communicate this awareness to anaesthetists. A form of spectral analysis of an electroencephalogram meets some of the criteria for an ideal index of anaesthetic depth     

Statistical monitoring of over-dispersed multivariate count data using approximate likelihood ratio tests

In this paper, we develop a statistical monitoring scheme for multivariate count data. In many applications involving multivariate count data, individual variables are not only correlated to each other, but also over-dispersed. Traditional statistical monitoring methods for multivariate count data that assume simple statistical models fail to fit the data collected when the underlying process is under normal working state, also referred to as the in-control state. Therefore, we propose a monitoring scheme which is based on the Poisson–multivariate Gaussian mixed model. Although such models are quite flexible, efficient statistical monitoring schemes for such models have not been developed. In this paper, we develop likelihood ratio test-based monitoring schemes that are shown to be superior to standard multivariate statistical monitoring schemes. The key challenge in developing likelihood ratio test for the Poisson–multivariate Gaussian mixed models is that the likelihood function can only be calculated by multidimensional numerical integration. We tackle this issue using an approximation of this complex likelihood function.     

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.     

A hybrid learning-based model for on-line monitoring and diagnosis of out-of-control signals in multivariate manufacturing processes

In multivariate statistical process control, most multivariate quality control charts are shown to be effective in detecting out-of-control signals based upon overall statistics. But these charts do not relieve the need for pinpointing the source(s) of the out-of-control signals. In addition, these charts cannot provide more detailed process information, such as quantitative abnormal assessment values and visualisation of process changes, which would be very useful for quality practitioners to locate the assignable causes that give rise to the out-of-control situation. In this study, a hybrid learning-based model has been investigated for monitoring and diagnosing out-of-control signals in a bivariate process. In this model, a minimum quantisation error (MQE) chart based on the self-organization map (SOM) neural network (NN) was developed for monitoring process changes (i.e., mean shifts), and a selective NN ensemble approach (DPSOEN) was developed for diagnosing signals that are judged as out-of-control signals by MQE charts. The simulation results demonstrate that the proposed model outperforms the conventional multivariate control scheme in terms of average run length (ARL), and can accurately classify the source(s) of out-of-control signals. An extensive experiment is also carried out to examine the effects of six statistical features on the performance of DPSOEN.     

Process monitoring based on classification tree and discriminant analysis

To cope with the computational intensity associated with classification tree analysis and the multicolinearity in the process data, a newly developed process monitoring scheme integrating classification tree and Fisher Discriminant Analysis (FDA) is developed. FDA extracts the most significant components in the original process data and achieves optimal discriminating among different faults. Classification tree uses the FDA scores, which are the lower dimensional representation produced by FDA, to separate observations into different fault classes. A stopping rule is applied to determine the optimal order of FDA. Two case studies are presented to illustrate the effectiveness of the proposed methods compared with the original classification tree. The new method generates better classification accuracy and uses less construction time.     

Composite multivariate quality control using a system of univariate, bivariate, and multivariate quality control rules.

We propose a composite multivariate quality control (CMQC) system to control simultaneously measured variables. This system is designed to detect unacceptable trends and systematic error in one or more variables, unacceptable random error in one or more variables, and unacceptable changes in the correlation structure in any pair of variables. It is also designed to be tolerant of missing data, to be capable of rejecting as few as one or as many as all variables in a run, and to provide the analyst with control statistics and graphics that logically relate to sources of analytical error. Quality control rules for univariate, multivariate, and correlation conditions are incorporated in the system, as are plots displaying CMQC statistic values and control limits for univariate, multivariate, and correlation parameters. We also discuss advantages of the CMQC over the T2 and principal component multivariate quality control methods. We demonstrate the CMQC procedure using data from a laboratory process in which 40 variables were measured during 40 characterization runs and 23 runs analyzing unknowns.     

Statistical monitoring and diagnosis of automatic controlled processes using dynamic PCA

As manufacturing quality has become a decisive factor in competing in a global market, statistical quality techniques such as statistical process control (SPC) are becoming very popular in industries. With advances in sensing and data capture technology, large volumes of data are being routinely collected in automatic controlled processes. There is a growing need for SPC monitoring and diagnosis in these environments, but an effective implementing scheme is still lacking. This research provides an integrated approach to simultaneously monitor and diagnose an automatic controlled process by using dynamic principal component analysis (DPCA) and minimax distance classifier. Through a step-by-step implementation procedure, the proposed scheme is expected to have an impact on many manufacturing industries with automatic process control (APC) or engineering process control (EPC).     

Performance updating of concrete bridges using proactive health monitoring methods

Uncertainties associated with modelling of deteriorating bridges strongly affect management decisions, such as inspection, maintenance and repair actions. These uncertainties can be reduced by the effective use of health monitoring systems, through which information regarding in situ performance can be incorporated in the management of bridges.The objectives of this paper are twofold; first, an improved chloride induced deterioration model for concrete bridges is proposed that can quantify degradation in performance soon after chlorides are deposited on the bridge, rather than when initiation of corrosion at the reinforcement level takes place. As a result, the implications of introducing proactive health monitoring can be assessed using probabilistic durability criteria. Thus, the second objective of the paper is to present a methodology for performance updating of deteriorating concrete bridges fitted with a proactive health monitoring system.This methodology is illustrated via a simple example of a typical bridge element, such as a beam or a part of a slab. The results highlight the benefits from introducing ‘smart’ technology in managing bridges subject to deterioration, and quantify the reduction in uncertainties and their subsequent effect on predictions of future bridge performance.     

Vision-based monitoring method using gray relational analysis

Intelligent surveillance system has become an important research topic in the field of computer vision. The authors propose a monitoring method based on the cellular model to monitor human activities in the indoor environment. The measured area of an indoor room is divided into several unit areas in which each unit area is considered as a simple cell in the cellular model. A rectangular box is then used to group those neighbouring active cells into a unit to represent a moving object. Since people usually walk without a fixed style and the colour of objects may be similar to that of the background, the distribution of active cells is often uncertain and incomplete. The authors therefore apply the gray relational analysis to detect and track multiple moving objects. Several experiments have been conducted to evaluate the performance of the proposed system. The experimental results show that the proposed system is highly effective in verifying and tracking multiple objects in real time.