Remote Near-Infrared Fuel Monitoring System

Hutzler, Scott A; Bessee, Gary B.

doi:10.21236/ada363918

Cited by 4 publications

(3 citation statements)

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“…The data set contains the NIR spectra of several diesel fuels (N = 40) obtained at the Southwest Research Institute (SWRI) on a project sponsored by the U.S. Army [29]. The fuels were originally scanned from the …”

Section: Diesel Data Setmentioning

confidence: 99%

PCA model building with missing data: New proposals and a comparative study

Folch-Fortuny

Arteaga

Ferrer

2015

Chemometrics and Intelligent Laboratory Systems

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Section: Diesel Data Setmentioning

confidence: 99%

PCA model building with missing data: New proposals and a comparative study

Folch-Fortuny

Arteaga

Ferrer

2015

Chemometrics and Intelligent Laboratory Systems

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“…The first one consists of the percentage composition of eight fatty acids: palmitic, palmitoleic, stearic, oleic, linoleic, linolenic, arachidic and eicosenoic, found in the lipid fraction of 75 olive oils of South Apulia (Italia) [19]. The second data set correspond to NIR spectra (wavelengths 750-1550 in 2 mm increments) of several diesel fuels (N = 40) obtained at the Southwest Research Institute (SWRI) on a project sponsored by the U.S. Army [20]. Finally, a multivariate data set with 10 variables and 100 observations, simulated from [21,22], is included.…”

Section: Data Setsmentioning

confidence: 99%

Missing Data Imputation Toolbox for MATLAB

Folch-Fortuny

Arteaga

Ferrer

2016

Chemometrics and Intelligent Laboratory Systems

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Here we introduce a graphical user-friendly interface to deal with missing values called Missing Data Imputation (MDI) Toolbox. This MATLAB toolbox allows imputing missing values, following missing completely at random patterns, exploiting the relationships among variables. In this way, principal component analysis (PCA) models are fitted iteratively to impute the missing data until convergence. Different methods, using PCA internally, are included in the toolbox: trimmed scores regression (TSR), known data regression (KDR), KDR with principal component regression (KDR-PCR), KDR with partial least squares regression (KDR-PLS), projection to the model plane (PMP), iterative algorithm (IA), modified nonlinear iterative partial least squares regression algorithm (NIPALS) and data augmentation (DA). MDI Toolbox presents a general procedure to impute missing data, thus can be used to infer PCA models with missing data, to estimate the covariance structure of incomplete data matrices, or to impute the missing values as a preprocessing step of other methodologies.

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“…Since this dataset was in the proposal of TSR for PCA-MB with missing data [109], we considered it an interesting case for comparison. It contains K= 401 wavelengths (750 -1550 in 2 mm increments) of N = 40 diesel fuels obtained at the Southwest Research Institute (SWRI) on a project sponsored by the U.S. Army [110].…”

Section: Real Datasetsmentioning

confidence: 99%

Statistical Machine Learning in Biomedical Engineering

González Cebrián

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This thesis, developed under a research personnel formation grant from the Universitat Politècnica de València, aims to propose and apply methodologies of Statistical Machine Learning in Biomedical Engineering contexts. This concept seeks to combine machine learning models with the classic understanding and interpretability of statistical reasoning, resulting in technological solutions for biomedical problems that go beyond solely optimizing the predictive performance of models. To achieve this, two main objectives have been outlined, which also structure the document: proposing novel methodologies within the umbrella of Statistical Machine Learning and applying solutions to real biomedical problems while keeping this philosophy in mind. These objectives have materialized into methodological contributions for simulating outliers and imputing missing data in the presence of outliers and applied contributions to real cases for improving healthcare processes, enhancing disease diagnosis and prognosis, and standardizing measurement procedures in biotechnological environments. These contributions are articulated in chapters corresponding to the aforementioned two main parts. Finally, the conclusions and future lines of research conclude the document, reiterating the main messages of the contributions and the overall doctoral thesis and laying the groundwork for future lines of inquiry stemming from the work conducted throughout the doctorate. ix Gracias, finalmente, a mis más antiguas amistades: mi familia. Gracias por la paciencia que habéis tenido conmigo en mis días mudos de deprimente atropello melancólico y en mis huidas hacia delante por la puerta de atrás. Incluyo en este grupo a Cristina, ya que con tu trabajo has puesto el mundo a mis pies cuando sentía que lo tenía totalmente encima. Papá y mamá, gracias por enseñarme a vivir volando libre. Y a Ángela, gracias por tu fe en mí; siempre has sido la luz de todos mis pozos. Ya solo me queda decir que espero que el trabajo esté a vuestra altura. Os quiero.

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Remote Near-Infrared Fuel Monitoring System

Cited by 4 publications

References 0 publications

PCA model building with missing data: New proposals and a comparative study

PCA model building with missing data: New proposals and a comparative study

Missing Data Imputation Toolbox for MATLAB

Statistical Machine Learning in Biomedical Engineering

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