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Records with Keyword: Multivariate Statistics
Supply Chain Monitoring Using Principal Component Analysis
Jing Wang, Christopher Swartz, Brandon Corbett, Kai Huang
July 16, 2020 (v1)
Keywords: monitoring, Multivariate Statistics, Supply Chain
Various types of risks exist in a supply chain, and disruptions could lead to economic loss or even breakdown of a supply chain without an effective mitigation strategy. The ability to detect disruptions early can help improve the resilience of the supply chain. In this paper, the application of principal component analysis (PCA) and dynamic PCA (DPCA) in fault detection and diagnosis of a supply chain system is investigated. In order to monitor the supply chain, data such as inventory levels, market demands and amount of products in transit are collected. PCA and DPCA are used to model the normal operating conditions (NOC). Two monitoring statistics, the Hotelling's T-squared and the squared prediction error (SPE), are used to detect abnormal operation of the supply chain. The confidence limits of these two statistics are estimated from the training data based on the $\chi^2$- distributions. The contribution plots are used to identify the variables with abnormal behavior when at le... [more]
A Review of Kernel Methods for Feature Extraction in Nonlinear Process Monitoring
Karl Ezra Pilario, Mahmood Shafiee, Yi Cao, Liyun Lao, Shuang-Hua Yang
February 12, 2020 (v1)
Keywords: Fault Detection, fault diagnosis, kernel CCA, kernel CVA, kernel FDA, kernel ICA, kernel PCA, kernel PLS, Machine Learning, Multivariate Statistics
Kernel methods are a class of learning machines for the fast recognition of nonlinear patterns in any data set. In this paper, the applications of kernel methods for feature extraction in industrial process monitoring are systematically reviewed. First, we describe the reasons for using kernel methods and contextualize them among other machine learning tools. Second, by reviewing a total of 230 papers, this work has identified 12 major issues surrounding the use of kernel methods for nonlinear feature extraction. Each issue was discussed as to why they are important and how they were addressed through the years by many researchers. We also present a breakdown of the commonly used kernel functions, parameter selection routes, and case studies. Lastly, this review provides an outlook into the future of kernel-based process monitoring, which can hopefully instigate more advanced yet practical solutions in the process industries.
Multivariate Analysis of Plasma Metabolites in Children with Autism Spectrum Disorder and Gastrointestinal Symptoms Before and After Microbiota Transfer Therapy
James B. Adams, Troy Vargason, Dae-Wook Kang, Rosa Krajmalnik-Brown, Juergen Hahn
December 16, 2019 (v1)
Subject: Biosystems
Keywords: autism spectrum disorder, biomarker, co-occurring conditions, fecal microbiota transplant, Fisher discriminant analysis, gastrointestinal symptoms, leave-one-out cross-validation, Multivariate Statistics, plasma metabolites
Current diagnosis of autism spectrum disorder (ASD) is based on assessment of behavioral symptoms, although there is strong evidence that ASD affects multiple organ systems including the gastrointestinal (GI) tract. This study used Fisher discriminant analysis (FDA) to evaluate plasma metabolites from 18 children with ASD and chronic GI problems (ASD + GI cohort) and 20 typically developing (TD) children without GI problems (TD − GI cohort). Using three plasma metabolites that may represent three general groups of metabolic abnormalities, it was possible to distinguish the ASD + GI cohort from the TD − GI cohort with 94% sensitivity and 100% specificity after leave-one-out cross-validation. After the ASD + GI participants underwent Microbiota Transfer Therapy with significant improvement in GI and ASD-related symptoms, their metabolic profiles shifted significantly to become more similar to the TD − GI group, indicating potential utility of this combination of plasma metabolites as a b... [more]
Rethinking Computing and Statistics Instruction with Vocareum and Gradescope
Alexander Dowling
November 22, 2019 (v2)
Subject: Education
Keywords: Active Learning, Classroom Technology, Education, Jupyter Notebooks, Multivariate Statistics, Numerical Methods, Python
I will share ongoing efforts to retool CBE 20258 Numerical and Statistical Analysis (required) to provide a scaffolding for all chemical engineering undergraduates to develop core competencies in computing, applied statistics, and mathematical modeling. Key aspects of the course redesign include i) modernizing content including the adoption of the Python programming language and Jupyter notebooks, ii) moving initial exposure to outside of the classroom, and iii) incorporating active learning in all class sessions. I will share how classroom technologies Vocareum and Gradescope have been critical to the success of the redesign by reducing grading time, giving students fast feedback, and enabling regular accountability.
Training All Chemical Engineers in Computing and Data Science
Alexander Dowling
November 11, 2019 (v3)
Subject: Education
Keywords: Active Learning, Multivariate Statistics, Numerical Methods, Python, Undergraduate Education
In this contribution, I will discuss ongoing efforts to retool the sophomore-level “Numerical and Statistical Analysis” course (required) to provide a scaffolding for all students to develop core competencies in computing, applied statistics, and mathematical modeling throughout their undergraduate experience and profession careers. Beginning in Spring 2019, we are transitioning from MATLAB to Python for several reasons including consistency with “Chemical Process Control” (junior, required) and college-wide electives in data science and statistical computing that already use Python. I will also share experiences using Jupyter notebooks and cloud-based computing platforms such as Colaboratory to incorporate active learning into lectures and tutorials and to remove technical barriers for students. Content and assignments have been reorganized to emphasize mastery of foundational skills in preference over content breadth. For example, students are now required to submit hand-written pseu... [more]
On the Use of Multivariate Methods for Analysis of Data from Biological Networks
Troy Vargason, Daniel P. Howsmon, Deborah L. McGuinness, Juergen Hahn
July 31, 2018 (v1)
Keywords: autism spectrum disorder, classification, Fisher discriminant analysis, Machine Learning, Multivariate Statistics, one carbon metabolism, probability density function, transsulfuration, urine toxic metals
Data analysis used for biomedical research, particularly analysis involving metabolic or signaling pathways, is often based upon univariate statistical analysis. One common approach is to compute means and standard deviations individually for each variable or to determine where each variable falls between upper and lower bounds. Additionally, p-values are often computed to determine if there are differences between data taken from two groups. However, these approaches ignore that the collected data are often correlated in some form, which may be due to these measurements describing quantities that are connected by biological networks. Multivariate analysis approaches are more appropriate in these scenarios, as they can detect differences in datasets that the traditional univariate approaches may miss. This work presents three case studies that involve data from clinical studies of autism spectrum disorder that illustrate the need for and demonstrate the potential impact of multivariate... [more]
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