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Meeting	MS&T21: Materials Science & Technology
Symposium	Materials Informatics for Images and Multi-dimensional Datasets
Presentation Title	Multivariate Statistical Analysis (MVSA) for Hyperspectral Images
Author(s)	Chuong Nguyen, Alp Manavbasi
On-Site Speaker (Planned)	Chuong Nguyen
Abstract Scope	Modern materials characterization techniques generate huge amount of data, often in the form of hyperspectral images. Traditional analyses break down these data sets into static spectra or images, and manually correlate them for information. With the advances of computing power, chemometrics, specifically MVSA for hyperspectral images, is increasingly used to automatically extract information using mathematical and statistical algorithms.This paper presents MVSA of data obtained by Auger electron spectroscopy (AES) and secondary ion mass spectrometry (SIMS) concerning surface treatment of aluminum. The data can be analyzed autonomously, or with human inputs. Ultimately, the analyses revealed important features of high performing surfaces for future applications.

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

Building a Database of Fatigue Fracture Images to train a CNN

Characterization of Additively Manufactured ZrB2-SiC Ultra High Temperature Ceramics via X-ray Microtomography

Graph Neural Networks for an Accurate and Interpretable Prediction of the Properties of Polycrystalline Materials

Machine Learning and Image Processing Techniques for Materials Evaluation

Machine Learning Ferroelectrics: Bayesianity, Parsimony, and Causality

Multivariate Statistical Analysis (MVSA) for Hyperspectral Images

Now On-Demand Only - Computational or Experimental? Interpreting X-ray Absorption and Diffraction Contrast for Massive Non-destructive 3D Grain Mapping of Metals in Laboratory CT

Open-source Hyper-dimensional Materials Analytics Using Hyperspy

Quantitative Comparisons of 2D Microstructures with the Wasserstein Metric

Spatial and Statistical Representation of Strain Localization as a Function of the 3D Microstructure Using Multi-modal and Multi-scale Data Merging

Training Deep-learning Models with 3D Microstructure Images to Predict Location-dependent Mechanical Properties in Additive Manufacturing

Understanding Degradation and Failure Mechanisms by Multiscale and Multiresolution Electron Microscopy

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