Developing a Relationship Between LIBS Spectra and Pit Volume for in Situ Dating of Geologic Samples
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The utilization of machine learning techniques has become commonplace in the analysis of optical emission spectra. These methods are often limited to variants of principal components analysis (PCA), partial-least squares (PLS), and artificial neural networks (ANNs). A plethora of other techniques exist and are well established in the world of data science, yet are seldom investigated for their use in spectroscopic problems. In this study, machine learning techniques were used to analyze optical emission spectra of laser-induced plasma from ceria pellets doped with silicon in order to predict silicon content. Additionally, a boosted regression ensemble model was created, and its predictive accuracy was compared to that of traditional PCA, PLS, and ANN regression models. Boosted regression tree ensembles yielded fits with R-squared (R2) values as high as 0.964 and mean-squared errors of prediction (MSEPs) as low as 0.074, providing the most accurate predictive model. Neural networks performed with slightly lower R2 values and higher MSEPs compared to the ensemble methods, thus indicating susceptibility to overfitting.
Automated NGFC system design tool set to develop optimized, next-generation solid oxide fuel cell (SOFC) based natural gas fuel cell (NGFC) system designs with >70% fuel efficiency and <30 g/kWh CO2 emissions
We will employ a machine learning approach with intelligent data mining and database construction to analyze enormous data repositories for identifying and extracting geographic-dependent cell design specifications from publicly accessible grid-scale energy storage usage databases in an automated way at scale.
This dataset contains covariance libraries created for SCALE. These will be distributed by providing documentation and metadata in GitLab repos hosted by ORNL (code.ornl.gov/scale/data/cov-libs), and the main datasets are hosted in S3-based ORNL servers. The data are also contained in Constellation. More documentation can be found in PUB ID 263037.
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Charger design that is 2-fault tolerant to catastrophic has been achieved for the Spacesuit Li-ion Battery with key features. Power supply control circuit and 2 microprocessors independently control against overcharge. 3 microprocessor control against undercharge (false positive: Go for EVA) conditions. 2 independent channels provide functional redundancy. Capable of charge balancing cell banks in series. Cell manufacturing and performance uniformity is excellent with both designs. Once a few outliers are removed, LV cells are slightly more uniform than MoliJ cells. If cell balance feature of charger is ever invoked, it will be an indication of a significant degradation issue, not a nominal condition.
In planetary exploration, in situ absolute geochronology is an important measurement. Thus far, on Mars, the age of the surface has largely been determined by crater density counting, which gives relative ages. These ages can have significant uncertainty as they depend on many poorly constrained parameters. More than that, the curves must be tied to absolute ages to relate geologic timescales on Mars to the rest of the solar system. Thus far, only the lost lander Beagle 2 was designed to conduct absolute geochronology measurements, though some recent attempts using MSL Curiosity show that this investigation is feasible (Reference Farley here) and should be strongly encouraged for future flight.
Absolute age determination isnecessary to check and calibratethe relative Martian chronologypresently available from meteoriticcrater counting.
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Chapter 9 from the book "Laser-Induced Breakdown Spectroscopy"
SciX 2021 Conference, Providence, RI, September 26 - October 1, 2021