Engineering topics

Moore, Emily R.

Publications and source records attributed to Moore, Emily R..

3 records

DOE OSTI2022

Seascape Interface Control Document

This paper serves as the Interface Control Document (ICD) for the Seascape automated test harness developed at Sandia National Laboratories. The primary purposes of the Seascape system are: (1) provide a place for accruing large, curated, labeled data sets useful for developing and evaluating detection and classification algorithms (including, but not limited to, supervised machine learning applications) (2) provide an automated structure for specifying, running and generating reports on algorithm performance. Seascape uses GitLab, Nexus, Solr, and Banana, open source software, together with code written in the Python language, to automatically provision and configure computational nodes, queue up jobs to accomplish algorithms test runs against the stored data sets, gather the results and generate reports which are then stored in the Nexus artifact server.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Seascape Interface Control Document

This all-inclusive document describes the components, installation, and usage of the Seascape system. Additionally, this manual outlines the step-by-step processes for setting up your own local instance of Seascape, incorporating new datasets and algorithms into Seascape, and how to use the system itself. A brief overview of Seascape is provided in Section 1.2. System components and the various roles of the intended users of the system are described in Section 1.3. Next, steps on how each role uses Seascape are explained in Section 2.1. Finally, the steps to incorporate data into Seascape-DB and an algorithm into Seascape-VV are outlined in Sections 2.2 and 2.3, respectively. Steps to set up an instance of Seascape can be found in Appendix A.1. Finally, Seascape usage can be found in Section 2.1. The appendix includes code examples, frequently asked questions, terminology, and a list of acronyms.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Machine Learning for Correlated Intelligence. LDRD SAND Report

The Machine Learning for Correlated Intelligence Laboratory Directed Research & Development (LDRD) Project explored competing a variety of machine learning (ML) classification techniques against a known, open source dataset through the use of a rapid and automated algorithm research & development (RD) infrastructure. This approach relied heavily on creating an infrastructure in which to provide a pipeline for automatic target recognition (ATR) ML algorithm competition. Results are presented for nine ML classifiers against a primary dataset using the pipeline infrastructure developed for this project. New approaches to feature set extraction are presented and discussed as well.

97 MATHEMATICS AND COMPUTING↗