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At least 19 records

Attitude Determination and Control System (ADCS) and Maintenance and Diagnostic System (MDS): A maintenance and diagnostic system for Space Station Freedom

The Maintenance and Diagnostic System (MDS) that is being developed at Honeywell to enhance the Fault Detection Isolation and Recovery system (FDIR) for the Attitude Determination and Control System on Space Station Freedom is described. The MDS demonstrates ways that AI-based techniques can be used to improve the maintainability and safety of the Station by helping to resolve fault anomalies that cannot be fully determined by built-in-test, by providing predictive maintenance capabilities, and by providing expert maintenance assistance. The MDS will address the problems associated with reasoning about dynamic, continuous information versus only about static data, the concerns of porting software based on AI techniques to embedded targets, and the difficulties associated with real-time response. An initial prototype was built of the MDS. The prototype executes on Sun and IBM PS/2 hardware and is implemented in the Common Lisp; further work will evaluate its functionality and develop mechanisms to port the code to Ada.

Toms, David

The MDS autonomous control architecture

We describe the autonomous control architecture for the JPL Mission Data System (MDS). MDS is a comprehensive new software infrastructure for supporting unmanned space exploration. The autonomous control architecture is one component of MDS designed to enable autonomous operations.

MDS autonomous control unmanned space exploration

Materials Data Science Ontology(MDS-Onto): Unifying Domain Knowledge in Materials and Applied Data Science

Ontologies have gained popularity in the scientific community as a way to standardize terminologies in organizations’ data. Although certain cohorts have created frameworks with rules and guidelines on creating ontologies, there exist significant variations in how Materials Science ontologies are currently developed. We seek to provide guidance in the form of a unified automated framework for developing interoperable and modular ontologies for Materials Data Science that simplifies the ontology terms matching by establishing a semantic bridge up to the Basic Formal Ontology(BFO). This framework provides key recommendations on how ontologies should be positioned within the semantic web, what knowledge representation language is recommended, and where ontologies should be published online to boost their findability and interoperability. Two fundamental components of the MDS-Onto framework are the bilingual package called FAIRmaterials for ontology creation and FAIRLinked, for FAIR data creation. To showcase the practical capabilities of FAIRmaterials, we present two exemplar domain ontologies of MDS-Onto: Synchrotron X-Ray Diffraction and Photovoltaics.

29 ENERGY PLANNING, POLICY, AND ECONOMY

Calibrating the WFPC2 astrometry for MDS

The Hubble Space Telescope (HST) Medium Deep Survey has optimized an automated procedure to associate and stack WFPC2 data. The coordinates listed in HST WFPC2 image and/or jitter file headers are often found to be insufficient to stack images for cosmic ray removal. We discuss results from software developed for the HST Medium Deep Survey (MDS) pipeline to evaluate these shifts by cross-correlation of the images. We will also discuss the distortion of the pre-cool-down WFPC2 field and attempts to derive absolute coordinates for MDS WFPC2 observations using the USNO-Al.0 half billion object catalog.

Ratnatunga, Kavan U.

MDS MIC Catalog Inputs

This viewgraph presentation reviews the inputs to the MDS Medical Information Communique (MIC) catalog. The purpose of the group is to provide input for updating the MDS MIC Catalog and to request that MMOP assign Action Item to other working groups and FSs to support the MITWG Process for developing MIC-DDs.

Johnson-Throop, Kathy A.

A lower bound for the decoder error probability of the linear MDS code

A lower bound for the decoder error probability (P sub E (u)) of a linear maximum distance separable (MDS) code is derived by counting the dominant types of decoding words around code words. It is shown that the lower bound derived is similar in form, and close numerically, to the upper bound derived.

Cheung, K.-M.

Additive Construction with Mobile Emplacement (ACME) / Automated Construction of Expeditionary Structures (ACES) Materials Delivery System (MDS)

The purpose of the Automated Construction of Expeditionary Structures, Phase 3 (ACES 3) project is to incorporate the Liquid Goods Delivery System (LGDS) into the Dry Goods Delivery System (DGDS) structure to create an integrated and automated Materials Delivery System (MDS) for 3D printing structures with ordinary Portland cement (OPC) concrete. ACES 3 is a prototype for 3-D printing barracks for soldiers in forward bases, here on Earth. The LGDS supports ACES 3 by storing liquid materials, mixing recipe batches of liquid materials, and working with the Dry Goods Feed System (DGFS) previously developed for ACES 2, combining the materials that are eventually extruded out of the print nozzle. Automated Construction of Expeditionary Structures, Phase 3 (ACES 3) is a project led by the US Army Corps of Engineers (USACE) and supported by NASA. The equivalent 3D printing system for construction in space is designated Additive Construction with Mobile Emplacement (ACME) by NASA.

Mueller, R. P.

Tracking system performance tests in the MDS era

Tracking system performance tests as developed to support DSN Mark III Data Subsystem implementation project and prepass readiness tests are described. The system test design, the rationale chosen for implementation, and the inevitable compromises imposed by schedule constraints and reserves are discussed.

Buckles, B. J.

Multidimensional scaling informed by F -statistic: Visualizing grouped microbiome data with inference

Multidimensional scaling (MDS) is a widely used dimensionality reduction technique in microbial ecology data analysis that captures the multivariate structure of the data while preserving pairwise distances between samples. While improvements in MDS have enhanced the ability to reveal group-specific data patterns, these MDS-based methods require prior assumptions for inference, limiting their application in general microbiome analysis. Here, in this study, we introduce a new MDS-based ordination method, “F-informed MDS,” which configures the data distribution based on the F-statistic, the ratio of dispersion between groups sharing common and different characteristics. Using semisynthetic datasets, we demonstrate that the proposed method is robust to hyperparameter selection while maintaining statistical significance throughout the ordination process. Various quality metrics for evaluating dimensionality reduction confirm that F-informed MDS is comparable to state-of-the-art methods in preserving both local and global data structures. Its application to a diatom-associated bacterial community suggests the role of this new method in interpreting the community’s response to the host. Our approach offers a well-founded refinement of MDS that aligns with statistical test results, which can be beneficial for broader multidimensional data analyses in microbiology and ecology. This new visualization tool can be incorporated into standard microbiome data analyses.

Biological and medical sciences