Special Coverage

Iodine-Compatible Hall Effect Thruster
Precision Assembly of Systems on Surfaces (PASS)
Development of a Novel Electrospinning System with Automated Positioning and Control Software
2016 Create The Future Design Contest Open For Entries
Clamshell Sampler
Shape Memory Alloy Rock Splitter
Deployable Extra-Vehicular Activity Platform (DEVAP) for Planetary Surfaces
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Anomaly Detection in Large Sets of High-Dimensional Symbol Sequences (sequenceMiner)

Ames Research Center, Moffett Field, California SequenceMiner was developed to address the problem of detecting and describing anomalies in large sets of high-dimensional symbol sequences. sequenceMiner works by performing unsupervised clustering (grouping) of sequences using the normalized longest common subsequence (LCS) as a similarity measure, followed by a detailed analysis of outliers to detect anomalies. sequenceMiner utilizes a new hybrid algorithm for computing the LCS that has been shown to outperform existing algorithms by a factor of five. sequenceMiner also includes new algorithms for outlier analysis that provide comprehensible indicators as to why a particular sequence was deemed to be an outlier. This provides analysts with a coherent description of the anomalies identified in the sequence, and why they differ from more normal sequences.

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Time-Scaled Activity Diagram Simulator

NASA’s Jet Propulsion Laboratory, Pasadena, California The Time-Scaled Activity Diagram Simulator software simulates and animates time-tagged events as an overlay on activity and internal block diagrams in MagicDraw as a plugin application. “Events” are quite generic and include atomic actions, compound actions, signal and message exchanges, invocations, and more. Events are specified in a schedule in a text file, which is ingested by the plug-in application. Concurrent and synchronized execution specified in the event schedule is illustrated within and across diagrams. Events correspond to nodes and edges of hierarchical activity models and corresponding structural decomposition. This software can be used to visually communicate the execution and timeline of any collection of scheduled events, including business, software, and spacecraft processes and procedures, and is applicable to most if not all missions.

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General-Use Nodal Network Solver (GUNNS)

GUNNS is a flow system modeling software package that combines nodal analysis and the hydraulic-electric analogy to simulate fluid, electrical, and thermal systems. Lyndon B. Johnson Space Center, Houston, Texas Crewed space vehicle training has always required highfidelity models of vehicle systems. Many of these systems, particularly of the “flow” variety, benefit from modeling the system as a whole, rather than relying only on the interactions between individual components. In general, system properties (temperatures, voltages, flow rates, etc.) are modeled both internal to and between physical components (valves, batteries, etc.). Lacking knowledge of the system atlarge, integration of flows between individual components is extremely difficult without incurring model instability and errors. The same analogous problem applies among fluid, thermal, and electrical systems. Nodal analysis is a method commonly used in industry to avoid this problem by representing these systems as a simultaneous system of equations.

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Modularized SIR-to-NetCDF Conversion Command-Line Tool

NASA’s Jet Propulsion Laboratory, Pasadena, California This innovation reads in a proprietary formatted binary data format known as Scatterometer Image Reconstruction (SIR), and converts these data files to netCDF-classic format. This software is provided as a command-line tool for Linux/UNIX-based systems, and includes built-in regression testing and command line options. The software is configured to output the netCDF files using a metadata conformance to Attribute Convention for Dataset Discovery (ACDD) and Climate and Forecasting (CF) version 1.6. This software is initially tested and designed for SIR files created for datasets originating from QuikSCAT and SeaWinds on ADEOS-II.

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Discovery Through Eigenbasis Modeling of Uninteresting Data

The system learns only what to ignore, reducing the possibility of missing the items of interest. NASA’s Jet Propulsion Laboratory, Pasadena, California When presented with a new data set, a common initial goal is to explore its contents in a discovery mode to find items of interest. However, each user who views the data set may have a different scientific goal in mind, and therefore a different desired prioritization of the items for examination. Further, as the users explore more of the data set, they accumulate concrete examples of what is or is not of interest. The goal of this work was to formalize this iterative approach to understanding large data sets, and instantiate it with methods capable of the necessary adaptation as the system iteratively acquires user feedback.

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Obstacle Avoidance Methods

NASA’s Jet Propulsion Laboratory, Pasadena, California Obstacle avoidance is a difficult problem due to the non-convex state constraints. Therefore, the feasible state space needs to be convexified, or split, into convex regions at which point the search for an optimal solution among those convex regions is done. Methods for obstacle avoidance include two mixed integer linear programming (MILP) methods (obstacle related method and path-related method) and a state-constraint convexification method.

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Bar Code Scanner to Supplement Property Inventory Process

There is no longer a need for any paper check-off sheets. Goddard Space Flight Center, Greenbelt, Maryland Within NASA Goddard, property inventories outside of the formal inventories had no automated method to perform quick checks of equipment location, especially during periods leading up to a formal inventory. One method that existed simply involved printing out an end-user’s equipment in a hard-copy form, and having each employee search for and annotate the results on the hard copy. These hard copies would then have to be returned to the property custodian. Information on the new whereabouts of equipment may or may not have been updated. This approach also relied on an honor system and the work styles of different individuals. Sometimes, multiple reminders needed to be sent to some employees to complete their inventory. In other cases, inventory was made more difficult because of the location of the equipment. A lot of property consists of test equipment in a lab environment. In this case, equipment is sometimes hard to access or grouped with numerous other pieces. These cases make it difficult to read off barcode numbers.

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