Automating the Training Development Process
Automating the task of training development always sounded like a great idea, and recently, a system was developed that actually does just that.
ATDS↗
SEARCH · Search NASA
Results for “Process Automation”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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APS Data Management System
As the capabilities of modern X-ray detectors and acquisition technologies increase, so do the data rates and volumes produced at synchrotron beamlines. This brings into focus a number of challenges related to managing data at such facilities, including data transfer, near real-time data processing, automated processing pipelines, data storage, handling metadata and remote user access to data. The Advanced Photon Source Data Management System software is designed to help beamlines deal with these issues. Finally, this paper presents the system architecture and describes its components and functionality; the system's current usage is discussed, examples of its use have been provided and future development plans are outlined.
97 MATHEMATICS AND COMPUTING↗
Automated data processing architecture for the Gemini Planet Imager Exoplanet Survey
The Gemini Planet Imager Exoplanet Survey (GPIES) is a multiyear direct imaging survey of 600 stars to discover and characterize young Jovian exoplanets and their environments. We have developed an automated data architecture to process and index all data related to the survey uniformly. An automated and flexible data processing framework, which we term the Data Cruncher, combines multiple data reduction pipelines (DRPs) together to process all spectroscopic, polarimetric, and calibration data taken with GPIES. With no human intervention, fully reduced and calibrated data products are available less than an hour after the data are taken to expedite follow up on potential objects of interest. The Data Cruncher can run on a supercomputer to reprocess all GPIES data in a single day as improvements are made to our DRPs. A backend MySQL database indexes all files, which are synced to the cloud, and a front-end web server allows for easy browsing of all files associated with GPIES. To help observers, quicklook displays show reduced data as they are processed in real time, and chatbots on Slack post observing information as well as reduced data products. Together, the GPIES automated data processing architecture reduces our workload, provides real-time data reduction, optimizes our observing strategy, and maintains a homogeneously reduced dataset to study planet occurrence and instrument performance.
79 ASTRONOMY AND ASTROPHYSICS↗
An Automated Sample Processing System for Planetary Exploration
An Automated Sample Processing System (ASPS) for wet chemistry processing of organic materials on the surface of Mars has been jointly developed by Ball Aerospace and the Jet Propulsion Laboratory. The mechanism has been built and tested to demonstrate TRL level 4. This paper describes the function of the system, mechanism design, lessons learned, and several challenges that were overcome.
traditional mechanical components↗
The Status and Promise of Advanced M&V: An Overview of “M&V 2.0” Methods, Tools, and Applications
Advanced measurement and verification (M&V) of energy efficiency savings, often referred to as M&V 2.0 or advanced M&V, is currently an object of much industry attention. Thus far, however, there has been a lack of clarity about what techniques M&V 2.0 includes, how those techniques differ from traditional approaches, what the key considerations are for their use, and what value propositions M&V 2.0 presents to different stakeholders. The objective of this paper is to provide background information and frame key discussion points related to advanced M&V. The paper identifies the benefits, methods, and requirements of advanced M&V and outlines key technical issues for applying these methods. It presents an overview of the distinguishing elements of M&V 2.0 tools and of how the industry is addressing needs for tool testing, consistency, and standardization, and it identifies opportunities for collaboration. In this paper, we consider two key features of M&V 2.0: (1) automated analytics that can provide ongoing, near-real-time savings estimates, and (2) increased data granularity in terms of frequency, volume, or end-use detail. Greater data granularity for large numbers of customers, such as that derived from comprehensive implementation of advanced metering infrastructure (AMI) systems, leads to very large data volumes. This drives interest in automated processing systems. It is worth noting, however, that automated processing can provide value even when applied to less granular data, such as monthly consumption data series. Likewise, more granular data, such as interval or end-use data, delivers value with or without automated processing, provided the processing is manageable. But it is the combination of greater data detail with automated processing that offers the greatest opportunity for value. Using M&V methods that capture load shapes together with automated processing1 can determine savings in near-real time to provide stakeholders with more timely and detailed information. This information can be used to inform ongoing building operations, provide early input on energy efficiency program design, or assess the impact of efficiency by location and time of day. Stakeholders who can make use of such information include regulators, energy efficiency program administrators, program evaluators, contractors and aggregators, building owners, the investment community, and grid planners. Although each stakeholder has its own priorities and challenges related to savings measurement and verification, the potential exists for all to draw from a single set of efficiency valuation data. Such an integrated approach could provide a base consistency across stakeholder uses.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Automated space processing payloads study. Volume 2, book 1: Technical report
The extent was investigated to which experiment hardware and operational requirements can be met by automatic control and material handling devices; payload and system concepts that make extensive use of automation technology are defined. Hardware requirements for each experiment were established and tabulated, and investigations of applicable existing hardware were documented. The capabilities and characteristics of industrial automation equipment, controls, and techniques are presented in the form of a summary of applicable equipment characteristics in three basic mutually-supporting formats. Facilities for performing groups of experiments are defined along with four levitation groups and three furnace groups; major hardware elements required to implement them were identified. A conceptual design definition of ten different automated processing facilities is presented along with the specific equipment to implement each facility and the design layouts of the different units. Constraints and packaging, weight, and power requirements for six payloads postulated for shuttle missions in the 1979 to 1982 time period were examined.
Source record↗
Automation and Process Improvement Enables a Small Team to Operate a Low Thrust Mission in Orbit Around the Asteroid Vesta
NASA's Dawn mission to the asteroid Vesta and dwarf planet Ceres launched September 27, 2007 and arrived at Vesta in July of 2011. This mission uses ion propulsion to achieve the necessary delta-V to reach and maneuver at Vesta and Ceres. This paper will show how the evolution of ground system automation and process improvement allowed a relatively small engineering team to transition from cruise operations to asteroid operations while maintaining robust processes. The cruise to Vesta phase lasted almost 4 years and consisted of activities that were built with software tools, but each tool was open loop and required engineers to review the output to ensure consistency. Additionally, this same time period was characterized by the evolution from manually retrieved and reviewed data products to automatically generated data products and data value checking. Furthermore, the team originally took about three to four weeks to design and build about four weeks of spacecraft activities, with spacecraft contacts only once a week. Operations around the asteroid Vesta increased the tempo dramatically by transitioning from one contact a week to three or four contacts a week, to fourteen contacts a week (every 12 hours). This was accompanied by a similar increase in activity complexity as well as very fast turn around activity design and build cycles. The design process became more automated and the tools became closed loop, allowing the team to build more activities without sacrificing rigor. Additionally, these activities were dependent on the results of flight system performance, so more automation was added to analyze the flight data and provide results in a timely fashion to feed the design cycle. All of this automation and process improvement enabled up the engineers to focus on other aspects of spacecraft operations, including spacecraft health monitoring and anomaly resolution.
ground system automation↗
HyperKube: A Kubernetes Based System for the Automation of Processing and Analysis of Hyperspectral Data Obtained from Multiple Hyperspectral Imaging Systems
Hyperspectral imagery is an emerging field of technology that has enormous potential for remote and proximal sensing in numerous areas of research. The plant phenotyping community is applying this technology to advance the throughput and accuracy of plant phenotypes based on airborne and lab-based hyperspectral imaging technology. Here we report an automated processing and analysis pipeline for four different hyperspectral imaging platforms, discuss the data issues involved, and present a strategy for computing and data architecture to handle hyperspectral data.
47 OTHER INSTRUMENTATION↗
Non-Contact Conductivity Measurement for Automated Sample Processing Systems
A new method has been developed for monitoring and control of automated sample processing and preparation especially focusing on desalting of samples before analytical analysis (described in more detail in Automated Desalting Apparatus, (NPO-45428), NASA Tech Briefs, Vol. 34, No. 8 (August 2010), page 44). The use of non-contact conductivity probes, one at the inlet and one at the outlet of the solid phase sample preparation media, allows monitoring of the process, and acts as a trigger for the start of the next step in the sequence (see figure). At each step of the muti-step process, the system is flushed with low-conductivity water, which sets the system back to an overall low-conductivity state. This measurement then triggers the next stage of sample processing protocols, and greatly minimizes use of consumables. In the case of amino acid sample preparation for desalting, the conductivity measurement will define three key conditions for the sample preparation process. First, when the system is neutralized (low conductivity, by washing with excess de-ionized water); second, when the system is acidified, by washing with a strong acid (high conductivity); and third, when the system is at a basic condition of high pH (high conductivity). Taken together, this non-contact conductivity measurement for monitoring sample preparation will not only facilitate automation of the sample preparation and processing, but will also act as a way to optimize the operational time and use of consumables
Beegle, Luther W.↗
An Updated Process for Automated Deepspace Conjunction Assessment
There is currently a high level of interest in the areas of conjunction assessment and collision avoidance from organizations conducting space operations. Current conjunction assessment activity is mainly focused on spacecraft and debris in the Earth orbital environment [1]. However, collisions are possible in other orbital environments as well [2]. This paper will focus on the current operations of and recent updates to the Multimission Automated Deep Space Conjunction Assessment Process (MADCAP) used at the Jet Propulsion Laboratory for NASA to perform conjunction assessment at Mars and the Moon. Various space agencies have satellites in orbit at Mars and the Moon with additional future missions planned. The consequences of collisions are catastrophically high. Intuitive notions predict low probability of collisions in these sparsely populated environments, but may be inaccurate due to several factors. Orbits of scientific interest often tend to have similar characteristics as do the orbits of spacecraft that provide a communications relay for surface missions. The MADCAP process is controlled by an automated scheduler which initializes analysis based on a set timetable or the appearance of new ephemeris files either locally or on the Deep Space Network (DSN) Portal. The process then generates and communicates reports which are used to facilitate collision avoidance decisions. The paper also describes the operational experience and utilization of the automated tool during periods of high activity and interest such as: the close approaches of NASA's Lunar Atmosphere & Dust Environment Explorer (LADEE) and Lunar Reconnaissance Orbiter (LRO) during the LADEE mission. In addition, special consideration was required for the treatment of missions with rapidly varying orbits and less reliable long term downtrack estimates; in particular this was necessitated by perturbations to MAVEN's orbit induced by the Martian atmosphere. The application of special techniques to non-operational spacecraft with large uncertainties is also studied. Areas for future work are also described. Although the applications discussed in this paper are in the Martian and Lunar environments, the techniques are not unique to these bodies and could be applied to other orbital environments.
collision↗
Johnson Space Center Research and Technology Annual Report 1998-1999
As the principle center for NASA's Human Exploration and Development of Space (HEDS) Enterprise, the Johnson Space Center (JSC) leads NASA development of human spacecraft, human support systems, and human spacecraft operations. An important element in implementing this mission, JSC has focused on developing the infrastructure and partnerships that enable the technology development for future NASA programs. In our efforts to develop key technologies, we have found that collaborative relationships with private industry and academia strengthen our capabilities, infuse innovative ideas, and provide alternative applications for our development projects. The American public has entrusted NASA with the responsibility for space technology development, and JSC is committed to the transfer of the technologies that we develop to the private sector for further development and application. It is our belief that commercialization of NASA technologies benefits both American industry and NASA through technology innovation and continued partnering. To this end, we present the 1998-1999 JSC Research and Technology Report. As your guide to the current JSC technologies, this report showcases the projects in work at JSC that may be of interest to U.S. industry, academia, and other government agencies (federal, state, and local). For each project, potential alternative uses and commercial applications are described. To aid in your search, projects are arranged according to the Major Product Groups used by CorpTech to classify and index types of industry. Some projects fall into multiple categories and are placed under the predominant category, for example, an artificial intelligence project is listed under the Computer Software category, while its function is to automate a process (Automation category).
Abbey, George W. S.↗
SolarAPP+ ® (Solar Automated Permit Processing Plus) [SWR-19-51]
SolarAPP+ ®: Safe and affordable home solar through permitting automation. NREL has launched a collaborative effort with key code officials, authorities having jurisdiction (AHJs), and the solar industry to develop an instant online solar permitting platform for code compliant residential systems. SolarAPP (Solar Automated Permit Processing) will: -Provide a flexible, web-based PV-permitting tool for residential systems at no-cost to AHJs. -Enable standardization of instant permitting processes. -Evaluate applications for safety and code compliance. -Offer opportunities to incorporate energy storage and expand to other market segments.
Cook, Jeffrey↗
Engineering, construction, and operations in space II; Proceedings of Space 90, the Second International Conference, Albuquerque, NM, Apr. 22-26, 1990. Vols. 1 & 2
Attention is given to such topics as the processing of lunar soils, the automated processing of extraterrestrial materials, lunar excavation and mining, lunar oxygen, lunar base development, lunar surface structures, planetary surface transportation, and Mars missions. Papers are also presented on the Space Station, space debris, robotics and automated processing, structures and structural response, Mission to Planet Earth, life support systems, space power, human factors, artificial gravity, and education.
Johnson, Stewart W.↗
Solar Automated Permit Processing Software for Distributed PV (Cooperative Research and Development Final Report, CRADA Number CRD-19-00825)
NREL and its project partners built and commercialized the novel Solar Automated Permitting Processing Plus (SolarAPP+) software platform that delivers instant residential rooftop solar permits. The tool provides installers with a standard portal for entering permit information for residential solar systems across all Authorities Having Jurisdiction (AHJs) using SolarAPP+, thereby providing a streamlined plan review form, transparency into permitting timelines, and required specifications. The project has proven to reduce permitting timelines from as many as 20 business days to zero, delivering solar projects faster in the four participating AHJs using the platform. Streamlining permitting timelines, all else equal, results in consumers installing their systems and reducing their electricity bills faster. The continued expansion of SolarAPP+ features and adoption nationwide will expand the benefits of the SolarAPP+ beyond the current user base.
14 SOLAR ENERGY↗
Automated Space Processing Payloads Study. Volume 1: Executive Summary
An investigation is described which examined the extent to which the experiment hardware and operational requirements can be met by automatic control and material handling devices; payload and system concepts are defined which make extensive use of automation technology. Topics covered include experiment requirements and hardware data, capabilities and characteristics of industrial automation equipment and controls, payload grouping, automated payload conceptual design, space processing payload preliminary design, automated space processing payloads for early shuttle missions, and cost and scheduling.
Source record↗
Knowledge Acquisition, Validation, and Maintenance in a Planning System for Automated Image Processing
A key obstacle hampering fielding of AI planning applications is the considerable expense of developing, verifying, updating, and maintainting the planning knowledge base (KB). Planning systems must be able to compare favorably in terms of software lifecycle costs to other means of automation such as scripts or rule-based expert systems. This paper describes a planning application of automated imaging processing and our overall approach to knowledge acquisition for this application.
artificial intelligence image processing knowledge↗
ORNL automates key process in plutonium-238 production
Oak Ridge National Laboratory scientists have automated part of the process of producing plutonium-238, which is used by NASA to fuel deep space exploration. Resolving this key bottleneck will help boost annual production of the radioisotope towards NASA’s goal of 1.5 kilograms of Pu-238 per year by 2025.
07 ISOTOPE AND RADIATION SOURCES↗
Fission gas bubble identification using MATLAB's image processing toolbox
Automated image processing routines have the potential to aid in the fuel performance evaluation process by eliminating bias in human judgment that may vary from person-to-person or sample-to-sample. This study presents several MATLAB based image analysis routines designed for fission gas void identification in post-irradiation examination of uranium molybdenum (U–Mo) monolithic-type plate fuels. Frequency domain filtration, enlisted as a pre-processing technique, can eliminate artifacts from the image without compromising the critical features of interest. This process is coupled with a bilateral filter, an edge-preserving noise removal technique aimed at preparing the image for optimal segmentation. Adaptive thresholding proved to be the most consistent gray-level feature segmentation technique for U–Mo fuel microstructures. The Sauvola adaptive threshold technique segments the image based on histogram weighting factors in stable contrast regions and local statistics in variable contrast regions. Once all processing is complete, the algorithm outputs the total fission gas void count, the mean void size, and the average porosity. The final results demonstrate an ability to extract fission gas void morphological data faster, more consistently, and at least as accurately as manual segmentation methods. - Highlights: •Automated image processing can aid in the fuel qualification process. •Routines are developed to characterize fission gas bubbles in irradiated U–Mo fuel. •Frequency domain filtration effectively eliminates FIB curtaining artifacts. •Adaptive thresholding proved to be the most accurate segmentation method. •The techniques established are ready to be applied to large scale data extraction testing.
36 MATERIALS SCIENCE↗