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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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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
NASA NTRS1997

The Automated Instrumentation and Monitoring System (AIMS): Design and Architecture

Whether a researcher is designing the 'next parallel programming paradigm', another 'scalable multiprocessor' or investigating resource allocation algorithms for multiprocessors, a facility that enables parallel program execution to be captured and displayed is invaluable. Careful analysis of such information can help computer and software architects to capture, and therefore, exploit behavioral variations among/within various parallel programs to take advantage of specific hardware characteristics. A software tool-set that facilitates performance evaluation of parallel applications on multiprocessors has been put together at NASA Ames Research Center under the sponsorship of NASA's High Performance Computing and Communications Program over the past five years. The Automated Instrumentation and Monitoring Systematic has three major software components: a source code instrumentor which automatically inserts active event recorders into program source code before compilation; a run-time performance monitoring library which collects performance data; and a visualization tool-set which reconstructs program execution based on the data collected. Besides being used as a prototype for developing new techniques for instrumenting, monitoring and presenting parallel program execution, AIMS is also being incorporated into the run-time environments of various hardware testbeds to evaluate their impact on user productivity. Currently, the execution of FORTRAN and C programs on the Intel Paragon and PALM workstations can be automatically instrumented and monitored. Performance data thus collected can be displayed graphically on various workstations. The process of performance tuning with AIMS will be illustrated using various NAB Parallel Benchmarks. This report includes a description of the internal architecture of AIMS and a listing of the source code.

Yan, Jerry C.↗
NASA NTRS1993

Operations Automation Using the Link Monitor and Control Operator Assistant

The Link Monitor & Control Operator Assistant (LMC OA) is a knowledge-based prototype system which uses Artificial Intelligence (AI) techniques to provide semi-automated monitor and control functions to support operations of the Deep Space Network (DSN) 70-Meter antenna at the Goldstone Deep Space Communications Camplex (DSCC).

Goldstone↗
NASA NTRS2014

Evaluating an Automated Approach for Monitoring Forest Disturbances in the Pacific Northwest from Logging, Fire and Insect Outbreaks with Landsat Time Series Data

Forests are the largest aboveground sink for atmospheric carbon (C), and understanding how they change through time is critical to reduce our C-cycle uncertainties. We investigated a strong decline in Normalized Difference Vegetation Index (NDVI) from 1982 to 1991 in Pacific Northwest forests, observed with the National Ocean and Atmospheric Administration's (NOAA) series of Advanced Very High Resolution Radiometers (AVHRRs). To understand the causal factors of this decline, we evaluated an automated classification method developed for Landsat time series stacks (LTSS) to map forest change. This method included: (1) multiple disturbance index thresholds; and (2) a spectral trajectory-based image analysis with multiple confidence thresholds. We produced 48 maps and verified their accuracy with air photos, monitoring trends in burn severity data and insect aerial detection survey data. Area-based accuracy estimates for change in forest cover resulted in producer's and user's accuracies of 0.21 +/- 0.06 to 0.38 +/- 0.05 for insect disturbance, 0.23 +/- 0.07 to 1 +/- 0 for burned area and 0.74 +/- 0.03 to 0.76 +/- 0.03 for logging. We believe that accuracy was low for insect disturbance because air photo reference data were temporally sparse, hence missing some outbreaks, and the annual anniversary time step is not dense enough to track defoliation and progressive stand mortality. Producer's and user's accuracy for burned area was low due to the temporally abrupt nature of fire and harvest with a similar response of spectral indices between the disturbance index and normalized burn ratio. We conclude that the spectral trajectory approach also captures multi-year stress that could be caused by climate, acid deposition, pathogens, partial harvest, thinning, etc. Our study focused on understanding the transferability of previously successful methods to new ecosystems and found that this automated method does not perform with the same accuracy in Pacific Northwest forests. Using a robust accuracy assessment, we demonstrate the difficulty of transferring change attribution methods to other ecosystems, which has implications for the development of automated detection/attribution approaches. Widespread disturbance was found within AVHRR-negative anomalies, but identifying causal factors in LTSS with adequate mapping accuracy for fire and insects proved to be elusive. Our results provide a background framework for future studies to improve methods for the accuracy assessment of automated LTSS classifications.

Landsat↗
NASA NTRS2009

Programs Automate Complex Operations Monitoring

Kennedy Space Center, just off the east coast of Florida on Merritt Island, has been the starting place of every human space flight in NASA s history. It is where the first Americans left Earth during Project Mercury, the terrestrial departure point of the lunar-bound Apollo astronauts, as well as the last solid ground many astronauts step foot on before beginning their long stays aboard the International Space Station. It will also be the starting point for future NASA missions to the Moon and Mars and temporary host of the new Ares series rockets designed to take us there. Since the first days of the early NASA missions, in order to keep up with the demands of the intricate and critical Space Program, the launch complex - host to the large Vehicle Assembly Building, two launch pads, and myriad support facilities - has grown increasingly complex to accommodate the sophisticated technologies needed to manage today s space missions. To handle the complicated launch coordination safely, NASA found ways to automate mission-critical applications, resulting in streamlined decision-making. One of these methods, management software called the Control Monitor Unit (CMU), created in conjunction with McDonnell Douglas Space & Defense Systems, has since left NASA, and is finding its way into additional applications.

Source record↗
DOE OSTI2021

Machine Learning for Automated Weld Quality Monitoring and Control (CRADA 461)

The emergence of Artificial Intelligence (AI) and Machine Learning (ML) methods may provide an alternative pathway to elicit the desired process-structure-property relationship. To achieve such relationship at an accelerated pace the use of data driven machine learning techniques are employed. This CRADA investigates the use and effectiveness of applying AI/ML techniques to a large dataset with the intention of creating a data-driven model that will predict processing parameters required to join lightweight materials at optimal performance.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Machine Learning for Automated Weld Quality Monitoring and Control

Resistance Spot Welding (RSW) is a critical process in the automotive industry, valued for its cost-effectiveness, short cycle time, and robustness. However, achieving consistent high-quality joints remains challenging due to the complex interplay of various factors, like materials, processes, and manufacturing uncertainties, etc. Under the collaborative project between Oak Ridge National Laboratory (ORNL) and General Motors (GM), we have developed a robust and expansible machine learning (ML) framework aimed at enhancing quality control in RSW. By harnessing the power of machine learning, we have developed the ability to ensure every aspect of the welding process, from the initial process design stage to the final weld joint quality. The framework operates by analyzing a variety of data streams, including in-line process signals, process parameters, materials, and postprocessed weld joint data. Through this analysis, the models have been trained to detect deviations from optimal quality standards, leveraging their ability to identify signature data patterns and anomalies within in-line signals and construct complex correlations between these signals and weld quality parameters. Meanwhile, the machine learning framework is designed to adapt to a variety of materials, including high strength steels and aluminum alloys, etc. Its flexible architecture facilitates the incorporation of diverse data sources and features, enabling precise modeling and prediction across a broad range of material properties and weld quality variables. The expansible ML frameworks represent a promising transformation in weld quality monitoring and control, empowering industry to achieve high levels of efficiency, consistency, and reliability in manufacturing.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Machine Learning for Automated Weld Quality Monitoring and Control

Resistance Spot Welding (RSW) is a critical process in the automotive industry, valued for its cost-effectiveness, short cycle time, and robustness. However, achieving consistent high-quality joints remains challenging due to the complex interplay of various factors, like materials, processes, and manufacturing uncertainties, etc. Under the collaborative project between Oak Ridge National Laboratory (ORNL) and General Motors (GM), we have developed a robust and expansible machine learning (ML) framework aimed at enhancing quality control in RSW. By harnessing the power of machine learning, we have developed the ability to ensure every aspect of the welding process, from the initial process design stage to the final weld joint quality. The framework operates by analyzing a variety of data streams, including in-line process signals, process parameters, materials, and postprocessed weld joint data. Through this analysis, the models have been trained to detect deviations from optimal quality standards, leveraging their ability to identify signature data patterns and anomalies within in-line signals and construct complex correlations between these signals and weld quality parameters. Meanwhile, the machine learning framework is designed to adapt to a variety of materials, including high strength steels and aluminum alloys, etc. Its flexible architecture facilitates the incorporation of diverse data sources and features, enabling precise modeling and prediction across a broad range of material properties and weld quality variables. The expansible ML frameworks represent a promising transformation in weld quality monitoring and control, empowering industry to achieve high levels of efficiency, consistency, and reliability in manufacturing.

42 ENGINEERING↗
NASA NTRS1991

MMU applications for automated rendezvous and capture

The Manned Maneuvering Unit (MMU) is a proven free flying platform that can operate in a piloted or unpiloted mode. The MMU is a possible candidate for an on orbit AR&C demonstration. A pilot can transition the system between manual and automated modes, then monitor the automated system for safety.

Whitsett, Edward↗
NASA NTRS1994

Automated Instrumentation and Monitoring of Data Movement for Parallel Programs

Writing efficient parallel programs is complicated by the need to select the right data structure alignments and distributions, which determine the nature and volume of inter-processor communications. A large number of performance tools for parallel programs have been developed recently to expose these inter-processor communications. However, none of them support performance views or provide statistics in terms of inter-processor data structure interactions. A performance tool that tracks the interaction between individual data structures and the context of these interactions is essential for understanding the performance of both explicit message passing programs and data-parallel languages such as HPF. In this paper we discuss the use of compiler front end tools for automatically tracking data structure movements in message passing programs, and low-overhead monitoring and postprocessing of such codes. We demonstrate that robust instrumentation and low overhead monitoring of inter-processor data structure movements is possible, with the use of a number of NAS benchmark codes, run on the i860 hypercube. We also show that the data so collected can be used effectively by post processing tools that expose performance bottlenecks using graphical displays and performance statistics.

Sarukkai, Sekhar↗
NASA NTRS1994

Tuning CAS Application using AIMS: An Automated Instrumentation and Monitoring System

To bring together NASA's scientists and engineers and their counterparts in industry, other government agencies, and academia working in the Computational AeroSciences (CAS) field. This workshop is part of the technology transfer plan of the High Performance Computing and Communications Program (HPCCP). Specific objectives of this Workshop are to: (1) communicate the goals and objectives of HPCCP in the area of CAS; (2) promote and disseminate CAS technology within the appropriate technical communities, including NASA, industry, academia, and other government labs; (3) help promote synergy among CAS scientists; and (4) permit feedback from peer researchers in issues pacing the CAS field in general and the HPCCP CAS program in particular.

Mehra, P.↗
NASA NTRS2003

Science Goal Driven Automation for NASA Missions: The Science Goal Monitor

Infusion of automation technologies into NASA s future missions will be essential not only to achieve substantial reduction in mission operations staff and costs, but also in order to both effectively handle an exponentially increasing volume of scientific data and to successfully meet dynamic, opportunistic scientific goals and objectives. Current spacecraft operations cannot respond to science driven events, such as intrinsically variable or short-lived phenomena in a timely manner. For such investigations, we must teach our platforms to dynamically understand, recognize, and react to the scientists goals. While much effort has gone into automating routine spacecraft operations to reduce human workload and hence costs, applying intelligent automation to the science side, i.e., science data acquisition, data analysis and reactions to that data analysis in a timely and still scientifically valid manner, has been relatively under-emphasized.

Korathkar, Anuradha↗
DOE OSTI2024

Automated Assembly Progress Monitoring in Modular Construction Factories Using Computer Vision-Based Instance Segmentation

Modular construction has recently gained interest as a transformative construction method. In this method, a large portion of the construction is performed inside factories, where processes are fast-paced and interdependent; therefore, any deviation from the schedule can delay the production. Such deviations are frequent in modular factories due to the labor-intensive nature of the tasks. This propagation of delays can be mitigated by continuously monitoring each process; however, current manual monitoring methods are laborious, and recently proposed contact sensor-based methods are intrusive to the work. In addition, recent computer vision-based monitoring methods inside factories are limited to detection algorithms that fail to provide the pixel-level accuracy required for assembly progress monitoring in highly occluded factory scenes, and they require a large number of manual annotations. Therefore, this paper proposes a method to monitor the installation of subassemblies in modular construction factories using mask R-CNN instance segmentation and improves the data efficiency of the model using a copy-paste augmentation method. This method was validated on the CCTV videos captured from a modular construction factory in the US, resulting in a 9% mAP improvement in segmentation.

computer vision↗
NASA NTRS1999

Non-Traditional Displays for Mission Monitoring

Advances in automation capability and reliability have changed the role of humans from operating and controlling processes to simply monitoring them for anomalies. However, humans are traditionally bad monitors of highly reliable systems over time. Thus, the human is assigned a task for which he is ill equipped. We believe that this has led to the dominance of human error in process control activities such as operating transportation systems (aircraft and trains), monitoring patient health in the medical industry, and controlling plant operations. Research has shown, though, that an automated monitor can assist humans in recognizing and dealing with failures. One possible solution to this predicament is to use a polar-star display that will show deviations from normal states based on parameters that are most indicative of mission health.

Trujillo, Anna C.↗
NASA NTRSDate not supplied

Cognitive Engineering in Training: Monitoring and Pilot-Automation Coordination in Complex Environments

This paper reports our investigation of flight path monitoring in aviation. We interviewed experienced pilots to understand the knowledge and skills underlying effective monitoring and we developed an example learning environment to improve these skills. We explore how design of pilot training and learning, like the design of interfaces and of the underlying automation, benefits from cognitive engineering methods and perspective. In aviation, monitoring and managing flight path are critical activities. The influences on flight path are complex and come from the autoflight system, from control actions by the pilot, and from external factors, including weather and Air Traffic Control (ATC). Indeed, inadequate flight path monitoring is a current aviation concern as it has been implicated in accidents and incidents. Effective piloting depends on strategies for noticing, understanding, and anticipating these influences to monitor and manage flight path. Lack of such skills reduces pilots' ability to maintain safety margin and resilience. Although flightdeck automation is intended to aid pilot understanding and prediction, the Fight Management Systems (FMS) can mislead as well as aid the pilot's understanding and projection of what will happen. In dynamic conditions, FMS predictions may be based on old or incomplete information. Understanding such vulnerabilities is an important part of pilot-autoflight coordination. The learning environment we developed is designed to help pilots proactively monitor and manage flight path. We consider how a broad cognitive engineering approach might inform the "what" and "how" of learning in dynamic work domains.

pilot-monitoring↗
DOE OSTI2024

Automated Framework for Groundwater Monitoring Using DWT with LSTM and Transformers

Environmental monitoring is critical for safeguarding public health and ecological well-being. Traditional data structuring and workflow monitoring methods consume significant time and effort, hindering timely insights and effective decision-making. Our study addresses this challenge by presenting an AI framework that automates data cleaning, structuring, and modeling processes, specifically targeting applications in groundwater monitoring. By leveraging automation for data processing and model training, our framework establishes a novel and efficient paradigm for environmental monitoring, with its potential application to the vast network of over a hundred Department of Energy Environmental Management (DoE-EM) cleanup sites across the country. It analyzes data streams from a network of groundwater Internet-of-Things (IoT) sensors deployed at the Savannah River Site (SRS) for prediction modeling. This allows human experts to focus on analysis and decision-making, ultimately leading to better environmental outcomes.The framework employs multivariate time-series forecasting methods to study and model the behavior of varying chemical analytes. The continuous learning process is enabled by utilizing deep learning techniques. It allows the framework to become more nuanced in its analysis over time, adapting to the specific characteristics of the environmental site and the evolving nature of contaminant behavior. Deep learning models known for sequence modeling, LSTM, and Transformers are employed for time series forecasting. Data processing and structuring are essential components significantly impacting the final model's performance. This hypothesis was proven by presenting a comparative analysis of model performance with processed and unprocessed data. The feature engineering approach utilized was the Discrete Wavelet Transform, which works well with time series data.

Discrete Wavelet Transform (DWT)↗