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At least 163 records · Page 9

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NASA NTRS2009

Modeling, Detection, and Disambiguation of Sensor Faults for Aerospace Applications

Sensor faults continue to be a major hurdle for systems health management to reach its full potential. At the same time, few recorded instances of sensor faults exist. It is equally difficult to seed particular sensor faults. Therefore, research is underway to better understand the different fault modes seen in sensors and to model the faults. The fault models can then be used in simulated sensor fault scenarios to ensure that algorithms can distinguish between sensor faults and system faults. The paper illustrates the work with data collected from an electro-mechanical actuator in an aerospace setting, equipped with temperature, vibration, current, and position sensors. The most common sensor faults, such as bias, drift, scaling, and dropout were simulated and injected into the experimental data, with the goal of making these simulations as realistic as feasible. A neural network based classifier was then created and tested on both experimental data and the more challenging randomized data sequences. Additional studies were also conducted to determine sensitivity of detection and disambiguation efficacy to severity of fault conditions.

Balaban, Edward↗

NASA NTRS2010

SSME Engine Controller Development and Evolution

The SSME engine controller is a redundant, micro-processor based control unit. The controller along with its embedded software provides closed loop control of the engine s thrust and mixture ratio during flight as well as configuring the engine state and operations during non-flight activities such as checkouts, and pre-start engine conditioning. It processes orbiter commands into engine actions and provides engine state and health information to the orbiter for immediate action, telemetry and recording. In addition the controller includes functions which continually monitor and manage engine health, and can activate a variety of fault mitigation actions to insure safe engine operation. The objective of this paper is to provide a description of the SSME engine controller as well as its developmental history and lessons learned

Abrams, Russ↗

NASA NTRS2013

NASA Software Engineering Benchmarking Study

To identify best practices for the improvement of software engineering on projects, NASA's Offices of Chief Engineer (OCE) and Safety and Mission Assurance (OSMA) formed a team led by Heather Rarick and Sally Godfrey to conduct this benchmarking study. The primary goals of the study are to identify best practices that: Improve the management and technical development of software intensive systems; Have a track record of successful deployment by aerospace industries, universities [including research and development (R&D) laboratories], and defense services, as well as NASA's own component Centers; and Identify candidate solutions for NASA's software issues. Beginning in the late fall of 2010, focus topics were chosen and interview questions were developed, based on the NASA top software challenges. Between February 2011 and November 2011, the Benchmark Team interviewed a total of 18 organizations, consisting of five NASA Centers, five industry organizations, four defense services organizations, and four university or university R and D laboratory organizations. A software assurance representative also participated in each of the interviews to focus on assurance and software safety best practices. Interviewees provided a wealth of information on each topic area that included: software policy, software acquisition, software assurance, testing, training, maintaining rigor in small projects, metrics, and use of the Capability Maturity Model Integration (CMMI) framework, as well as a number of special topics that came up in the discussions. NASA's software engineering practices compared favorably with the external organizations in most benchmark areas, but in every topic, there were ways in which NASA could improve its practices. Compared to defense services organizations and some of the industry organizations, one of NASA's notable weaknesses involved communication with contractors regarding its policies and requirements for acquired software. One of NASA's strengths was its software assurance practices, which seemed to rate well in comparison to the other organizational groups and also seemed to include a larger scope of activities. An unexpected benefit of the software benchmarking study was the identification of many opportunities for collaboration in areas including metrics, training, sharing of CMMI experiences and resources such as instructors and CMMI Lead Appraisers, and even sharing of assets such as documented processes. A further unexpected benefit of the study was the feedback on NASA practices that was received from some of the organizations interviewed. From that feedback, other potential areas where NASA could improve were highlighted, such as accuracy of software cost estimation and budgetary practices. The detailed report contains discussion of the practices noted in each of the topic areas, as well as a summary of observations and recommendations from each of the topic areas. The resulting 24 recommendations from the topic areas were then consolidated to eliminate duplication and culled into a set of 14 suggested actionable recommendations. This final set of actionable recommendations, listed below, are items that can be implemented to improve NASA's software engineering practices and to help address many of the items that were listed in the NASA top software engineering issues. 1. Develop and implement standard contract language for software procurements. 2. Advance accurate and trusted software cost estimates for both procured and in-house software and improve the capture of actual cost data to facilitate further improvements. 3. Establish a consistent set of objectives and expectations, specifically types of metrics at the Agency level, so key trends and models can be identified and used to continuously improve software processes and each software development effort. 4. Maintain the CMMI Maturity Level requirement for critical NASA projects and use CMMI to measure organizations developing software for NASA. 5.onsolidate, collect and, if needed, develop common processes principles and other assets across the Agency in order to provide more consistency in software development and acquisition practices and to reduce the overall cost of maintaining or increasing current NASA CMMI maturity levels. 6. Provide additional support for small projects that includes: (a) guidance for appropriate tailoring of requirements for small projects, (b) availability of suitable tools, including support tool set-up and training, and (c) training for small project personnel, assurance personnel and technical authorities on the acceptable options for tailoring requirements and performing assurance on small projects. 7. Develop software training classes for the more experienced software engineers using on-line training, videos, or small separate modules of training that can be accommodated as needed throughout a project. 8. Create guidelines to structure non-classroom training opportunities such as mentoring, peer reviews, lessons learned sessions, and on-the-job training. 9. Develop a set of predictive software defect data and a process for assessing software testing metric data against it. 10. Assess Agency-wide licenses for commonly used software tools. 11. Fill the knowledge gap in common software engineering practices for new hires and co-ops.12. Work through the Science, Technology, Engineering and Mathematics (STEM) program with universities in strengthening education in the use of common software engineering practices and standards. 13. Follow up this benchmark study with a deeper look into what both internal and external organizations perceive as the scope of software assurance, the value they expect to obtain from it, and the shortcomings they experience in the current practice. 14. Continue interactions with external software engineering environment through collaborations, knowledge sharing, and benchmarking.

Rarick, Heather L.↗

NASA NTRS2002

The scope and direction of health informatics

Health Informatics (HI) is a dynamic discipline based on the medical sciences, information sciences, and cognitive sciences. Its domain can broadly be defined as medical information management. The purpose of this paper is to provide an overview of this domain, discuss the current "state of the art," and indicate the likely growth areas for health informatics. The sources of information used in this paper are selected publications from the literature of Health Informatics, HI 5300: Introduction to Health Informatics, which is a course from the Department of Health Informatics at the University of Texas Houston Health Sciences Center, and the author's personal experience in practicing telemedicine and implementing an electronic medical record at the NASA-Johnson Space Center. The conclusion is that the direction of Health Informatics is in the direction of data management, transfer, and representation via electronic medical records and the Internet.

Medical Informatics Applications↗

NASA NTRSDate not supplied

Managing Multi-Instrument Data Streams in Secure Environments

The capture and curation of all primary instrument data is a potentially valuable source of added insight into experiments or diagnostics in laboratory experiments. The data can, when properly curated, enable analysis beyond the current practice that uses just a subset of the as-measured data. Complete curated data can also be input for machine learning and other data exploration tools. Conveniently storing and accessing instrument data requires that the instruments are connected to databases and users through a networking infrastructure. This infrastructure needs to accommodate a wide array of instruments which can range from single laboratory mounted probes for environment monitoring to computers managing multiple instruments. These resources may also include mobile devices on which researchers record instrument and experiment state related notes. These varied data sources bring with them the challenges of different communications capabilities and protocols as well as the primary data typically being produced in proprietary formats. These challenges are further compounded when the instruments need to operate in secure environments such as required in national laboratories. We will discuss the SmartLab, an ongoing effort to set up a system for instrument and simulation data curation at NASA Langley Research Center. We will outline the challenges faced in managing the data sources required for ongoing research activities and the solutions that are being considered and implemented to address those challenges.

instrument data management↗

DOE OSTI2026

Bayesian Physics Informed Spatio-Temporal Network for Streamflow Data Imputation

Reliable reconstruction of incomplete streamflow records is critical for improving hydrological forecasting, flood preparedness, and water resource management. However, large observational gaps and uncertainties in governing physical parameters limit the accuracy of traditional statistical and machinelearning imputation frameworks. To address these challenges, we develop a Bayesian Physics-Informed Spatio-Temporal Network (BPI-STNet) that jointly captures spatial and temporal dependencies while enforcing hydrologic consistency through embedded physical constraints. The framework integrates a GraphSAGE-LSTM architecture to model spatial connectivity across gauges and temporal flow dynamics, coupled with a Bayesian update mechanism to estimate uncertain parameters in a simplified water-balance framework. Unlike conventional physics-informed networks that rely on sampling-based posterior estimation, BPI-STNet derives an analytic solution to the inverse problem, allowing closed-form Bayesian updates of uncertain parameters Λ={α,β,k} using Gaussian priors and likelihoods. Applied to daily observations from the Susquehanna River Basin (1980-2022), BPI-STNet achieves substantial improvements over a purely data-driven RGNN baseline, which reduced RMSE by 23 % and MAE by 9 %, and achieving an average NSE values up to 0.96. The results demonstrate that coupling Bayesian inference with physics-informed learning yields physically consistent, uncertainty-aware reconstructions that preserve the temporal persistence and statistical distribution of observed flows. The proposed framework establishes a generalizable paradigm for data-sparse hydrologic systems where both data fidelity and physical interpretability are essential.

Krishnan Kutty Ambika, Anukesh [ORNL] (ORCID:00000↗

DOE OSTI2025

Financial Analysis of the High Flow Experiment conducted at the Glen Canyon Dam during Water Year 2023

The Glen Canyon Dam (GCD) is a Colorado River Storage Project (CRSP) power resource that is a component of the Salt Lake City Area Integrated Projects (SLCA/IP). The 2016 record of decision (ROD) for the GCD long-term experimental and management plan (LTEMP) final Environmental Impact Statement (EIS) specified criteria for GCD monthly water releases, daily and hourly operating limits, and experimental releases. This report examines the financial implications of the high flow experiment (HFE) conducted at GCD during the spring of Water Year (WY) 2023 as required by the LTEMP HFE Protocol. This report is part of a series of reports that describe the financial costs of LTEMP experimental releases since the 2016 ROD was adopted in January 2017. Previous reports analyzed the impact of several past HFEs and Bug Flow Experiments. This report focuses on the HFE conducted in April 2023. For this experimental release, financial costs of approximately $1.33 million were incurred because the HFE required sustained water releases exceeding the power plant’s maximum turbine flow rate. In addition, during the experiment, operators were not allowed to shape GCD power production, either to follow Firm Electric Service (FES) customer day-ahead energy deliveries or to respond to market prices. This study identifies the main factors contributing to the HFE costs and examines the interdependencies among these factors. It applies an integrated set of tools to estimate Western Area Power Administration (WAPA) financial impacts by simulating GCD under two types of cases; namely, (1) a “With Experiment” case that mimics the operations that actually occurred and (2) a “Without Experiment” case that simulates operations under the assumption that the HFE did not occur. The “With Experiment” case mimics operations during the HFE and the entire month the HFE occurred. It complies with LTEMP hourly and daily operating criteria. The “Without Experiment” case assumes that the HFE did not occur. The monthly water release volume is assumed to be identical under both cases. The Colorado River Storage Project Python-based model (CRiSPPy) model was the main modeling tool used to simulate the dispatch of the GCD hydropower plant and associated water releases from Lake Powell. In the modeling process, the research team used extensive data sets and historical information on SLCA/IP power plant characteristics, hydrologic conditions, and WAPA’s power purchases and sales prices. In addition to estimating the financial impact of the HFE, the team used the CRiSPPy model to gain insights into the interplay among ROD operating criteria, exceptions made to criteria to accommodate the HFE, and WAPA operating practices.

13 HYDRO ENERGY↗

DOE OSTI2025

Financial Analysis of the Smallmouth Bass Flows implemented at the Glen Canyon Dam during Water Year 2024

The Glen Canyon Dam (GCD) is a Colorado River Storage Project (CRSP) power resource that is a component of the Salt Lake City Area Integrated Projects (SLCA/IP). The 2016 record of decision (ROD) for the GCD long-term experimental and management plan (LTEMP) final Environmental Impact Statement (EIS) specifies criteria for GCD monthly water releases, daily and hourly operating limits, and experimental releases. This report presents a financial analysis of the Smallmouth bass (Micropterus dolomieu) (SMB) flows implemented at GCD during Water Year (WY) 2024. These bypass flows were introduced by the U.S. Bureau of Reclamation (USBR) as an emergency response to the growing threat posed by invasive SMB in the Colorado River ecosystem downstream of the dam. SMB are a non-native predatory species that pose a significant threat to native fish populations, including the endangered humpback chub (Gila cypha). The thermal regime below GCD, typically cold due to hypolimnetic releases from Lake Powell, has historically served as a thermal barrier limiting SMB establishment. However, persistently low reservoir levels in recent years have reduced stratification in Lake Powell, allowing warmer water to be released downstream. This has enabled SMB to spawn successfully below the dam, prompting urgent ecological concerns. To mitigate the risk of SMB proliferation, the USBR implemented a series of bypass flows in WY 2024. Drawn from a lower elevation than the penstocks, the bypass structures released cooler water downstream. These short-duration bypass flows aimed to keep temperatures cool enough to prevent SMB from spawning, thereby reducing the ecological threat posed by this invasive species. Although motivated by ecological objectives, these bypass flows came with financial tradeoffs. Releasing water through the bypass structures instead of the turbines at GCD reduced hydropower generation, resulting in a significantly lower financial position for Western Area Power Administration (WAPA), which is responsible for marketing the electricity produced by the GCD Powerplant. This report analyzes the financial impact of the SMB flows implemented from July to November 2024. These experimental releases led to an estimated financial cost of approximately $18.9 million, primarily driven by the substantial volume of water diverted through the bypass structures. This study applies an integrated set of tools to estimate WAPA financial impacts by simulating GCD under two types of cases; namely, (1) a “With Experiment” case that mimics the water operations that actually occurred, including the SMB bypass flows, and (2) a “Without Experiment” case that simulates operations under the assumption that the SMB flows did not occur. Both cases comply with LTEMP hourly and daily operating criteria, and the monthly water release volumes are assumed to be identical under both cases. The Colorado River Storage Project Python-based model (CRiSPPy) model was the main modeling tool used to simulate the dispatch of the GCD hydropower plant and associated water releases from Lake Powell. In the modeling process, the research team used extensive data sets and historical information on SLCA/IP power plant characteristics, hydrologic conditions, and WAPA’s power purchases and sales prices.

13 HYDRO ENERGY↗

DOE OSTI2026

Financial Analysis of the Smallmouth Bass Flows implemented at the Glen Canyon Dam during 2025

Ploussard, Quentin [Argonne National Laboratory (A↗

NASA NTRS1985

An optical disk archive for a data base management system

An overview is given of a data base management system that can catalog and archive data at rates up to 50M bits/sec. Emphasis is on the laser disk system that is used for the archive. All key components in the system (3 Vax 11/780s, a SEL 32/2750, a high speed communication interface, and the optical disk) are interfaced to a 100M bits/sec 16-port fiber optic bus to achieve the high data rates. The basic data unit is an autonomous data packet. Each packet contains a primary and secondary header and can be up to a million bits in length. The data packets are recorded on the optical disk at the same time the packet headers are being used by the relational data base management software ORACLE to create a directory independent of the packet recording process. The user then interfaces to the VAX that contains the directory for a quick-look scan or retrieval of the packet(s). The total system functions are distributed between the VAX and the SEL. The optical disk unit records the data with an argon laser at 100M bits/sec from its buffer, which is interfaced to the fiber optic bus. The same laser is used in the read cycle by reducing the laser power. Additional information is given in the form of outlines, charts, and diagrams.

Thomas, Douglas T.↗

NASA NTRSDate not supplied

Enhancing Air Traffic Control Planning with Automatic Speech Recognition

The decisions made during the Federal Aviation Administration Air Traffic Control System Command Center's planning teleconferences hold significant sway over the National Airspace System. Held every two hours, these teleconferences convene air traffic managers and stakeholders from across the nation to discuss airspace conditions, weather, and constraints, leading to the formulation and adjustment of traffic management initiatives. Given the critical nature of these decisions, the need for accurate and efficient record-keeping is paramount. In recent years, the application of automatic speech recognition has gained popularity across diverse industries, including aviation. While traditional applications focus on transcribing air traffic control communication, this paper explores a unique application of automatic speech recognition by converting the audio from planning teleconferences into text transcriptions. This innovative approach addresses key challenges in the field, presenting potential benefits for quality assurance, real-time participation, and downstream natural language processing tasks. A notable breakthrough in the machine learning community, namely the transformer neural network architecture, forms the backbone of the proposed solution in this paper. The transformer architecture's role in this research represents a paradigm shift in the efficiency of automatic speech recognition models. By reducing the amount of in-domain training data required, this architecture allows for the fine-tuning of such models like Whisper, originally pretrained on vast English speech datasets. The adaptability of the transformer architecture proves invaluable in capturing the nuances of aviation terminology and specific language used in planning teleconferences. Leveraging the Whisper model as a baseline, our research details the fine-tuning and validation using a dataset comprising 20 hours of meticulously transcribed planning teleconferences. Notably, the baseline pretrained Whisper model exhibited a word error rate of 18.77%. Through the fine-tuning process, the model achieved a substantial improvement, demonstrating an impressive performance with a reduced word error rate of 6.82%. This substantial decrease in WER not only highlights the effectiveness of the transformer architecture but also emphasizes the practical advancements achieved through the application of automatic speech recognition in this specific domain. The utilization of automatic speech recognition in planning teleconferences in this work introduces several novelties. Firstly, the creation of text transcriptions offers a valuable tool for quality assurance and facilitates the efficient review of teleconferences. This is an important aspect of the proposed solution, given the time-sensitive and high-stakes nature of decisions made during these meetings. Furthermore, text-searchable transcriptions provide a streamlined approach for locating and validating critical information, potentially saving hours of manual effort in searching through audio recordings. Moreover, our research identifies a key use case for external facilities and stakeholders. In situations where attendance at the planning teleconference is not feasible, having access to text transcriptions in real-time or shortly after the teleconference ends, proves to be a time-saving and informative resource. This feature enhances collaboration and ensures that stakeholders can stay abreast of important discussions and decisions even in their absence. Despite the efficiency gains facilitated by the transformer architecture in automatic speech recognition technology, it is essential to acknowledge the human factors in data creation. Subject matter experts play a crucial role in accurately transcribing planning teleconferences due to the specificity and complexity of the information discussed. The research dataset, consisting of 20 hours of transcribed planning teleconferences, forms the foundation for fine-tuning and validating the Whisper model. The achieved word error rate of 6.82% demonstrates promising advancements, particularly in recognizing essential aviation terminology within the teleconferences. In conclusion, this paper presents a comprehensive exploration of the application of automatic speech recognition in Air Traffic Control System Command Center planning teleconferences, leveraging the transformer architecture for enhanced efficiency. The novel contributions lie in the improved accessibility of decision-making records, real-time participation opportunities for external stakeholders, and the potential for downstream natural language processing advancements. As the aviation industry continues to evolve, the integration of automatic speech recognition technologies holds the promise of revolutionizing decision-making processes and contributing to the overall safety and efficiency of air traffic management.

ATM↗

NASA NTRS1975

Results of phase one of land use information Delphi study

The Land Use Management Information System (LUMIS) is being developed for the city portion of the Santa Monica mountains. LUMIS incorporates data developed from maps and aerial photos as well as traditional land based data associated with routine city and county record keeping activities and traditional census data. To achieve the merging of natural resource data with governmental data LUMIS is being designed in accordance with restrictions associated with two other land use information systems currently being constructed by Los Angeles city staff. The two city systems are LUPAMS (Land Use Planning and Management System) which is based on data recorded by the County Assessor's office for each individual parcel of land in the city, and Geo-BEDS, a geographically based environmental data system.

Paul, C. K.↗

NASA NTRS2016

Cyber Security for the Spaceport Command and Control System: Vulnerability Management and Compliance Analysis

With the rapid development of the Internet, the number of malicious threats to organizations is continually increasing. In June of 2015, the United States Office of Personnel Management (OPM) had a data breach resulting in the compromise of millions of government employee records. The National Aeronautics and Space Administration (NASA) is not exempt from these attacks. Cyber security is becoming a critical facet to the discussion of moving forward with projects. The Spaceport Command and Control System (SCCS) project at the Kennedy Space Center (KSC) aims to develop the launch control system for the next generation launch vehicle in the coming decades. There are many ways to increase the security of the network it uses, from vulnerability management to ensuring operating system images are compliant with securely configured baselines recommended by the United States Government.

Cyber Security↗

NASA NTRS2004

Cryogenic Fluid Management: 2000-2004

This custom bibliography from the NASA Scientific and Technical Information Program lists a sampling of records found in the NASA Aeronautics and Space Database. The scope of this topic includes cooling technologies for precision astronomical sensors and advanced spacecraft, as well as propellant storage and transfer in space. This area of focus is one of the enabling technologies as defined by NASA's Report of the President's Commission on Implementation of United States Space Exploration Policy, published in June 2004.

Source record↗

NASA NTRS1975

ITOS VHRR on-board data compression study

Data compression methods for ITOS VHRR data were studied for a tape recorder record-and playback application. A playback period of 9 minutes was assumed with a nominal 18 minute record period for a 2-to-1 compression ratio. Both analog and digital methods were considered with the conclusion that digital methods should be used. Two system designs were prepared. One is a PCM system and the other is an entropy-coded predictive-quantization, sometimes called entropy-coded DPCM or just DPCM, system. Both systems use data management principles to transmit only the necessary data. Both systems use a medium capacity standard tape recorder from specifications provided by the technical officer. The 10 to the 9th power bit capacity of the recorder is the basic limitation on the compression ratio. Both systems achieve the minimum desired 2 to 1 compression ratio. A slower playback rate can be used with the DPCM system due to a higher compression factor for better link performance at a given CNR in terms of bandwidth utilization and error rate. The report is divided into two parts. The first part summarizes the theoretical conclusions of the second part and presents the system diagrams. The second part is a detailed analysis based upon an empirically derived random process model arrived at from specifications and measured data provided by the technical officer.

Gray, R. M.↗

NASA NTRS1993

Value added data archiving

Researchers in the Molecular Sciences Research Center (MSRC) of Pacific Northwest Laboratory (PNL) currently generate massive amounts of scientific data. The amount of data that will need to be managed by the turn of the century is expected to increase significantly. Automated tools that support the management, maintenance, and sharing of this data are minimal. Researchers typically manage their own data by physically moving datasets to and from long term storage devices and recording a dataset's historical information in a laboratory notebook. Even though it is not the most efficient use of resources, researchers have tolerated the process. The solution to this problem will evolve over the next three years in three phases. PNL plans to add sophistication to existing multilevel file system (MLFS) software by integrating it with an object database management system (ODBMS). The first phase in the evolution is currently underway. A prototype system of limited scale is being used to gather information that will feed into the next two phases. This paper describes the prototype system, identifies the successes and problems/complications experienced to date, and outlines PNL's long term goals and objectives in providing a permanent solution.

Berard, Peter R.↗

NASA NTRS2020

Medics: Medical Decision Support System for Long-Duration Space Exploration

The Autonomous Medical Operations (AMO) group at NASA Ames is developing a “medical decision support system” to enable astronauts on long-duration exploration missions to operate autonomously. The system will support clinical actions by providing medical interpretation advice and procedural recommendations during emergent care and clinical work performed by crew. The current state of development of the system, called MedICS (Medical Interpretation Classification and Segmentation) includes two separate aspects: a set of machine learning diagnostic models trained to analyze organ images and patient health records, and an interface to ultrasound diagnostic hardware and to medical repositories. Three sets of images of different organs and medical records were utilized for training machine learning models for various analyses, as follows: 1. Pneumothorax condition (collapsed lung). The trained model provides a positive or negative diagnosis of the condition. 2. Carotid artery occlusion. The trained model produces a diagnosis of 5 different occlusion levels (including “normal”). 3. Ocular retinal images. The model extracts optic disc pixels (image segmentation). This is a precursor step for advanced autonomous fundus clinical evaluation algorithms to be implemented in FY20. 4. Medical health records. The model produces a differential diagnosis for any particular individual, based on symptoms and other health and demographic information. A probability is calculated for each of 25 most common conditions. The same model provides the likelihood of survival. All results are provided with a confidence level. Item 1 images were provided by the US Army and were part of a data set for the clinical treatment of injured battlefield soldiers. This condition is relevant to possible space mishaps, due to pressure management issues. Item 2 images were provided by Houston Methodist Hospital, and item 3 health records were acquired from the MIT laboratory of computational physiology. The machine learning technology utilized is deep multilayer networks (Deep Learning), and new models will continue to be produced, as relevant data is made available and specific health needs of astronaut crews are identified. The interfacing aspects of the system include a GUI for running the different models, and retrieving and storing data, as well as support for integration with an augmented reality (AR) system deployed at JSC by Tietronix Software Inc. (HoloLens). The AR system provides guidance for the placement of an ultrasound transducer that captures images to be sent to the MedICS system for diagnosis. The image captured and the associated diagnosis appear in the technician’s AR visual display.

Colombano, Silvano↗

DOE OSTI2025

Automated Inventory Solutions in End-User IT Support

Managing IT equipment by hand is prone to errors and delays, severely impacting operational continuity and productivity. Manual inventory systems often result in time delays, inconsistent record-keeping, equipment shortages, and increased workloads for IT staff. At Savannah River National Laboratory (SRNL), my internship focused on creating an automated inventory management solution using Microsoft Power Automate and SharePoint Lists. This solution seamlessly integrates with the existing Microsoft 365 infrastructure, thus eliminating the need for additional software purchases or dedicated server space. By providing real-time updates and reducing manual data entry, the new system ensures a more reliable and maintainable approach to IT asset management.

Information Technology↗