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At least 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Space Flown Rodent Liver RNA Sequencing Data for Machine Learning in Space Biology Research

High-throughput nucleic acid sequencing (DNA-seq, RNA-seq) has become widespread in biomedical research due to the growing availability and affordability of these assays. Data analysis has been accelerated in recent years by the adoption of artificial intelligence (AI) and machine learning (ML) techniques by biomedical researchers. In space biology research, RNAseq datasets from space-flown experimental samples are critical for characterizing the gene expression aberrations associated with exposure to spaceflight stressors. However, space biological experiments tend to be very low sample size, so identifying proper AI/ML algorithms for sequencing data analysis is an ongoing challenge since these algorithms typically require large sample size. The NASA Science Mission Directorate (SMD) has started the “Benchmark Initiative for AI/ML”, focused on creating datasets meant for three main applications: 1) scientific benchmarking, which finds the best algorithm for a specific problem; 2) application benchmarking, which measures algorithm performance against a set of parameters; and 3) system benchmarking, which evaluates performance of hardware and software architecture. These scientific benchmarks consist of an AI-ready dataset and a reference implementation on a specific scientific question. In this work, we focused on generating standardized datasets to allow the scientific community to benchmark AI/ML algorithms in the domain of space biology. We present here a standardized, AI-ready, publicly available benchmark dataset for space biology RNA-seq data as a collaboration between the NASA AI4LS (Artificial Intelligence for Life Sciences) working group. and NASA’s SMD. This dataset consists of space-flown and ground control mouse liver found in the NASA GeneLab omics database. However, to amplify the small sample number (n=112 samples) for ML purposes, we employ Gaussian noise and a generative adversarial network to extend this dataset to 6,000 synthetic samples, matching the original gene expression characteristics.

James Casaletto↗
NASA NTRSDate not supplied

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge, reduce risk, and support safe, productive human space missions. Through the powerful emerging computer science approaches of artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in biomedical science and engineered astronaut health systems, to enable Earth-independence and autonomy of mission operations. We present a decadal view of AI/ML architecture to support deep space mission goals, developed in concert with leaders in the field. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing hardware, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated real-time mission biomonitoring, and 8) a Precision Space Health system. Cutting-edge AI/ML approaches that can be integrated to support these domains include active learning, explainable AI, adaptive learning, causal inference, knowledge graphs, federated learning, transfer learning, and large language models. Finally, we present results from several current ML projects that are underway in the field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics data to phenotypic data using an ensemble model to infer causality of spaceflight rodent liver health disruption, 2) usage of explainable ML to interrogate the muscular underpinnings of spaceflight muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human space health risk leveraging rodent bone datasets, 4) usage of large pre-trained models connecting biomedical knowledgebases with small spaceflight datasets to understand gene-to-gene interaction networks, and 5) a suite of benchmarked open science datasets (spaceflight mouse liver; radiation DNA damage) enabling programmers to identify the best ML algorithms to answer space biological science questions.

space biology↗
NASA NTRSDate not supplied

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge, reduce risk, and support safe, productive human space missions. Through the powerful emerging computer science approaches of artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in biomedical science and engineered astronaut health systems, to enable Earth-independence and autonomy of mission operations. We present a decadal view of AI/ML architecture to support deep space mission goals, developed in concert with leaders in the field. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing hardware, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated real-time mission biomonitoring, and 8) a Precision Space Health system. Cutting-edge AI/ML approaches that can be integrated to support these domains include active learning, explainable AI, adaptive learning, causal inference, knowledge graphs, federated learning, transfer learning, and large language models. Finally, we present results from several current ML projects that are underway in the field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics data to phenotypic data using an ensemble model to infer causality of spaceflight rodent liver health disruption, 2) usage of explainable ML to interrogate the muscular underpinnings of spaceflight muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human space health risk leveraging rodent bone datasets, 4) usage of large pre-trained models connecting biomedical knowledgebases with small spaceflight datasets to understand gene-to-gene interaction networks, and 5) a suite of benchmarked open science datasets (spaceflight mouse liver; radiation DNA damage) enabling programmers to identify the best ML algorithms to answer space biological science questions.

space biology↗
NASA NTRSDate not supplied

Creating Benchmark Data for Artificial Intelligence and Machine Learning Space Biology Research

To identify an appropriate AI/ML approach for a specific problem, the best practice is to measure algorithm performance through the benchmarking process. A scientific benchmark consists of an AI-ready dataset and a reference implementation on a specific scientific question. The NASA Science Mission Directorate (SMD) has started the “Benchmark Initiative for AI/ML to create scientific benchmark datasets in three applications: 1) scientific benchmarking, which finds the best algorithm for a specific problem; 2) application benchmarking, which measures algorithm performance against a set of parameters; and 3) system benchmarking, which evaluates performance of hardware and software architecture. Currently, there are no standardized datasets available to benchmark AI/ML algorithms in the domain of space biology. In this work, we constructed two AI/ML-ready biological datasets from experiments in space-flown mice: cellular imaging and RNA-seq. First, radiation-exposed immune cells harbor DNA damage foci that can be fluorescently marked to visualize the amount of damage following exposure to ionizing radiation. However, such large datasets are difficult to analyze visually, due to imaging inconsistencies and human bias, and classical image processing approaches can fail on imaging artifacts. AI/ML are therefore exciting alternative, providing the speed of machines and the accuracy of humans. We have made this dataset available at https://registry.opendata.aws/bps_microscopy/. Second, high-throughput nucleic acid sequencing (DNA-seq, RNA-seq) has become widespread in biomedical research due to the growing availability and affordability of these assays. However, most sequencing datasets suffer from high dimensionality and low sample count. In this work, we used a generative adversarial network to synthesize a standardized, AI-ready, publicly available benchmark dataset for space biology RNA-seq data with sufficient space-flown and ground control mouse liver samples from NASA GeneLab. This dataset is available at https://registry.opendata.aws/bps_rnaseq/. These datasets are now fully open the Space Biology community to test their favorite AI/ML approaches.

James Casaletto↗
NASA NTRS2025

A Minority Report Submitted as an Addendum to the Report of the Mars 2020 Organic Contamination Panel

This Minority Report (MR) presents seven findings in addition to or contrary to the main OCP report: 1. Contamination control for the Mars 2020 cache must be strict. Mars’ surface is known to be organics-poor from laboratory studies, Mars meteorite analyses, and from four previous NASA missions. It is imperative that contamination control measures are enacted that enable reliable and robust detection of potential biomarker compounds at the ppb level. 2. Since Mars 2020 is a sample return mission and analyses of samples in the returnable cache are expected to occur after return to Earth, positive controls are not recommended for flight on Mars 2020 unless a compelling case can be made for their use. 3. The findings of previous panels dedicated to organic compound analysis in Martian samples (OCSSG, ND-SAG, SDT) recommend TOC limits between 10-40 ppb. The MR finds that the lower limit of 10 ppb is recommended and that insufficient justification is given by the OCP Panel Report (PR) to raise the TOC limit to 40 ppb. 4. Analytical capability is sufficiently advanced that analytical capability is an irrelevant consideration with respect to differentiating between 10 and 40 ppb TOC. 5. Perceived contamination control challenges are an irrelevant consideration for raising the TOC contamination limit from 10 to 40 ppb since those challenges, and the procedures to ameliorate them, will exist regardless of whether the limit is 10 or 40 ppb. 6. The “dilution cleaning” method has not been adequately proven for utilization on the Mars 2020 mission. Shortcomings have been identified in terms of peer review, method verification, analytical and testing approach, application to space flight hardware, and performance under Martian conditions. The method should be revisited and independently tested using statistically and analytically robust methods, and scrutinized in a rigorous peer review process. 7. The Mars 2020 mission claims considerable heritage from the Mars Science Laboratory (MSL) mission, but MSL contamination control efforts contain significant errors and implementation discrepancies that would imperil the Mars 2020 caching mission were they repeated. A standing contamination control panel should be formed to provide independent oversight for the Mars 2020 mission.

Mars Sample Return↗
DOE OSTI2018

Particle Flow and PUPPI in the Level-1 Trigger at CMS for the HL-LHC

With the planned addition of tracking information to the Compact Muon Solenoid (CMS) Level-1 trigger for the High-Luminosity Large Hadron Collider (HL-LHC), the trigger algorithms can be completely reconceptualized. We explore the feasibility of using particle flow-like reconstruction and pileup per particle identification (PUPPI) pileup mitigation at the hardware trigger level. This represents a new type of multi-subdetector pattern recognition challenge for the HL-LHC. We present proof-of-principle studies on both physics and hardware-resource performance of a prototype algorithm for use by CMS in the HL-LHC era.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2019

System and method for vehicle energy estimation, adaptive control and routing

In various embodiments the present disclosure provides a system and a method for estimating energy usage in a vehicle. The system includes hardware that perform certain operations that include receiving data associated with a route to be taken by the vehicle. The operations include determining, based on the data associated with the route and a state of the vehicle, an estimate corresponding to the energy that would be used by the vehicle when traversing a segment of the route.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

Development of programmable logic in a rapid, automated, and repeatable fashion for safety-related applications - 385

Digital safety platform considerations for power plant modernization or new build efforts involve a burdensome task of implementing a large number of software-based functions onto a designated platform to match the plant's architecture. This problem is not unique to any particular safety-related platform solution, but is prevalent across the landscape of current Qualified and Licensed Safety-related Instrumentation and Control (I and C) Solutions. As an example when implementing a RG 1.97 compliant Post Accident Monitoring System (PAMS) solution with the Lockheed Martin Nuclear Protection and Control (NuPAC) safety platform, 72 unique software loads are needed to configure the Qualified Display System (QDS). Large scale development and customization efforts to upgrade existing plant functions or systems require the licensee to assume the cost and technical risk necessary to undergo a digital based upgrade. Often the cost, schedule, and technical risk outweighed the benefit of modernization efforts due to the unknown hurdles and past projects that overran cost and schedule projections. Lockheed Martin's Engineering Production Suite (EPS) minimizes the licensee risk and expense associated with modernization efforts through a developer's toolkit for rapid, automated, and repeatable development of Programmable Logic used to implement plant functions and architectures for use on the Lockheed Martin NuPAC platform. This paper describes a use case where by EPS can create system level representations, simulate system functions, partition logic designs to accommodate hardware constraints through logic partitioning, automate the assembly of Application Specific Programmable Logic (ASPL), generate system test cases, program the NuPAC hardware, and perform testing throughout the design workflow. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Hydrogen Production, Grid Integration, and Scaling for the Future

The project will explore near and long-term visions towards the commercialization of grid integrated electrolysis systems to inform deployment across the planning, procurement, and operation stages of hydrogen production on the grid. It will leverage NREL's state-of-the-art 1.25 MW polymer electrolyte membrane (PEM) electrolyzer system to characterize system performance in relevant scenarios, also creating a digital twin for emulation in the Advanced Research on Integrated Energy Systems (ARIES) virtual environment and performing hardware-in-the-loop (HIL) testing of pilot scale, decentralized, and centralized hydrogen systems.

electrolysis systems↗
DOE OSTI2023

Introduction of a Variable Inductance Transformer for the Design of Resonant Power Converters

Magnetic integration is a hot topic in power electronics that concerns the use of a transformer’s leakage and magnetizing inductances purposefully in isolated power electronic converters, thereby giving the opportunity to save the cost and footprint of any additional inductor. This is of prime interest, especially in CLLLC resonant converters which require up to three inductors. For a complete integration of these inductances, the concept of a variable inductance transformer (VIT) is introduced in this thesis. A VIT is an adaptive magnetic structure that facilitates an easy adjustment of both magnetizing and leakage inductances to meet their desired values. However, for a more promising design, an accurate estimation of these inductances is necessary. While the evaluation of magnetizing inductance is quite straightforward, the calculation of leakage inductance is rather convoluted, because the leakage inductance is influenced by both the winding layout and the operating frequency. In this thesis, three new semi-analytical methods for calculating the frequency-independent leakage inductance, and a novel semi-analytical method for evaluating the frequency-dependent leakage inductance are proposed. These methods can calculate the respective leakage inductances of a VIT within an outstanding ±5% uncertainty. Finally, a bidirectional CLLLC resonant dc-dc converter is investigated for the constant current constant voltage (CCCV) charging of the next-generation 900 V traction battery of an electric vehicle. A new voltage gain equation is derived for designing the CLLLC resonant tank, and a small-signal model is presented for designing the variable-frequency feedback controller. Furthermore, a new methodology to design a VIT is developed to overcome the challenges associated with small coupling coefficients and guarantee a complete magnetic integration of the tank inductances. All theoretical results presented herein are verified through simulations and experiments performed on hardware prototypes designed in the lab.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2000

Performance of Adsorption - Based CO2 Acquisition Hardware for Mars ISRU

Chemical processing of the dusty, low-pressure Martian atmosphere typically requires conditioning and compression of the gases as first steps. A temperature-swing adsorption process can perform these tasks using nearly solid-state hardware and with relatively low power consumption compared to alternative processes. In addition, the process can separate the atmospheric constituents, producing both pressurized CO2 and a buffer gas mixture of nitrogen and argon. To date we have developed and tested adsorption compressors at scales appropriate for the near-term robotic missions that will lead the way to ISRU-based human exploration missions. In this talk we describe the characteristics, testing, and performance of these devices. We also discuss scale-up issues associated with meeting the processing demands of sample return and human missions.

Finn, John E.↗
NASA NTRS1998

FPGA Based Reconfigurable ATM Switch Test Bed

Various issues associated with "FPGA Based Reconfigurable ATM Switch Test Bed" are presented in viewgraph form. Specific topics include: 1) Network performance evaluation; 2) traditional approaches; 3) software simulation; 4) hardware emulation; 5) test bed highlights; 6) design environment; 7) test bed architecture; 8) abstract sheared-memory switch; 9) detailed switch diagram; 10) traffic generator; 11) data collection circuit and user interface; 12) initial results; and 13) the following conclusions: Advances in FPGA make hardware emulation feasible for performance evaluation, hardware emulation can provide several orders of magnitude speed-up over software simulation; due to the complexity of hardware synthesis process, development in emulation is much more difficult than simulation and requires knowledge in both networks and digital design.

Chu, Pong P.↗
DOE OSTI2021

Understanding HPC Benchmark Performance on Intel Broadwell and Cascade Lake Processors

Hardware platforms in high performance computing are constantly getting more complex to handle even when considering multicore CPUs alone. Numerous features and configuration options in the hardware and the software environment that are relevant for performance are not even known to most application users or developers. Microbenchmarks, i.e., simple codes that fathom a particular aspect of the hardware, can help to shed light on such issues, but only if they are well understood and if the results can be reconciled with known facts or performance models. The insight gained from microbenchmarks may then be applied to real applications for performance analysis or optimization. In this paper we investigate two modern Intel x86 server CPU architectures in depth: Broadwell EP and Cascade Lake SP. We highlight relevant hardware configuration settings that can have a decisive impact on code performance and show how to properly measure on-chip and off-chip data transfer bandwidths. The new victim L3 cache of Cascade Lake and its advanced replacement policy receive due attention. Finally we use DGEMM, sparse matrix-vector multiplication, and the HPCG benchmark to make a connection to relevant application scenarios.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2002

The Microwave Anisotropy Probe (MAP) Guidance, Navigation, and Control Hardware Suite

The on-orbit success of the Microwave Anisotropy Probe (MAP) Guidance, Navigation, and Control System can partially be attributed to the performance of a hardware suite chosen to meet the complex attitude determination and control requirements of the mission. To meet these requirements, a diverse set of components was used. The set included two Lockheed Martin AST-201 star trackers, two Kearfott Two-Axis Rate Assemblies mounted to provide X, Y and redundant Z-axis rates, two Adcole Digital Sun Sensor heads sharing one set of electronics, twelve Adcole Coarse Sun Sensor eyes, three Ithaco E-sized Reaction Wheel Assemblies, a Propulsion Subsystem that employed eight Primex Rocket Engine Modules, and a pair of Goddard-designed Attitude Control Electronics which connect all of the components to the spacecraft processor. The performance of this hardware is documented, as are some of the spacecraft accommodations and lessons learned that came from working with this particular set of hardware.

Ward, David K.↗
DOE OSTI2024

Shifting Between Compute and Memory Bounds: A Compression-Enabled Roofline Model

In the evolving landscape of high-performance computing, especially to fight the end of Moore’s Law and Dennard’s Scaling, the ability to shift between compute-bound and memory-bound states is critical for enhancing adaptability and flexibility to diverse system and domain-specific architectures. Such capability is vital for optimizing performance across distinguished hardware configurations, such as accelerators, memory hierarchies, and cache systems. Despite that ad hoc optimization techniques, such as compressed/approximate computation, have been enabled for compute-/data-intensive computing for improved performance in distinct hardware settings, there lacks an understanding of 1) the rational behind performance improvement; 2) capability of different optimizations; 3) what optimization to respond to specific computational and memory demands. This work proposes a compression-enabled roofline model to facilitate this adaptability with data compression techniques to balance and transform between computational and memory demands. This model enables applications to adjust in response to the specific strengths and limitations of the underlying hardware and system to optimize resource utilization. The effectiveness of this approach is demonstrated with matrix multiplication kernels on different input sizes, with turning on/off various compression techniques, including 1) low-precision floating point; 2) sparse matrix formulation; and 3) compressed arrays with ZFP. By reducing memory transfer volumes and cache misses and increasing data locality and computational intensity through compression, the specific roofline model can transform between compute and memory bounds to align more efficiently with system capabilities. This advancement not only improves overall performance but also maximizes adaptability in diverse computing environments.

Naraparaju, Ramasoumya [University of Washington]↗
NASA NTRS1976

Bosch CO2 Reduction System Development

Development of a Bosch process CO2 reduction unit was continued, and, by means of hardware modifications, the performance was substantially improved. Benefits of the hardware upgrading were demonstrated by extensive unit operation and data acquisition in the laboratory. This work was accomplished on a cold seal configuration of the Bosch unit.

Holmes, R. F.↗
NASA NTRS1976

Long life testing of spare Mariner Venus '67 hardware

The faultless performance of the Mariner Venus '67 Power Conditioning Electronics (PCE) throughout six years of continuous operation in a simulated space environment is reported. Weekly functional tests supplemented by daily monitoring verified that the PCE equipment can perform to its intended functions for at least six years without apparent performance degradation. Performance throughout the test period was very stable, there are no circuit or redundancy improvements to be considered. When the equipment was examined after the test was completed, there was no evidence of any physical damage nor any difficulty in disconnecting the wiring connectors.

Silverman, S. W.↗