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At least 1,549 records · Page 86

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3D Scanning System to Assess Gravity-Dependent Body Shape Changes

The human body shows unique morphological changes when exposed to different gravity conditions, including muscle atrophy, fluid shift, and spinal elongation. Such changes need to be incorporated for human-system integration in the vehicle habitat, garment, and spacesuit designs, as inaccurate body measurements can result in suboptimal crew protection that can potentially decrease injury tolerance. However, measurement tools have not been available for accurate assessments of body shape changes. This work aimed to develop a prototype 3-D body scanning system with the configuration and performance optimized for in-flight crewmember body scanning. A scan hardware system was developed using Intel RealSense commercial off-the-shelf 3D sensors. The sensor parameters were iteratively optimized and tested to obtain the performance level needed for body scanning. A scan booth structure was fabricated, with the overall size 4 x 4 x 8 feet. The specific number of sensors and mounting positions were determined by iterative simulations, which indicated that 16 cameras can capture 94% and 96% of body surface area from the 1st percentile female and 99th percentile male crew population subject. The mounted sensors were linked through a mix of USB-C and USB-3 cables and operated for data acquisition from a Linux laptop computer. A software prototype was developed using Python and Tkinter graphical user interface toolkit. A calibration procedure was also built using a panel of QR codes. A computer vision tool detected and decoded the unique ID and pattern locations of the QR codes. The calibration information determined the position and orientation of the 3D sensors with respect to each other. The scanner performance was assessed using a set of 3D printed custom manikins. The manikin size and shape were derived from the previous ISS study, which measured the crewmembers’ anthropometry changes across the different flight phases. The average anthropometric measurements at the pre-flight and flight day 15 were sampled and projected onto the 1st percentile female and 99th percentile male body shapes. Another pair of manikins were also 3D printed to simulate the neutral body posture, estimated from ISS microgravity environments. A preliminary analysis assessed the performance of the newly developed scanner against the reference scanner, which has been used at the NASA JSC for crew and test subject anthropometry. Although the new scanner data showed several artifacts and missing geometries in the occluded body areas such as armpits and crotch, overall shape matched with the reference scan. When the manikin surface coordinates were compared, a root mean square error of 1.3 cm was observed from the manikin torso segment. Linear measurements including the stature, knee height and circumference measurements at the chest and calf showed differences from the reference scan measurements, ranging between 0.3 and 0.9 cm. Overall, this work demonstrated a development framework for an in-flight scanner with design and operation optimized for crewmember body scanning. Further improvement can potentially provide previously unavailable anthropometric data from different gravitational environments, including 0-g, 1/6-g, and 1-g. Such data can improve suit fit, habitat design, exercise efficacy quantification and sizing of orthostatic intolerance garments.

K H Kim↗

NASA NTRS2024

Transfer-AE: A novel autoencoder-based impact detection model for structural digital twin

Accurately detecting the location and intensity of impacts is crucial for ensuring structural safety. Currently, AI-based structural impact detection methods are widely used for their excellent detection accuracy. However, their generalization capability is limited by the scenarios present in the training data. Many complex and dangerous impact scenarios are difficult to conduct real-world experiments on to collect sufficient samples. To capture all impact scenarios and fully leverage the advantages of AI-based detection technologies, advanced methods involve combining real-world structural monitoring data with corresponding numerical models to construct digital twins. These methods continuously refine the created numerical models with limited real-world data and provide diverse impact scenarios through numerical model simulations. However, there are inevitable differences between digital models and physical models that are challenging to correct through mechanical means. This discrepancy in data distribution between the two models significantly hinders the application of digital twin technology in impact/event identification tasks. To address this challenge, this study proposes a novel model based on autoencoders, named Transfer-AE. Transfer-AE encodes the common features of digital twins in the latent space to bridge the uncertainty gap at a macro scale between numerical models and physical models and synchronously fits the magnitude and location of the impact load in the decoder. This enables consistent detection results for the same impact event, whether the sample comes from the numerical model or the physical model. Transfer-AE includes two operating modes: Mode 1 has a fixed computational complexity with stable inference speed, but the training cost and difficulty increase with data distribution. Mode 2's computational complexity increases with data distribution, but it has a fixed training cost and speed. In both cases involving the geodesic dome structure simulating a deep space habitat and the IASC-ASCE benchmark structure, Transfer-AE demonstrated the best performance in impact localization and quantification tasks compared to mainstream domain-adaptive transfer models.

Chengjia Han↗

DOE OSTI2020

Long-haul high rate quantum key distribution

Multiple bit values can be encoded on a single photon in a quantum key distribution (QKD) system using a plurality of sidebands of an optical carrier frequency. Computational and conjugate bases can be defined, and photons decoded based on a selected state from either basis. If n sidebands are available, as many as log2 n bits can be encoded on a single photon. Errors in detected bit values due to selection of an incorrect basis state or other errors can be at least partially corrected by bit distillation to identity bit strings for which a transmitter and a receiver record the same values, without insecure transmission of these values.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

Adaptive neural network management system

A method and computer system for managing a neural network. Data is sent into an input layer in a portion of layers of nodes in the neural network. The data moves on an encode path through the portion such that an output layer in the portion outputs encoded data. The encoded data is sent into the output layer on a decode path through the portion back to the input layer to obtain a reconstruction of the data by the input layer. A determination is made as to whether an undesired amount of error has occurred in the output layer based on the data sent into the input layer and the reconstruction of the data. A number of new nodes is added to the output layer when a determination is present that the undesired amount of the error occurred, enabling reducing the error using the number of the new nodes.

Draelos, Timothy J.↗

DOE OSTI2021

Long-haul high rate quantum key distribution

Multiple bit values can be encoded on a single photon in a quantum key distribution (QKD) system using a plurality of sidebands of an optical carrier frequency. Computational and conjugate bases can be defined, and photons decoded based on a selected state from either basis. If n sidebands are available, as many as log2n bits can be encoded on a single photon. Errors in detected bit values due to selection of an incorrect basis state or other errors can be at least partially corrected by bit distillation to identity bit strings for which a transmitter and a receiver record the same values, without insecure transmission of these values.

Nordholt, Jane Elizabeth↗

DOE OSTI2022

Bayesian State-Space Modeling Framework for Understanding and Predicting Golden Eagle Movements Using Telemetry Data

Predicting raptor movements through a wind power plant under given atmospheric and topographical conditions is a crucial first step in the overall goal of quantifying the risk of turbine-related collisions and mortalities. Extracting behavioral traits of golden eagles (Aquila chrysaetos) from telemetry data requires the fusion of noisy and sparse movement data (location, heading, velocity) with a stochastic mathematical representation of the eagles' decision-making processes. In this study, we framed this problem in a Bayesian state-space framework where both observations and decision-making are assumed to be stochastic processes connected through hidden states (mode of flight, intent), and the unknown model parameters are assumed to be random variables that are calibrated using the available telemetry data. This framework allowed for rigorous consideration of underlying uncertainties while allowing for both data and prior biological knowledge to contribute to a probabilistic and predictive agent-based movement model. We implemented and applied the Bayesian framework to understand movement behavior of 23 GPS-tagged golden eagles travelling in the western US for years 2019 and 2020. Our preliminary findings show that the Bayesian state-space framework provides a robust inverse modeling apparatus to decode eagle behavioral characteristics from telemetry data. This study was primarily aimed at verifying and validating the framework with selected golden eagle tracks (both long- and short-ranged), with future research aimed at extending the framework to include multi-mode flight, consideration of atmospheric data and uplift mechanisms, eagle-to-eagle interaction, and eagle-to-turbine interaction.

Bayesian modeling↗

DOE OSTI2022

Detecting CAN Masquerade Attacks with Signal Clustering Similarity

Vehicular Controller Area Networks (CANs) are susceptible to cyber attacks of different levels of sophistication. Fabrication attacks are the easiest to administer—an adversary simply sends (extra) frames on a CAN—but also the easiest to detect because they disrupt frame frequency. To overcome time-based detection methods, adversaries must administer masquerade attacks by sending frames in lieu of (and therefore at the expected time of) benign frames but with malicious payloads. Research efforts have proven that CAN attacks, and masquerade attacks in particular, can affect vehicle functionality. Examples include causing unintended acceleration, deactivation of vehicle’s brakes, as well as steering the vehicle. We hypothesize that masquerade attacks modify the nuanced correlations of CAN signal time series and how they cluster together. Therefore, changes in cluster assignments should indicate anomalous behavior. We confirm this hypothesis by leveraging our previously developed capability for reverse engineering CAN signals (i.e., CAN-D [Controller Area Network Decoder]) and focus on advancing the state of the art for detecting masquerade attacks by analyzing time series extracted from raw CAN frames. Specifically, we demonstrate that masquerade attacks can be detected by computing time series clustering similarity using hierarchical clustering on the vehicle’s CAN signals (time series) and comparing the clustering similarity across CAN captures with and without attacks. We test our approach in a previously collected CAN dataset with masquerade attacks (i.e., the ROAD dataset) and develop a forensic tool as a proof of concept to demonstrate the potential of the proposed approach for detecting CAN masquerade attacks.

Moriano Salazar, Pablo↗

DOE OSTI2022

Devices and methods for increasing the speed and efficiency at which a computer is capable of modeling a plurality of random walkers using a particle method

A method for increasing a speed or energy efficiency at which a computer is capable of modeling a plurality of random walkers. The method includes defining a virtual space in which a plurality of virtual random walkers will move among different locations in the virtual space. The method also includes either assigning a corresponding set of ringed neurons in a spiking neural network to a corresponding virtual random walker, or assigning a corresponding set of ringed neurons to a point in the virtual space. Movement of a given virtual random walker is tracked by decoding differences between states of individual neurons in a corresponding given set of ringed neurons. A virtual random walk of the plurality of virtual random walkers is executed using the spiking neural network.

Aimone, James Bradley↗

DOE OSTI2022

Continuous variable quantum secret sharing

Continuous variable quantum secret sharing (CV-QSS) technologies are described that use laser sources and homodyne detectors. Here, a Gaussian-modulated coherent state (GMCS) prepared by one device passes through secure stations of other devices sequentially on its way to a trusted device, and each of the other devices coherently adds a locally prepared, independent GMCS to the group of propagating GMCSs. Finally, the trusted device measures both the amplitude and the phase quadratures of the received group of coherent GMCSs using double homodyne detectors. The trusted device suitably uses the measurement results to establish a secure key for encoding secret messages to be broadcast to the other devices. The devices cooperatively estimate, based on signals corresponding to their respective Gaussian modulations, the trusted device's secure key, so that the cooperative devices can decode the broadcast secret messages with the secure key.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Method and apparatus for front end gather/scatter memory coalescing

A system for processing gather and scatter instructions can implement a front-end subsystem, a back-end subsystem, or both. The front-end subsystem includes a prediction unit configured to determine a predicted quantity of coalesced memory access operations required by an instruction. A decode unit converts the instruction into a plurality of access operations based on the predicted quantity, and transmits the plurality of access operations and an indication of the predicted quantity to an issue queue. The back-end subsystem includes a load-store unit that receives a plurality of access operations corresponding to an instruction, determines a subset of the plurality of access operations that can be coalesced, and forms a coalesced memory access operation from the subset. A queue stores multiple memory addresses for a given load-store entry to provide for execution of coalesced memory accesses.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Method and apparatus for back end gather/scatter memory coalescing

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Method for simultaneous characterization and expansion of reference libraries for small molecule identification

A variational autoencoder (VAE) has been developed to learn a continuous numerical, or latent, representation of molecular structure to expand reference libraries for small molecule identification. The VAE has been extended to include a chemical property decoder, trained as a multitask network, to shape the latent representation such that it assembles according to desired chemical properties. The approach is unique in its application to metabolomics and small molecule identification, focused on properties that are obtained from experimental measurements (m/z, CCS) paired with its training paradigm, which involves a cascade of transfer learning iterations. First, molecular representation is learned from a large dataset of structures with m/z labels. Next, in silico property values are used to continue training. Finally, the network is further refined by being trained with the experimental data. The trained network is used to predict chemical properties directly from structure and generate candidate structures with desired chemical properties. The network is extensible to other training data and molecular representations, and for use with other analytical platforms, for both chemical property and feature prediction as well as molecular structure generation.

Colby, Sean M.↗

DOE OSTI2023

Combustion control using spiking neural networks

A system that controls a combustion engine stores network vectors in a memory that represent diverse and distinct spiking neural networks. The system decodes the network vectors and trains and evaluates the spiking neural networks. The system duplicates selected network vectors and crosses-over the duplicated network vectors that represent modified spiking neural networks. The system mutates the crossed-over duplicated network vectors by randomly modifying one or more portions of the crossing-over duplicated network vectors. The system meter exhaust gas into an intake manifold when an engine temperature exceeds a threshold, an engine load exceeds a threshold, an engine's rotation-per-minute rate exceeds a threshold, and a fuel flow exceeds a threshold. The system modifies fuel flow into an engine's combustion chamber on a cycle-to-cycle basis by the trained spiking neural network.

Puente, Brian P. Maldonado↗

DOE OSTI2023

Tunable plasmonic color device and method of making the same

A plasmonic system is disclosed. The system includes at least one polarizer that is configured to provide at least one linearly polarized broadband light beam, an anisotropic plasmonic metasurface (APM) assembly having a plurality of nanoantennae each having a predetermined orientation with respect to a global axis representing encoded digital data, the APM assembly configured to receive the at least one linearly polarized broadband light beam and by applying localized surface plasmon resonance reflect light with selectable wavelengths associated with the predetermined orientations of the nanoantennae, and at least one analyzer that is configured to receive the reflected light with selectable wavelength, wherein the relative angles between each of the at least one analyzers and each of the at least one polarizers are selectable with respect to the global axis, thereby allowing decoding of the digital data.

Kildishev, Alexander V.↗

DOE OSTI2023

Quantum frequency processor for provable cybersecurity

Methods of quantum key distribution include receiving a frequency bin photon at a location, selecting a frequency bin photon quantum key distribution measurement basis, with a quantum frequency processor, performing a measurement basis transformation on the received frequency bin photon so that the frequency bin photon is measurable in the selected frequency bin photon quantum key distribution measurement basis, and detecting the frequency bin photon in the selected quantum key distribution measurement basis and assigning a quantum key distribution key value based on the detection to a portion of a quantum key distribution key. Apparatus and methods for encoding, decoding, transmitting, and receiving frequency bin photons are disclosed.

Lukens, Joseph M.↗

DOE OSTI2023

Filtering micro-operations for a micro-operation cache in a processor

A processor includes a micro-operation cache having a plurality of micro-operation cache entries for storing micro-operations decoded from instruction groups and a micro-operation filter having a plurality of micro-operation filter table entries for storing identifiers of instruction groups for which the micro-operations are predicted dead on fill if stored in the micro-operation cache. The micro-operation filter receives an identifier for an instruction group. The micro-operation filter then prevents a copy of the micro-operations from the first instruction group from being stored in the micro-operation cache when a micro-operation filter table entry includes an identifier that matches the first identifier.

Scrbak, Marko↗

DOE OSTI2024

Decarbonization Dilemmas: Deliberating Difficult Decisions in Laboratory Design

Dive into the depths of design and decision-making, while we discuss the decarbonization of laboratory buildings! Delve into the dense domain of laboratory design and operations, where every development presents a diverse array of dilemmas and delights. Join us for these dynamic sessions focused on decoding the secrets of sustainable success. Dig deep into the dynamic world of heat pump designs and the delicate balance of heating and cooling loads. Debate between constant and variable fume hood designs, where these decisions determine outcomes. Discover the divergent paths of HVAC system implementation, from the deployment of chilled beams to the diverse array of different terminal unit types. But don't delay; decisive action is demanded for these goals! Dare to dream of decarbonization as we direct discussions on retrofitting existing building stock versus innovative new design approaches. Delve into the depths of debate and emerge with a decisive strategy for sustainable success. Discuss recent discoveries in development from experts associated with existing laboratory buildings with decarbonization goals. These insights and lessons learned will help determine the path forward in our industry's drive for decarbonization designs. Decarbonization is no easy task, but with determination, dedication, and devotion, we can defy the odds and forge a brighter future for laboratory design and operations. Let's dare to decarbonize together!

decarbonization↗

DOE OSTI2024

Uncertainty-aware Continuous Implicit Neural Representations for Remote Sensing Object Counting

Many existing object counting methods rely on density map estimation (DME) of the discrete grid representation by decoding extracted image semantic features from designed convolutional neural networks (CNNs). Relying on discrete density maps not only leads to information loss dependent on the original image resolution, but also has a scalability issue when analyzing high-resolution images with cubically increasing memory complexity. Furthermore, none of the existing methods can offer reliable uncertainty quantification (UQ) for the derived count estimates. To overcome these limitations, we design UNcertainty-aware, hypernetwork-based Implicit neural representations for Counting (UNIC) to assign probabilities and the corresponding counting confidence over continuous spatial coordinates. We derive a sampling-based Bayesian counting loss function and develop the corresponding model training algorithm. UNIC outperforms existing methods on the Remote Sensing Object Counting (RSOC) dataset with reliable UQ and improved interpretability of the derived count estimates. Our code is available at https://github.com/SiyuanXu-tamu/UNIC.

97 MATHEMATICS AND COMPUTING↗