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At least 1,801 records · Page 100
All sourcesNASA NTRSDOE OSTI
NASA NTRS2006

Assessment of Postflight Locomotor Performance Utilizing a Test of Functional Mobility: Strategic and Adaptive Responses

Space flight induces adaptive modification in sensorimotor function, allowing crewmembers to operate in the unique microgravity environment. This adaptive state, however, is inappropriate for a terrestrial environment. During a re-adaptation period upon their return to Earth, crewmembers experience alterations in sensorimotor function, causing various disturbances in perception, spatial orientation, posture, gait, and eye-head coordination. Following long duration space flight, sensorimotor dysfunction would prevent or extend the time required to make an emergency egress from the vehicle; compromising crew safety and mission objectives. We are investigating two types of motor learning that may interact with each other and influence a crewmember's ability to re-adapt to Earth's gravity environment. In strategic learning, crewmembers make rapid modifications in their motor control strategy emphasizing error reduction. This type of learning may be critical during the first minutes and hours after landing. In adaptive learning, long-term plastic transformations occur, involving morphological changes and synaptic modification. In recent literature these two behavioral components have been associated with separate brain structures that control the execution of motor strategies: the strategic component was linked to the posterior parietal cortex and the adaptive component was linked to the cerebellum (Pisella, et al. 2004). The goal of this paper was to demonstrate the relative contributions of the strategic and adaptive components to the re-adaptation process in locomotor control after long duration space flight missions on the International Space Station (ISS). The Functional Mobility Test (FMT) was developed to assess crewmember s ability to ambulate postflight from an operational and functional perspective. Sixteen crewmembers were tested preflight (3 sessions) and postflight (days 1, 2, 4, 7, 25) following a long duration space flight (approx 6 months) on the ISS. We have further analyzed the FMT data to characterize strategic and adaptive components during the postflight readaptation period. Crewmembers walked at a preferred pace through an obstacle course set up on a base of 10 cm thick medium density foam (Sunmate Foam, Dynamic Systems, Inc., Leicester, NC). The 6.0m X 4.0m course consisted of several pylons made of foam; a Styrofoam barrier 46.0cm high that crewmembers stepped over; and a portal constructed of two Styrofoam blocks, each 31cm high, with a horizontal bar covered by foam and suspended from the ceiling which was adjusted to the height of the crewmember s shoulder. The portal required crewmembers to bend at the waist and step over a barrier simultaneously. All obstacles were lightweight, soft and easily knocked over. Crewmembers were instructed to walk through the course as quickly and as safely as possible without touching any of the objects on the course. This task was performed three times in the clockwise direction and three times in the counterclockwise direction that was randomly chosen. The dependent measures for each trial were: time to complete the course (seconds) and the number of obstacles touched or knocked down. For each crewmember, the time to complete each FMT trial from postflight days 1, 2, 4, 7 and 25 were further analyzed. A single logarithmic curve using a least squares calculation was fit through these data to produce a single comprehensive curve (macro). This macro curve composed of data spanning 25 days, illustrates the re-adaptive learning function over the longer time scale term. Additionally, logarithmic curves were fit to the 6 data trials within each individual post flight test day to produce 5 separate daily curves. These micro curves, produced from data obtained over the course of minutes, illustrates the strategic learning function exhibited over a relative shorter time scale. The macro curve for all subjects exhibited adaptive motor learning patterns over the 25 day period. Howev, 9/16 crewmembers exhibited significant strategic motor learning patterns in their micro curves, as defined by m > 1 in the equation of the line y=m*LN(x) +b. These data indicate that postflight recovery in locomotor function involves both strategic and adaptive mechanisms. Future countermeasures will be designed to enhance both recovery processes.

Warren, L. E.↗
NASA NTRS2017

Optimal Control Surface Layout for an Aeroservoelastic Wingbox

This paper demonstrates a technique for locating the optimal control surface layout of an aeroservoelastic Common Research Model wingbox, in the context of maneuver load alleviation and active utter suppression. The combinatorial actuator layout design is solved using ideas borrowed from topology optimization, where the effectiveness of a given control surface is tied to a layout design variable, which varies from zero (the actuator is removed) to one (the actuator is retained). These layout design variables are optimized concurrently with a large number of structural wingbox sizing variables and control surface actuation variables, in order to minimize the sum of structural weight and actuator weight. Results are presented that demonstrate interdependencies between structural sizing patterns and optimal control surface layouts, for both static and dynamic aeroelastic physics.

Stanford, Bret K.↗
NASA NTRS2016

Post-Test Inspection of NASA's Evolutionary Xenon Thruster Long Duration Test Hardware: Ion Optics

A Long Duration Test (LDT) was initiated in June 2005 as a part of NASA's Evolutionary Xenon Thruster (NEXT) service life validation approach. Testing was voluntarily terminated in February 2014, with the thruster accumulating 51,184 hours of operation, processing 918 kg of xenon propellant, and delivering 35.5 MN-s of total impulse. The post-test inspection objectives for the ion optics were derived from the original NEXT LDT test objectives, such as service life model validation, and expanded to encompass other goals that included verification of in situ measurements, test issue root causes, and past design changes. The ion optics cold grid gap had decreased only by an average of 7% of pretest center grid gap, so efforts to stabilize NEXT grid gap were largely successful. The upstream screen grid surface exhibited a chamfered erosion pattern. Screen grid thicknesses were ≥ 86% of the estimated pretest thickness, indicating that the screen grid has substantial service life remaining. Deposition was found on the screen aperture walls and downstream surfaces that was primarily composed of grid material and back-sputtered carbon, and this deposition likely caused the minor decreases in screen grid ion transparency during the test. Groove depths had eroded through up to 35% of the accelerator grid thickness. Minimum accelerator aperture diameters increased only by about 5-7% of the pretest values and downstream surface diameters increased by about 24-33% of the pretest diameters. These results suggest that increasing the accelerator aperture diameters, improving manufacturing tolerances, and masking down the perforated diameter to 36 cm were successful in reducing the degree of accelerator aperture erosion at larger radii.

Ion Thruster↗
NASA NTRS2017

Playbook Data Analysis Tool: Collecting Interaction Data from Extremely Remote Users

Typically, user tests for software tools are conducted in person. At NASA, the users may be located at the bottom of the ocean in a pressurized habitat, above the atmosphere in the International Space Station, or in an isolated capsule on a simulated asteroid mission. The Playbook Data Analysis Tool (P-DAT) is a human-computer interaction (HCI) evaluation tool that the NASA Ames HCI Group has developed to record user interactions with Playbook, the group's existing planning-and-execution software application. Once the remotely collected user interaction data makes its way back to Earth, researchers can use P-DAT for in-depth analysis. Since a critical component of the Playbook project is to understand how to develop more intuitive software tools for astronauts to plan in space, P-DAT helps guide us in the development of additional easy-to-use features for Playbook, informing the design of future crew autonomy tools.P-DAT has demonstrated the capability of discreetly capturing usability data in amanner that is transparent to Playbook’s end-users. In our experience, P-DAT data hasalready shown its utility, revealing potential usability patterns, helping diagnose softwarebugs, and identifying metrics and events that are pertinent to Playbook usage aswell as spaceflight operations. As we continue to develop this analysis tool, P-DATmay yet provide a method for long-duration, unobtrusive human performance collectionand evaluation for mission controllers back on Earth and researchers investigatingthe effects and mitigations related to future human spaceflight performance.

in-flight monitoring↗
NASA NTRS2019

Crazy Antennas

Everyone here is familiar with traditional antennas, time-honored favorites like dipoles and solid parabolic reflectors. But occasionally, circumstances call for something peculiar. This paper will describe a number of unusual antennas for particular communications scenarios that have been developed at the NASA Glenn Research over the past decade or so. The list includes: a K-band scanning ferroelectric reflectarray; a UHF "Vivaldi" for cellular connectivity to unmanned aerial vehicles; a Ku-band array that develops a top-hat pattern to feed a zone plate antenna; an active antenna that toggles between Iridium and GPS bands; a VHF hybrid spiral/dipole for orientation determination on Venus; and a Ku-band deployable reflector that strongly resembles a giant beach ball. Design strategy and performance results will be included, and trends towards cognitive antennas will be discussed.

Romanofsky, Robert↗
NASA NTRS2021

Post-test Inspection of NASA’s Evolutionary Xenon Thruster Long Duration Test Hardware: Ion Optics

A Long Duration Test (LDT) was initiated in June 2005 as a part of NASA’s Evolutionary Xenon Thruster (NEXT) service life validation approach. Testing was voluntarily terminated in February 2014, with the thruster accumulating 51,184 hours of operation, processing 918 kg of xenon propellant, and delivering 35.5 MN-s of total impulse. The post-test inspection objectives for the ion optics were derived from the original NEXT LDT test objectives, such as service life model validation, and expanded to encompass other goals that included verification of in situ measurements, test issue root causes, and past design changes. The ion optics cold grid gap had decreased only by an average of 7% of pretest center grid gap, so efforts to stabilize NEXT grid gap were largely successful. The upstream screen grid surface exhibited a chamfered erosion pattern. Screen grid thicknesses were ≥ 86% of the estimated pretest thickness, indicating that the screen grid has substantial service life remaining. Deposition was found on the screen aperture walls and downstream surfaces that was primarily composed of grid material and back-sputtered carbon, and this deposition likely caused the minor decreases in screen grid ion transparency during the test. Groove depths had eroded through up to 35% of the accelerator grid thickness. Minimum accelerator aperture diameters increased only by about 5-7% of the pretest values and downstream surface diameters increased by about 24-33% of the pretest diameters. These results suggest that increasing the accelerator aperture diameters, improving manufacturing tolerances, and masking down the perforated diameter to 36 cm were successful in reducing the degree of accelerator aperture erosion at larger radii.

George C. Soulas↗
DOE OSTI2020

Joint Design of Multimodal Transit Networks and Shared Autonomous Mobility Fleets

Providing quality transit service to travelers in low-density areas, particularly travelers without personal vehicles, is a constant challenge for transit agencies. The advent of fully-autonomous vehicles (AVs) and their inclusion in mobility service fleets may allow transit agencies to offer better service and/or reduce their own capital and operational costs. This study focuses on the problem of allocating resources between transit patterns and operating (or subsidizing) shared-use AV mobility services (SAMSs) in a large metropolitan area. To address this question, a joint transit network redesign and SAMS fleet size determination problem (JTNR-SFSDP) is introduced, and a bi-level mathematical programming formulation and solution approach are presented. The upper-level problem modifies a transit network frequency setting problem (TNFSP) formulation via incorporating SAMS fleet size as a decision variable and allowing the removal of bus routes. The lower-level problem consists of a dynamic combined mode choice-traveler assignment problem (DCMC-TAP) formulation. The heuristic solution procedure involves solving the upper-level problem using a nonlinear programming solver and solving the lower-level problem using an iterative agent-based assignment-simulation approach. To illustrate the effectiveness of the modeling framework, this study uses traveler demand from Chicago along with the regions existing multimodal transit network. The computational results indicate significant traveler benefits, in terms of improved average traveler wait times, associated with optimizing the joint design of multimodal transit networks and SAMS fleets compared with the initial transit network design.

bi-level programming↗
NASA NTRS1994

Operations and support cost modeling of conceptual space vehicles

The University of Dayton is pleased to submit this annual report to the National Aeronautics and Space Administration (NASA) Langley Research Center which documents the development of an operations and support (O&S) cost model as part of a larger life cycle cost (LCC) structure. It is intended for use during the conceptual design of new launch vehicles and spacecraft. This research is being conducted under NASA Research Grant NAG-1-1327. This research effort changes the focus from that of the first two years in which a reliability and maintainability model was developed to the initial development of an operations and support life cycle cost model. Cost categories were initially patterned after NASA's three axis work breakdown structure consisting of a configuration axis (vehicle), a function axis, and a cost axis. A revised cost element structure (CES), which is currently under study by NASA, was used to established the basic cost elements used in the model. While the focus of the effort was on operations and maintenance costs and other recurring costs, the computerized model allowed for other cost categories such as RDT&E and production costs to be addressed. Secondary tasks performed concurrent with the development of the costing model included support and upgrades to the reliability and maintainability (R&M) model. The primary result of the current research has been a methodology and a computer implementation of the methodology to provide for timely operations and support cost analysis during the conceptual design activities.

Ebeling, Charles↗
NASA NTRS1999

Laser Interferometer Space Antenna (LISA) Far Field Phase Patterns

The Laser Interferometer Space Antenna (LISA) for the detection of Gravitational Waves is a very long baseline interferometer, which will measure the changes in the distance of a five million kilometer arm to picometer accuracies. Knowledge of the phase deviations from a spherical wave and what causes these deviations are needed considerations in (as a minimum) the design of the telescope and in determining pointing requirements. Here we will present the far field phase deviations from a spherical wave for given Zernike aberrations of the exit pupil and discuss how these results affect the choice of a telescope design.

Waluschka, Eugene↗
DOE OSTI2025

Machine Learning for Anomaly Detection in Neural Network Security and SRF Cavities

This dissertation explores the development and deployment of machine learning approaches to address critical challenges in anomaly detection across two distinct domains: neural network security in federated learning settings and cavity behavior analysis in particle accelerator operations at Jefferson Lab in Newport News, Virginia. Anomaly detection identifies deviations from expected patterns, safeguarding systems in cybersecurity, industry, and research against malicious activities and failures. This dissertation demonstrates how our machine learning approaches enhance detection accuracy and efficiency in both neural network security and industrial applications. First, we investigate vulnerabilities in deep neural networks deployed in federated learning. Although federated learning preserves user privacy by training models locally, it remains vulnerable to backdoor attacks, in which malicious participants embed hidden triggers that induce targeted misbehavior. We propose a self-supervised contrastive learning framework to detect and mitigate such backdoor attacks. In our experiments, this method achieves higher detection accuracy and lower false positive rates than existing defenses, while operating without access to local model updates or original training data and thus preserving the privacy guarantees of the federated setting. Second, we address the operational reliability of superconducting radio-frequency (SRF) cavities at the Continuous Electron Beam Accelerator Facility (CEBAF). Our research leverages an unsupervised learning approach, combined with Principal Component Analysis (PCA) and k-means clustering, to identify anomalous behaviors in SRF cavities. Our method detects subtle anomalous behavior by analyzing SRF signal data. This knowledge allows for the early detection and resolution of potential faults, significantly improving the efficiency and reliability of operations. Third, we extend these insights to time-series anomaly detection more broadly. We design a contrastive-learning based model tailored to increasingly dynamic environments and academic research. This model improves detection accuracy in settings that require real-time monitoring and predictive maintenance. Our research underscores the broader applicability and impact of advanced machine learning techniques in anomaly detection. By extracting meaningful patterns from complex data, machine learning can significantly enhance security in distributed neural networks and improve the efficiency of particle accelerator operations. This dissertation serves as a stepping stone for future investigations into the vast possibilities of anomaly detection, inspiring further exploration and development of machine learning techniques in this field.

Ferguson, Hal [Old Dominion University]↗
DOE OSTI2021

Ce IV 70 Oxosulfate Rings, Frameworks, Supramolecular Assembly, and Redox Activity**

Abstract M IV molecular oxo‐clusters (M=Zr, Hf, Ce, Th, U, Np, Pu) are prolific in bottoms‐up material design, catalysis, and elucidating reaction pathways in nature and in synthesis. Here we introduce Ce 70 , a wheel‐shaped oxo‐cluster, [Ce IV 70 (OH) 36 (O) 64 (SO 4 ) 60 (H 2 O) 10 ] 4− . Ce 70 crystallizes into intricate high pore volume frameworks with divalent transition metals and Ce‐monomer linkers. Eight crystal‐structures feature four framework types in which the Ce 70 ‐rings are linked as propellers, in offset‐stacks, in a tartan pattern, and as isolated rings. Small‐angle X‐ray scattering of Ce 70 dissolved in butylamine, with and without added cations (Ce IV , alkaline earths, Mn II ), shows the metals‘ differentiating roles in ring linking, leading to supramolecular assemblies. The large acidic pores and abundant terminal sulfates provide ion‐exchange behavior, demonstrated with U IV and Nd III . Frameworks featuring Ce III/IV ‐monomer linkers demonstrate both oxidation and reduction. This study opens the door to mixed‐metal, highly porous framework catalysts, and new clusters for metal‐organic framework design

Colliard, Ian↗
DOE OSTI2021

PISCES-RF: a liquid-cooled high-power steady-state helicon plasma device

Radio-frequency (RF) driven helicon plasma sources can produce relatively high-density plasmas (n > 1019 m -3 ) at relatively moderate powers (<2 kW) in argon. However, to produce similar high-density plasmas for fusion relevant gases such as hydrogen (H), deuterium (D) and helium (He), much higher RF powers are needed. For very high RF powers, thermal issues of the RF-transparent dielectric window, used in the RF source design, limit the plasma operation timescales. To mitigate this constraint, we have designed, built and tested a novel helicon plasma source assembly with a fully liquid-cooled RF-transparent window which allows steady state operations at high power (up to 20 kW) and successfully produces high-density plasma with both argon and H. Deionized (DI) water, flowing between two concentric dielectric RF windows, is used as the coolant. We show that a full azimuthal blanket of DI water does not prevent high-density plasma production. From calorimetry on the DI water, we measure the net heat removed by the coolant at steady state conditions. Using infra-red imaging, we calculate the constant plasma heat deposition and measure the final steady state temperature distribution patterns on the inner surface of the ceramic layer. The heat deposition pattern follows the helical shape of the antenna. We also show the consistency between the heat absorbed by the DI water, as measured by calorimetry, and the total heat due to the combined effect of the plasma heating and the absorbed RF. These results are being used to answer critical engineering questions for the 200 kW RF device materials plasma exposure experiment being designed at the Oak Ridge National Laboratory as a next generation plasma material interaction device.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

The ELISA Detectability and Potency of Pegfilgrastim Decrease in Physiological Conditions: Key Roles for Aggregation and Individual Variability

PEGylated recombinant human granulocyte colony stimulating factor (pegflgrastim) is used clinically to accelerate immune reconstitution following chemotherapy and is being pursued for biosimilar development. One challenge to overcome in pegflgrastim biosimilar development is establishing pharmacokinetic (PK) similarity, which is partly due to the degree of PK variability. We herein report that commercially available G-CSF and PEG ELISA detection kits have different capacities to detect pegflgrastim aggregates that rapidly form in vitro in physiological conditions. These aggregates can be observed using SDS-PAGE, size-exclusion chromatography, dynamic light scattering, and real-time NMR analysis and are associated with decreased bioactivity as reflected by reduced drug-induced cellular proliferation and STAT3 phosphorylation. Furthermore, individual variability in the stability and detectability of pegflgrastim in human sera is also observed. Pegflgrastim levels display marked subject variability in sera from healthy donors incubated at 37°C. The stability patterns of pegflgrastim closely match the stability patterns of flgrastim, consistent with a key role for pegflgrastim’s G-CSF moiety in driving formation of inactive aggregates. Taken together, our results indicate that individual variability and ELISA specificity for inactive aggregates are key factors to consider when designing and interpreting studies involving the measurement of serum pegflgrastim concentrations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Proton beam position measurement in air using a BPM

A Beam Position Monitor (BPM) is potentially useful to measure the position and phase of the beam in air in a non-destructive way. An air-gap BPM in experiments, such as beam-induced radioactive waste management and dynamic radiography applications, where a so-called air gap is needed, can be utilized to measure the beam position and phase. In this study, a stripline BPM was used in the air-gap of an 800 MeV proton beam transport line. The downstream end of the primary beamline exit window was made of a thin aluminum plate and allowed the beam to travel 1.2 m in ambient air before re-entering into a vacuum drift section. Such a configuration was arranged to examine the BPM effectiveness in atmospheric temperature and pressure where ionization of air occurs. In this study, a high energy (800 MeV), high current (0.6 A beam peak current/pulse) proton beam of 5 mm radius was transported in the air. The beam position relative to the axis was measured by detecting the signature of the beam in a nanosecond scale. This nanosecond scale detection ability was useful to identify other signals such as plasma effects. The BPM signals were processed at a frequency of 201 MHz; thus, one gets a stronger response in a stripline pattern as it was used in this study instead of a dot-type BPM. Experimental data show that the BPM works well in air, but ionization of air or plasma formation could not be measured over the BPM signal. The design, construction, and performance of a BPM in air environment are presented.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Continuous wave room temperature operation of the 2 $μ$m GaSb-based photonic crystal surface emitting diode lasers

Continuous wave room temperature operation of 2 μm GaSb-based photonic-crystal surface-emitting diode lasers has been realized. The deep etched square mesa devices showed threshold current densities of 500 A/cm 2 at 20 °C. The epi-side down mounted lasers generated above 10 mW of output power in the continuous wave regime and tens of milliwatts in pulses from the 200 × 200 μm 2 aperture. Here, the breakthrough in the device performance parameters was achieved thanks to a highly homogeneous air-pocket retaining epitaxial regrowth process optimized for a specifically designed antimonide diode laser heterostructure. The nanofabrication method utilizing low temperature atomic hydrogen surface cleaning yielded low disorder square lattice of droplet-shaped voids covered by uniform p-cladding layer. The laser emission spectrum as well as near/far field patterns demonstrated peculiar features presumably linked to deformation of the void shape during regrowth and formation of the array of filaments.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2022

Surface coating by mechanofusion modulates bulk charging pathways and battery performance of Ni-rich layered cathodes

Ni-rich layered oxides as high-capacity battery cathodes suffer from degradation at high voltages. Here, we utilize a dry surface modification method, mechanofusion (MF), to achieve enhanced battery stability. The simplicity, high yield, and flexibility make it cost-effective and highly attractive for processing at the industrial scale. The underlying mechanisms responsible for performance improvement are unveiled by a systematic study combining multiple probes, e.g., 3D nano-tomography, spectroscopic imaging, in situ synchrotron diffraction, and finite element analysis (FEA). MF affects the bulk crystallography by introducing partially disordered structure, microstrain, and local lattice variation. Furthermore, the crack initiation and propagation pattern during delithiation are regulated and the overall mechanical fracture is reduced after such surface coating. We validate that MF can alter the bulk charging pathways. Such a synergic effect between surface modification and bulk charge distribution is fundamentally important for designing next-generation battery cathode materials.

25 ENERGY STORAGE↗
DOE OSTI2023

Foundations of machine learning for low-temperature plasmas: methods and case studies

Abstract Machine learning (ML) and artificial intelligence have proven to be an invaluable tool in tackling a vast array of scientific, engineering, and societal problems. The main drivers behind the recent proliferation of ML in practically all aspects of science and technology can be attributed to: (a) improved data acquisition and inexpensive data storage; (b) exponential growth in computing power; and (c) availability of open-source software and resources that have made the use of state-of-the-art ML algorithms widely accessible. The impact of ML on the field of low-temperature plasmas (LTPs) could be particularly significant in the emerging applications that involve plasma treatment of complex interfaces in areas ranging from the manufacture of microelectronics and processing of quantum materials, to the LTP-driven electrification of the chemical industry, and to medicine and biotechnology. This is primarily due to the complex and poorly-understood nature of the plasma-surface interactions in these applications that pose unique challenges to the modeling, diagnostics, and predictive control of LTPs. As the use of ML is becoming more prevalent, it is increasingly paramount for the LTP community to be able to critically analyze and assess the concepts and techniques behind data-driven approaches. To this end, the goal of this paper is to provide a tutorial overview of some of the widely-used ML methods that can be useful, amongst others, for discovering and correlating patterns in the data that may be otherwise impractical to decipher by human intuition alone, for learning multivariable nonlinear data-driven prediction models that are capable of describing the complex behavior of plasma interacting with interfaces, and for guiding the design of experiments to explore the parameter space of plasma-assisted processes in a systematic and resource-efficient manner. We illustrate the utility of various supervised, unsupervised and active learning methods using LTP datasets consisting of commonly-available, information-rich measurements (e.g. optical emission spectra, current–voltage characteristics, scanning electron microscope images, infrared surface temperature measurements, Fourier transform infrared spectra). All the ML demonstrations presented in this paper are carried out using open-source software; the datasets and codes are made publicly available. The FAIR guiding principles for scientific data management and stewardship can accelerate the adoption and development of ML in the LTP community.

Physics↗
DOE OSTI2024

An explainable variational autoencoder model for three-dimensional acoustic emission source localization in hollow cylindrical structures

We introduce an explainable variational autoencoder for three-dimensional (3D) localization of acoustic emission sources in hollow cylindrical structures, with an unsupervised approach. This research capitalizes on multi-arrival waveforms generated by helical path propagation in cylindrical geometries to enable efficient two-receiver localization. By integrating the modal characteristics of Lamb modes under multi-path conditions, we demonstrate that two sets of time-of-arrival differences and peak amplitudes extracted from one receiver can serve as effective localization features. This initial approach identifies four potential source locations, highlighting the feasibility of two-receiver source localization using traditional feature extraction methods. However, direct extraction can be challenging when mode overlaps occur, complicating the localization process. To address this, our work proposes a novel waveform-based method. This method leverages the consistent dispersion characteristics within isotropic materials, where each unique combination of mode arrival times and peak amplitudes constructs a distinct waveform. This distinctiveness overcomes the ambiguities associated with mode overlaps, significantly enhancing the method’s precision and robustness. Our approach adopts a data-driven strategy for waveform-based localization using variational autoencoder (VAE). VAE discerns waveform patterns for localization, while also addressing data uncertainties. The VAE’s encoder and decoder networks capture the localization process and the source’s influence on waveform generation, respectively, guiding latent variables to segregate waveforms by source in the latent space. The design of the learning process focuses on specific localization characteristics to enhance result explainability. Localization predictions are generated by projecting test waveforms, not included in the training set, onto a trained latent space. The prediction is determined using a nearest-neighbor approach based on the closest latent representation of a source. Validation with pencil-lead-break tests on a metallic pipe confirmed our method’s effectiveness, achieving an averaged 3D localization accuracy of 0.84.

Lee, Guan-Wei↗

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