Search NASA⌕ Search

SEARCH · Search NASA

Results for “Tracking error”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Limiter-aware adaptive excitation control for hydropower generators with legacy voltage regulation systems

Hydropower units often rely on legacy excitation systems with fixed automatic voltage regulator (AVR) settings, but parameter drift, changing operating conditions, and evolving grid-service demands can degrade terminal-voltage regulation over time. This problem should be addressed because poor voltage regulation affects reactive-power support, system stability, and reliable plant operation, and replacing installed AVR and limiter logic is costly and disruptive in practice. To address this issue, this paper proposes a retrofit-friendly enhancement for hydropower generator excitation systems that combines adaptive tuning of the AVR middle lead–lag branch with a small proportional-integral correction for removing steady bias and slow drift. The adaptive laws are normalized, projection-bounded, and equipped with leakage, while adaptation is enabled only in the middle branch. Thus, the intent of the underexcitation and overexcitation limiters, as well as legacy current and rate limits, remains unchanged. Stability is analyzed under incremental performance assumptions, yielding boundedness and asymptotic tracking in the absence of saturation. The method is evaluated using real operational data from a utility-scale hydropower unit at Rocky Reach Dam, Unit C-8. Compared with the fixed AVR baseline, the proposed method reduces terminal-voltage mean squared error by about 70%, removes long periods of steady-state bias, and improves transient tracking while coexisting with limiter actions. These results show that substantial voltage-control improvement can be achieved on existing hydropower units with minimal integration effort and without replacing the installed AVR structure.

ESST5B model↗
DOE OSTI2019

On-Track Measurement of Road Load Changes in Two Close-Following Vehicles: Methods and Results

As emerging automated vehicle technology is making advances in safety and reliability, engineers are also exploring improvements in energy efficiency with this new paradigm. Powertrain efficiency receives due attention, but also impactful is finding ways to reduce driving losses in coordinated-driving scenarios. Efforts focused on simulation to quantify road load improvements require a sufficient amount of background validation work to support them. This study uses a practical approach to directly quantify road load changes by testing the coordinated driving of two vehicles on a test track at various speeds (64, 88, 113 km/h) and vehicle time gaps (0.3 to 1.3 s). Axle torque sensors were used to directly measure the load required to maintain steady-state speeds while following a lead vehicle at various gap distances. Through trial and error, test methods were developed that appear to provide satisfactory results, considering the challenges of track testing under real-world conditions (wind, weather, temperature changes, etc.). We found that total road load was reduced by about 10-12% at an optimum gap time of 0.25 to 0.4 s. Challenges that were encountered included less repeatability of load measurements at short gap distances and powertrain mode switching (from a hybrid vehicle) that rendered the 88-km/h results less useful. This study will provide the framework for further investigations on coordinated-driving road load using track testing methods.

Duoba, Michael↗
DOE OSTI2025

Demonstration of an AI-driven workflow for dynamic x-ray spectroscopy

X-ray absorption near edge structure (XANES) spectroscopy is a powerful technique for characterizing the chemical state and symmetry of individual elements within materials, but requires collecting data at many energy points which can be time-consuming. While adaptive sampling methods exist for efficiently collecting spectroscopic data, they often lack domain-specific knowledge about the structure of XANES spectra. Here we demonstrate a knowledge-injected Bayesian optimization approach for adaptive XANES data collection that incorporates understanding of spectral features like absorption edges and pre-edge peaks. We show this method accurately reconstructs the absorption edge of XANES spectra using only 15–20% of the measurement points typically needed for conventional sampling, while maintaining the ability to determine the x-ray energy of the sharp peak after the absorption edge with errors less than 0.03 eV, the absorption edge with errors less than 0.1 eV; and overall root-mean-square errors less than 0.005 compared to traditionally sampled spectra. Our experiments on battery materials and catalysts demonstrate the method’s effectiveness for both static and dynamic XANES measurements, improving data collection efficiency and enabling better time resolution for tracking chemical changes. This approach advances the degree of automation in XANES experiments, reducing the common errors of under- or over-sampling points near the absorption edge and enabling dynamic experiments that require high temporal resolution or limited measurement time.

Bayesian optimization↗
DOE OSTI2018

Remote Distributed Vibration Sensing Through Opaque Media Using Permanent Magnets

Vibration sensing is critical for a variety of applications from structural fatigue monitoring to understanding the modes of airplane wings. In particular, remote sensing techniques are needed for measuring the vibrations of multiple points simultaneously, assessing vibrations inside opaque metal vessels, and sensing through smoke clouds and other optically challenging environments. Here, in this paper, we propose a method which measures high-frequency displacements remotely using changes in the magnetic field generated by permanent magnets. We leverage the unique nature of vibration tracking and use a calibrated local model technique developed specifically to improve the frequency-domain estimation accuracy. The results show that two-dimensional local models surpass the dipole model in tracking high-frequency motions. A theoretical basis for understanding the effects of electronic noise and error due to correlated variables is generated in order to predict the performance of experiments prior to implementation. Simultaneous measurements of up to three independent vibrating components are shown. The relative accuracy of the magnet-based displacement tracking with respect to the video tracking ranges from 40 to 190 μm when the maximum displacements approach ±5 mm and when sensor-to-magnet distances vary from 25 to 36 mm. Finally, vibration sensing inside an opaque metal vessel and mode shape changes due to damage on an aluminum beam are also studied using the wireless permanent-magnet vibration sensing scheme.

42 ENGINEERING↗
DOE OSTI2015

WE-AB-BRA-12: Virtual Endoscope Tracking for Endoscopy-CT Image Registration

Purpose: The use of endoscopy in radiotherapy will remain limited until we can register endoscopic video to CT using standard clinical equipment. In this phantom study we tested a registration method using virtual endoscopy to measure CT-space positions from endoscopic video. Methods: Our phantom is a contorted clay cylinder with 2-mm-diameter markers in the luminal surface. These markers are visible on both CT and endoscopic video. Virtual endoscope images were rendered from a polygonal mesh created by segmenting the phantom’s luminal surface on CT. We tested registration accuracy by tracking the endoscope’s 6-degree-of-freedom coordinates frame-to-frame in a video recorded as it moved through the phantom, and using these coordinates to measure CT-space positions of markers visible in the final frame. To track the endoscope we used the Nelder-Mead method to search for coordinates that render the virtual frame most similar to the next recorded frame. We measured the endoscope’s initial-frame coordinates using a set of visible markers, and for image similarity we used a combination of mutual information and gradient alignment. CT-space marker positions were measured by projecting their final-frame pixel addresses through the virtual endoscope to intersect with the mesh. Registration error was quantified as the distance between this intersection and the marker’s manually-selected CT-space position. Results: Tracking succeeded for 6 of 8 videos, for which the mean registration error was 4.8±3.5mm (24 measurements total). The mean error in the axial direction (3.1±3.3mm) was larger than in the sagittal or coronal directions (2.0±2.3mm, 1.7±1.6mm). In the other 2 videos, the virtual endoscope got stuck in a false minimum. Conclusion: Our method can successfully track the position and orientation of an endoscope, and it provides accurate spatial mapping from endoscopic video to CT. This method will serve as a foundation for an endoscopy-CT registration framework that is clinically valuable and requires no specialized equipment.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

SU-D-210-05: The Accuracy of Raw and B-Mode Image Data for Ultrasound Speckle Tracking in Radiation Therapy

Purpose: For ultrasound speckle tracking there is some evidence that the envelope-detected signal (the main step in B-mode image formation) may be more accurate than raw ultrasound data for tracking larger inter-frame tissue motion. This study investigates the accuracy of raw radio-frequency (RF) versus non-logarithmic compressed envelope-detected (B-mode) data for ultrasound speckle tracking in the context of image-guided radiation therapy. Methods: Transperineal ultrasound RF data was acquired (with a 7.5 MHz linear transducer operating at a 12 Hz frame rate) from a speckle phantom moving with realistic intra-fraction prostate motion derived from a commercial tracking system. A normalised cross-correlation template matching algorithm was used to track speckle motion at the focus using (i) the RF signal and (ii) the B-mode signal. A range of imaging rates (0.5 to 12 Hz) were simulated by decimating the imaging sequences, therefore simulating larger to smaller inter-frame displacements. Motion estimation accuracy was quantified by comparison with known phantom motion. Results: The differences between RF and B-mode motion estimation accuracy (2D mean and 95% errors relative to ground truth displacements) were less than 0.01 mm for stable and persistent motion types and 0.2 mm for transient motion for imaging rates of 0.5 to 12 Hz. The mean correlation for all motion types and imaging rates was 0.851 and 0.845 for RF and B-mode data, respectively. Data type is expected to have most impact on axial (Superior-Inferior) motion estimation. Axial differences were <0.004 mm for stable and persistent motion and <0.3 mm for transient motion (axial mean errors were lowest for B-mode in all cases). Conclusions: Using the RF or B-mode signal for speckle motion estimation is comparable for translational prostate motion. B-mode image formation may involve other signal-processing steps which also influence motion estimation accuracy. A similar study for respiratory-induced motion would also be prudent. This work is support by Cancer Research UK Programme Grant C33589/A19727.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Simulation investigation of multipactor in metal components for space application with an improved secondary emission model

Effects of the secondary electron emission (SEE) phenomenon of metal surface on the multipactor analysis of microwave components are investigated numerically and experimentally in this paper. Both the secondary electron yield (SEY) and the emitted energy spectrum measurements are performed on silver plated samples for accurate description of the SEE phenomenon. A phenomenological probabilistic model based on SEE physics is utilized and fitted accurately to the measured SEY and emitted energy spectrum of the conditioned surface material of microwave components. Specially, the phenomenological probabilistic model is extended to the low primary energy end lower than 20 eV mathematically, since no accurate measurement data can be obtained. Embedding the phenomenological probabilistic model into the Electromagnetic Particle-In-Cell (EM-PIC) method, the electronic resonant multipacting in microwave components can be tracked and hence the multipactor threshold can be predicted. The threshold prediction error of the transformer and the coaxial filter is 0.12 dB and 1.5 dB, respectively. Simulation results demonstrate that the discharge threshold is strongly dependent on the SEYs and its energy spectrum in the low energy end (lower than 50 eV). Multipacting simulation results agree quite well with experiments in practical components, while the phenomenological probabilistic model fit both the SEY and the emission energy spectrum better than the traditionally used model and distribution. The EM-PIC simulation method with the phenomenological probabilistic model for the surface collision simulation has been demonstrated for predicting the multipactor threshold in metal components for space application.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Combined Mesonet and Tracker

Title: Combined Mesonet and Trackers (UNL Mobile Mesonets) Authors University of Nebraska PI: Adam Houston, UNL Professor (ahouston2@unl.edu) Mailing Address: 126 Bessey Hall P.O. Box 880340 Lincoln, NE 68588-0340 CoMeT Overview The University of Nebraska-Lincoln operates three Combined Mesonet and Tracker (CoMeTs). CoMeTs are Ford Explorers (model years 2015, 2017, and 2019) with forward-mounted suites of meteorological sensors and dual moonroofs, combining the capability of a mobile mesonet to collect near-surface observations with the capability of an unmanned aircraft systems (UAS) tracker vehicle, which enables an observer in the second row of seats to see the aircraft and maintain compliance with Federal Aviation Administration policies on UAS operation. The CoMeTs collect observations of slow temperature and humidity at ~2 m above ground level (AGL) using a Vaisala HMP155A, fast temperature at ~2 m AGL using a Campbell Scientific 109SS-L thermistor, pressure at ~2.5 m AGL using a Vaisala PTB210, wind speed and direction at ~3.25 m AGL using an R.M. Young 05103 propeller anemometer, and vehicle heading using a KVH Industries C-100 fluxgate compass (Barbieri et al. 2019). The HMP155A and 109SS-L thermistor are shielded and aspirated within a U-tube (Waugh and Frederickson 2010; Houston et al. 2016). This list of sensors is also included in the CoMeT data file metadata. Manufacturer specifications for these instruments are given in Table 1 of Hanft and Houston (2018). The reported measured quantities are summarized below.CoMeT-3 was funded through an equipment allocation included in the NSF TORUS award (AGS-1824649). Instrument Description The specific sensors included on each CoMeT are summarized in the table at the end of this section. In general each CoMeT collects observations of slow temperature and humidity at ~2 m above ground level (AGL) using a Vaisala HMP155, fast temperature at ~2 m AGL using a Campbell Scientific 109SS-L thermistor, pressure at ~2.5 m AGL using a Vaisala PTB210 barometer with a Gill pressure port, wind speed and direction at ~3.25 m AGL using an R.M. Young 05103 propeller anemometer, position using a Garmin 19x HVS receiver, and vehicle heading using a KVH Industries C-100 fluxgate compass. The HMP155 and 109SS are shielded and aspirated within a U-tube (Waugh and Frederickson 2010; Houston et al. 2016). Fast temperature and corrected RH measurements (using sensors housed within the U-tube) have a time constant of 10-12 s based on data collected across a temperature and RH shock during the CLOUD-MAP 2017 calibration/validation tests on June 26, 2017. Vehicle speed was < 10 kts for this test. CoMeT-1 CoMeT-2 CoMeT-3 Slow Temperature Slow RH Vaisala HMP155A-L20-PT Part #: 22280-7 Vaisala HMP155E Part #: E1AA11A0B1A1A0A Vaisala HMP155E Part #: E1AA11A0B1A1A0A Fast temperature Campbell Scientific 109SS-L20-PT Part #: 21448-3 Campbell Scientific 109SS-L12-PW Part #: 21448-109 Campbell Scientific 109SS-L12-PW Part #: 21448-150 Pressure Vaisala PTB-210 Part #: A1A1B Gill Pressure Port Part #: 61002 Vaisala PTB-210 Part #: A1A1B Gill Pressure Port Part #: 61002 Vaisala PTB-210 Part #: A1A1B Gill Pressure Port Part #: 61002 Wind RM Young 05103-L20-PT Part #: 18435-310 RM Young 05103-L20-PW Part #: 18435-244 RM Young 05103-L20-PW Part #: 18435-244 GPS Garmin GPS 19x HVS (NMEA 0183) Part #: 010-01010-00 Garmin GPS 19x HVS (NMEA 0183) Part #: 010-01010-00 Garmin GPS 19x HVS (NMEA 0183) Part #: 010-01010-00 Compass KVH C-100 Part #: 01-0177-15 KVH C-100 Part #: 01-0177-15 KVH C-100 Part #: 01-0177-15 Logger Campbell Scientific CR6-NA-XT-SW Part #: 28385-9 Campbell Scientific CR6-WIFI-XT-SW Part #: 28385-6 Campbell Scientific CR6-WIFI-XT-SW Part #: 28385-6 Data Collection and Real-Time Processing The reported measured quantities are summarized in the table below. Quantity Units Source Epoch time Seconds GPS Latitude and longitude Degrees GPS Altitude m GPS Pressure hPa PTB210 Temperature (fast) deg C 109SS-L Temperature (slow) deg C HMP155 RH (slow) % HMP155 Vehicle speed m/s GPS Vehicle heading deg C-100 and GPS In addition to the measured variables, several derived variables are calculated. Corrected/fast relative humidity (%) Relative humidity is adjusted to the fast temperature following Richardson et al. (1998) and Houston et al. (2016). Water vapor mixing ratio (g/kg) Dew point temperature (&deg;C) Potential temperature (Kelvin) Virtual potential temperature (Kelvin) Equivalent potential temperature (Kelvin) Regular intercomparisons between all three CoMeTs were performed during TORUS 2019. Comparisons were also conducted between CoMeT-1 and CoMeT-2 during LAPSE-RATE (2018) on 14 July. In these intercomparisons, the vehicles were parked adjacent to each other aligned perpendicular to (and facing into) the wind. To minimize engine heating effects, intercomparisons were only conducted when the wind speed was >10 kts. Data Format Original data files for each deployment are saved as text files and then converted to NetCDF. NetCDF versions have units that are CF compliant and may not match the original units in the txt files. The naming convention for the NetCDF files is as follows: UNL.CoMeT3.{deployment date YYYYMMDD}.{start time of observation collection in UTC HHMM}.L2.{post-processing codes}.cdf example: UNL.CoMeT3.20190627.1931.L2.g1.f1.cdf Post-processing codes are included to track modifications to the raw data. These codes are closely connected to error flags associated with each record. Each letter corresponds to a particular instrument: g: GPS p: Barometer tf: Fast temperature ts: Slow temperature rh: Relative humidity f: Compass w: Wind monitor a: All instruments Each number corresponds to a particular post-processing action described more below. Measured and derived variables are included in the following table. Variable Heading Standard Name Units time Time seconds since 00:00:00, 01-01-1970 Alt Altitude meters lat Latitude degrees north lon Longitude degrees east fast_temp Air Temperature kelvin slow_temp Air Temperature kelvin pressure Air Pressure pascals logger_RH Relative Humidity percent calc_corr_RH Relative Humidity percent wind_speed Wind Speed meters per second wind_dir Wind From Direction degrees vehicle_dir Vehicle Direction degrees dewpoint Dew Point Temperature kelvin mixing_ratio Humidity Mixing Ratio g/g theta Air Potential Temperature kelvin theta_v Virtual Potential Temperature kelvin theta_e Equivalent Potential Temperature Kelvin error_flag The error_flag variable is a string that matches the post-processing codes listed above. All instruments will have an associated code, but will have a &ldquo;0&rdquo; if the datum is unchanged from the initial processed value. Error Codes The following table summarizes the error codes for data collected before 2020: Error Code Relevant CoMeT Description g1 1,2,3 Exact correction. GPS position and time reprocessed from raw data g2 1 As far as we can tell this is an exact correction to an error in the GPS time. During the correct time periods the time suddenly went backwards ~250s and stayed at this offset for 750s when it corrected itself. The offset was applied to the &ldquo;time warp&rdquo; period. p1 2 Approximate correction. Hole in the pressure tube connecting the pressure port to the barometer. Resulted in erroneously low air pressure measurements when the vehicle was in motion. Derived variables recalculated (dew point temperature [e depends on qv and p], water vapor mixing ratio, potential temperature, virtual potential temperature, equivalent potential temperature) a1 3 Exact correction. Missing data reprocessed from raw data a2 1 Bug fix to bias correction for ts1, ts2, and rh1: water vapor mixing ratio was off by a factor of 10 and virtual potential temperature was wrong because of this. f1 3 No correction, missing data. Fluxgate compass inoperable. Wind speed and direction calculated using GPS-derived vehicle heading instead. rh1 1 Approximate correction. Constant bias of +1.7% removed from relative humidity. Derived variables recalculated (corrected/fast relative humidity, dew point temperature, water vapor mixing ratio, virtual potential temperature, equivalent potential temperature) ts1 1 Approximate correction. Constant bias of +0.6 K removed from slow temperature. Derived variables recalculated (corrected/fast relative humidity, dew point temperature, water vapor mixing ratio, virtual potential temperature, equivalent potential temperature) ts2 1 Approximate correction. Constant bias of +1.0 K removed. Derived variables recalculated (corrected/fast relative humidity, dew point temperature, water vapor mixing ratio, virtual potential temperature, equivalent potential temperature) References Bolton, D., 1980: The Computation of Equivalent Potential Temperature. Mon. Wea. Rev., 108, 1046&ndash;1053, https://doi.org/10.1175/1520-0493(1980)108<1046:TCOEPT>2.0.CO;2. Hanft, W., and A. L. Houston, 2018: An Observational and Modeling Study of Mesoscale Air Masses with High Theta-E. Mon. Wea. Rev., 146, 2503&ndash;2524, https://doi.org/10.1175/MWR-D-17-0389.1.Wexler Houston, A. L., R. J. Laurence III, T. W. Nichols, S. Waugh, B. Argrow, and C. L. Ziegler, 2016: Intercomparison of unmanned aircraft-borne and mobile mesonet atmospheric sensors. Journal of Atmospheric and Oceanic Technology. 33, 1569-1582, doi: 10.1175/JTECH-D-15-0178.1. Lowe, P. R., 1977: An Approximating Polynomial for the Computation of Saturation Vapor Pressure. J. Applied Meteorology, 16, 100&ndash;103. Richardson, S. J., S. E. Frederickson, F. V. Brock, and J. A. Brotzge, 1998: Combination temperature and relative humidity probes: Avoiding large air temperature errors and associated relative humidity errors. Preprints, 10th Symp. On Meteorological Observations and Instrumentation, Phoenix, AZ, Amer. Meteor. Soc., 278&ndash;283. Waugh, S., and S. E. Frederickson, 2010: An improved aspirated temperature system for mobile meteorological observations, especially in severe weather. 25th Conf. on Severe Local Storms, Denver, CO, Amer. Meteor. Soc., P5.2. [Available online at https://ams.confex.com/ams/25SLS/techprogram/paper_176205.htm.]

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Detecting and tracking drift in quantum information processors

Abstract If quantum information processors are to fulfill their potential, the diverse errors that affect them must be understood and suppressed. But errors typically fluctuate over time, and the most widely used tools for characterizing them assume static error modes and rates. This mismatch can cause unheralded failures, misidentified error modes, and wasted experimental effort. Here, we demonstrate a spectral analysis technique for resolving time dependence in quantum processors. Our method is fast, simple, and statistically sound. It can be applied to time-series data from any quantum processor experiment. We use data from simulations and trapped-ion qubit experiments to show how our method can resolve time dependence when applied to popular characterization protocols, including randomized benchmarking, gate set tomography, and Ramsey spectroscopy. In the experiments, we detect instability and localize its source, implement drift control techniques to compensate for this instability, and then demonstrate that the instability has been suppressed.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

High-precision control of tracked field robots in the presence of unknown traction coefficients

Accurate steering through crop rows that avoids crop damage is one of the most important tasks for agricultural robots utilized in various field operations, such as monitoring, mechanical weeding, or spraying. In practice, varying soil conditions can result in off-track navigation due to unknown traction coefficients so that it can cause crop damage. To address this problem, this work presents the development, application, and experimental results of a real-time receding horizon estimation and control (RHEC) framework applied to a fully autonomous mobile robotic platform to increase its steering accuracy. Recent advances in cheap and fast microprocessors, as well as advances in solution methods for nonlinear optimization problems, have made nonlinear receding horizon control (RHC) and receding horizon estimation (RHE) methods suitable for field robots that require high-frequency (milliseconds) updates. A real-time RHEC framework is developed and applied to a fully autonomous mobile robotic platform designed by the authors for in-field phenotyping applications in sorghum fields. Nonlinear RHE is used to estimate constrained states and parameters, and nonlinear RHC is designed based on an adaptive system model that contains time-varying parameters. The capabilities of the real-time RHEC framework are verified experimentally, and the results show an accurate tracking performance on a bumpy and wet soil field. Finally, the mean values of the Euclidean error and required computation time of the RHEC framework are equal to 0.0423 m and 0.88 ms, respectively.

42 ENGINEERING↗
DOE OSTI2016

SU-F-J-18: Feasibility of Open Mask Immobilization with Optical Imaging Guidance (OIG) for H&N Radiotherapy

Purpose: Full face and neck thermoplastic masks provide standard-of-care immobilization for patients receiving H&N IMRT. However, these masks are uncomfortable and increase skin dose. The purpose of this pilot study was to investigate the feasibility and setup accuracy of open face and neck mask immobilization with OIG. Methods: Ten patients were consented and enrolled to this IRB-approved protocol. Patients were immobilized with open masks securing only forehead and chin. Standard IMRT to 60–70 Gy in 30 fractions were delivered in all cases. Patient simulation information, including isocenter location and CT skin contours, were imported to a commercial OIG system. On the first day of treatment, patients were initially set up to surface markings and then OIG referenced to face and neck skin regions of interest (ROI) localized on simulation CT images, followed by in-room CBCT. CBCTs were acquired at least weekly while planar OBI was acquired on the days without CBCT. Following 6D robotic couch correction with kV imaging, a new optical real-time surface image was acquired to track intrafraction motion and to serve as a reference surface for setup at the next treatment fraction. Therapists manually recorded total treatment time as well as couch shifts based on kV imaging. Intrafractional ROI motion tracking was automatically recorded. Results: Setup accuracy of OIG was compared with CBCT results. The setup error based on OIG was represented as a 6D shift (vertical/longitudinal/lateral/rotation/pitch/roll). Mean error values were −0.70±3.04mm, −0.69±2.77mm, 0.33±2.67 mm, −0.14±0.94 o, −0.15±1.10o and 0.12±0.82o, respectively for the cohort. Average treatment time was 24.1±9.2 minutes, comparable to standard immobilization. The amplitude of intrafractional ROI motion was 0.69±0.36 mm, driven primarily by respiratory neck motion. Conclusion: OGI can potentially provide accurate setup and treatment tracking for open face and neck immobilization. Study accrual and patient/provider satisfaction survey collection remain ongoing. This study is supported by VisionRT, Ltd.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

TU-F-CAMPUS-T-02: Vernier Picket Fence Test: A Non-Imaging Method to Localize the Radiation Isocenter with Submillimeter Accuracy

Purpose: The purpose of this study is to propose a new non-imaging method to localize the radiation isocenter with submillimeter accuracy. Methods: The Vernier picket fence (VPF) is a multileaf collimator (MLC) picket fence sequence in which the fence spacing is 1/N smaller than the detector spacing of the QA phantom, where N is the magnification factor, typically set to 10 or 20. Similar to reading a Vernier caliper, the user can easily achieve the resolution of 1/N of the detector spacing by visually inspecting the maximum signal. To achieve higher accuracy, a Gaussian model was used to interpolate the peak position, which can fall between adjacent detectors. In two separate tests, precise MLC offsets and imprecise couch offsets were applied to a 2D detector array (MapCheck, Sun Nuclear Corp., Melbourne, Florida) to introduce setup errors. Two vertical VPF fields were delivered with collimator angles at 0° and 90° to detect the lateral and longitudinal setup errors, respectively. For a rotational QA phantom, an additional lateral VPF field is needed to detect the vertical setup error for three-dimensional capabilities. Results: With N set to 20 and a detector spacing of 5 mm for MapCheck, the resolution of the VPF’s visual analysis is 0.25 mm. With the Gaussian interpretation, the VPF can achieve an accuracy of 0.02 mm, as shown by the MLC offset test. The couch offset test measured the couch hysteresis and demonstrated that the setup error detected by the VPF differed from the ExacTrac™ (Brainlab AG, Feldkirchen, Germany) optical tracking by 0.055 mm in the lateral direction and 0.041 mm in the longitudinal direction on average. The VPF was also shown to be feasible in the vertical direction as well. Conclusion: This study verified the VPF as a non-imaging method to localize the radiation isocenter with submillimeter accuracy. Funding is in part by the Portland Chapter of the Achievement Rewards for College Scientists. The content is solely the responsibility of the authors, and does not necessarily represent the official views of the sponsors. The authors declare no conflict of interest.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Towards Native Execution of Deep Learning on a Leadership-Class HPC System

Large parallel machines generally offer the best parallel performance with "native execution" that is achieved using codes developed with the optimized compilers, communication libraries, and runtimes offered on the machines. In this paper, we report and analyze performance results from native execution of deep learning on a leadership-class high-performance computing (HPC) system. Using our new code called DeepEx, we present a study of the parallel speed up and convergence rates of learning achieved with native parallel execution. In the trade-off between computational parallelism and synchronized convergence, we first focus on maximizing parallelism while still obtaining convergence. Scaling results are reported from execution on up to 15,000 GPUs using two scientific data sets from atom microscopy and protein folding applications, and also using the popular ImageNet data set. In terms of the traditional measure of parallel speed up, excellent scaling is observed up to 12,000 GPUs. Additionally, accounting for convergence rates of deep learning accuracy or error, a deep learning-specific metric called "learning speed up" is also tracked. The performance results indicate the need to evaluate parallel deep learning execution in terms of learning speed up, and point to additional directions for improved exploitation of high-end HPC systems.

Yoginath, Srikanth↗
DOE OSTI2015

SU-E-T-138: Automated Chart Review Module Including Cross-Vendor Data Transfer Verification Developed for IHE-RO Initiative

Purpose: To follow the Integrating Healthcare Enterprise - Radiation Oncology (IHE-RO) initiative of proper cross-vendor technology integration, an automated chart checker (ACC) was developed. ACC compares extracted data from an approved patient plan in the Eclipse treatment planning system (TPS) against data existing in the Mosaiq treatment management system (TMS). ACC automatically analyzes these parameters using built-in quality checklists to provide further aid in chart review. Methods: Eclipse TPS data are obtained using Eclipse scripting API (ESAPI) while Mosaiq TMS data are obtained from a radiotherapy-treatment-planning (RTP) file. Using this information, ACC identifies TPS-TMS discrepancies in 18 primary beam parameters including MU, energy, jaw positions, gantry angle, table angle, accessories, and bolus for up to 31 beams. Next, approximately 40 items from traditional quality checklists are evaluated such as prescription consistency, DRR graticule placement, plan approval status, global max dose, and dose tracking coefficients. Parameters were artificially modified to determine if ACC would detect an error in data transfer and to test each component of quality checklists. Results: Using ESAPI scripting and RTP file-processing, ACC was able to properly aggregate data from TPS and TMS for up to 31 beams. Errors were artificially introduced into each plan parameter, and ACC was able to successfully detect all of them within seconds. Next, ACC was able to successfully detect mistakes in the chart by identifying deviations with its quality checklists, within seconds. Conclusion: ACC effectively addresses the potential issue of faulty cross-vendor data transfer, as described by IHE-RO. In addition, ACC was also able to detect deviations from its built-in quality checklists. ACC is already an invaluable tool for efficient and standardized chart review and will continue to improve as its incorporated checklists become more comprehensive.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

On infinite tensor networks, complementary recovery and type II factors

We initiate a study of local operator algebras at the boundary of infinite tensor networks, using the mathematical theory of inductive limits. In particular, we consider tensor networks in which each layer acts as a quantum code with complementary recovery, a property that features prominently in the bulk-to-boundary maps intrinsic to holographic quantum error-correcting codes. In this case, we decompose the limiting Hilbert space and the algebras of observables in a way that keeps track of the entanglement in the network. As a specific example, we describe this inductive limit for the holographic Harlow-Pastawski-Preskill-Yoshida code model and relate its algebraic and error-correction features. We find that the local algebras in this model are given by the hyperfinite type II$_\infty$ factor. Next, we discuss other networks that build upon this framework and comment on a connection between type II factors and stabilizer circuits. We conclude with a discussion of multiscale entanglement renormalization ansatz networks in which complementary recovery is broken. We argue that this breaking possibly permits a limiting type III von Neumann algebra, making them more suitable ansätze for approximating subregions of quantum field theories.

holographic dualities↗
DOE OSTI2017

Estimation and Fusion for Tracking Over Long-Haul Links Using Artificial Neural Networks

In a long-haul sensor network, sensors are remotely deployed over a large geographical area to perform certain tasks, such as tracking and/or monitoring of one or more dynamic targets. A remote fusion center fuses the information provided by these sensors so that a final estimate of certain target characteristics – such as the position – is expected to possess much improved quality. In this paper, we pursue learning-based approaches for estimation and fusion of target states in longhaul sensor networks. In particular, we consider learning based on various implementations of artificial neural networks (ANNs). Finally, the joint effect of (i) imperfect communication condition, namely, link-level loss and delay, and (ii) computation constraints, in the form of low-quality sensor estimates, on ANN-based estimation and fusion, is investigated by means of analytical and simulation studies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Optical eye tracking system for real-time noninvasive tumor localization in external beam radiotherapy

Purpose: External beam radiotherapy currently represents an important therapeutic strategy for the treatment of intraocular tumors. Accurate target localization and efficient compensation of involuntary eye movements are crucial to avoid deviations in dose distribution with respect to the treatment plan. This paper describes an eye tracking system (ETS) based on noninvasive infrared video imaging. The system was designed for capturing the tridimensional (3D) ocular motion and provides an on-line estimation of intraocular lesions position based on a priori knowledge coming from volumetric imaging. Methods: Eye tracking is performed by localizing cornea and pupil centers on stereo images captured by two calibrated video cameras, exploiting eye reflections produced by infrared illumination. Additionally, torsional eye movements are detected by template matching in the iris region of eye images. This information allows estimating the 3D position and orientation of the eye by means of an eye local reference system. By combining ETS measurements with volumetric imaging for treatment planning [computed tomography (CT) and magnetic resonance (MR)], one is able to map the position of the lesion to be treated in local eye coordinates, thus enabling real-time tumor referencing during treatment setup and irradiation. Experimental tests on an eye phantom and seven healthy subjects were performed to assess ETS tracking accuracy. Results: Measurements on phantom showed an overall median accuracy within 0.16 mm and 0.40° for translations and rotations, respectively. Torsional movements were affected by 0.28° median uncertainty. On healthy subjects, the gaze direction error ranged between 0.19° and 0.82° at a median working distance of 29 cm. The median processing time of the eye tracking algorithm was 18.60 ms, thus allowing eye monitoring up to 50 Hz. Conclusions: A noninvasive ETS prototype was designed to perform real-time target localization and eye movement monitoring during ocular radiotherapy treatments. The device aims at improving state-of-the-art invasive procedures based on surgical implantation of radiopaque clips and repeated acquisition of X-ray images, with expected positive effects on treatment quality and patient outcome.

60 APPLIED LIFE SCIENCES↗