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At least 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Real-World Driving Features for Identifying Intelligent Driver Model Parameters

Driver behavior models play a significant role in representing different driving styles and the associated relationships with traffic patterns and vehicle energy consumption in simulation studies. The models often serve as a proxy for baseline human driving when assessing energy-saving strategies that alter vehicle velocity. Such models are especially important in connectivity-enabled energy-saving strategy research because they can easily adapt to changing driving conditions like posted speed limits or change in traffic light state. While numerous driver models exist, parametric driver models provide the flexibility required to represent variability in real-world driving through different combinations of model parameters. These model parameters must be informed by a representative set of parameter values for the driver model to adequately represent a real-world driver. It stands to reason that determining the parameter values from real-world driving data would serve the purpose of representing a real-world driver. Although the published literature is replete with techniques and consequences of using real-world driving data to determine parameter values for parametric driver models, none have explored them in the context of using shorter driving features where the parameter values may change over the course of a single trip for the same driver. In this study we consider the “intelligent driver model” (IDM) as our driver behavior model and use real-world driving data from the Transportation Secure Data Center (TSDC) maintained at the National Renewable Energy Laboratory (NREL). The TSDC includes real-world travel data from across the United States, from which NREL has created a wide range of driving routes consisting of road features such as speed limits, stop locations, and turn locations. The real-world driving data are categorized into different driving regimes and extracted into driving features. The driving features are then used to calibrate the parameter values for the IDM. The distribution of the parameters and the relationships among them are reported. The insights obtained from this study enable judicious usage of IDM or similar parametric driver models to represent baseline human driver behavior in simulations.

27 ARPA - Advanced Research Projects Agency-Energy↗
DOE OSTI2021

Real-World Driving Features for Identifying Intelligent Driver Model Parameters: Preprint

Driver behavior models play a significant role in representing different driving styles and the associated relationships with traffic patterns and vehicle energy consumption in simulation studies. The models often serve as a proxy for baseline human driving when assessing energy-saving strategies that alter vehicle velocity. Such models are especially important in connectivity-enabled energy-saving strategy research because they can easily adapt to changing driving conditions like posted speed limits or change in traffic light state. While numerous driver models exist, parametric driver models provide the flexibility required to represent variability in real-world driving through different combinations of model parameters. These model parameters must be informed by a representative set of parameter values for the driver model to adequately represent a real-world driver. It stands to reason that determining the parameter values from real-world driving data would serve the purpose of representing a real-world driver. While the published literature is replete with techniques and consequences of using real-world driving data to determine parameter values for parametric driver models, none have explored them in the context of using shorter driving features where the parameter values may change over the course of a single trip for the same driver. In this study we consider the “Intelligent Driver Model” (IDM) as the driver behavior model to explore and real-world driving data from the Transportation Secure Data Center (TSDC) maintained at the National Renewable Energy Laboratory (NREL). The TSDC includes real-world travel data from across the United States, from which NREL has created a wide range of driving routes consisting of road features such as speed limits, stop locations and turn locations. The real-world driving data are categorized into different driving regimes and extracted into driving features. The driving features are then used to calibrate the parameter values for the IDM. The distribution of the parameters and the relationships among them are reported. The insights obtained from this study enable judicious usage of IDM or similar parametric driver models to represent baseline human driver behavior in simulations.

27 ARPA - Advanced Research Projects Agency-Energy↗
DOE OSTI2024

Bayes_Opt-SWMM: A Gaussian process-based Bayesian optimization tool for real-time flood modeling with SWMM

Real-time flood model plays a pivotal role in averting urban flood damage, particularly when there is minimal lead time for preparatory measures. However, urban flood modeling in real-time often contends with inherent uncertainties arising from input data uncertainty and parameter ambiguities. Here this study introduces a real-time calibration (RTC) tool called Bayes_Opt-SWMM, specifically tailored for real-time urban flood modeling and uncertainty optimization. This tool leverages the Gaussian process-based Bayesian optimization algorithm and interfaces seamlessly with the Stormwater Management Model (SWMM). It integrates real-time model forcing data and flood monitoring collected through sensors and gauges which are strategically placed within critical locations of urban drainage systems. Our approach hinges on the Surrogate Model based Uncertainty Optimization (SMUO) concept, providing an avenue for enhancing real-time flood modeling. Bayes_Opt-SWMM runs the optimization process using a surrogate model called Gaussian Process emulator with two inference methods: (1) the Gaussian Process (GP) model and (2) Markov Chain Monte Carlo (MCMC) algorithm in GP model (GP_MCMC). Furthermore, three acquisition functions, namely Expected Improvement (EI), Maximum Probability of Improvement (MPI), and Lower Confidence Bound (LCB), facilitate optimal parameter fitting within the surrogate models. The efficiency of GP-based surrogate models in learning SWMM model parameters, leads to an improved uncertainty quantification and accelerated real-time flood modeling in urban areas. Overall, Bayes_Opt-SWMM emerges as a cost-effective and valuable tool for real-time flood modeling and monitoring, with significant potential for managing intelligent storm water systems in urban environments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

NSTX-U Advances in Real-Time C++11 on Linux

Programming languages like C and Ada combined with proprietary embedded operating systems have dominated the real-time application space for decades. The new C++11standard includes native, language-level support for concurrency, a required feature for any nontrivial event-oriented real-time software. Threads, Locks, and Atomics now exist to provide the necessary tools to build the structures that make up the foundation of a complex real-time system. The National Spherical Torus Experiment Upgrade (NSTX-U) at the Princeton Plasma Physics Laboratory (PPPL) is breaking new ground with the language as applied to the needs of fusion devices. A new Digital Coil Protection System (DCPS) will serve as the main protection mechanism for the magnetic coils, and it is written entirely in C++11 running on Concurrent Computer Corporation's real-time operating system, RedHawk Linux. It runs over 600 algorithms in a 5 kHz control loop that determine whether or not to shut down operations before physical damage occurs. To accomplish this, NSTX-U engineers developed software tools that do not currently exist elsewhere, including real-time atomic synchronization, real-time containers, and a real-time logging framework. Together with a recent (and carefully configured) version of the GCC compiler, these tools enable data acquisition, processing, and output using a conventional operating system to meet a hard real-time deadline (that is, missing one periodic is a failure) of 200 microseconds.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Generalizing synthetic data-trained acoustic predictive models to real-world measurements

Acoustic Resonance Spectroscopy (ARS) is highly sensitive to structural properties such as material, geometry, and environmental conditions; as a consequence, it can noninvasively measure internal properties that are unobservable by most other methods. Because of its sensing capabilities and low implementation cost and complexity, ARS has potential as a paradigm shift in noninvasive sensing, characterization, and monitoring applications. However, extracting specific properties from ARS measurements, comprising the vibration spectrum of a test object, is challenging due to the sensitivity of the spectra to other structural changes not being measured, e.g. manufacturing tolerances, component coupling, environmental variation, etc. Neural Networks are promising tools for identifying trends in ARS measurements, but their training typically requires large datasets, which are often impractical to obtain for real-world systems. Synthetic data can be simulated efficiently, but discrepancies between synthetic and real-world data frequently lead to poor generalization when testing on the real-world data. We propose a novel ARS model training framework that enables networks trained exclusively on synthetic ARS data to generalize effectively to real-world measurements. Our approach leverages the Correlation Alignment (CORAL) technique to enforce the extraction of features common to both synthetic and real-world domains. As a case study, we demonstrate noninvasive ARS-based pressure measurements in sealed systems. Finite element method (FEM) simulations were used to generate synthetic training data across diverse vessel configurations and pressure conditions, and model performance was then tested on real-world measurements. We demonstrate that robust machine learning models for ARS can be developed without large real-world datasets, significantly broadening the applicability of ARS for noninvasive sensing. Moreover, the approach is extensible to other sensing modalities where synthetic data are abundant but real-world data are limited.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Leveraging Real-World Driving Data for Design and Impact Evaluation of Energy Efficient Control Strategies

Modeling and simulation are crucial in the development of advanced energy efficient control strategies. Utilizing real-world driving data as the underlying basis for control design and simulation lends veracity to projected real-world energy savings. Standardized drive cycles are limited in their utility for evaluating advanced driving strategies that utilize connectivity and on-vehicle sensing, primarily because they are typically intended for evaluating emissions and fuel economy under controlled conditions. Real-world driving data, because of its scale, is a useful representation of various road types, driving styles, and driving environments. The scale of real-world data also presents challenges in effectively using it in simulations. A fast and efficient simulation methodology is necessary to handle the large number of simulations performed for design analysis and impact evaluation of control strategies. In this study, two methods are presented of leveraging real-world data in both design optimization of energy efficient control strategies and in evaluating the real-world impact of those control strategies upon large-scale deployment. Through these methodologies, strategies with highest impact on energy savings were selected to be implemented as control algorithms. The developed algorithms were incorporated into a vehicle dynamics and powertrain control architecture implemented on a Cadillac CT6 demonstration vehicle. The control algorithms were then exercised on real-world driving scenarios to determine their impact on collective energy savings. The methodology utilizes the large-scale driving data sets maintained by the National Renewable Energy Laboratory to extract real-world driving scenarios and efficient simulation software tools. The insights obtained through this research help in guiding technology selection for energy efficient driving controls.

ARPA-E↗
NASA NTRS2005

Validation Of The Airspace Concept Evaluation System Using Real World Data

This paper discusses the process of performing a validation of the Airspace Concept Evaluation System (ACES) using real world historical flight operational data. ACES inputs are generated from select real world data and processed to create a realistic reproduction of a single day of operations within the National Airspace System (NAS). ACES outputs are then compared to real world operational metrics and delay statistics for the reproduced day. Preliminary results indicate that ACES produces delays and airport operational metrics similar to the real world with minor variations of delay by phase of flight. ACES is a nation-wide fast-time simulation tool developed at NASA Ames Research Center. ACES models and simulates the NAS using interacting agents representing center control, terminal flow management, airports, individual flights, and other NAS elements. These agents pass messages between one another similar to real world communications. This distributed agent based system is designed to emulate the highly unpredictable nature of the NAS, making it a suitable tool to evaluate current and envisioned airspace concepts. To ensure that ACES produces the most realistic results, the system must be validated. There is no way to validate future concepts scenarios using real world historical data, but current day scenario validations increase confidence in the validity of future scenario results. Each operational day has unique weather and traffic demand schedules. The more a simulation utilizes the unique characteristic of a specific day, the more realistic the results should be. ACES is able to simulate the full scale demand traffic necessary to perform a validation using real world data. Through direct comparison with the real world, models may continuee to be improved and unusual trends and biases may be filtered out of the system or used to normalize the results of future concept simulations.

Zelinski, Shannon↗
NASA NTRS2005

Development of a Real-Time GPS/Seismic Displacement Meter: Applications to Civilian Infrastructure in Orange and Western Riverside Counties, California

We propose a three-year applications project that will develop an Integrated Real-Time GPS/Seismic System and deploy it in Orange and Western Riverside Counties, spanning three major strike-slip faults in southern California (San Andreas, San Jacinto, and Elsinore) and significant populations and civilian infrastructure. The system relying on existing GPS and seismic networks will collect and analyze GPS and seismic data for the purpose of estimating and disseminating real-time positions and total ground displacements (dynamic, as well as static) during all phases of the seismic cycle, from fractions of seconds to years. Besides its intrinsic scientific use as a real-time displacement meter (transducer), the GPS/Seismic System will be a powerful tool for local and state decision makers for risk mitigation, disaster management, and structural monitoring (dams, bridges, and buildings). Furthermore, the GPS/Seismic System will become an integral part of California's spatial referencing and positioning infrastructure, which is complicated by tectonic motion, seismic displacements, and land subsidence. Finally, the GPS/Seismic system will also be applicable to navigation in any environment (land, sea, or air) by combining precise real-time instantaneous GPS positioning with inertial navigation systems. This development will take place under the umbrella of the California Spatial Reference Center, in partnership with local (Counties, Riverside County Flood and Water Conservation District, Metropolitan Water District), state (Caltrans), and Federal agencies (NGS, NASA, USGS), the geophysics community (SCIGN/SCEC2), and the private sector (RBF Consulting). The project will leverage considerable funding, resources, and R&D from SCIGN, CSRC and two NSF-funded IT projects at UCSD and SDSU: RoadNet (Real-Time Observatories, Applications and Data Management Network) and the High Performance Wireless Research and Education Network (HPWREN). These two projects are funded to develop both the wireless networks and the integrated, seamless, and transparent information management system that will deliver seismic, geodetic, oceanographic, hydrological, ecological, and physical data to a variety of end users in real-time in the San Diego region. CSRC is interested in providing users access to real-time, accurate GPS data for a wide variety of applications including RTK surveying/GIS and positioning of moving platforms such as aircraft and emergency vehicles. SCIGN is interested in upgrading sites to high-frequency real-time operations for rapid earthquake response and GPS seismology. The successful outcome of the project will allow the implementation of similar systems elsewhere, particularly in plate boundary zones with significant populations and civilian infrastructure. CSRC would like to deploy the GPS/Seismic System in other parts of California, in particular San Diego, Los Angeles County and the San Francisco Bay Area.

Bock, Yehuda↗
DOE OSTI2011

Towards a Real-Time Cluster Computing Infrastructure

Real-time computing has traditionally been considered largely in the context of single-processor and embedded systems, and indeed, the terms real-time computing, embedded systems, and control systems are often mentioned in closely related contexts. However, real-time computing in the context of multinode systems, specifically high-performance, cluster-computing systems, remains relatively unexplored, largely due to the fact that until now, there has not been a need for such an environment. In this paper, we motivate the need for a cluster computing infrastructure capable of supporting computation over large datasets in real-time. Our motivating example is an analytical framework to support the next generation North American power grid, which is growing both in size and complexity. With streaming sensor data in the future power grid potentially reaching rates on the order of terabytes per day, the task of analyzing this data subject to real-time guarantees becomes a daunting task which will require the power of high-performance cluster computing capable of functioning under real-time constraints. One specific challenge that such an environment presents is the need for real-time networked communication between cluster nodes. In this paper, we discuss the need for real-time high-performance cluster computation, along with our work-in-progress towards an infrastructure which will ultimately enable such an environment.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2019

Improving Operational Efficiency, Compliance and Community Perception with Real-time Data - 19260

The nuclear and medical radiation industry has always faced a significant burden in effectively balancing exposure and productivity. Maintenance operations and development of new techniques require significant staffing resources to monitor and review personnel exposures and changes in operational radiation levels. Currently utilised monitoring technology and review techniques remain unchanged since the 1950's, relying upon mostly analogue instrumentation and paper reporting. After utilising isolated-area monitoring during a reactor decommissioning project it became apparent that current systems created false assurance as the units continually failed without warning due to 'dirty' power. These instrument failures could have easily led to an unnecessary exposure while operators continued to assume they were being safely monitored. This paper is a philosophical discussion on how current shortcomings could be addressed, security increased, and exposure risks minimised, by utilising real-time monitoring and automated reporting. The community and media expectations of reported data and nuclear monitoring systems continue to be coloured by sensational technology seen in movies and emerging technology available through smart homes and smart cities initiatives. For the general public to be more engaged with real radiation levels and effects, the nuclear industry needs to more effectively communicate safe levels. Organisations can have regain control of exposure levels discussions through the controlled release of background and expected exposure level data. An increase in real world monitoring data communicated in a controlled and more effective way will aid in the general public accepting, and being educated about, the minimal increase above natural background levels of the nuclear industry and other radiation facilities. This is particularly as Australia is in the process of implementing its Nuclear Waste Facility, with community acceptance the number one inhibitor of being able to move forward. Storage, mobile assets, and processing all present unique management, security, exposure, and telemetry challenges for a facility operations manager. Active monitoring and automated review of the monitoring data can alert operators to any unexpected events before any resultant increases in radiation levels cause an unplanned exposure to operators or the environment. Integration of radiation monitoring data with global positioning telemetry enables better organisational awareness of mobile asset location and early detection of any potential shifting and reduction in shielding integrity of sources and encapsulated waste during transport. Additionally, the real-time monitoring can be set-up to enhance an organisations' or nation's security capabilities. For instance, the real time monitor will send an automated alert in the case of an unexpected movement of a source through a monitored facility or for an unscheduled movement of a monitored vehicle. There are further benefits to be gained in control, ALARA principles and effluent management by increasing real-time monitoring capabilities through the stages of the waste cycle. Increasing operational effectiveness while delivering operational cost savings sounds like an attractive proposition. With increased access to operational area radiation data comes drastically improved real-time management of exposures for new, and experimental practices. Increased live data can be utilised to identify optimum practices for reducing operational and environmental exposures or releases. This increased operational data effectively comes with the added benefit of a reduction in the required health physics personnel resources for general operations. By reducing exposure and the detachment of required health physics resources from routine operations, there is greater availability for these resources to support maintenance operations and project-based work. The review of this increased data will create internal capability to identify low-- and high-risk activities, resulting in the ability to reduce the number of highly skilled workers in an industry where the availability of this talent is declining. During my time as the Radiation Safety and Nuclear Security Manager at a research facility, I implemented a real-time monitoring system. The system identified a secondary risk that had previously been unidentified and posed a higher exposure and security risk than the radiation practice being monitored. What stops industry from implementing technology that encourages agile work practices, while minimising security risks and personnel exposure? (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

Towards Improved Real-Time Observability of Behind-Meter PhotoVoltaic Systems: A Data-Driven Approach

The stochasticity in energy supply introduced by increased PV penetration in grid distribution systems can lead to frequent and sudden supply-demand imbalances, thereby causing voltage and frequency fluctuations. Enhanced observability, in real-time, of grid attributes such as real and reactive power flows, power injections/consumption and voltages is becoming increasingly essential to ensure smooth grid operation under these new large-scale PV integration scenarios. Two significant challenges prevent grid operators from achieving this objective: 1) Computational complexity of quasi-static timeseries simulations, and 2) Partial observability of behind-meter grid assets. In this work, we take a step towards this objective by enhancing the observability of smart inverter equipped PV systems located behind AMI meters. Smart inverters convert the DC output of PV systems into real and reactive power, the ratio of which is governed by a power factor setting. This in turn is governed by the real-time voltage at its terminal, as per a pre-set volt/var curve. While it is possible to infer the power factor setting - thereby, real and reactive power injection of the PV system - at each time interval by quasi-static time series simulations, such methods are not scalable to large grid sizes. We propose to reduce the reliance on simulations by developing a deep neural network based data-driven model which predicts the time dependent power factors of the PV systems in the grid. The model uses a small amount of simulated data to learn the dependence of power factor on 1) load and apparent PV power injection (root mean square of real and reactive power) data, and 2) AMI net-load data (for BTM PV assets). Our results show that our model can predict power factors with mean absolute error of less than 6.2% under full observability scenario and under 8.6% under the partial observability scenario. Thus, our data-driven model can reduce the amount of simulations required to compute real and reactive injections from PV systems in the distribution system with or without complete information.

14 SOLAR ENERGY↗
DOE OSTI2022

Benchmarking Real-Time Control Platforms Using a Matlab/Simulink Coder with Applications in the Control of DC/AC Switched Power Converters

In the control of DC/AC switched power converters (SPC), one of the most important aspects to be considered is the selection of the real-time control platform. The real-time control platform must be able to meet the high performance efficiency and regulation requirements of the DC/AC SPC, as these typically operate at switching frequencies in the order of kHz to MHz. For this reason, the hardware characteristics of the ADC and PWM, and the processing capacity of the real-time control platform are of vital importance when implementing advanced digital controllers that maintain voltage and current levels within regulatory standards. In this context, we aimed to perform a comparative study of the computation times of different real-time control platforms when implementing different control strategies for DC/AC switched power converters. We also analyzed the impact of the real-time control platforms on the THD of the voltages generated by the DC/AC switched power converters. With the help of this paper, researchers and developers will have criteria to select which real-time control platform to use in real-time control for DC/AC SPC applications.

42 ENGINEERING↗
NASA NTRSDate not supplied

Formal Verification of the Interaction Between Semi-Algebraic Sets and Real Analytic Functions

Semi-algebraic sets and real analytic functions are fundamental concepts in Real Algebraic Geometry and Real Analysis, respectively. These concepts interact in the study of Differential Equations, where the real analytic solution to a differential equation is known to enter or exit a semi-algebraic set in a predicable way. Motivated to enhance the capability to reason about differential equations in the Prototype Verification System (PVS), a formalization of multivariate polynomials, semi-algebraic sets, and real analytic functions is developed. The favorable way that a real analytic function enters and exits a semi-algebraic set is proven. It is further shown that if the function is assumed to be smooth, a slightly weaker assumption than real analytic, these favorable interactions with semi-algebraic sets may fail.

Real analytic functions↗
DOE OSTI2013

Accuracy of Real-time Couch Tracking During 3-dimensional Conformal Radiation Therapy, Intensity Modulated Radiation Therapy, and Volumetric Modulated Arc Therapy for Prostate Cancer

Purpose: To evaluate the accuracy of real-time couch tracking for prostate cancer. Methods and Materials: Intrafractional motion trajectories of 15 prostate cancer patients were the basis for this phantom study; prostate motion had been monitored with the Calypso System. An industrial robot moved a phantom along these trajectories, motion was detected via an infrared camera system, and the robotic HexaPOD couch was used for real-time counter-steering. Residual phantom motion during real-time tracking was measured with the infrared camera system. Film dosimetry was performed during delivery of 3-dimensional conformal radiation therapy (3D-CRT), step-and-shoot intensity modulated radiation therapy (IMRT), and volumetric modulated arc therapy (VMAT). Results: Motion of the prostate was largest in the anterior-posterior direction, with systematic ( N-Ary-Summation ) and random ({sigma}) errors of 2.3 mm and 2.9 mm, respectively; the prostate was outside a threshold of 5 mm (3D vector) for 25.0%{+-}19.8% of treatment time. Real-time tracking reduced prostate motion to N-Ary-Summation =0.01 mm and {sigma} = 0.55 mm in the anterior-posterior direction; the prostate remained within a 1-mm and 5-mm threshold for 93.9%{+-}4.6% and 99.7%{+-}0.4% of the time, respectively. Without real-time tracking, pass rates based on a {gamma} index of 2%/2 mm in film dosimetry ranged between 66% and 72% for 3D-CRT, IMRT, and VMAT, on average. Real-time tracking increased pass rates to minimum 98% on average for 3D-CRT, IMRT, and VMAT. Conclusions: Real-time couch tracking resulted in submillimeter accuracy for prostate cancer, which transferred into high dosimetric accuracy independently of whether 3D-CRT, IMRT, or VMAT was used.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2015

Combining of different data pools for calculating a reliable POD for real defects

Real defects are essential for the evaluation of the reliability of non destructive testing (NDT) methods, especially in relation to the integrity of components. But in most of the cases the amount of available real defects is not sufficient to evaluate the system. Model-assisted and transfer functions are one way to handle that challenge. This study is focused on a combination of different data pools to create a sufficient amount of data for the reliability estimation. A widespread approach for calculating the Probability of Detection (POD) was used on a radiographic testing (RT) method. The highest contrast to noise ratio (CNR) of each indication is usually selected as the signal in the 'â vs. a' (signal-response) approach for RT. By combining real and artificial defects (flat bottom holes, side drill holes, flat bottom squares, notches, etc) in RT the highest signals are close to each other, but the process of creating and evaluating real defects is much more complex. The solution is seen in the combination of real and artificial data using a weighted least square approach. The weights for real or artificial data were based on the importance, the value and the different detection behavior of the different data. For comparison, the alternative combination through the Bayesian Updating was also applied. As verification, a data pool with a large amount of real data was available. In an advanced approach for evaluating the digital RT data, the size of the indication (perpendicular to the X-ray beam) was introduced as additional information. The signal now consists of the CNR and the area of the indication. The detectability is changing depending on the area of the indication, a fact that was ignored in the previous POD calculations for RT. This points out that a weighted least square approach to pool the data might no longer be adequate. The Bayesian Updating of the estimated parameters of the relationship between the signal field (the area of the indication) and the geometry of the defects is seen as the appropriate model to combine the different defect types in a useful and meaningful way. This work was carried out together with the Finnish company for spent nuclear fuel and waste management - Posiva Oy. The digital RT is one of the NDT methods that might be used for the inspection of the weld of the copper canister to be used for the spent nuclear fuel in the Scandinavian concept of final disposal.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2016

SU-G-BRA-17: Tracking Multiple Targets with Independent Motion in Real-Time Using a Multi-Leaf Collimator

Purpose: Multiple targets with large intrafraction independent motion are often involved in advanced prostate, lung, abdominal, and head and neck cancer radiotherapy. Current standard of care treats these with the originally planned fields, jeopardizing the treatment outcomes. A real-time multi-leaf collimator (MLC) tracking method has been developed to address this problem for the first time. This study evaluates the geometric uncertainty of the multi-target tracking method. Methods: Four treatment scenarios are simulated based on a prostate IMAT plan to treat a moving prostate target and static pelvic node target: 1) real-time multi-target MLC tracking; 2) real-time prostate-only MLC tracking; 3) correcting for prostate interfraction motion at setup only; and 4) no motion correction. The geometric uncertainty of the treatment is assessed by the sum of the erroneously underexposed target area and overexposed healthy tissue areas for each individual target. Two patient-measured prostate trajectories of average 2 and 5 mm motion magnitude are used for simulations. Results: Real-time multi-target tracking accumulates the least uncertainty overall. As expected, it covers the static nodes similarly well as no motion correction treatment and covers the moving prostate similarly well as the real-time prostate-only tracking. Multi-target tracking reduces >90% of uncertainty for the static nodal target compared to the real-time prostate-only tracking or interfraction motion correction. For prostate target, depending on the motion trajectory which affects the uncertainty due to leaf-fitting, multi-target tracking may or may not perform better than correcting for interfraction prostate motion by shifting patient at setup, but it reduces ∼50% of uncertainty compared to no motion correction. Conclusion: The developed real-time multi-target MLC tracking can adapt for the independently moving targets better than other available treatment adaptations. This will enable PTV margin reduction to minimize health tissue toxicity while remain tumor coverage when treating advanced disease with independently moving targets involved. The authors acknowledge funding support from the Australian NHMRC Australia Fellowship and NHMRC Project Grant No. APP1042375.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-G-JeP3-07: Real-Time Image Guided Radiation Therapy for Heterotopic Ossification in Patients After Hip Replacement

Purpose: To demonstrate the feasibility of using CBCT in a real-time image guided radiation therapy (IGRT) for single fraction heterotopic ossification (HO) in patients after hip replacement. In this real-time procedure, all steps, from simulation, imaging, planning to treatment delivery, are performed at the treatment unit in one appointment time slot. This work promotes real-time treatment to create a paradigm shift in the single fraction radiation therapy. Methods: An integrated real-time IGRT for HO was developed and tested for radiation treatment of heterotopic ossification for patient after hip replacement. After CBCT images are acquired at the linac, and sent to the treatment planning system, the physician determines the field and/or draws a block. Subsequently, a simple 2D AP/PA plan with prescription of 700 cGy is created on-the-fly for physician to review. Once the physician approves the plan, the patient is treated on the same simulation position. This real-time treatment requires the team of attending physician, physicist, therapists, and dosimetrist to work in harmony to achieve all the steps in a timely manner. Results: Ten patients have been treated with this real-time treatment, having the same beams arrangement treatment plan and prescription as our clinically regular CT-based 2D plans. The average time for these procedures are 52.9 ±10.7 minutes from the time patient entered the treatment room until s/he exited, and 37.7 ±8.6 minutes from starting CBCT until last beam delivered. Conclusion: The real-time IGRT for HO treatment has been tested and implemented to be a clinically accepted procedure. This one-time appointment greatly enhances the waiting time, especially when patients in high level of pain, and provides a convenient approach for the whole clinical staff. Other disease sites will be also tested with this new technology.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

How Low Can You Go? Using Synthetic 3D Imagery to Drastically Reduce Real-World Training Data for Object Detection

Deep convolutional neural networks (DCNNs) currently provide state-of-the-art performance on image classification and object detection tasks, and there are many global security mission areas where such models could be extremely useful. Crucially, the success of these models is driven in large part by the widespread availability of high-quality open source data sets such as Image Net, Common Objects in Context (COCO), and KITTI, which contain millions of images with thousands of unique labels. However, global security relevant objects-of-interest can be difficult to obtain: relevant events are low frequency and high consequence; the content of relevant images is sensitive; and adversaries and proliferators seek to obscure their activities. For these cases where exemplar data is hard to come-by, even fine-tuning an existing model with available data can be effectively impossible. Recent work demonstrated that models can be trained using a combination of real-world and synthetic images generated from 3D representations; that such models can exceed the performance of models trained using real-world data alone; and that the generated images need not be perfectly realistic (Tremblay, et al., 2018). However, this approach still required hundreds to thousands of real-world images for training and fine tuning, which for sparse, global security-relevant datasets can be an unrealistic hurdle. In this research, we validate the performance and behavior of DCNN models as we drive the number of real-world images used for training object detection tasks down to a minimal set. We perform multiple experiments to identify the best approach to train DCNNs from an extremely small set of real-world images. In doing so, we: Develop state-of-the-art, parameterized 3D models based on real-world images and sample from their parameters to increase the variance in synthetic image training data; Use machine learning explainability techniques to highlight and correct through targeted training the biases that result from training using completely synthetic images; and Validate our results by comparing the performance of the models trained on synthetic data to one another, and to a control model created by fine-tuning an existing ImageNet-trained model with a limited number (hundreds) of real-world images.

97 MATHEMATICS AND COMPUTING↗