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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.

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At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Point pattern match-based change detection in a constellation of previously detected objects

A method and system is provided that applies attribute- and topology-based change detection to objects that were detected on previous scans of a medium. The attributes capture properties or characteristics of the previously detected objects, such as location, time of detection, detection strength, size, elongation, orientation, etc. The locations define a three-dimensional network topology forming a constellation of previously detected objects. The change detection system stores attributes of the previously detected objects in a constellation database. The change detection system detects changes by comparing the attributes and topological consistency of newly detected objects encountered during a new scan of the medium to previously detected objects in the constellation database. The change detection system may receive the attributes of the newly detected objects as the objects are detected by an object detection system in real time.

47 OTHER INSTRUMENTATION↗
DOE OSTI2015

Dose planning objectives in anal canal cancer IMRT: the TROG ANROTAT experience

Intensity modulated radiotherapy (IMRT) is ideal for anal canal cancer (ACC), delivering high doses to irregular tumour volumes whilst minimising dose to surrounding normal tissues. Establishing achievable dose objectives is a challenge. The purpose of this paper was to utilise data collected in the Assessment of New Radiation Oncology Treatments and Technologies (ANROTAT) project to evaluate the feasibility of ACC IMRT dose planning objectives employed in the Australian situation. Ten Australian centres were randomly allocated three data sets from 15 non-identifiable computed tomography data sets representing a range of disease stages and gender. Each data set was planned by two different centres, producing 30 plans. All tumour and organ at risk (OAR) contours, prescription and dose constraint details were provided. Dose–volume histograms (DVHs) for each plan were analysed to evaluate the feasibility of dose planning objectives provided. All dose planning objectives for the bone marrow (BM) and femoral heads were achieved. Median planned doses exceeded one or more objectives for bowel, external genitalia and bladder. This reached statistical significance for bowel V30 (P = 0.04), V45 (P < 0.001), V50 (P < 0.001), external genitalia V20 (P < 0.001) and bladder V35 (P < 0.001), V40 (P = 0.01). Gender was found to be the only significant factor in the likelihood of achieving the bowel V50 (P = 0.03) and BM V30 constraints (P = 0.04). The dose planning objectives used in the ANROTAT project provide a good starting point for ACC IMRT planning. To facilitate clinical implementation, it is important to prioritise OAR objectives and recognise factors that affect the achievability of these objectives.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2013

THE DENSITY OF MID-SIZED KUIPER BELT OBJECT 2002 UX25 AND THE FORMATION OF THE DWARF PLANETS

The formation of the largest objects in the Kuiper belt, with measured densities of ∼1.5 g cm{sup –3} and higher, from the coagulation of small bodies, with measured densities below 1 g cm{sup –3}, is difficult to explain without invoking significant porosity in the smallest objects. If such porosity does occur, measured densities should begin to increase at the size at which significant porosity is no longer supported. Among the asteroids, this transition occurs for diameters larger than ∼350 km. In the Kuiper belt, no density measurements have been made between ∼350 km and ∼850 km, the diameter range where porosities might first begin to drop. Objects in this range could provide key tests of the rock fraction of small Kuiper belt objects (KBOs). Here we report the orbital characterization, mass, and density determination of the 2002 UX25 system in the Kuiper belt. For this object, with a diameter of ∼650 km, we find a density of 0.82 ± 0.11 g cm{sup –3}, making it the largest solid known object in the solar system with a measured density below that of pure water ice. We argue that the porosity of this object is unlikely to be above ∼20%, suggesting a low rock fraction. If the currently measured densities of KBOs are a fair representation of the sample as a whole, creating ∼1000 km and larger KBOs with rock mass fractions of 70% and higher from coagulation of small objects with rock fractions as low as those inferred from 2002 UX25 is difficult.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

ScaleML: Machine Learning based Heap Memory Object Scaling Prediction

Memory subsystem contributes 28-40% of total energy consumption. Several studies investigated energy prediction and consumption via profiling memory object access patterns. However, such profiling leads to higher energy consumption due to intense memory object-level profiling to achieve high prediction accuracy. Further, memory object access pattern prediction has been considered through analyzing the variation between memory object access patterns, referred to as scaling rate. The existing techniques for scaling rate prediction, such as Linear Scaling Rate (LSR), suffer from a high error rate in prediction with changes in access patterns, which leads to a high error rate of energy consumption prediction. In this paper, we compare and evaluate several memory object access pattern prediction models including LSR and machine learning (ML) models. Further, we propose SCALEML, a heap memory object scaling rate prediction mechanism that employs an ML model to achieve high access pattern prediction accuracy with variations in memory object access patterns. We evaluate SCALEML using various application benchmarks. The experimental results show that SCALEML achieves about 20% higher accuracy than the LSR model for predicting the scaling rate of object access patterns and energy estimation.

Park, Joongeon↗
DOE OSTI2014

Change Detection in Constellations of Buried Objects Extracted From Ground-Penetrating Radar Data

Detection of deliberately buried objects in ground-penetrating radar (GPR) data acquired along a path is a clutter-limited problem. Detection-false alarm rate performance can be improved by replacing the detection statistic with a change statistic that incorporates information from previous path traversals. A constellation matching approach is developed for buried-object change detection in GPR data. Network topologies of buried objects detected in GPR data from previous path traversals are maintained in a constellation database. Localized groups of buried objects newly detected on the latest path traversal are matched to the constellation. Buried objects from the latest path traversal whose locations or strengths cannot be reconciled with the constellation are identified as changes. The system has one component that generates constellation databases offline and another component suitable for change detection in real time. It can tolerate paths with significant translational misalignments. The system uses the following: 1) a customized translational relaxation algorithm for point pattern matching that incorporates detection strength and a probabilistic uncertainty model for buried-object location into the objective function and 2) a change statistic that accounts for the magnitude of change relative to predicted detection strength. A constellation database can typically be generated offline from a single path traversal roughly two orders of magnitude faster than the time typically required for a vehicle to travel the extent of the path. Database sizes are typically four to five orders of magnitude smaller than the data sets of GPR signal scans or focused 3-D GPR images that they were generated from. On bumpy dirt roads buried exclusively with nonmetallic objects at various depths, detection-false alarm rate performance is shown to be significantly better for our change statistics than for our detection statistics.

47 OTHER INSTRUMENTATION↗
DOE OSTI2015

Imaging, object detection, and change detection with a polarized multistatic GPR array

A polarized detection system performs imaging, object detection, and change detection factoring in the orientation of an object relative to the orientation of transceivers. The polarized detection system may operate on one of several modes of operation based on whether the imaging, object detection, or change detection is performed separately for each transceiver orientation. In combined change mode, the polarized detection system performs imaging, object detection, and change detection separately for each transceiver orientation, and then combines changes across polarizations. In combined object mode, the polarized detection system performs imaging and object detection separately for each transceiver orientation, and then combines objects across polarizations and performs change detection on the result. In combined image mode, the polarized detection system performs imaging separately for each transceiver orientation, and then combines images across polarizations and performs object detection followed by change detection on the result.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Transparent Object Tracking Benchmark

Visual tracking has achieved considerable progress in recent years. However, current research in the field mainly focuses on tracking of opaque objects, while little attention is paid to transparent object tracking. In this paper, we make the first attempt in exploring this problem by proposing a Transparent Object Tracking Benchmark (TOTB). Specifically, TOTB consists of 225 videos (86K frames) from 15 diverse transparent object categories. Each sequence is manually labeled with axis-aligned bounding boxes. To the best of our knowledge, TOTB is the first benchmark dedicated to transparent object tracking. In order to understand how existing trackers perform and to provide comparison for future research on TOTB, we extensively evaluate 25 state-of-the-art tracking algorithms. The evaluation results exhibit that more efforts are needed to improve transparent object tracking. Besides, we observe some nontrivial findings from the evaluation that are discrepant with some common beliefs in opaque object tracking. For example, we find that deeper features are not always good for improvements. Moreover, to encourage future research, we introduce a novel tracker, named TransATOM, which leverages transparency features for tracking and surpasses all 25 evaluated approaches by a large margin. By releasing TOTB, we expect to facilitate future research and application of transparent object tracking in both the academia and industry.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Systems and methods for automated depowdering and transfer of additively manufactured objects

Systems and methods for additive manufacturing of objects are disclosed. The system includes an object builder subsystem configured to additively manufacture an object in a powder bed, an object extraction subsystem configured to extract the object from the powder bed, and a depowdering subsystem configured to remove powder from the object while the object is gripped by the gripper assembly. The object extraction subsystem includes a gripper assembly comprising two or more gripper members that each include a perforated mesh structure.

Elliott, Amelia M.↗
DOE OSTI2016

Visual-Attention-Based Background Modeling for Detecting Infrequently Moving Objects

Motion is one of the most important cues to separate foreground objects from the background in a video. Using a stationary camera, it is usually assumed that the background is static, while the foreground objects are moving most of the time. However, in practice, the foreground objects may show infrequent motions, such as abandoned objects and sleeping persons. Meanwhile, the background may contain frequent local motions, such as waving trees and/or grass. Such complexities may prevent the existing background subtraction algorithms from correctly identifying the foreground objects. We propose a new approach that can detect the foreground objects with frequent and/or infrequent motions. Specifically, we use a visual-attention mechanism to infer a complete background from a subset of frames and then propagate it to the other frames for accurate background subtraction. Furthermore, we develop a feature-matching-based local motion stabilization algorithm to identify frequent local motions in the background for reducing false positives in the detected foreground. The proposed approach is fully unsupervised, without using any supervised learning for object detection and tracking. Extensive experiments on a large number of videos have demonstrated that the proposed approach outperforms the state-of-the-art motion detection and background subtraction methods in comparison.

42 ENGINEERING↗
DOE OSTI2025

Regen: An object layout regenerator on large-scale production HPC systems

This article proposes an object layout regenerator called Regen which regenerates and removes the object layout dynamically to improve the read performance of applications. Regen first detects frequent access patterns from the I/O requests of the applications. Second, Regen reorganizes the objects and regenerates or preallocates new object layouts according to the identified access patterns. Finally, Regen removes or reuses the obsolete or regenerated object layouts as necessary. As a result, Regen accelerates access to objects by providing a flexible object layout. We implement Regen as a framework on top of Proactive Data Container (PDC) and evaluate it on Cori supercomputer, a production-scale HPC system, by using realistic HPC I/O benchmarks. The experimental results show that Regen improves the I/O performance by up to 16.92 × compared with an existing system.

Distributed file system↗
DOE OSTI2025

Quantum approximate multi-objective optimization

The goal of multi-objective optimization is to understand optimal trade-offs between competing objective functions by finding the Pareto front, that is, the set of all Pareto-optimal solutions, where no objective can be improved without degrading another one. Multi-objective optimization can be challenging classically, even if the corresponding single-objective optimization problems are efficiently solvable. Thus, multi-objective optimization represents a compelling problem class to analyze with quantum computers. Here we use a low-depth quantum approximate optimization algorithm to approximate the optimal Pareto front of certain multi-objective weighted maximum-cut problems. We demonstrate its performance on an IBM Quantum computer, as well as with matrix product state numerical simulation, and show its potential to outperform classical approaches.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

ODDS: Real-Time Object Detection Using Depth Sensors on Embedded GPUs

Detecting objects that are carried when someone enters or exits a room is very useful for a wide range of smart building applications including safety, security, and energy efficiency. While there has been a significant amount of work on object recognition using large-scale RGB image datasets, RGB cameras are too privacy invasive in many smart building applications and they work poorly in the dark. Additionally, deep object detection networks require powerful and expensive GPUs. We propose a novel system that we call ODDS (Object Detector using a Depth Sensor) that can detect objects in real-time using only raw depth data on an embedded GPU, e.g., NVIDIA Jetson TX1. Hence, our solution is significantly less privacy invasive (even if the sensor is compromised) and less expensive, while maintaining a comparable accuracy with state of the art solutions. Specifically, we resort to training a deep convolutional neural network using raw depth images, with curriculum based learning to improve accuracy by considering the complexity and imbalance in object classes and developing a sparse coding based technique that speeds up the system ~2× with minimal loss of accuracy. Based on a complete implementation and real-world evaluation, we see ODDS achieve 80.14% mean average precision in object detection in real-time (5-6 FPS) on a Jetson TX1.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Performance Comparison of Object Detection Networks for Shrapnel Identification in Ultrasound Images

Ultrasound imaging is a critical tool for triaging and diagnosing subjects but only if images can be properly interpreted. Unfortunately, in remote or military medicine situations, the expertise to interpret images can be lacking. Machine-learning image interpretation models that are explainable to the end user and deployable in real time with ultrasound equipment have the potential to solve this problem. We have previously shown how a YOLOv3 (You Only Look Once) object detection algorithm can be used for tracking shrapnel, artery, vein, and nerve fiber bundle features in a tissue phantom. However, real-time implementation of an object detection model requires optimizing model inference time. Here, we compare the performance of five different object detection deep-learning models with varying architectures and trainable parameters to determine which model is most suitable for this shrapnel-tracking ultrasound image application. We used a dataset of more than 16,000 ultrasound images from gelatin tissue phantoms containing artery, vein, nerve fiber, and shrapnel features for training and evaluating each model. Every object detection model surpassed 0.85 mean average precision except for the detection transformer model. Overall, the YOLOv7tiny model had the higher mean average precision and quickest inference time, making it the obvious model choice for this ultrasound imaging application. Other object detection models were overfitting the data as was determined by lower testing performance compared with higher training performance. In summary, the YOLOv7tiny object detection model had the best mean average precision and inference time and was selected as optimal for this application. Next steps will implement this object detection algorithm for real-time applications, an important next step in translating AI models for emergency and military medicine.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Multiple-objective Reinforcement Learning for Inverse Design and Identification

The aim of inverse chemical design is to develop new molecules with given optimized molecular properties or objectives. Recently, generative deep learning (DL) networks are considered as the state-of-the-art in inverse chemical design and have achieved early success in generating molecular structures with desired properties in the pharmaceutical and material chemistry fields. However, satisfying a large number (> 10 objectives) of molecular objectives is a limitation of current generative models. To improve the model’s ability to handle a large number of molecule design objectives, we developed a Reinforcement Learning (RL) based generative framework to optimize chemical molecule generation. Our use of Curriculum Learning (CL) to fine-tune the pre-trained generative network allowed the model to satisfy up to 21 objectives and increase the generative network’s robustness. The experiments show that the proposed multiple-objective RL-based generative model can correctly identify unknown molecules with an 83% to 100% success rate, compared to the baseline approach of 0%. Additionally, this proposed generative model is not limited to just chemistry research challenges; we anticipate that problems that utilize RL with multiple objectives will benefit from this framework.

reinforcement learning, generative model, inverse ↗
DOE OSTI2021

Multi-objective Eco-Routing Model Development and Evaluation for Battery Electric Vehicles.

This paper develops a multi-objective eco-routing algorithm (combined eco- and travel time-optimum routing) for battery electric vehicles (BEVs) and internal combustion engine vehicles (ICEVs) and investigates the network-wide impacts of the proposed multi-objective Nash optimum (user equilibrium) traffic assignment on a large-scale network. Eco-routing is a technique that finds the most energy efficient route. ICEV and BEV energy consumption patterns are significantly different with regard to their sensitivity to driving cycles. Unlike ICEVs, BEVs are more energy efficient on low-speed arterial trips compared to highway trips. Different energy consumption patterns require different eco-routing strategies for ICEVs and BEVs. This study found that single objective eco-routing could significantly reduce the energy consumption of BEVs but also significantly increase their average travel time. Consequently, the study developed a multi-objective routing model (eco- and travel time-routing) to improve both energy and travel time measures. The model introduced a link cost function that uses the specification of the value of time and the cost of fuel/energy. The simulation study found that multi-objective routing could reduce the BEV energy consumption by 13.5%, 14.2%, 12.9%, and 10.7%, as well as ICEV fuel consumption by 0.1%, 4.3%, 3.4%, and 10.6% for “not congested, “slightly congested,” “moderately congested,” and “highly congested” conditions, respectively. The study also found that multi-objective user equilibrium routing reduced the average vehicle travel time by up to 10.1% compared to the standard user equilibrium traffic assignment for highly congested conditions, producing a solution closer to the system optimum traffic assignment. The results indicate that the proposed multi-objective eco-routing strategy can reduce vehicle fuel/energy consumption effectively with minimum impacts on travel times for both BEVs and ICEVs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2013

A multi-objective programming model for assessment the GHG emissions in MSW management

Highlights: • The multi-objective multi-period optimization model. • The solution approach for the generation of the Pareto front with mathematical programming. • The very detailed description of the model (decision variables, parameters, equations). • The use of IPCC 2006 guidelines for landfill emissions (first order decay model) in the mathematical programming formulation. - Abstract: In this study a multi-objective mathematical programming model is developed for taking into account GHG emissions for Municipal Solid Waste (MSW) management. Mathematical programming models are often used for structure, design and operational optimization of various systems (energy, supply chain, processes, etc.). The last twenty years they are used all the more often in Municipal Solid Waste (MSW) management in order to provide optimal solutions with the cost objective being the usual driver of the optimization. In our work we consider the GHG emissions as an additional criterion, aiming at a multi-objective approach. The Pareto front (Cost vs. GHG emissions) of the system is generated using an appropriate multi-objective method. This information is essential to the decision maker because he can explore the trade-offs in the Pareto curve and select his most preferred among the Pareto optimal solutions. In the present work a detailed multi-objective, multi-period mathematical programming model is developed in order to describe the waste management problem. Apart from the bi-objective approach, the major innovations of the model are (1) the detailed modeling considering 34 materials and 42 technologies, (2) the detailed calculation of the energy content of the various streams based on the detailed material balances, and (3) the incorporation of the IPCC guidelines for the CH{sub 4} generated in the landfills (first order decay model). The equations of the model are described in full detail. Finally, the whole approach is illustrated with a case study referring to the application of the model in a Greek region.

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

The Role of Presented Objects in Deriving Color Preference Criteria from Psychophysical Studies

Of the many “components” of a color rendering measure, one is perhaps the most important: the set of color samples (spectral reflectance functions) that are employed as a standardized means of evaluating and rating a light source. At the same time, a standardized set of color samples can never apply perfectly to a real space or a real set of observed objects, meaning there will always be some level of mismatch between the predicted and observed color shifts. This mismatch is important for lighting specifiers to consider, but even more critical for experiments that seek to evaluate the relationship between color rendering measures and human perception. This article explores how the color distortions of three possible experimental object sets compare to the color distortions predicted using the color evaluation samples of IES TM-30-15 (TM-30). The experimental object sets include those from Royer and colleagues [2016], a set of produce (10 fruits and vegetables), and the X-rite Color Checker Classic. The differences are traced back to properties of the samples sets, such as the coverage of color space, average chroma level, and specific spectral features. The consequence of the differences, that the visual evaluation is based on color distortions that are substantially different from what is predicted, can lead to inaccurate criteria or models of a given perception, such as preference. To minimize the error in using criteria or models when specifying color rendering attributes for a given application, the criteria or models should be developed using a set of experimental objects that matches the typical objects of the application as closely as possible. Alternatively, if typical objects of an application cannot be reasonably determined, an object set that matches the distortions predicted by TM-30 as close as possible is likely to provide the most meaningful results.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

A serendipitous all sky survey for bright objects in the outer solar system

We use seven year's worth of observations from the Catalina Sky Survey and the Siding Spring Survey covering most of the northern and southern hemisphere at galactic latitudes higher than 20° to search for serendipitously imaged moving objects in the outer solar system. These slowly moving objects would appear as stationary transients in these fast cadence asteroids surveys, so we develop methods to discover objects in the outer solar system using individual observations spaced by months, rather than spaced by hours, as is typically done. While we independently discover eight known bright objects in the outer solar system, the faintest having V=19.8±0.1, no new objects are discovered. We find that the survey is nearly 100% efficient at detecting objects beyond 25 AU for V≲19.1 (V≲18.6 in the southern hemisphere) and that the probability that there is one or more remaining outer solar system object of this brightness left to be discovered in the unsurveyed regions of the galactic plane is approximately 32%.

79 ASTRONOMY AND ASTROPHYSICS↗