Search NASA⌕ Search

SEARCH · Search NASA

Results for “task”

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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Multi-task deep reinforcement learning for intelligent multi-zone residential HVAC control

In this short communication, a data-driven deep reinforcement learning (deep RL) method is applied to minimize HVAC users’ energy consumption costs while maintaining users’ comfort. The applied deep RL method's efficiency is enhanced by conducting multi-task learning that can achieve an economic control strategy for a multi-zone residential HVAC system in both cooling and heating scenarios. The applied multi-task deep RL method is compared with a rule-based benchmark case and a single-task deep deterministic policy gradient algorithm to verify its effective and generalized application in optimizing HVAC operation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Coarse-to-fine Task-driven Inpainting for Geoscience Images

The processing and recognition of geoscience images have wide applications. Most of existing researches focus on understanding the high-quality geoscience images by assuming that all the images are clear. However, in many real-world cases, the geoscience images might contain occlusions during the image acquisition. This problem actually implies the image inpainting problem in computer vision and multimedia. As far as we know, all the existing image inpainting algorithms learn to repair the occluded regions for a better visualization quality, they are excellent for natural images but not good enough for geoscience images, and they never consider the following geoscience task when developing inpainting methods. Here, this paper aims to repair the occluded regions for a better geoscience task performance and advanced visualization quality simultaneously, without changing the current deployed deep learning based geoscience models. Because of the complex context of geoscience images, we propose a coarse-to-fine encoder-decoder network with the help of designed coarse-to-fine adversarial context discriminators to reconstruct the occluded image regions. Due to the limited data of geoscience images, we propose a MaskMix based data augmentation method, which augments inpainting masks instead of augmenting original images, to exploit the limited geoscience image data. The experimental results on three public geoscience datasets for remote sensing scene recognition, cross-view geolocation and semantic segmentation tasks respectively show the effectiveness and accuracy of the proposed method. The code is available at: https://github.com/HMS97/Task-driven-Inpainting.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Multi-Task Reinforcement Learning for Distribution System Voltage Control With Topology Changes

This letter proposes a multi-task deep reinforcement learning (DRL) approach for distribution system voltage regulation considering topology changes via PV smart inverter control. The key idea is to encode the topology as an additional state for the DRL and leverage the multi-task learning scheme for joint learning of all task control policies. Unlike other DRL-based methods, our approach is robust to different topologies. Comparison results on the modified IEEE 123-node system demonstrate the enhanced robustness of the proposed method.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2011

Design and Implementation of “Many Parallel Task” Hybrid Subsurface Model

Continuum scale models have been used to study subsurface flow, transport, and reactions for many years. Recently, pore scale models, which operate at scales of individual soil grains, have been developed to more accurately model pore scale phenomena, such as precipitation, that may not be well represented at the continuum scale. However, particle-based models become prohibitively expensive for modeling realistic domains. Instead, we are developing a hybrid model that simulates the full domain at continuum scale and applies the pore model only to areas of high reactivity. The hybrid model uses a dimension reduction approach to formulate the mathematical exchange of information across scales. Since the location, size, and number of pore regions in the model varies, an adaptive Pore Generator is being implemented to define pore regions at each iteration. A fourth code will provide data transformation from the pore scale back to the continuum scale. These components are coupled into a single hybrid model using the SWIFT workflow system. Our hybrid model workflow simulates a kinetic controlled mixing reaction in which multiple pore-scale simulations occur for every continuum scale timestep. Each pore-scale simulation is itself parallel, thus exhibiting multi-level parallelism. Our workflow manages these multiple parallel tasks simultaneously, with the number of tasks changing across iterations. It also supports dynamic allocation of job resources and visualization processing at each iteration. We discuss the design, implementation and challenges associated with building a scalable, Many Parallel Task, hybrid model to run efficiently on thousands to tens of thousands of processors.

workflow, Many task, Subsurface↗
DOE OSTI2013

A Framework for Load Balancing of Tensor Contraction Expressions via Dynamic Task Partitioning

In this paper, we introduce the Dynamic Load-balanced Tensor Contractions (DLTC), a domain-specific library for efficient task parallel execution of tensor contraction expressions, a class of computation encountered in quantum chemistry and physics. Our framework decomposes each contraction into smaller unit of tasks, represented by an abstraction referred to as iterators. We exploit an extra level of parallelism by having tasks across independent contractions executed concurrently through a dynamic load balancing run- time. We demonstrate the improved performance, scalability, and flexibility for the computation of tensor contraction expressions on parallel computers using examples from coupled cluster methods.

Tensor contraction, domain-specific language, dyna↗
DOE OSTI2019

Harnessing billions of tasks for a scalable portable hydrodynamic simulation of the merger of two stars

We propose a highly scalable demonstration of a portable asynchronous many-task programming model and runtime system applied to a grid-based adaptive mesh refinement hydrodynamic simulation of a double white dwarf merger with 14 levels of refinement that spans 17 orders of magnitude in astrophysical densities. The code uses the portable C++ parallel programming model that is embodied in the HPX library and being incorporated into the ISO C++ standard. The model reflects a significant shift from existing bulk synchronous parallel programming models under consideration for exascale systems. Through the use of the Futurization technique, seemingly sequential code is transformed into wait-free asynchronous tasks. We demonstrate the potential of our model by showing results from strong scaling runs on National Energy Research Scientific Computing Center’s Cori system (658,784 Intel Knight’s Landing cores) that achieve a parallel efficiency of 96.8% using billions of asynchronous tasks.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

TASK ALLOCATION IN GEO-DISTRIBUTED CYBER-PHYSICAL SYSTEMS

This paper studies the task allocation algorithm for a distributed test facility (DTF), which aims to assemble geo-distributed cyber (software) and physical (hardware in the loop components into a prototype cyber-physical system (CPS). This allows low cost testing on an early conceptual prototype (ECP) of the ultimate CPS (UCPS) to be developed. The DTF provides an instrumentation interface for carrying out reliability experiments remotely such as fault propagation analysis and in-situ testing of hardware and software components in a simulated environment. Unfortunately, the geo-distribution introduces an overhead that is not inherent to the UCPS, i.e. a significant time delay in communication that threatens the stability of the ECP and is not an appropriate representation of the behavior of the UCPS. This can be mitigated by implementing a task allocation algorithm to find a suitable configuration and assign the software components to appropriate computational locations, dynamically. This would allow the ECP to operate more efficiently with less probability of being unstable due to the delays introduced by geo-distribution. The task allocation algorithm proposed in this work uses a Monte Carlo approach along with Dynamic Programming to identify the optimal network configuration to keep the time delays to a minimum.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

ASME Non-metallic Component Degradation and Failure Task Group

ASME Non-metallic Component Degradation and Failure Task Group background, component failure and component functionality, the nature of graphite, damage tolerance, AGR design, damage tolerance, non-metallic component degradation and failure task group, task group progress, monitoring and examination, link between degradation processes and their impact on core components in the array of graphite components, the link between degradation processes and RIM options, and a way forward.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Predicting Search Task Difficulty through a Discrete‐Time Action Log Representation on Spectrum Kernel

ABSTRACT Predicting perceived difficulty on a web search task is an open problem in the interactive information retrieval field. A common approach to tackle it, is through features obtained from full search sessions, which are then used to train classification models. In this poster we attempt to predict perceived task difficulty at different stages of the search process. To do so, we use the spectrum kernel for support vector machine (SVM) classification. Our preliminary results suggest that by using behavioral data from the first query segment, it is possible to provide timely classifications of whether a search task is perceived as hard or easy.

Gacitúa, Daniel↗
DOE OSTI2018

A Task-Optimized Neural Network Replicates Human Auditory Behavior, Predicts Brain Responses, and Reveals a Cortical Processing Hierarchy

A core goal of auditory neuroscience is to build quantitative models that predict cortical responses to natural sounds. Reasoning that a complete model of auditory cortex must solve ecologically relevant tasks, we optimized hierarchical neural networks for speech and music recognition. The best-performing network contained separate music and speech pathways following early shared processing, potentially replicating human cortical organization. The network performed both tasks as well as humans and exhibited human-like errors despite not being optimized to do so, suggesting common constraints on network and human performance. The network predicted fMRI voxel responses substantially better than traditional spectrotemporal filter models throughout auditory cortex. It also provided a quantitative signature of cortical representational hierarchy—primary and non-primary responses were best predicted by intermediate and late network layers, respectively. Here the results suggest that task optimization provides a powerful set of tools for modeling sensory systems.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Development of a mnemonic discrimination task using naturalistic stimuli with applications to aging and preclinical Alzheimer's disease

Most tasks test memory within the same day, however, most forgetting occurs after 24 h. Further, testing memory for simple words or objects does not mimic real-world memory experiences. We designed a memory task showing participants video clips of everyday kinds of experiences, including positive, negative, and neutral stimuli, and tested memory immediately and 24 h later. During the memory test, we included repeated and similar stimuli to tax both target recognition and lure discrimination ability. Participants’ memory was worse after 24 h, especially the ability to discriminate similar stimuli. Emotional videos were better remembered when tested immediately, however, after 24 h we find gist versus detail trade-offs in emotional forgetting. We also applied this paradigm to a sample of cognitively normal older adults that also underwent amyloid and tau PET imaging. We found that older adults performed worse on the task compared to young adults. While both young and older adults showed similar patterns of forgetting of repeated emotional and neutral clips, older adults showed preserved neutral compared to emotional discrimination after 24 h. Further, lure discrimination performance correlated with medial temporal lobe tau in older adults with preclinical Alzheimer’s disease. These results suggest factors such as time between encoding and retrieval, emotion, and similarity influence memory performance and should be considered when examining memory performance for an accurate picture of memory function and dysfunction.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2011

Exploring Fine-Grained Task-based Execution on Multi-GPU Systems

Many-core Graphics Processing Units (GPUs) have been utilized as the computation engine in many scientific fields due to the high peak performance, cost effectiveness, and the availability of user friendly programming environments, e.g., NVIDIA CUDA. However, the conventional data parallel GPU programming paradigm cannot satisfactorily address issues such as load balancing and GPU resource utilization due to the irregular and unbalanced workload patterns exhibited in some applications. In this paper, we explore the design space of task-based solutions for multi-GPU systems. By employing finer-grained tasks than what is supported in the current CUDA, and allowing task sharing, our solutions enable dynamic load balancing. We evaluate our solutions with a Molecular Dynamics application with different atom distributions (from uniform distribution to highly non-uniform distribution). Experimental results obtained on a 4-GPU system show that, for non-uniform distributed systems, our solutions achieve excellent speedup, and significant performance improvement over other solutions based on the standard CUDA APIs.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2016

Extending the Roofline Model for Asynchronous Many-Task Runtimes

New architectural trends and extreme scale parallelism challenge the efficient mapping of applications to large scale and emerging systems. Fine-grained Asynchronous Manytask Runtimes (AMR) offer a unique flexibility to hide the varying latencies of contentious operations, and facilitate the effective exploitation of abundant resources. By decomposing work into smaller asynchronous chunks, the impact of unpredictable operations can be mitigated through a tighter overlapping of tasks. Unfortunately, the cost of this decomposition comes at a potentially steep price in the form of runtime overhead which can at times rival the cost of the computation itself. This leads to the question, how large should a task be? A common practice for application developers is to experimentally determine the granularity of a task after a code has been parallelized. Instead, we propose a new methodology based on an extended Roofline model to provide practical upper bounds on the throughput performance of an application. First, we extend the Roofline model to support not only latency hiding analysis, but also a multidimensional amortized analysis. By combining this new methodology with a serial application and an AMR implementation, we can predict the worst case runtime overhead attribution of individual runtime features prior to the development of parallel code. Thus, this runtimecentric methodology can provide a vehicle for application/runtime codesign by providing a comprehensive bottleneck analysis based on existing runtime features.

Suetterlein, Joshua D.↗
DOE OSTI2016

In-Staging Data Placement for Asynchronous Coupling of Task-Based Scientific Workflows, In: 2016 Second International Workshop on Extreme Scale Programming Models and Middlewar (ESPM2)

Coupled application workflows composed of applications implemented using task-based models present new coupling and data exchange challenges, due to the asynchronous interaction and coupling behaviors between tasks of the component applications. In this paper, we present an adaptive data placement approach that addresses these challenges by dynamically adjusting to the asynchronous coupling patterns. Specifically, it places data across a set of staging cores/nodes with an awareness of the application-specific data locality requirements and the runtime task executions at these staging cores/nodes, with the goal of reducing end-to-end execution time and data movement overhead of the workflow. We experimentally demonstrate the effectiveness of our approach on the Titan Cray XK7 system using representative data coupling patterns derived from current scientific workflows. The evaluation demonstrates that our approach efficiently improves performance by reducing the time-to-solution and increasing the quality of insights for scientific discovery.

Computer Science↗
DOE OSTI2022

MAD: Self-Supervised Masked Anomaly Detection Task for Multivariate Time Series

In this paper, we introduce Masked Anomaly Detection (MAD), a general self-supervised learning task for multivariate time series anomaly detection. With the increasing availability of sensor data from industrial systems, being able to detecting anomalies from streams of multivariate time series data is of significant importance. Given the scarcity of anomalies in real-world applications, the majority of literature has been focusing on modeling normality. The learned normal representations can empower anomaly detection as the model has learned to capture certain key underlying data regularities. A typical formulation is to learn a predictive model, i.e., use a window of time series data to predict future data values. In this paper, we propose an alternative self-supervised learning task. By randomly masking a portion of the inputs and training a model to estimate them using the remaining ones, MAD is an improvement over the traditional left-to-right next step prediction (NSP) task. Our experimental results demonstrate that MAD can achieve better anomaly detection rates over traditional NSP approaches when using exactly the same neural network (NN) base models, and can be modified to run as fast as NSP models during test time on the same hardware, thus making it an ideal upgrade for many existing NSP-based NN anomaly detection models.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Synchro-Waveform-Based Event Identification Using Multi-Task Time-Frequency Transform Networks

Influenced by the transient dynamics and reduced inertia characteristics of high-penetration renewable energy systems, power system events frequently exhibit distinct characteristics such as high-frequency components including wide-band oscillations and hyper-harmonics. This makes standard systems face challenges including significant latency and reduced accuracy due to limited data resolution. However, current methods face significant limitations, including insufficient pattern capture ability, low noise immunity, limited feature learning, and restricted localization capabilities, thereby hindering real-time performance. To tackle this issue, this paper proposed a novel synchro-waveform-based event identification approach via a Multi-task Time-frequency Transform Network (MTTNet). Initially, a Time-frequency Transform Block (TTB) is developed to extract both local and global information. The TTB leverages both Fourier and S-transforms to derive comprehensive time-frequency information from synchro-waveforms. Subsequently, a multi-task learning strategy is employed to identify the type and distinguish localization of events. Integrating the TTB and multi-task learning, the MTTNet is designed for synchro-waveform-based event identification, incorporating an adaptive weighting strategy and simplified computation for the S-transform. Two different datasets, comprising simulated and actual synchro-waveforms, are collected from the IEEE 123 bus system and a real-world high-penetration renewable energy system using a universal grid analyzer. Extensive experiments on various conditions are carried out. In conclusion, results demonstrated that the MTTNet consistently surpasses both basic and advanced baselines, with maximum improvements of 13.24% and 9.86%, respectively, while reducing the calculation burden by 15-19 times to achieve real-time event identification.

Event identification↗
DOE OSTI2012

Functionality and operation of fluoroscopic automatic brightness control/automatic dose rate control logic in modern cardiovascular and interventional angiography systems: A Report of Task Group 125 Radiography/Fluoroscopy Subcommittee, Imaging Physics Committee, Science Council

Task Group 125 (TG 125) was charged with investigating the functionality of fluoroscopic automatic dose rate and image quality control logic in modern angiographic systems, paying specific attention to the spectral shaping filters and variations in the selected radiologic imaging parameters. The task group was also charged with describing the operational aspects of the imaging equipment for the purpose of assisting the clinical medical physicist with clinical set-up and performance evaluation. Although there are clear distinctions between the fluoroscopic operation of an angiographic system and its acquisition modes (digital cine, digital angiography, digital subtraction angiography, etc.), the scope of this work was limited to the fluoroscopic operation of the systems studied. The use of spectral shaping filters in cardiovascular and interventional angiography equipment has been shown to reduce patient dose. If the imaging control algorithm were programmed to work in conjunction with the selected spectral filter, and if the generator parameters were optimized for the selected filter, then image quality could also be improved. Although assessment of image quality was not included as part of this report, it was recognized that for fluoroscopic imaging the parameters that influence radiation output, differential absorption, and patient dose are also the same parameters that influence image quality. Therefore, this report will utilize the terminology ''automatic dose rate and image quality'' (ADRIQ) when describing the control logic in modern interventional angiographic systems and, where relevant, will describe the influence of controlled parameters on the subsequent image quality. A total of 22 angiography units were investigated by the task group and of these one each was chosen as representative of the equipment manufactured by GE Healthcare, Philips Medical Systems, Shimadzu Medical USA, and Siemens Medical Systems. All equipment, for which measurement data were included in this report, was manufactured within the three year period from 2006 to 2008. Using polymethylmethacrylate (PMMA) plastic to simulate patient attenuation, each angiographic imaging system was evaluated by recording the following parameters: tube potential in units of kilovolts peak (kVp), tube current in units of milliamperes (mA), pulse width (PW) in units of milliseconds (ms), spectral filtration setting, and patient air kerma rate (PAKR) as a function of the attenuator thickness. Data were graphically plotted to reveal the manner in which the ADRIQ control logic responded to changes in object attenuation. There were similarities in the manner in which the ADRIQ control logic operated that allowed the four chosen devices to be divided into two groups, with two of the systems in each group. There were also unique approaches to the ADRIQ control logic that were associated with some of the systems, and these are described in the report. The evaluation revealed relevant information about the testing procedure and also about the manner in which different manufacturers approach the utilization of spectral filtration, pulsed fluoroscopy, and maximum PAKR limitation. This information should be particularly valuable to the clinical medical physicist charged with acceptance testing and performance evaluation of modern angiographic systems.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2012

Report of the Task Group 186 on model-based dose calculation methods in brachytherapy beyond the TG-43 formalism: Current status and recommendations for clinical implementation

The charge of Task Group 186 (TG-186) is to provide guidance for early adopters of model-based dose calculation algorithms (MBDCAs) for brachytherapy (BT) dose calculations to ensure practice uniformity. Contrary to external beam radiotherapy, heterogeneity correction algorithms have only recently been made available to the BT community. Yet, BT dose calculation accuracy is highly dependent on scatter conditions and photoelectric effect cross-sections relative to water. In specific situations, differences between the current water-based BT dose calculation formalism (TG-43) and MBDCAs can lead to differences in calculated doses exceeding a factor of 10. MBDCAs raise three major issues that are not addressed by current guidance documents: (1) MBDCA calculated doses are sensitive to the dose specification medium, resulting in energy-dependent differences between dose calculated to water in a homogeneous water geometry (TG-43), dose calculated to the local medium in the heterogeneous medium, and the intermediate scenario of dose calculated to a small volume of water in the heterogeneous medium. (2) MBDCA doses are sensitive to voxel-by-voxel interaction cross sections. Neither conventional single-energy CT nor ICRU/ICRP tissue composition compilations provide useful guidance for the task of assigning interaction cross sections to each voxel. (3) Since each patient-source-applicator combination is unique, having reference data for each possible combination to benchmark MBDCAs is an impractical strategy. Hence, a new commissioning process is required. TG-186 addresses in detail the above issues through the literature review and provides explicit recommendations based on the current state of knowledge. TG-43-based dose prescription and dose calculation remain in effect, with MBDCA dose reporting performed in parallel when available. In using MBDCAs, it is recommended that the radiation transport should be performed in the heterogeneous medium and, at minimum, the dose to the local medium be reported along with the TG-43 calculated doses. Assignments of voxel-by-voxel cross sections represent a particular challenge. Electron density information is readily extracted from CT imaging, but cannot be used to distinguish between different materials having the same density. Therefore, a recommendation is made to use a number of standardized materials to maintain uniformity across institutions. Sensitivity analysis shows that this recommendation offers increased accuracy over TG-43. MBDCA commissioning will share commonalities with current TG-43-based systems, but in addition there will be algorithm-specific tasks. Two levels of commissioning are recommended: reproducing TG-43 dose parameters and testing the advanced capabilities of MBDCAs. For validation of heterogeneity and scatter conditions, MBDCAs should mimic the 3D dose distributions from reference virtual geometries. Potential changes in BT dose prescriptions and MBDCA limitations are discussed. When data required for full MBDCA implementation are insufficient, interim recommendations are made and potential areas of research are identified. Application of TG-186 guidance should retain practice uniformity in transitioning from the TG-43 to the MBDCA approach.

61 RADIATION PROTECTION AND DOSIMETRY↗