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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Driver Distraction Behavior Detection using a Vision Transformer Model based on Transfer Learning Strategy

Driver distraction behavior is one of the critical factors in traffic accidents. Thus, advanced driver state detection system has become the focus in the field of intelligent vehicle. However, in practical applications, insufficient samples of driving distraction behaviors bring great challenges to training a personalized behavior distraction detection model for a specific driver. To this end, a novel transformer model based on a transfer learning strategy is proposed in this paper to accurately recognize driver distraction behavior. Inspired by the effect of the transformer network in visual recognition, we firstly present a transformer behavior distraction detection system to identify the behavior categories that cause driver distraction. Then, for the specific driving dataset in practical application scenarios, the transfer learning strategy is introduced into the driver distraction detection model to further train the general transformer network. The effectiveness of the transformer based on the transfer learning strategy is validated compared with other traditional deep learning methods. The results show that the proposed detection method has better generalization ability and higher accuracy.

Fang, Zhenwu↗
DOE OSTI2017

Chemical Sensing Strategies for Real-Time Monitoring of Transformer Oil: A Review

Power transformers are a central component in the field of energy distribution and transmission. The early recognition of incipient faults in operating transformers is substantially cost effective by lessening impromptu blackouts. A standout amongst the most responsive and dependable strategies utilized for assessing the health of oil filled electrical equipment is dissolved gas analysis (DGA). Nowadays, there is an expanding requirement for better nonintrusive diagnostic and online monitoring tools to survey the internal state of the transformers. Chemical sensors are viewed as a key innovation for condition monitoring of transformer health, coordinating the non-invasiveness with typical sensor features, such as cost, usability, portability, and the integration with the data networks. Low-cost chemical sensors-based DGA techniques are expected to drastically augment the diagnostic abilities empowering the deployment on a broader range of oil filled power assets. The recent development involves both specific sensors designed to detect individual dissolved gas in transformer oil and non-specific sensors, operated in near ambient conditions, with the potential to be applied in a DGA system. In this paper, general background and operating guidelines of DGA are presented to address the origin of the gas formation, methods for their detection and the interpretation of the results by data analytics. The recent significant interest and advancements in chemical sensors to DGA applications are reviewed. Lastly, future research perspectives and challenges for the development of novel DGA chemical sensors are also discussed.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Challenges to Rural Service Transformers on Increased Electric Vehicle Charging Infrastructure

As Electric Vehicle (EV) electric charging demand increases so to does the corresponding load requirements for charging the vehicles. The expansion of power distribution assets is therefore a critical issue to address on whether rural service transformers can handle the increase in EV load demand. Large-scale EV deployment is likely to cause problems in the localized distributions systems bringing challenges such as increased load demand, increased system losses, and additional voltage drops. This paper investigates how rural service transformer resiliency plays an important role in these challenges. EV charging can create new load peaks exceeding the service transformer's rated capacity, thereby accelerating equipment aging. EV charging can also both positively and negatively affect transformer aging. The core purpose of this paper is to investigate that change in service life of rural service transformers.

Mukherjee, Srijib↗
DOE OSTI2014

HSLOT: The HERCULES Scriptable Loop Transformations Engine

HSLOT arms users with a rich set of configurable transformation directives, to be used as-they-are or to be specialized and combined into powerful custom transformations. We offer a plethora of loop transformations, which includes both the classic set (unroll, fuse, fission, tile, and so on) as well as unique ones (specialize, swap nest, split, fork, and so on) that are not found in other state-of-the-art systems. We show how HSLOT enables more transformations such as merging two loops that cannot be fused because of data dependencies and how HSLOT can be used in a simple and systematic fashion to improve memory accesses and expose better parallelism. To use our system, users simply annotate loops with the transformations sequence and compile with our Open64-based HSLOTimplementing Fortran compiler, HSLF90, which produces both object files and optionally source. We describe our experiment results using a set of scientific kernels written in Fortran with HSLOT directives on AMD 32 core system.

Kartsaklis, Christos↗
DOE OSTI2024

Complex-valued universal linear transformations and image encryption using spatially incoherent diffractive networks

As an optical processor, a diffractive deep neural network (D2NN) utilizes engineered diffractive surfaces designed through machine learning to perform all-optical information processing, completing its tasks at the speed of light propagation through thin optical layers. With sufficient degrees of freedom, D2NNs can perform arbitrary complex-valued linear transformations using spatially coherent light. Similarly, D2NNs can also perform arbitrary linear intensity transformations with spatially incoherent illumination; however, under spatially incoherent light, these transformations are nonnegative, acting on diffraction-limited optical intensity patterns at the input field of view. Here, we expand the use of spatially incoherent D2NNs to complex-valued information processing for executing arbitrary complex-valued linear transformations using spatially incoherent light. Through simulations, we show that as the number of optimized diffractive features increases beyond a threshold dictated by the multiplication of the input and output space-bandwidth products, a spatially incoherent diffractive visual processor can approximate any complex-valued linear transformation and be used for all-optical image encryption using incoherent illumination. The findings are important for the all-optical processing of information under natural light using various forms of diffractive surface-based optical processors.

36 MATERIALS SCIENCE↗
DOE OSTI2015

MO-G-304-02: Knowledge Based DVH Prediction Using a Geometric Dose Transform

Purpose: To demonstrate a novel method for predicting patient dose-volume histograms (DVHs) using a prior database of optimized radiotherapy treatment plans. Such predicted DVHs could be useful for automating treatment planning. Methods: Our initial demonstration utilized a database of 100 prostate intensity-modulated radiotherapy (IMRT) data-sets. Each data-set contained a CT image with contours of the planning target volume (PTV), rectum, and bladder, the parameters of a clinically approved IMRT plan, and a corresponding simulated dose distribution. We applied a novel geometric transformation to remove the influence of the PTV size, shape, and location on the dose distribution. We termed the transformed distribution the geometrically normalized dose distribution (GNDD). This normalization transform was applied to 80 data-sets randomly selected from the database, and a population GNDD was computed as the average. Next, the population GNDD was mapped onto each of the remaining 20 patient datasets using the reverse of the geometric normalization transform, and predicted DVHs were calculated from the reverse transformed dose distributions (GNDD-DVHs). In addition, a state of the art machine learning based method from the literature was tested for comparison. Results: DVH prediction accuracy was quantified by calculating the relative root mean squared error (rRMSE) on predicted DVHs for the 20 test patients using their known DVHs. For bladder, rectum, and PTV average rRMSEs for the GNDD method were 9.7 ± 4.2%, 13.9 ± 6.0%, and 2.3 ± 0.5% respectively. Prediction results using GNDD were roughly equivalent to that from the machine learning method. Conclusion: We developed a new method for predicting DVH curves from a database of prior patient plans. We demonstrated that our simple approach achieves accuracy comparable to a method using a complicated machine learning based approach.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Pseudo-differential representation of the metaplectic transform and its application to fast algorithms

The metaplectic transform (MT), also known as the linear canonical transform, is a unitary integral mapping that is widely used in signal processing and can be viewed as a generalization of the Fourier transform. For a given function $ψ$ on an $N$-dimensional continuous space q , the MT of $ψ$ is parameterized by a rotation (or more generally, a linear symplectic transformation) of the 2$N$-dimensional phase space ( q,p ), where p is the wavevector space dual to q . In this study, we derive a pseudo-differential form of the MT. For small-angle rotations, or near-identity transformations of the phase space, it readily yields asymptotic differential representations of the MT, which are easy to compute numerically. Rotations by larger angles are implemented as successive applications of $K \gg 1$ small-angle MTs. The algorithm complexity scales as $O(KN^3N_p)$ where $N_p$ is the number of grid points. Here, we present a numerical implementation of this algorithm and discuss how to mitigate the associated numerical instabilities.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

RF Gap Transformation in PARMILA

The PARMILA (Phase And Radial Motion in Ion Linear Accelerators) code is used to design ion linacs and simulate the beam dynamics therein. The original code was developed in the 1960’s and later the Los Alamos Accelerator Code Group developed a PC version that has been used worldwide. Recently, in an effort to modernize the code and use it as the basis for a high-performance beam dynamics simulator with the LANSCE linac, the details of the code have come under additional scrutiny. PARMILA is a “z code” that uses various transformations to approximately represent the effect of the beam line element on the beam. In the case of the RF gap, it treats the particle dynamics inside the gap as a combination of drift + kick at the mid-plane + drift. Recently, we uncovered an undocumented keyword “off-exact” in PARMILA that enables the code to use the full modified-Bessel functions instead of low-order approximations in the RF gap transformation. This change improved the accuracy of the transformation for off-axis particles. However, a discrepancy between the analytical formula and the code was found for the transformation of the transverse coordinates. The purpose of this tech note is to document the derivation of the gap transformation and the related DTL cell length calculation used in the PARMILA code.

43 PARTICLE ACCELERATORS↗
DOE OSTI2016

Wavelet Transforms using VTK-m

These are a set of slides that deal with the topics of wavelet transforms using VTK-m. First, wavelets are discussed and detailed, then VTK-m is discussed and detailed, then wavelets and VTK-m are looked at from a performance comparison, then from an accuracy comparison, and finally lessons learned, conclusion, and what is next. Lessons learned are the following: Launching worklets is expensive; Natural logic of performing 2D wavelet transform: Repeat the same 1D wavelet transform on every row, repeat the same 1D wavelet transform on every column, invoke the 1D wavelet worklet every time: num_rows x num_columns; VTK-m approach of performing 2D wavelet transform: Create a worklet for 2D that handles both rows and columns, invoke this new worklet only one time; Fast calculation, but cannot reuse 1D implementations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Optical Fiber Sensor Technology Development and Field Validation for Distribution Transformer and Other Grid Asset Health Monitoring

Power transformers are critical pieces of infrastructure in the electric grid that are both extremely expensive and difficult to replace. These transformers often have long lead times for replacement, and failures can create long service disruptions. This project has developed a new suite of sensors designed to give early warning of the impending failure of these important power transformers before it is too late and a major failure occurs. By using novel fiber optic sensors instead of conventional existing technologies, we are able to measure transformer characteristics indicative of impending failures in ways that were not previously possible. These new optical fiber-based sensors are completely immune to the strong magnetic fields present in power transformers, as well as being capable of using distributed measurement techniques. Distributed measurement techniques enable the fiber to return information all along its length as opposed to only collecting data at a single point; as would a thermocouple or standard pressure sensor.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Defining a Platform Approach and Market Participation: Data Driven Business Models for Solid State Transformer-Based Synthetic Inertia and Voltage Stability Controls (CRADA Final Report, Project 1, Mod 1)

The primary objective of this project is to determine the incremental value created with the medium voltage solid-state transformer (MV SST) technology to different stakeholders in view of the updated DER grid regulations. This includes studying the benefits of the MV SST technology in a range of use cases for EV and DER penetration including (1) “corridor charging” for EVs and (2) solar plus storage (FERC 2222). The potential customers of this technology include utilities for EV charging, DER installers who must meet utility interconnection requirements, balancing authorities, and DER aggregators. The traditional transformers on the grid could be a limiting factor for the EV-grid integration as the distribution transformers were not designed to handle the dynamic and fluctuating EV charging loads. Thus, the issues such as voltage fluctuations, increased losses and reduced efficiency [1] can negatively impact the grid operation. To address these challenges, transformers with flexibility and adaptability become imperative to meet the evolving energy demands. In this regard, the concept of Medium Voltage Solid-State Transformers.

14 SOLAR ENERGY↗
DOE OSTI2024

Transformer Neural Networks with Spatiotemporal Attention for Predictive Control and Optimization of Industrial Processes

In the context of real-time optimization and model predictive control of industrial systems, machine learning, and neural networks represent cutting-edge tools that hold promise for enhancing dynamic modeling. This work presents a novel transformer neural network architecture for real-time optimization and model predictive control. This network design includes a modified attention mechanism inspired by positional embedding attention from vision transformers and task-specific modifications to the input-output structure of the transformer’s decoder stack. Experiments were conducted using data from a 450 MW coal-fired power plant to evaluate this approach's effectiveness. The transformer neural network was compared with conventional recurrent models, including GRU and LSTM. The transformer exhibited a 6% increase in the R-squared (R2) value of predictions and an 83% reduction in mean squared error (MSE). Computation time was also reduced by 84% compared to conventional recurrent models.

Gallup, Ethan R.↗
DOE OSTI2016

SacB-SacR Gene Cassette As the Negative Selection Marker to Suppress Agrobacterium Overgrowth in Agrobacterium -Mediated Plant Transformation

Agrobacterium overgrowth is a common problem in Agrobacterium -mediated plant transformation. To suppress the Agrobacterium overgrowth, various antibiotics have been used during plant tissue culture steps. The antibiotics are expensive and may adversely affect plant cell differentiation and reduce plant transformation efficiency. The SacB-SacR proteins are toxic to most Agrobacterium tumefaciens strains when they are grown on culture medium supplemented with sucrose. Therefore, SacB-SacR genes can be used as negative selection markers to suppress the overgrowth of A. tumefaciens in the plant tissue culture process. We generated a mutant A. tumefaciens strain GV2260 ( recA-SacB/R ) that has the SacB-SacR cassette inserted into the bacterial genome at the recA gene locus. The mutant Agrobacterium strain is sensitive to sucrose but maintains its ability to transform plant cells in both transient and stable transformation assays. We demonstrated that the mutant strain GV2260 ( recA-SacB/R ) can be inhibited by sucrose that reduces the overgrowth of Agrobacterium and therefore improves the plant transformation efficiency. We employed GV2260 ( recA-SacB/R ) to generate stable transgenic N. benthamiana plants expressing a CRISPR-Cas9 for knocking out a WRKY transcription factor.

Biochemistry & Molecular Biology↗
DOE OSTI2022

Pitch-Angle Diffusion in the Earth’s Magnetosphere Organized by the Mozer-Transformed Coordinate System

In the Earth’s dipole magnetosphere finite-gyroradius effects produce a shift of the atmospheric loss cone away from the direction of the magnetic field. This loss-cone shift is theoretically described by the “Mozer transform”, which is based upon the curvature drift of particles crossing the equatorial plane. For positive ions the northern and southern loss cones both shift westward and for electrons the northern and southern loss cones both shift eastward. This loss-cone shift is part of a coordinate-system transform, with the transformed coordinates better organizing the behavior of particle orbits in the dipole magnetic field (e.g. first adiabatic invariants, mirror heights, and bounce times). In this report it is demonstrated that the transformed coordinate system also properly organizes pitch-angle diffusion. This improved organization of the diffusion is true whether the angular scattering is produced by plasma-wave scattering or by field-line-curvature (FLC) scattering. It is shown that FLC scattering and the loss cone shift are linked, so that if FLC scattering is occurring, there is a loss cone shifted away from the magnetic-field direction and the Mozer-transformed coordinates are needed.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Solute Transport and Transformation in an Intermittent, Headwater Mountain Stream with Diurnal Discharge Fluctuations

Time-variable discharge is known to control both transport and transformation of solutes in the river corridor. Still, few studies consider the interactions of transport and transformation together. Here, we consider how diurnal discharge fluctuations in an intermittent, headwater stream control reach-scale solute transport and transformation as measured with conservative and reactive tracers during a period of no precipitation. One common conceptual model is that extended contact times with hyporheic zones during low discharge conditions allows for increased transformation of reactive solutes. Instead, we found tracer timescales within the reach were related to discharge, described by a single discharge-variable StorAge Selection function. We found that Resazurin to Resorufin (Raz-to-Rru) transformation is static in time, and apparent differences in reactive tracer were due to interactions with different ages of storage, not with time-variable reactivity. Overall we found reactivity was highest in youngest storage locations, with minimal Raz-to-Rru conversion in waters older than about 20 h of storage in our study reach. Therefore, not all storage in the study reach has the same potential biogeochemical function and increasing residence time of solute storage does not necessarily increase reaction potential of that solute, contrary to prevailing expectations.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1971

Multi-dimensional real Fourier transform

Four subroutines compute one-dimensional and multi-dimensional Fourier transforms for real data, multi-dimensional complex Fourier transforms, and multi-dimensional sine, cosine and sine-cosine transforms. Subroutines use Cooley-Tukey fast Fourier transform. In all but one-dimensional case, transforms are calculated in up to six dimensions.

Krogh, F. T.↗
NASA NTRS1972

Relations between Haar and Walsh/Hadamard transforms.

Relations between the Haar and Walsh/Hadamard (W/H) transforms, which are proved, show that for some applications the Haar transform performs as well as, and faster than, the W/H transform. These relations yield a family of orthogonal transforms including the Haar and W/H transforms with a common fast algorithm.

Fino, B. J.↗
NASA NTRS1973

Pseudocolor transformation of ERTS imagery

One of the photographic techniques which shows great promise as an aid in interpreting ERTS imagery is pseudocolor transformation. It is a process where each shade of gray in an original black-and-white image is seen as a different color in the transformation. The well known ERTS-1 MSS image of the Monterey Bay-San Francisco area was transformed using a technique which requires only two intermediate separations. Possible faults were delineated on an overlay of the transformation before referring to geologic maps. The results were quite remarkable in that all large active or recently active faults shown on the latest geologic map of California were interpreted from the image for all, or much, of their length. Perhaps the most interesting result was the Reliz fault. The fault is shown as covered; however, a lineation corresponding to the position of the fault is visible on the image. The usefulness of ERTS image in identifying recently active faults is demonstrable. Although the faults are also visible in the unenhanced image, they are clearly accentuated and more easily mapped on the pseudocolor transformation.

Lamar, J. V.↗