DSIF station control and data equipment
Deep Space Information Facility equipment for processing spacecraft telemetry and command data
DATA PROCESSING↗
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Results for “deep data”
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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A Transfer Learning Strategy for Improving the Data Efficiency of Deep Reinforcement Learning Control in Smart Buildings
Reinforcement learning (RL) is a powerful tool that has shown promising results in many domains such as robotics and game-playing. Because RL algorithms learn optimal control policies by continuously interacting with their environments, these algorithms require a lot of data to learn, which limits their application to a wide range of domains. For this reason, there is an immense need for improving the training and data efficiency of RL. Towards addressing this research gap, this paper proposes a transfer learning (TL) approach to improve the efficiency of the RL algorithms by reducing data need and, thus, reducing training time. To demonstrate the proposed approach, a knowledge transfer from a set of buildings to another building was conducted. The results show that the proposed TL approach is a promising method that can efficiently harness the information from similar RL tasks and reduce the data needs of RL algorithms.
Amasyali, Kadir↗
Comparisons of Cloud In-Situ Microphysical Properties of Deep Convective Clouds to Appendix D/P, Using Data from the HAIC-HIWC and HIWC RADAR I Flight Campaigns
In-situ cloud data from three international flight campaigns is compared to the new Federal Aviation Administration (FAA) Title 14 Code of Federal Regulations Part 33 Appendix D mixed-phase/glaciated environmental envelope (1), and the corresponding identical European Aviation Safety Agency (EASA) CS-25 Appendix P envelope (2) (hereafter "Appendix D/P"). Appendix D/P consists of a temperature-altitude envelope, a 99th percentile total water content (TWC99) envelope at the 17.4 Nm distance scale, a distance factor to estimate TWC99 values at other distance scales, ice crystal median mass diameter (MMD), and recommended liquid water content (LWC) levels in mixed-phase icing conditions. The dataset is from 45 flight missions in 3 tropical locations, with 472 runs in approximately 115 clouds, providing about 29,600 Nm of in-cloud data in deep convection over four targeted temperature intervals: 10, 30, 40, and 50 +5 C. The measurements span altitueds from about 17,000' to 39,000'. The comparisons will serve as a basis for regulatory and industry assessment of the efficacy of Appendix D/P.
Strapp, J. Walter↗
Comparisons of Cloud In-Situ Microphysical Properties of Deep Convective Clouds to Appendix D/P using Data from the HAIC-HIWC and HIWC-RADAR I Flight Campaigns
In-situ cloud data from three international flight campaigns are compared to the Federal Aviation Administration Title 14 Code of Federal Regulations Part 33 Appendix D mixed-phase/glaciated environmental envelope, and the corresponding identical European Aviation Safety Agency CS-25 Appendix P envelope. The appendices consist of a temperature-altitude envelope, a 99th percentile total water content envelope at the 17.4 Nm distance scale, a distance factor for estimation at other distance scales, ice crystal median mass diameter, and recommended liquid water content levels in mixed-phase icing conditions. The data were collected during 54 flights out of one subtropical and two tropical locations, with 472 runs from about 17,000’ to 39,000’ in approximately 115 clouds. The campaigns provide about 29,600 Nm of in situ data in deep convection over four targeted temperature intervals: -10, -30, -40, and -50, all ± 5 C. The dataset is a modern and unique documentation of the ice crystal icing environment, and results described in this article will contribute to regulatory and industry assessment of Appendices D and P.
Ice Crystal Icing↗
Greatly enhanced deep space mission data return using verv large DSN arrays
The purposes of the Array System are: To fulfill the mission needs for higher data rates in the post-2010 era - Reduce the cost per data bit by two orders of magnitude. Specific Goal: Achieve a factor of 10 increase in DSN reception capability per decade from 2012 through 2027 (an average of 1 dB per year).
array↗
Machine-learned closure of URANS for stably stratified turbulence: connecting physical timescales & data hyperparameters of deep time-series models
Stably stratified turbulence (SST), a model that is representative of the turbulence found in the oceans and atmosphere, is strongly affected by fine balances between forces and becomes more anisotropic in time for decaying scenarios. Moreover, there is a limited understanding of the physical phenomena described by some of the terms in the Unsteady Reynolds-Averaged Navier–Stokes (URANS) equations—used to numerically simulate approximate solutions for such turbulent flows. Rather than attempting to model each term in URANS separately, it is attractive to explore the capability of machine learning (ML) to model groups of terms, i.e. to directly model the force balances. We develop deep time-series ML for closure modeling of the URANS equations applied to SST. We consider decaying SST which are homogeneous and stably stratified by a uniform density gradient, enabling dimensionality reduction. We consider two time-series ML models: long short-term memory and neural ordinary differential equation. Both models perform accurately and are numerically stable in a posteriori (online) tests. Furthermore, we explore the data requirements of the time-series ML models by extracting physically relevant timescales of the complex system. We find that the ratio of the timescales of the minimum information required by the ML models to accurately capture the dynamics of the SST corresponds to the Reynolds number of the flow. The current framework provides the backbone to explore the capability of such models to capture the dynamics of high-dimensional complex dynamical system like SST flows.
97 MATHEMATICS AND COMPUTING↗
Towards automated and real-time multi-object detection of anguilliform fishes from sonar data using YOLOv8 deep learning algorithm
Eels (Anguilla spp.), including American eels (Anguilla rostrata), European eels (Anguilla anguilla), and Japanese eels (Anguilla japonica), are species of critical management and regulatory concern due to their vulnerability to various stressors during downstream migrations. Accurate and efficient detection of migrating eels can improve our understanding of fish behaviors and fish-hydraulic structure interactions, thereby facilitating the design, operation, and optimization of more effective downstream passage facilities from both biological and economic perspectives. However, a real-time, automated framework for detecting migrating eels in real-world applications is currently lacking. Leveraging imaging sonar as a reliable technology for fish passage monitoring, field data are acquired using imaging sonar and then converted to single sonar frames/images for subsequent analysis. In this study, a framework based on the You Only Look Once Version 8 (YOLOv8)-based convolutional neural network is proposed for multi-object detection of eels and non-eel fish using the sonar images after image subtraction and additional wavelet denoising. The results from both training and testing phases demonstrate that the framework's ability can successfully detect both eels and non-eel fish in preprocessed sonar images, achieving F1-scores and mAP@0.50 exceeding 0.84. Additionally, the incorporation of wavelet denoising during preprocessing slightly improve detection performance. Furthermore, the transferability of this framework from eel to lamprey detection is demonstrated to be feasible given the similar morphological characteristics of these two species. Overall, the proposed framework achieves accurate and efficient detection of migrating eels, providing reliable and real-time information that can help conserve vulnerable eel and eel-like populations.
Deep learning↗
Predicting plug loads with occupant count data through a deep learning approach
Predictive control has gained increasing attention for its ability to reduce energy consumption and improve occupant comfort in buildings. The plug loads prediction is a key component for the predictive building controls, as plug loads is a major source of internal heat gains in buildings. This study proposed a novel method to apply the Long-Short-Term-Memory (LSTM) Network, a special form of Recurrent Neural Network, to predict plug loads. The occupant count and the time have been confirmed to drive the plug load profile and thus selected as the features for the plug load prediction. The LSTM network was trained and tested with ground truth occupant count data collected from a real office building in Berkeley, California. Results from the LSTM network markedly improve the prediction accuracy compared with traditional linear regression methods and the classical Artificial Neural Network. 95% of 1-hour predictions from LSTM network are within ±1 kW of the actual plug loads, given the average plug loads during the office hour is 8.6 kW. The CV(RMSE) of the predicted plug load is 11% for the next hour, and 20% for the next eight hours. Lastly, we compared four prediction approaches with the office building we monitored: LSTM vs. ARIMA, with occupant counts vs. without occupant counts. It was found, the prediction error of the LSTM approach is around 4% less than the ARIMA approach. Using occupant counts as an exogenous input could further reduce the prediction error by 5%-6%. The findings of this paper could shed light on the plug load prediction for building control optimizations such as model-predictive control.
Occupant count↗
Identifying Trends in Deep Space Network Monitor Data
A computer program has been developed that analyzes Deep Space Network monitor data, looking for changes of trends in critical parameters. This program represents a significant improvement over the previous practice of manually plotting data and visually inspecting the resulting graphs to identify trends. This program uses proven numerical techniques to identify trends. When a statistically significant trend is detected, then it is characterized by means of a symbol that can be used by pre-existing model-based reasoning software. The program can perform any of the following functions: Given an expectation that data in a given list should exhibit an upward, downward, constant, or unknown trend, it can determine whether the data do or do not follow such a trend. Given a list of data, it can identify which of the aforementioned trends the data follow. Given two lists of data, it can determine whether or not both follow the same trend. This program can be executed on a variety of computers. It can be distributed in either source code or binary code form. It must be run in conjunction with any one of a number of Lisp compilers that are available commercially or as shareware.
James, Mark↗
Adaptive Data-Driven Deep-Learning Surrogate Model for Frontal Polymerization in Dicyclopentadiene
Frontal polymerization (FP) is a self-sustaining curing process that enables rapid and energy-efficient manufacturing of thermoset polymers and composites. Computational methods conventionally used to simulate the FP process are time-consuming, and repeating simulations are required for sensitivity analysis, uncertainty quantification, or optimization of the manufacturing process. Here, in this work, we develop an adaptive surrogate deep-learning model for FP of dicyclopentadiene (DCPD), which predicts the evolution of temperature and degree of cure orders of magnitude faster than the finite-element method (FEM). The adaptive algorithm provides a strategy to select training samples efficiently and save computational costs by reducing the redundancy of FEM-based training samples. The adaptive algorithm calculates the residual error of the FP governing equations using automatic differentiation of the deep neural network. A probability density function expressed in terms of the residual error is used to select training samples from the Sobol sequence space. The temperature and degree of cure evolution of each training sample are obtained by a 2D FEM simulation. The adaptive method is more efficient and has a better prediction accuracy than the random sampling method. With the well-trained surrogate neural network, the FP characteristics (front speed, shape, and temperature) can be extracted quickly from the predicted temperature and degree-of-cure fields.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Big, Deep, and Smart Data in Scanning Probe Microscopy
Scanning probe microscopy techniques open the door to nanoscience and nanotechnology by enabling imaging and manipulation of structure and functionality of matter on nanometer and atomic scales. We analyze the discovery process by SPM in terms of information flow from tip-surface junction to the knowledge adoption by scientific community. Furthermore, we discuss the challenges and opportunities offered by merging of SPM and advanced data mining, visual analytics, and knowledge discovery technologies.
77 NANOSCIENCE AND NANOTECHNOLOGY↗
Quantitative analysis of EXAFS data sets using deep reinforcement learning
Not provided.
Science & Technology - Other Topics↗
Neural networks to learn protein sequence–function relationships from deep mutational scanning data
Understanding the relationship between protein sequence and function is necessary to design new and useful proteins with applications in bioenergy, medicine, and agriculture. The mapping from sequence to function is tremendously complex because it involves thousands of molecular interactions that are coupled over multiple lengths and timescales. We show that neural networks can learn the sequence–function mapping from large protein datasets. Neural networks are appealing for this task because they can learn complicated relationships from data, make few assumptions about the nature of the sequence–function relationship, and can learn general rules that apply across the length of the protein sequence. We demonstrate that learned models can be applied to design new proteins with properties that exceed natural sequences.
59 BASIC BIOLOGICAL SCIENCES↗
The Cosmic Ultraviolet Baryon Survey (CUBS) – III. Physical properties and elemental abundances of Lyman-limit systems at z < 1
ABSTRACT We present a systematic investigation of physical conditions and elemental abundances in four optically thick Lyman-limit systems (LLSs) at z = 0.36–0.6 discovered within the Cosmic Ultraviolet Baryon Survey (CUBS). Because intervening LLSs at z < 1 suppress far-UV (ultraviolet) light from background QSOs, an unbiased search of these absorbers requires a near-UV-selected QSO sample, as achieved by CUBS. CUBS LLSs exhibit multicomponent kinematic structure and a complex mix of multiphase gas, with associated metal transitions from multiple ionization states such as C ii, C iii, N iii, Mg ii, Si ii, Si iii, O ii, O iii, O vi, and Fe ii absorption that span several hundred km s−1 in line-of-sight velocity. Specifically, higher column density components (log N(H i)/cm−2≳ 16) in all four absorbers comprise dynamically cool gas with $\langle T \rangle =(2\pm 1) \times 10^4\,$K and modest non-thermal broadening of $\langle b_\mathrm{nt} \rangle =5\pm 3\,$km s−1. The high quality of the QSO absorption spectra allows us to infer the physical conditions of the gas, using a detailed ionization modelling that takes into account the resolved component structures of H i and metal transitions. The range of inferred gas densities indicates that these absorbers consist of spatially compact clouds with a median line-of-sight thickness of $160^{+140}_{-50}$ pc. While obtaining robust metallicity constraints for the low density, highly ionized phase remains challenging due to the uncertain $N\mathrm{(H\, {\small I})}$, we demonstrate that the cool-phase gas in LLSs has a median metallicity of $\mathrm{[\alpha /H]_{1/2}}=-0.7^{+0.1}_{-0.2}$, with a 16–84 percentile range of [α/H] = (−1.3, −0.1). Furthermore, the wide range of inferred elemental abundance ratios ([C/α], [N/α], and [Fe/α]) indicate a diversity of chemical enrichment histories. Combining the absorption data with deep galaxy survey data characterizing the galaxy environment of these absorbers, we discuss the physical connection between star-forming regions in galaxies and diffuse gas associated with optically thick absorption systems in the z < 1 circumgalactic medium.
Zahedy, Fakhri S. (ORCID:0000000178692551)↗
Intra-class data augmentation with deep generative models of threat objects in baggage radiographs
Explore the source record for details and available documents.
Komkov, Heidi Baumgartner↗
Data for Natural Deep Eutectic Solvents (NADES) Assisted Deconstruction of Oilcane Bagasse for High Lipid and Sugar Recovery
In a novel approach, metabolically engineered sugarcane “Oilcane” has been investigated for fractionation of lipid and cellulose-rich pulp, using certain Natural deep eutectic solvents (NADES). The exploration of eco-friendly solvents are at the forefront of harnessing the biofuel potential of modern bioenergy crops. For this, six combinations of NADES were prepared using choline chloride (ChCl) as HBA and lactic acid (LA), oxalic acid (OA) and glycerol (Gly) as HBD and were further explored for pretreatment of oilcane bagasse in a molar ratio of 1:1 and 1:2. The impact of NADES ratio, biomass loading (10–50%), residence time (1–2 h), and temperature (90–140 °C) were evaluated for delignification, lipid content, sugar release after enzymatic hydrolysis. The finding demonstrated that under the optimal condition of ChCl: LA (1:2 molar ratio), 140 °C with 2 h retention time, the lipid content in the pre-treated substrate was increased to 2.5-fold (∼8% w/w) and > 80% glucose yield was achieved after 72 h of hydrolysis of pre-treated bagasse. High solid loading (∼50%) during pretreatment resulted in a similar glucose yield. Furthermore, recycling studies demonstrated that nearly 95 to 98% NADES could be recycled after each pretreatment for up to five consecutive cycles without any significant loss in chemical structure as confirmed by 1H NMR and FT IR. FT IR and XRD analyses of native and pre-treated biomass were performed to visualize the morphological changes during NADES pretreatment and their impact on sugar yield. The findings of the study may be used to establish NADES-based biorefinery for the valorization of lipids, and carbohydrates for fuels and chemicals production.
Conversion↗
Intra-class data augmentation with deep generative models of threat objects in baggage radiographs
Explore the source record for details and available documents.
Komkov, Heidi Baumgartner↗