Results for “Selective classification” — Page 10

NASA NTRS1986

The physical basis for spectral variations in thermal infrared emittance of silicates and application to remote sensing

The use of infrared spectroscopy for the remote characterization of planetary surfaces has received attention due to efforts in the investigation of these bodies from space. In the 8 to 14 micron region, a depression in the emittance spectra of rocks (sometimes called reststrahlen) is related to the fundamental stretching vibrations of Si-O bonds and shifts in the locations of this feature are ascribed to variations in rock composition. Thus, it should be possible to investigate, quantify, and model the relationships of reststrahlen spectral band location through silicate mineralogical composition to rock classification. This concept will be tested first through the use of laboratory-acquired data on the infrared spectra and mineralogy of selected mineral and rock samples. As a suitable classification model is developed, it will be tested through overflights of appropriate rock outcrops using the Thermal Infrared Multispectral Scanner (TIMS).

Walter, Louis S.↗

DOE OSTI2010

Storage Viability and Optimization Web Service

Non-residential sectors offer many promising applications for electrical storage (batteries) and photovoltaics (PVs). However, choosing and operating storage under complex tariff structures poses a daunting technical and economic problem that may discourage potential customers and result in lost carbon and economic savings. Equipment vendors are unlikely to provide adequate environmental analysis or unbiased economic results to potential clients, and are even less likely to completely describe the robustness of choices in the face of changing fuel prices and tariffs. Given these considerations, researchers at Lawrence Berkeley National Laboratory (LBNL) have designed the Storage Viability and Optimization Web Service (SVOW): a tool that helps building owners, operators and managers to decide if storage technologies and PVs merit deeper analysis. SVOW is an open access, web-based energy storage and PV analysis calculator, accessible by secure remote login. Upon first login, the user sees an overview of the parameters: load profile, tariff, technologies, and solar radiation location. Each parameter has a pull-down list of possible predefined inputs and users may upload their own as necessary. Since the non-residential sectors encompass a broad range of facilities with fundamentally different characteristics, the tool starts by asking the users to select a load profile from a limited cohort group of example facilities. The example facilities are categorized according to their North American Industry Classification System (NAICS) code. After the load profile selection, users select a predefined tariff or use the widget to create their own. The technologies and solar radiation menus operate in a similar fashion. After these four parameters have been inputted, the users have to select an optimization setting as well as an optimization objective. The analytic engine of SVOW is LBNL?s Distributed Energy Resources Customer Adoption Model (DER-CAM), which is a mixed-integer linear program (MILP) written and executed in the General Algebraic Modeling System (GAMS) optimization software. LBNL has released version 1.2.0.11 of SVOW. Information can be found at http://der.lbl.gov/microgrids-lbnl/current-project-storage-viability-website.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2023

Ultracool dwarfs candidates based on 6 yr of the Dark Energy Survey data

ABSTRACT We present a sample of 19 583 ultracool dwarf candidates brighter than z ≤23 selected from the Dark Energy Survey DR2 coadd data matched to VHS DR6, VIKING DR5, and AllWISE covering ∼ 480 deg2. The ultracool candidates were first pre-selected based on their (i–z), (z–Y), and (Y–J) colours. They were further classified using a method that compares their optical, near-infrared, and mid-infrared colours against templates of M, L, and T dwarfs. 14 099 objects are presented as new L and T candidates and the remaining objects are from the literature, including 5342 candidates from our previous work. Using this new and deeper sample of ultracool dwarf candidates we also present: 20 new candidate members to nearby young moving groups and associations, variable candidate sources and four new wide binary systems composed of two ultracool dwarfs. Finally, we also show the spectra of 12 new ultracool dwarfs discovered by our group and presented here for the first time. These spectroscopically confirmed objects are a sanity check of our selection of ultracool dwarfs and photometric classification method.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

Streamlined spatial and environmental expression signatures characterize the minimalist duckweed Wolffia australiana

Single-cell genomics permits a new resolution in the examination of molecular and cellular dynamics, allowing global, parallel assessments of cell types and cellular behaviors through development and in response to environmental circumstances, such as interaction with water and the light–dark cycle of the Earth. Here, we leverage the smallest, and possibly most structurally reduced, plant, the semiaquaticWolffia australiana, to understand dynamics of cell expression in these contexts at the whole-plant level. We examined single-cell-resolution RNA-sequencing data and foundWolffiacells divide into four principal clusters representing the above- and below-water-situated parenchyma and epidermis. Although these tissues share transcriptomic similarity with model plants, they display distinct adaptations thatWolffiahas made for the aquatic environment. Within this broad classification, discrete subspecializations are evident, with select cells showing unique transcriptomic signatures associated with developmental maturation and specialized physiologies. Assessing this simplified biological system temporally at two key time-of-day (TOD) transitions, we identify additional TOD-responsive genes previously overlooked in whole-plant transcriptomic approaches and demonstrate that the core circadian clock machinery and its downstream responses can vary in cell-specific manners, even in this simplified system. Distinctions between cell types and their responses to submergence and/or TOD are driven by expression changes of unexpectedly few genes, characterizingWolffiaas a highly streamlined organism with the majority of genes dedicated to fundamental cellular processes.Wolffiaprovides a unique opportunity to apply reductionist biology to elucidate signaling functions at the organismal level, for which this work provides a powerful resource.

Biochemistry & Molecular Biology↗

DOE OSTI2017

Measurement of the $\nu _{\mu }$ energy spectrum with IceCube-79

IceCube is a neutrino observatory deployed in the glacial ice at the geographic South Pole. The ν μ energy unfolding described in this paper is based on data taken with IceCube in its 79-string configuration. A sample of muon neutrino charged-current interactions with a purity of 99.5% was selected by means of a multivariate classification process based on machine learning. The subsequent unfolding was performed using the software Truee. The resulting spectrum covers an E ν -range of more than four orders of magnitude from 125 GeV to 3.2 PeV. Compared to the Honda atmospheric neutrino flux model, the energy spectrum shows an excess of more than 1.9σ in four adjacent bins for neutrino energies E ν ≥ 177.8TeV. The obtained spectrum is fully compatible with previous measurements of the atmospheric neutrino flux and recent IceCube measurements of a flux of high-energy astrophysical neutrinos.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

NASA NTRS1977

Remote sensing data processing - Two years ago, today, and two years from today

Certain technical problems arising in the recent past (1975) in the field of the processing of remote sensing data are reviewed including approaches to the analysis of Landsat MSS data and technical difficulties which must be overcome to achieve operational data processing. The current status of remote sensing data processing is then examined with emphasis on such current technical issues as training selection and labeling, sampling schemes and classification and mensuration. Hardware projections are made for the near future (1979) relative to the development of remote sensing data processing.

Holmes, Q. A.↗

NASA NTRS1983

Mapping project on land use changes in the carboniferous region of Santa Catarina

The utilization of remote sensing data for monitoring land use changes by means of digital image analysis is described. The following data were utilized: LANDSAT data from September 4, 1975, April 24, 1978, and September 8, 1981; LANDSAT paper photography data; area IV color photographs; IBGE topography maps, and auxiliary data about the Brazilian state of Santa Catarina. Three kinds of analyses of digital images were carried out. The project identified and mapped major classes of land use areas including urban areas, coal deposits, agricultural areas, forests, lakes, and flood plains. Five areas directly affected by coal exploration southeast of Santa Catarina are identified and described. In addition, the classification system used for organizing data about land cover in a hierarchical arrangement is presented. The project made use of two remote sensing data sources: data of MSS spectral (Mulitspectral Scanner System)/LANDSAT on a scale of 1:100,000 with approximately 80 m resolution, and infrared color aerial photographs on a scale of 1:45,000 with approximately 5 m resolution. Therefore, the classification system included three levels, two selected to be compatible with aerial photography data and the third to conform to the resolution of MSS/LANDSAT.

Valeriano, D. D.↗

NASA NTRS1990

Airborne MSS for land cover classification II

A basic methodology for land cover classification using airborne multispectral scanner (MSS) imagery is outlined. This includes waveband selection and radiometric calibration; correction for scan angle and atmosphere; training and classification and accuracy assessment. Refinements to this basic methodology include per-field sampling and the addition of low-pass filtering, image texture, prior probabilities and two dates of imagery. For a study area in upland England, eight land covers were classified with a mean accuracy of 52.6 percent using the basic methodology. This was increased to 79.0 percent by using a suitability refined methodology. Per-field sampling accounted for the largest proportion of this increase.

Curran, Paul J.↗

NASA NTRS1996

Evaluation of Star Identification Techniques

A number of different strategies are used or have been suggested for identifying star fields atitude determination in space. We offer a general classification of the existing techniques and select three representative algorithms for more comprehensive evaluation. In addition, we describe a software simulation environment we developed for the design, paramter determination, and evaluation of star identification algorithms. The identification rates and performance on the three algorithms are presented over a variety of noise conditions using two different sized onboard catalogs.

star↗

NASA NTRS2022

Science Autonomy for Ocean Worlds Astrobiology: A Perspective

Astrobiology missions to ocean worlds in our solar system must overcome both scientific and technological challenges due to extreme temperature and radiation conditions, long communication times, and limited bandwidth. While such tools could not replace ground-based analysis by science and engineering teams, machine learning algorithms could enhance the science return of these missions through development of autonomous science capabilities. Examples of science autonomy include onboard data analysis and subsequent instrument optimization, data prioritization (for transmission), and real-time decision-making based on data analysis. Similar advances could be made to develop streamlined data processing software for rapid ground-based analyses. Here we discuss several ways machine learning and autonomy could be used for astrobiology missions, including landing site selection, prioritization and targeting of samples, classification of “features” (e.g., proposed biosignatures) and novelties (uncharacterized, “new” features, which may be of most interest to agnostic astrobiological investigations), and data transmission.

ocean worlds↗

DOE OSTI2025

Contrasting Time-Frequency Representations for Unknown Waveform Detection

In real-world applications like spectrum management and interference detection, dealing with unseen electromagnetic waveforms is critical. Although some methods attempt to simulate open set data using generator models, they face challenges in generating synthetic samples for open set while simultaneously selecting an optimal discriminator for accurate classification. This results in difficulties capturing distinctive features across classes, especially in dynamic scenarios where new classes emerge. To detect unseen waveforms, we propose combining time and frequency domain features with cosine similarity loss to enhance feature distinctiveness and enabling more accurate predictions. This approach efficiently captures more comprehensive information than single-domain representations or approaches without cosine loss. Additionally, our model avoids generic feature vectors by extracting class-specific features during training, resulting in improved class representation. The experiment results show that this combined feature approach with cosine loss outperforms single-domain models and improves accuracy by 10\% over models without cosine loss.

99 - GENERAL AND MISCELLANEOUS↗

DOE OSTI2025

Contrasting Time-Frequency Representations for Unknown Waveform Detection

Identifying unseen electromagnetic waveforms is critical for many applications, like interference management, electronic warfare and spectrum management. Traditionally this is done using statistical methods for anomaly detection, which has evolved to deep learning models for identifying the unseen data, formally termed as open set recognition. Some prior methods use a generative model to emulate open set data, which face challenges in generating synthetic samples for open set while simultaneously selecting an optimal discriminator for accurate classification. To alleviate this issue, we propose a discriminative model that effectively combines time and frequency domain features of communication signals for accurate predictions. We further introduce a cosine similarity loss that makes the domain specific features unique to enhance the prediction rate. Additionally, our model avoids generic feature vectors by extracting class-specific features during training, resulting in improved class representation. The experiment results show that this combined feature approach with cosine loss outperforms single-domain models and improves accuracy by 10% over models without cosine loss.

99 - GENERAL AND MISCELLANEOUS↗

DOE OSTI2019

Data-driven Feature Analysis in Control Design for Series-Compensated Transmission Systems

One challenge in power-system control designs is the gap between numerical model-based analysis and complex real-world power systems. With increased data and measurements being collected from power systems, data-driven analysis (e.g., machine learning) may provide an alternative approach to reveal hidden information through learning from the real system data, and provide insights for better control scheme design during the utility planning process. Data-driven feature analysis was performed to evaluate the relationships between series compensation, power generation, and path flows in a real transmission system, as well as temporal patterns. The main data-driven analysis methods, including statistical cross-correlation, multinomial logistical regression, and classification and regression trees, were integrated for feature selection and developing predictive models of series compensation. Analysis results demonstrated the effectiveness of the proposed methodology in feature analysis and the potential to help improve power-system control scheme design.

42 ENGINEERING↗

NASA NTRS2015

Sensor Selection for Aircraft Engine Performance Estimation and Gas Path Fault Diagnostics

This paper presents analytical techniques for aiding system designers in making aircraft engine health management sensor selection decisions. The presented techniques, which are based on linear estimation and probability theory, are tailored for gas turbine engine performance estimation and gas path fault diagnostics applications. They enable quantification of the performance estimation and diagnostic accuracy offered by different candidate sensor suites. For performance estimation, sensor selection metrics are presented for two types of estimators including a Kalman filter and a maximum a posteriori estimator. For each type of performance estimator, sensor selection is based on minimizing the theoretical sum of squared estimation errors in health parameters representing performance deterioration in the major rotating modules of the engine. For gas path fault diagnostics, the sensor selection metric is set up to maximize correct classification rate for a diagnostic strategy that performs fault classification by identifying the fault type that most closely matches the observed measurement signature in a weighted least squares sense. Results from the application of the sensor selection metrics to a linear engine model are presented and discussed. Given a baseline sensor suite and a candidate list of optional sensors, an exhaustive search is performed to determine the optimal sensor suites for performance estimation and fault diagnostics. For any given sensor suite, Monte Carlo simulation results are found to exhibit good agreement with theoretical predictions of estimation and diagnostic accuracies.

Gas Turbine Engines↗

NASA NTRS2015

Sensor Selection for Aircraft Engine Performance Estimation and Gas Path Fault Diagnostics

This paper presents analytical techniques for aiding system designers in making aircraft engine health management sensor selection decisions. The presented techniques, which are based on linear estimation and probability theory, are tailored for gas turbine engine performance estimation and gas path fault diagnostics applications. They enable quantification of the performance estimation and diagnostic accuracy offered by different candidate sensor suites. For performance estimation, sensor selection metrics are presented for two types of estimators including a Kalman filter and a maximum a posteriori estimator. For each type of performance estimator, sensor selection is based on minimizing the theoretical sum of squared estimation errors in health parameters representing performance deterioration in the major rotating modules of the engine. For gas path fault diagnostics, the sensor selection metric is set up to maximize correct classification rate for a diagnostic strategy that performs fault classification by identifying the fault type that most closely matches the observed measurement signature in a weighted least squares sense. Results from the application of the sensor selection metrics to a linear engine model are presented and discussed. Given a baseline sensor suite and a candidate list of optional sensors, an exhaustive search is performed to determine the optimal sensor suites for performance estimation and fault diagnostics. For any given sensor suite, Monte Carlo simulation results are found to exhibit good agreement with theoretical predictions of estimation and diagnostic accuracies.

Gas Turbine Engines↗

NASA NTRS2016

Sensor Selection for Aircraft Engine Performance Estimation and Gas Path Fault Diagnostics

This paper presents analytical techniques for aiding system designers in making aircraft engine health management sensor selection decisions. The presented techniques, which are based on linear estimation and probability theory, are tailored for gas turbine engine performance estimation and gas path fault diagnostics applications. They enable quantification of the performance estimation and diagnostic accuracy offered by different candidate sensor suites. For performance estimation, sensor selection metrics are presented for two types of estimators including a Kalman filter and a maximum a posteriori estimator. For each type of performance estimator, sensor selection is based on minimizing the theoretical sum of squared estimation errors in health parameters representing performance deterioration in the major rotating modules of the engine. For gas path fault diagnostics, the sensor selection metric is set up to maximize correct classification rate for a diagnostic strategy that performs fault classification by identifying the fault type that most closely matches the observed measurement signature in a weighted least squares sense. Results from the application of the sensor selection metrics to a linear engine model are presented and discussed. Given a baseline sensor suite and a candidate list of optional sensors, an exhaustive search is performed to determine the optimal sensor suites for performance estimation and fault diagnostics. For any given sensor suite, Monte Carlo simulation results are found to exhibit good agreement with theoretical predictions of estimation and diagnostic accuracies.

Diagnosis↗

DOE OSTI2018

Galaxy populations in the most distant SPT-SZ clusters - I. Environmental quenching in massive clusters at $1.4\lesssim z\lesssim1.7$

We present first results from a galaxy population study in the highest redshift galaxy clusters identified in the 2500 deg$^2$ South Pole Telescope Sunyaev Zel'dovich effect (SPT-SZ) survey. The cluster selection is to first order independent of galaxy properties, making the SPT-SZ sample particularly well suited for cluster galaxy population studies. We carry out a 4-band imaging campaign with the {\it Hubble} and {\it Spitzer} Space Telescopes of the five $z\gtrsim 1.4$, S/N$_{SZE}>$5 clusters, that are among the rarest most massive clusters known at this redshift. All five show clear overdensities of red galaxies whose colors agree with the initial cluster redshift estimates. The highest redshift cluster in this sample, SPT-CLJ0459-4947 at $z\sim1.72$, is the most distant $M_{500}>10^{14}~M_{\odot}$ ICM-selected cluster discovered thus far, and is one of only three known clusters in this mass range at $z\gtrsim 1.7$, regardless of selection. Based on UVJ-like photometric classification of quiescent and star-forming galaxies, the passive fraction in the cluster central regions ($r/r_{500}<0.7$) is higher than in the field at the same redshift, with corresponding environmental quenching efficiencies typically in the range $\sim0.5-0.8$ for stellar masses $\log(M/M_{\odot})>10.85$. We have explored the impact of emission from star formation on the selection of this sample, concluding that all five clusters studied here would still have been detected with S/N$_{SZE}>$5, even if they had the same passive fraction as measured in the field. Our results thus point towards an efficient suppression of star formation in massive galaxies in the central regions of the most massive clusters, occurring already earlier than $z\sim1.5$. [Abridged]

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2019

Galaxy populations in the most distant SPT-SZ clusters: I. Environmental quenching in massive clusters at 1.4 ≲ z ≲ 1.7

Here, we present the first results from a galaxy population study in the highest redshift galaxy clusters identified in the 2500 deg 2 South Pole Telescope Sunyaev Zel’dovich effect (SPT-SZ) survey, which is sensitive to M 500 ≳ 3 × 10 14 M ⊙ clusters from z ~ 0.2 out to the highest redshifts where such massive structures exist. The cluster selection is to first order independent of galaxy properties, making the SPT-SZ sample particularly well suited for cluster galaxy population studies. We carried out a four-band imaging campaign with the Hubble and Spitzer Space Telescopes of the five z ≳ 1.4, S/N SZE > 5 clusters, that are among the rarest most massive clusters known at this redshift. All five clusters show clear overdensities of red galaxies whose colors agree with the initial cluster redshift estimates, although one (SPT-CLJ0607–4448) shows a galaxy concentration much less prominent than the others. The highest redshift cluster in this sample, SPT-CLJ0459–4947 at z ~ 1.72, is the most distant M 500 > 10 14 M ⊙ cluster discovered thus far through its intracluster medium, and is one of only three known clusters in this mass range at z ≳ 1.7, regardless of selection. Based on UVJ-like photometric classification of quiescent and star-forming galaxies, we find that the quiescent fraction in the cluster central regions ( r/r 500 < 0.7) is higher than in the field at the same redshift, with corresponding environmental quenching efficiencies typically in the range ~0.5 – 0.8 for stellar masses log( M/M ⊙) > 10.85. We have explored the impact of emission from star formation on the selection of this sample, concluding that all five clusters studied here would still have been detected with S/N SZE > 5, even if they had the same quiescent fraction as measured in the field. Our results thus point towards an efficient suppression of star formation in the central regions of the most massive clusters, occurring already earlier than z ~ 1.5.

79 ASTRONOMY AND ASTROPHYSICS↗