Gaussian Mixture Models for Information Integration: Toward Gaze-Informed Information Foraging Models for Imagery Analysis.
Abstract not provided.
Chen, Maximillian Gene↗
SEARCH · Engineering Papers
Results for “INFORMATION”
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.
BioEarth: Envisioning and developing a new regional earth system model to inform natural and agricultural resource management
As managers of agricultural and natural resources are confronted with uncertainties in global change impacts, the complexities associated with the interconnected cycling of nitrogen, carbon, and water present daunting management challenges. Existing models provide detailed information on specific sub-systems (land, air, water, economics, etc). An increasing awareness of the unintended consequences of management decisions resulting from interconnectedness of these sub-systems, however, necessitates coupled regional earth system models (EaSMs). Decision makers’ needs and priorities can be integrated into the model design and development processes to enhance decision-making relevance and "usability" of EaSMs. BioEarth is a current research initiative with a focus on the U.S. Pacific Northwest region that explores the coupling of multiple stand-alone EaSMs to generate usable information for resource decision-making. Direct engagement between model developers and non-academic stakeholders involved in resource and environmental management decisions throughout the model development process is a critical component of this effort. BioEarth utilizes a "bottom-up" approach, upscaling a catchment-scale model to basin and regional scales, as opposed to the "top-down" approach of downscaling global models utilized by most other EaSM efforts. This paper describes the BioEarth initiative and highlights opportunities and challenges associated with coupling multiple stand-alone models to generate usable information for agricultural and natural resource decision-making.
agricultural↗
Simulating precipitation in the Northeast United States using a climate-informed K -nearest neighbour algorithm
Not provided.
Water Resources↗
Using Systems Theoretic Process Analysis and Causal Analysis to Map and Manage Organizational Information to Enable Digitalization and Information Automation
The overarching goal of this Light Water Reactor Sustainability Program–supported research and development project is to provide planning tools and comprehensive guidance to utilities considering or undertaking full nuclear plant modernization. The results of this research will provide the nuclear industry with a comprehensive and usable solution, including guidance, lessons learned, methods, and planning tools. This research is currently working to provide guidance on digitalization and information automation to enable the evolution of data to information, insight, and action—thereby allowing utilities to operate safely and cost-competitively with all other electrical generation sources. Light Water Reactor Sustainability Program researchers have also recently started investigating how human and technology integration principles, information automation, and digitalization enable data evolution. These researchers are currently in the process of validating the use of System-Theoretic Process Analysis to define high-level safety constraints in the United States Nuclear Regulatory Commission’s problem identification and resolution process (i.e., a plant compliance information gathering activity). The next step in this research, which is described in the following sections of this report, is to map out data evolution in a use case to identify inefficiencies in another aspect of plant compliance information gathering and communication activities—event investigations and root cause analyses.
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
CARETS: A prototype regional environmental information system. Volume 7: Land use information and air quality planning
The pilot national land use information system developed by the U.S. Geological Survey in the Central Atlantic Regional Ecological Test Site project has provided an improved technique for estimating emissions, diffusion, and impact patterns of sulfur dioxide (S0 2 ) and particulate matter. Implementation of plans to control air quality requires land use information, which, until this time, has been inadequate. The pilot system, however, provided data for updating information on the sources of point and area emissions of S0 2 and particulate matter affecting the Norfolk--Portsmouth area of Virginia for the 1971-72 winter (Dec.-Jan.-Feb.) and the annual 1972 period, and for a future annual period--1985. This emission information is used as input to the Air Quality Display Model of the Environmental Protection Agency to obtain diffusion and impact patterns for the three periods previously mentioned. The results are: (1) During the 1971-72 winter, estimated SO 2 amounts over an area with a SW-NE-axis in the central section of Norfolk exceeded both primary and secondary levels; (2) future annual levels of SO 2 , estimated by anticipated residential development and point-source changes, are not expected to cause serious deterioration of the region's present air quality; and (3) for the 1971-72 winter and annual 1972 period the diffusion results showed that both primary and secondary standards for particulate matter are regularly exceeded in central Norfolk and Portsmouth. In additions on the bas is of current control programs, the 1985 levels of particulate matter are expected to exceed the presently established secondary air quality standards throughout central Norfolk and Portsmouth and in certain areas of Virginia Beach. The land use information can be used to estimate emissions for inputs to diffusion models and to interpret the implications of diffusion patterns for: (1) Implementing various control strategies (2) selecting sites of air sampling stations, and (3) predicting the effects that proposed changes in land use might have on emission patterns and air quality.
Remote sensing↗
Information management challenges of the EOS Data and Information System
An overview of the current information management concepts that are embodied in the plans for the Earth Observing System Data and Information System (EOSDIS) is presented, and some of the technology development and application areas that are envisioned to be particularly challenging are introduced. The Information Management System (IMS) is the EOSDIS element that provides the primary interface between the science users and the data products and services of EOSDIS. The goals of IMS are to define a clear and complete set of functional requirements and to apply innovative methods and technologies to satisfy them. The information management functions are described in detail, and some applicable technolgies are discussed. Some of the general issues affecting the successful development and operation of the information management element are addressed.
Mcdonald, Kenneth R.↗
Earth science information: Planning for the integration and use of global change information
Activities and accomplishments of the first six months of the Consortium for International Earth Science Information Network (CIESIN's) 1992 technical program have focused on four main missions: (1) the development and implementation of plans for initiation of the Socioeconomic Data and Applications Center (SEDAC) as part of the EOSDIS Program; (2) the pursuit and development of a broad-based global change information cooperative by providing systems analysis and integration between natural science and social science data bases held by numerous federal agencies and other sources; (3) the fostering of scientific research into the human dimensions of global change and providing integration between natural science and social science data and information; and (4) the serving of CIESIN as a gateway for global change data and information distribution through development of the Global Change Research Information Office and other comprehensive knowledge sharing systems.
Lousma, Jack R.↗
Government Information Locator Service (GILS). Draft report to the Information Infrastructure Task Force
This is a draft report on the Government Information Locator Service (GILS) to the National Information Infrastructure (NII) task force. GILS is designed to take advantage of internetworking technology known as client-server architecture which allows information to be distributed among multiple independent information servers. Two appendices are provided -- (1) A glossary of related terminology and (2) extracts from a draft GILS profile for the use of the American National Standard Information Retrieval Application Service Definition and Protocol Specification for Library Applications.
Source record↗
Recommendations for a Cockpit Display that Integrates Weather Information with Traffic Information
This effort was supported by the System-Wide Accident Prevention element of NASA s Aviation Safety Program. This document may serve as a first step toward the goal of integrating traffic, weather, and terrain information; it provides recommendations for a cockpit display that integrates weather information with traffic information. While some of the recommendations are general enough to be used for any type of operations, these recommendations are targeted for Federal Aviation Regulations Part 121 Operations. The document is organized in the following manner. First, weather information is discussed as an independent subject matter, and recommendations are presented for presenting weather in the cockpit. Second, traffic is discussed independently, but this discussion essentially reviews work on the display of traffic in the cockpit. Third, recommendations for the cockpit integration of weather and traffic information are discussed. Fourth, several research groups are recognized for their efforts in developing systems that are relevant to the current discussion. Finally, closing remarks provide suggestions for future efforts.
Comerford, Doreen A.↗
Dimension-independent likelihood-informed MCMC
Many Bayesian inference problems require exploring the posterior distribution of high-dimensional parameters that represent the discretization of an underlying function. This work introduces a family of Markov chain Monte Carlo (MCMC) samplers that can adapt to the particular structure of a posterior distribution over functions. Two distinct lines of research intersect in the methods developed here. First, we introduce a general class of operator-weighted proposal distributions that are well defined on function space, such that the performance of the resulting MCMC samplers is independent of the discretization of the function. Second, by exploiting local Hessian information and any associated low-dimensional structure in the change from prior to posterior distributions, we develop an inhomogeneous discretization scheme for the Langevin stochastic differential equation that yields operator-weighted proposals adapted to the non-Gaussian structure of the posterior. The resulting dimension-independent and likelihood-informed (DILI) MCMC samplers may be useful for a large class of high-dimensional problems where the target probability measure has a density with respect to a Gaussian reference measure. Two nonlinear inverse problems are used to demonstrate the efficiency of these DILI samplers: an elliptic PDE coefficient inverse problem and path reconstruction in a conditioned diffusion.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Framework for modeling high-impact, low-frequency power grid events to support risk-informed decisions
Not Available
Veeramany, Arun↗
The structural information filtered features (SIFF) potential: Maximizing information stored in machine-learning descriptors for materials prediction
Machine learning inspired potentials continue to improve the ability for predicting structures of materials. However, many challenges still exist, particularly when calculating structures of disordered systems. These challenges are primarily due to the rapidly increasing dimensionality of the feature-vector space which in most machine-learning algorithms is dependent on the size of the structure. In this article, we present a feature-engineered approach that establishes a set of principles for representing potentials of physical structures (crystals, molecules, and clusters) in a feature space rather than a physically motivated space. Our goal in this work is to define guiding principles that optimize information storage of the physical parameters within the feature representations. In this manner, we focus on keeping the dimensionality of the feature space independent of the number of atoms in the structure. Our Structural Information Filtered Features (SIFF) potential represents structures by utilizing a feature vector of low-correlated descriptors, which correspondingly maximizes information within the descriptor. We present results of our SIFF potential on datasets composed of disordered (carbon and carbon–oxygen) clusters, molecules with C 7 O 2 H 2 stoichiometry in the GDB9-14B dataset, and crystal structures of the form (Al x Ga y In z ) 2 O 3 as proposed in the NOMAD Kaggle competition. Our potential's performance is at least comparable, sometimes significantly more accurate, and often more efficient than other well-known machine-learning potentials for structure prediction. However, primarily, we offer a different perspective on how researchers should consider opportunities in maximizing information storage for features.
36 MATERIALS SCIENCE↗
Dark matter vs. neutrinos: the effect of astrophysical uncertainties and timing information on the neutrino floor
Future multi-tonne Direct Detection experiments will be sensitive to solar neutrino induced nuclear recoils which form an irreducible background to light Dark Matter searches. Indeed for masses around 6 GeV the spectra of neutrinos and Dark Matter are so similar that experiments are said to run into a neutrino floor, for which sensitivity increases only marginally with exposure past a certain cross section. In this work we show that this floor can be overcome using the different annual modulation expected from solar neutrinos and Dark Matter. Specifically for cross sections below the neutrino floor the DM signal is observable through a phase shift and a smaller amplitude for the time-dependent event rate. This allows the exclusion power to be improved by up to an order of magnitude for large exposures. In addition we demonstrate that, using only spectral information, the neutrino floor exists over a wider mass range than has been previously shown, since the large uncertainties in the Dark Matter velocity distribution make the signal spectrum harder to distinguish from the neutrino background. However for most velocity distributions it can still be surpassed using timing information, and so the neutrino floor is not an absolute limit on the sensitivity of Direct Detection experiments.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Physics-Informed Deep Neural Network Method for Limited Observability State Estimation
The precise knowledge regarding the state of the power grid is important in order to ensure optimal and reliable grid operation. Specifically, knowing the state of the distribution grid becomes increasingly important as more renewable energy sources are connected directly into the distribution network, increasing the fluctuations of the injected power. In this paper, we consider the case when the distribution grid becomes partially observable, due to for example cyber attacks, and the state estimation problem is under-determined. We present a new methodology that leverages a deep neural network (DNN) to estimate the grid state. The standard DNN training method is modified to explicitly incorporate the physical information of the grid topology and line/shunt admittance. We show that our method leads to a superior accuracy of the estimation when compared to the case when no physical information is provided. Finally, we compare the performance of our method to the standard state estimation approach, which is based on the weighted least squares with pseudo-measurements, and show that our method performs significantly better with respect to the estimation accuracy.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Using spectral methods to obtain particle size information from optical data: applications to measurements from CARES 2010
Abstract. Multi-wavelength in situ aerosol extinction, absorption and scattering measurements made at two ground sites during the 2010 Carbonaceous Aerosols and Radiative Effects Study (CARES) are analyzed using a spectral deconvolution method that allows extraction of particle-size-related information, including the fraction of extinction produced by the fine-mode particles and the effective radius of the fine mode. The spectral deconvolution method is typically applied to analysis of remote sensing measurements. Here, its application to in situ measurements allows for comparison with more direct measurement methods and validation of the retrieval approach. Overall, the retrieved fine-mode fraction and effective radius compare well with other in situ measurements, including size distribution measurements and scattering and absorption measurements made separately for PM1 and PM10, although there were some periods during which the different methods yielded different results. One key contributor to differences between the results obtained is the alternative, spectrally based definitions of fine and coarse modes from the optical methods, relative to instruments that use a physically defined cut point. These results indicate that for campaigns where size, composition and multi-wavelength optical property measurements are made, comparison of the results can result in closure or can identify unusual circumstances. The comparison here also demonstrates that in situ multi-wavelength optical property measurements can be used to determine information about particle size distributions in situations where direct size distribution measurements are not available.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Uncertainty-informed selection of CMIP6 Earth System Model subsets for use in multisectoral and impact models
Earth system models (ESMs) and general circulation models (GCMs) are heavily used to provide inputs to sectoral impact and multisector dynamic models, which include representations of energy, water, land, economics, and their interactions. Therefore, representing the full range of model uncertainty, scenario uncertainty, and interannual variability that ensembles of these models capture is critical to the exploration of the future co-evolution of the integrated human–Earth system. The pre-eminent source of these ensembles has been the Coupled Model Intercomparison Project (CMIP). With more modeling centers participating in each new CMIP phase, the size of the model archive is rapidly increasing, which can be intractable for impact modelers to effectively utilize due to computational constraints and the challenges of analyzing large datasets. In this work, we present a method to select a subset of the latest phase, CMIP6, featuring models for use as inputs to a sectoral impact or multisector dynamics models, while prioritizing preservation of the range of model uncertainty, scenario uncertainty, and interannual variability in the full CMIP6 ensemble results. This method is intended to help impact modelers select climate information from the CMIP archive efficiently for use in downstream models that require global coverage of climate information. This is particularly critical for large-ensemble experiments of multisector dynamic models that may be varying additional features beyond climate inputs in a factorial design, thus putting constraints on the number of climate simulations that can be used. We focus on temperature and precipitation outputs of CMIP6 models, as these are two of the most used variables among impact models, and many other key input variables for impacts are at least correlated with one or both of temperature and precipitation (e.g., relative humidity). Besides preserving the multi-model ensemble variance characteristics, we prioritize selecting CMIP6 models in the subset that preserve the very likely distribution of equilibrium climate sensitivity values as assessed by the latest Intergovernmental Panel on Climate Change (IPCC) report. This approach could be applied to other output variables of climate models and, possibly when combined with emulators, offers a flexible framework for designing more efficient experiments on human-relevant climate impacts. It can also provide greater insight into the properties of existing CMIP6 models.
Snyder, Abigail C.↗
Remote Sensing Information Sciences Research Group: Santa Barbara Information Sciences Research Group, year 4
Information Sciences Research Group (ISRG) research continues to focus on improving the type, quantity, and quality of information which can be derived from remotely sensed data. Particular focus in on the needs of the remote sensing research and application science community which will be served by the Earth Observing System (EOS) and Space Station, including associated polar and co-orbiting platforms. The areas of georeferenced information systems, machine assisted information extraction from image data, artificial intelligence and both natural and cultural vegetation analysis and modeling research will be expanded.
Estes, John E.↗
Government Information Quarterly. Volume 7, no. 2: National Aeronautics and Space Administration Scientific and Technical Information Programs. Special issue
NASA scientific and technical information (STI) programs are discussed. Topics include management of information in a research and development agency, the new space and Earth science information systems at NASA's archive, scientific and technical information management, and technology transfer of NASA aerospace technology to other industries.
Hernon, Peter↗