Search NASA⌕ Search

SEARCH · Search NASA

Results for “Information extraction”

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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS1980

Pattern recognition for remote sensing - Progress and prospects

An overview is given of the current state of automatic image pattern recognition as applied to remote sensing of the earth's resources. The framework for the discussion is provided by four important aspects of the remote sensing problem: scene information content, characterization of scene information, information extraction methods, and the net value of extractable information. Outstanding problems are surveyed, as are the prospects for future developments. The effect of increasingly complex data bases and the rapidly evolving digital computer technology are highlighted.

Swain, P. H.↗
NASA NTRS1995

Knowledge Discovery and Data Mining: An Overview

The process of knowledge discovery and data mining is the process of information extraction from very large databases. Its importance is described along with several techniques and considerations for selecting the most appropriate technique for extracting information from a particular data set.

data mining knowledge discovery data search↗
NASA NTRS2004

Intensity-based rock detection for acquiring onboard rover science

Missions to Mars already under study call for very long autonomous traverses (on the order of hundreds of meters per sol) during which there will be limited contact with Earth. The onboard Autonomous Science Investigation System (OASIS) is a technology for increasing science return during rover traverses by prioritizing science opportunities. By prioritizing data for downlink onboard, it is expected that the set of images selected for downlink by OASIS will have a consistently higher scientific interest then any set of equal number of images of the same traverse obtained using random or periodic sampling. Thus, OASIS can be used to increase the science returned from a long traverse. OASIS prioritizes data and identifies science opportunities by extracting information from image data and then using this information to prioritize the data and identify potential new science opportunities. Currently, the information extracted from the images is the location of the rocks in the image and several properties of these rocks such as size, albedo, texture and shape, In this paper we give an overview of the role of rock detection in the OASIS system and describe the functionality of the rock detector. We then present results on representative images

Judd, Michele↗
NASA NTRS1974

Extracting depth information from a stereo pair

An approach to extracting depth information out of a stereo pair of images is described. Special camera pair configurations are shown to be effective in reducing complexity. A simple algorithm which pairs objects in the two images and allows verification is described.

Yakimovsky, Y.↗
DOE OSTI2025

A Model Based Approach to Extract Health Information from Textual Data

In current nuclear power plants (NPPs) a large amount of condition-based data is being generated and stored to assess and monitor component health and performance. The format of this data can be either numeric (e.g., pump vibration data) or textual (e.g., condition report which assess component health). While assessing component health from numeric data can be performed with a large variety of methods, the extraction of information from textual data still remains a challenge. Natural language processing (NLP) methods are starting to be deployed in current NPPs mainly to filter out incident reports (IRs) that are not safety related by employing supervised machine learning methods. However, these methods do not really provide the quantitative information that might be contained in IRs. This paper presents an approach to extract information from textual data (e.g., from IRs, maintenance reports) that is based on NLP data analytics methods coupled with model-based system engineer (MBSE) models. NLP methods are employed to perform syntactic and semantic analyses. Syntactic analysis analyzes the grammatical structure of a sentence; such analysis includes: part of speech (POS) tagging (i.e., identification of grammatic elements of each string - e.g., nouns, verbs), named entity recognition (i.e., identification of text entities - e.g., names, dates, events), and relation extraction (e.g., coreference resolution). On the other hand, semantic analysis is designed to analyze the logic structure of a sentence. Through a specific set of rules, our methods can identify whether a sentence contains health information of a component (e.g., degraded performance, anomaly behavior) or the causal relationship between two events (i.e., a cause-effect pair). An innovative element of our approach is that semantic analysis relies on MBSE models to identify links between textual elements. MBSE are diagrams designed to represent system and component dependencies (from both a form and functional point of view). In our approach, MBSE models emulate system engineer knowledge about component/system architecture. This paper presents in detail how the integration of NLP methods and MBSE models is performed. Few analysis examples focusing on centrifugal pumps are presented.

97 - MATHEMATICS AND COMPUTING↗
NASA NTRS1991

Noncontact Measurements Of Torques In Shafts

Additional information extracted from eddy-current proximeter. Positioned over rotating shaft, measures both displacement of and torsion in shaft. Torque applied to shaft calculable from output of proximeter. Possible to extract torsion information from existing tape-recorded proximeter data.

Schwartzbart, Aaron↗
NASA NTRS1992

A comparison of techniques for extracting emissivity information from thermal infrared data for geologic studies

This article evaluates three techniques developed to extract emissivity information from multispectral thermal infrared data. The techniques are the assumed Channel 6 emittance model, thermal log residuals, and alpha residuals. These techniques were applied to calibrated, atmospherically corrected thermal infrared multispectral scanner (TIMS) data acquired over Cuprite, Nevada in September 1990. Results indicate that the two new techniques (thermal log residuals and alpha residuals) provide two distinct advantages over the assumed Channel 6 emittance model. First, they permit emissivity information to be derived from all six TIMS channels. The assumed Channel 6 emittance model only permits emissivity values to be derived from five of the six TIMS channels. Second, both techniques are less susceptible to noise than the assumed Channel 6 emittance model. The disadvantage of both techniques is that laboratory data must be converted to thermal log residuals or alpha residuals to facilitate comparison with similarly processed image data. An additional advantage of the alpha residual technique is that the processed data are scene-independent unlike those obtained with the other techniques.

Hook, Simon J.↗
NASA NTRS2000

Information Theoretic Extraction of EEG Features for Monitoring Subject Attention

The goal of this project was to test the applicability of information theoretic learning (feasibility study) to develop new brain computer interfaces (BCI). The difficulty to BCI comes from several aspects: (1) the effective data collection of signals related to cognition; (2) the preprocessing of these signals to extract the relevant information; (3) the pattern recognition methodology to detect reliably the signals related to cognitive states. We only addressed the two last aspects in this research. We started by evaluating an information theoretic measure of distance (Bhattacharyya distance) for BCI performance with good predictive results. We also compared several features to detect the presence of event related desynchronization (ERD) and synchronization (ERS), and concluded that at least for now the bandpass filtering is the best compromise between simplicity and performance. Finally, we implemented several classifiers for temporal - pattern recognition. We found out that the performance of temporal classifiers is superior to static classifiers but not by much. We conclude by stating that the future of BCI should be found in alternate approaches to sense, collect and process the signals created by populations of neurons. Towards this goal, cross-disciplinary teams of neuroscientists and engineers should be funded to approach BCIs from a much more principled view point.

Principe, Jose C.↗
NASA NTRS1974

Image data processing of earth resources management

The Earth Resources Technology Satellite (ERTS-1) was launched on July 23, 1972. Since that date, the vehicle has been acquiring and transmitting to earth a vast amount of information relating to the earth's resources. Scientific investigators throughout the world have been analyzing photographic products produced from this data with significant results. Recently, there has been a major shift of emphasis from the visual analysis of photo products to the use of sophisticated digital information extraction techniques. Specialized equipment, such as the GE IMAGE 100 System, has been developed that operates directly from Computer Compatible Tapes, (CCT’s), which contain preprocessed digital data acquired from the ERTS spacecraft. Analysis of CCT data avoids the losses in image resolution and quality which are inherent to all photo processing techniques, thereby greatly increasing the utility of ERTS data. Future ground processing systems will turn out fully corrected (radiometric and geometric) digital tapes which will further increase the utility of ERTS data. This paper is presented in two parts: the first part deals with the subject of present and future generations of image processing and information extraction systems under development at the General Electric Company; the second part describes in more detail the design and operation of GE1s interactive multispectral information extraction systems, IMAGE 100, and discusses results of analyses of ERTS data over a number of U.S. sites.

A W DeSio↗
NASA NTRSDate not supplied

Knowledge Discovery for Early Failure Assessment of Complex Engineered Systems Using Natural Language Processing

Emerging complex engineered systems may have unexpected safety issues due to novel operational environments, increasing autonomy, human-machine interaction, and other factors. To prevent failures in operation or testing that necessitate costly redesign, it is desirable to predict likely failure modes early in the design process. Text-based information about past engineering failures presents one possible solution by facilitating the retrieval of information that can inform new designs. However, identifying documents containing relevant information and extracting required information can be prohibitively time-consuming when implemented at scale. In this research, an automated natural language processing-based framework is proposed to discover relevant knowledge from documents containing failure-related design information. Documents containing usable information are filtered using sentiment analysis based on a custom lexicon specialized for engineering design and by filtering out documents containing only irrelevant topics. Next, from the identified usable documents, information relating to engineering failures, contributing factors that can be controlled at design time (“risk factors”), and recommended preventative actions are extracted. Semantic similarity is then used to group similar pieces of extracted information for improved generalizability. The proposed framework is applied to NASA’s Lessons Learned Information System (LLIS). The framework can be used to identify documents containing usable failure-related design information from other databases, extract relevant information from these documents, and generalize the acquired knowledge such that it can be applied to novel systems.

Sequoia R. Andrade↗
NASA NTRS2010

Monitoring System for Storm Readiness and Recovery of Test Facilities: Integrated System Health Management (ISHM) Approach

Severe weather events are likely occurrences on the Mississippi Gulf Coast. It is important to rapidly diagnose and mitigate the effects of storms on Stennis Space Center's rocket engine test complex to avoid delays to critical test article programs, reduce costs, and maintain safety. An Integrated Systems Health Management (ISHM) approach and technologies are employed to integrate environmental (weather) monitoring, structural modeling, and the suite of available facility instrumentation to provide information for readiness before storms, rapid initial damage assessment to guide mitigation planning, and then support on-going assurance as repairs are effected and finally support recertification. The system is denominated Katrina Storm Monitoring System (KStorMS). Integrated Systems Health Management (ISHM) describes a comprehensive set of capabilities that provide insight into the behavior the health of a system. Knowing the status of a system allows decision makers to effectively plan and execute their mission. For example, early insight into component degradation and impending failures provides more time to develop work around strategies and more effectively plan for maintenance. Failures of system elements generally occur over time. Information extracted from sensor data, combined with system-wide knowledge bases and methods for information extraction and fusion, inference, and decision making, can be used to detect incipient failures. If failures do occur, it is critical to detect and isolate them, and suggest an appropriate course of action. ISHM enables determining the condition (health) of every element in a complex system-of-systems or SoS (detect anomalies, diagnose causes, predict future anomalies), and provide data, information, and knowledge (DIaK) to control systems for safe and effective operation. ISHM capability is achieved by using a wide range of technologies that enable anomaly detection, diagnostics, prognostics, and advise for control: (1) anomaly detection algorithms and strategies, (2) fusion of DIaK for anomaly detection (model-based, numerical, statistical, empirical, expert-based, qualitative, etc.), (3) diagnostics/prognostics strategies and methods, (4) user interface, (5) advanced control strategies, (6) integration architectures/frameworks, (7) embedding of intelligence. Many of these technologies are mature, and they are being used in the KStorMS. The paper will describe the design, implementation, and operation of the KStorMS; and discuss further evolution to support other needs such as condition-based maintenance (CBM).

Figueroa, Fernando↗
DOE OSTI2024

Development of message passing-based graph convolutional networks for classifying cancer pathology reports

Abstract Background Applying graph convolutional networks (GCN) to the classification of free-form natural language texts leveraged by graph-of-words features (TextGCN) was studied and confirmed to be an effective means of describing complex natural language texts. However, the text classification models based on the TextGCN possess weaknesses in terms of memory consumption and model dissemination and distribution. In this paper, we present a fast message passing network (FastMPN), implementing a GCN with message passing architecture that provides versatility and flexibility by allowing trainable node embedding and edge weights, helping the GCN model find the better solution. We applied the FastMPN model to the task of clinical information extraction from cancer pathology reports, extracting the following six properties: main site, subsite, laterality, histology, behavior, and grade. Results We evaluated the clinical task performance of the FastMPN models in terms of micro- and macro-averaged F1 scores. A comparison was performed with the multi-task convolutional neural network (MT-CNN) model. Results show that the FastMPN model is equivalent to or better than the MT-CNN. Conclusions Our implementation revealed that our FastMPN model, which is based on the PyTorch platform, can train a large corpus (667,290 training samples) with 202,373 unique words in less than 3 minutes per epoch using one NVIDIA V100 hardware accelerator. Our experiments demonstrated that using this implementation, the clinical task performance scores of information extraction related to tumors from cancer pathology reports were highly competitive.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1990

Information sciences experiment system

The rapid expansion of remote sensing capability over the last two decades will take another major leap forward with the advent of the Earth Observing System (Eos). An approach is presented that will permit experiments and demonstrations in onboard information extraction. The approach is a non-intrusive, eavesdropping mode in which a small amount of spacecraft real estate is allocated to an onboard computation resource. How such an approach allows the evaluation of advanced technology in the space environment, advanced techniques in information extraction for both Earth science and information science studies, direct to user data products, and real-time response to events, all without affecting other on-board instrumentation is discussed.

Katzberg, Stephen J.↗
DOE OSTI2020

Survey of Current State of the Art Entity-Relation Extraction Tools

In the area of information extraction from text data, there exists a number of tools with the capability of extracting entities, topics, and their relationships with one another from both structured and unstructured text sources. Such information has endless uses in a number of domains, however, the solutions to getting this information are still in early stages and has room for improvement. The topic has been explored from a research perspective by academic institutions, as well as formal tool creation from corporations but has not made much advancement since the early 2000's. Overall, entity extraction, and the related topic of entity linking, is common among these tools, though with varying degrees of accuracy, while relationship extraction is more difficult to find and seems limited to same sentence analysis. In this report, we take a look at the top state of the art tools currently available and identify their capabilities, strengths, and weaknesses. We explore the common algorithms in the successful approaches to entity extraction and their ability to efficiently handle both structured and unstructured text data. Finally, we highlight some of the common issues among these tools and summarize the current ability to extract relationship information.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Auto Procedure Parsing: A Natural Language Processing Approach

Nuclear Power Plant (NPP) operating procedure is “a set of rules that describes how actions on the plant should be made if a certain system goal should be accomplished. U.S. NPPs use paper-based procedures (PBPs). PBPs are difficult to use. Common errors with PBPs are: following the wrong procedure, omit a step etc. Computer based procedures (CPBs) offer great improvement in ensuring plant safety. Some studies rely on experienced operators to understand the procedure content and then reorganize the procedure with digitally executable capabilities. Other studies utilize the procedure format to design rules to extract information from the operating procedures. Existing studies in procedure parsing are manual, laborious and extract limited information. This study aims to automatically extract critical information from operating procedures for generating computer interpretable representation of procedures. Such representation can further be used for automatic dynamic human reliability analysis and CPB design etc.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS2015

Neptune’s Dynamic Atmosphere from Kepler K2 Observations: Implications for Brown Dwarf Light Curve Analyses

Observations of Neptune with the Kepler Space Telescope yield a 49-day light curve with 98% coverage at a 1-minute cadence. A significant signature in the light curve comes from discrete cloud features. We compare results extracted from the light curve data with contemporaneous disk-resolved imaging of Neptune from the Keck 10-meter and Hubble Space Telescope. The direct comparison validates the zonal wind profile and cloud feature variability information extracted from the light curve. Neptune’s clouds vary in location and intensity on short and long time scales, with large discrete storms dominating the light curves; smaller or fainter clouds contribute to its variability. This has implications for the interpretation of information extracted from light curves of directly imaged exoplanets and cloudy brown dwarfs.

Simon, Amy A.↗
DOE OSTI2021

Corrosion Detection LDRD Seedling (Final Report)

Automation techniques for recognizing material defects are very common in fields like infrastructure survey and civil engineering; however, the defects are usually easily distinguishable from their background, on a larger scale (e.g. cement bridges), and very specific. Extracting information about a defect that is near or equal to the background of the data can be extremely difficult. Images with heavy background corrosion, pitting, cracking, and man-made defects (such as welding) makes extracting information on a specific defect near-impossible. This is especially true when the defect is on the micron level and the dynamic range of your image taking instrument is low. So, a simplified, Java-based Fiji™ (ImageJ) defect detection program, dependent on a ‘flag’ system, was proposed for data that has a micron level hairline crack that is near or equal to the background. The utilization of the built-in functions, plugins, and macro scripting capability of the Java-based software sends a series of flags to the user that help to identify if the surface contains a defect of interest and to characterize that defect.

42 ENGINEERING↗