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At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Data mining and computational screening of Rashba-Dresselhaus splitting and optoelectronic properties in two-dimensional perovskite materials

Recent developments highlighting the promise of two-dimensional perovskites have vastly increased the compositional search space in the perovskite family. This presents a great opportunity for the realization of highly performant devices and practical challenges associated with the identification of candidate materials. High-fidelity computational screening offers great value in this regard. In this study, we carry out a multiscale computational workflow, generating a dataset of two-dimensional perovskites in the Dion-Jacobson and Ruddlesden-Popper phases. Our dataset comprises ten B-site cations, four halogens, and over 20 organic cations across over 2000 materials. We compute electronic properties, thermoelectric performance, and numerous geometric characteristics. Furthermore, we introduce a framework for the high-throughput computation of Rashba-Dresselhaus splitting. Finally, we use this dataset to train machine learning models for the accurate prediction of band gaps, candidate Rashba-Dresselhaus materials, and partial charges. The work presented herein can aid future investigations of two-dimensional perovskites with targeted applications in mind.

14 SOLAR ENERGY↗
NASA NTRS2001

General Temporal Knowledge for Planning and Data Mining

We consider the architecture of systems that combine temporal planning and plan execution and introduce a layer of temporal reasoning that potential1y improves both the communication between humans and such systems, and the performance of the temporal planner itself. In particular, this additional layer simultaneously supports more flexibility in specifying and maintaining temporal constraints on plans within an uncertain and changing execution environment, and the ability to understand and trace the progress of plan execution. It is shown how a representation based on single set of abstractions of temporal information can be used to characterize the reasoning underlying plan generation and execution interpretation. The complexity of such reasoning is discussed.

Morris, Robert↗
NASA NTRS2005

Faults Discovery By Using Mined Data

Fault discovery in the complex systems consist of model based reasoning, fault tree analysis, rule based inference methods, and other approaches. Model based reasoning builds models for the systems either by mathematic formulations or by experiment model. Fault Tree Analysis shows the possible causes of a system malfunction by enumerating the suspect components and their respective failure modes that may have induced the problem. The rule based inference build the model based on the expert knowledge. Those models and methods have one thing in common; they have presumed some prior-conditions. Complex systems often use fault trees to analyze the faults. Fault diagnosis, when error occurs, is performed by engineers and analysts performing extensive examination of all data gathered during the mission. International Space Station (ISS) control center operates on the data feedback from the system and decisions are made based on threshold values by using fault trees. Since those decision-making tasks are safety critical and must be done promptly, the engineers who manually analyze the data are facing time challenge. To automate this process, this paper present an approach that uses decision trees to discover fault from data in real-time and capture the contents of fault trees as the initial state of the trees.

Lee, Charles↗
NASA NTRS2016

Data Mining and Knowledge Discover - IBM Cognitive Alternatives for NASA KSC

Skillful tools in cognitive computing to transform industries have been found favorable and profitable for different Directorates at NASA KSC. In this study is shown how cognitive computing systems can be useful for NASA when computers are trained in the same way as humans are to gain knowledge over time. Increasing knowledge through senses, learning and a summation of events is how the applications created by the firm IBM empower the artificial intelligence in a cognitive computing system. NASA has explored and applied for the last decades the artificial intelligence approach specifically with cognitive computing in few projects adopting similar models proposed by IBM Watson. However, the usage of semantic technologies by the dedicated business unit developed by IBM leads these cognitive computing applications to outperform the functionality of the inner tools and present outstanding analysis to facilitate the decision making for managers and leads in a management information system.

Cognitive computing↗
NASA NTRS2016

Unsupervised Spatio-Temporal Data Mining Framework for Burned Area Mapping

A method reduces processing time required to identify locations burned by fire by receiving a feature value for each pixel in an image, each pixel representing a sub-area of a location. Pixels are then grouped based on similarities of the feature values to form candidate burn events. For each candidate burn event, a probability that the candidate burn event is a true burn event is determined based on at least one further feature value for each pixel in the candidate burn event. Candidate burn events that have a probability below a threshold are removed from further consideration as burn events to produce a set of remaining candidate burn events.

Boriah, Shyam↗
NASA NTRS2017

Cross-Cutting Risk Framework: Mining Data for Common Risks Across the Portfolio

The National Aeronautics and Space Administration (NASA) defines risk management as an integrated framework, combining risk-informed decision making and continuous risk management to foster forward-thinking and decision making from an integrated risk perspective. Therefore, decision makers must have access to risks outside of their own project to gain the knowledge that provides the integrated risk perspective. Through the Goddard Space Flight Center (GSFC) Flight Projects Directorate (FPD) Business Change Initiative (BCI), risks were integrated into one repository to facilitate access to risk data between projects. With the centralized repository, communications between the FPD, project managers, and risk managers improved and GSFC created the cross-cutting risk framework (CCRF) team. The creation of the consolidated risk repository, in parallel with the initiation of monthly FPD risk managers and risk governance board meetings, are now providing a complete risk management picture spanning the entire directorate. This paper will describe the challenges, methodologies, tools, and techniques used to develop the CCRF, and the lessons learned as the team collectively worked to identify risks that FPD programs projects had in common, both past and present.

risk-informed decision making↗
NASA NTRS2020

Systems Development, Data Mining, and Knowledge Discovery

I worked as a NASA OSTEM virtually during the Fall 2020 term. Working in the IT division under my mentor Dr. Ali Shaykhian, our overall goal for the duration of this internship is to get a better understanding of the Visual Basic Language and how it can be used to make forms and collaboration with other workers to be more dynamics. I also worked with 3 other interns throughout this internship, using Microsoft Office to help each other to get a better understanding of how we approached our projects individually. Every week, my mentor Dr. Ali assigned me a task and a goal to finish by the end of each week. My overall project was figuring out a way to make email submissions and emails in general more dynamic for the NASA database. For example, instead of only using the same generic email for hundreds of different workers, a code can be used to send an individual email with more personalization such as each recipient’s name and personal info. I have also been assigned to create an email graphical user interface by the end of this internship. For these tasks to be made possible, I had to learn more about the capabilities of Microsoft Office, such as Macros in Microsoft Excel and Visual Basic in both Microsoft Excel and Word. Although it was challenging at first, I developed new programming skilled in a new language and actually made it possible to create this code along the way. Alongside doing the individual assignments, Dr. Ali also assigned up to replicate other intern’s work to get a better understanding of their approach and learn how to do it ourselves.

Janelisse Morales Gonzalez↗
NASA NTRS2012

Physics Mining of Multi-Source Data Sets

Powerful new parallel data mining algorithms can produce diagnostic and prognostic numerical models and analyses from observational data. These techniques yield higher-resolution measures than ever before of environmental parameters by fusing synoptic imagery and time-series measurements. These techniques are general and relevant to observational data, including raster, vector, and scalar, and can be applied in all Earth- and environmental science domains. Because they can be highly automated and are parallel, they scale to large spatial domains and are well suited to change and gap detection. This makes it possible to analyze spatial and temporal gaps in information, and facilitates within-mission replanning to optimize the allocation of observational resources. The basis of the innovation is the extension of a recently developed set of algorithms packaged into MineTool to multi-variate time-series data. MineTool is unique in that it automates the various steps of the data mining process, thus making it amenable to autonomous analysis of large data sets. Unlike techniques such as Artificial Neural Nets, which yield a blackbox solution, MineTool's outcome is always an analytical model in parametric form that expresses the output in terms of the input variables. This has the advantage that the derived equation can then be used to gain insight into the physical relevance and relative importance of the parameters and coefficients in the model. This is referred to as physics-mining of data. The capabilities of MineTool are extended to include both supervised and unsupervised algorithms, handle multi-type data sets, and parallelize it.

Helly, John↗
NASA NTRS2017

Mining Twitter Data to Augment NASA GPM Validation

The Twitter data stream is an important new source of real-time and historical global information for potentially augmenting the validation program of NASA's Global Precipitation Measurement (GPM) mission. There have been other similar uses of Twitter, though mostly related to natural hazards monitoring and management. The validation of satellite precipitation estimates is challenging, because many regions lack data or access to data, especially outside of the U.S. and in remote and developing areas. The time-varying set of "precipitation" tweets can be thought of as an organic network of rain gauges, potentially providing a widespread view of precipitation occurrence. Twitter provides a large source of crowd for crowdsourcing. During a 24-hour period in the middle of the snow storm this past March in the U.S. Northeast, we collected more than 13,000 relevant precipitation tweets with exact geolocation. The overall objective of our project is to determine the extent to which processed tweets can provide additional information that improves the validation of GPM data. Though our current effort focuses on tweets and precipitation, our approach is general and applicable to other social media and other geophysical measurements. Specifically, we have developed an operational infrastructure for processing tweets, in a format suitable for analysis with GPM data; engaged with potential participants, both passive and active, to "enrich" the Twitter stream; and inter-compared "precipitation" tweet data, ground station data, and GPM retrievals. In this presentation, we detail the technical capabilities of our tweet processing infrastructure, including data abstraction, feature extraction, search engine, context-awareness, real-time processing, and high volume (big) data processing; various means for "enriching" the Twitter stream; and results of inter-comparisons. Our project should bring a new kind of visibility to Twitter and engender a new kind of appreciation of the value of Twitter by the science research communities.

validatio↗
NASA NTRS2005

A Testbed Demonstration of an Intelligent Archive in a Knowledge Building System

The last decade's influx of raw data and derived geophysical parameters from several Earth observing satellites to NASA data centers has created a data-rich environment for Earth science research and applications. While advances in hardware and information management have made it possible to archive petabytes of data and distribute terabytes of data daily to a broad community of users, further progress is necessary in the transformation of data into information, and information into knowledge that can be used in particular applications in order to realize the full potential of these valuable datasets. In examining what is needed to enable this progress in the data provider environment that exists today and is expected to evolve in the next several years, we arrived at the concept of an Intelligent Archive in context of a Knowledge Building System (IA/KBS). Our prior work and associated papers investigated usage scenarios, required capabilities, system architecture, data volume issues, and supporting technologies. We identified six key capabilities of an IA/KBS: Virtual Product Generation, Significant Event Detection, Automated Data Quality Assessment, Large-Scale Data Mining, Dynamic Feedback Loop, and Data Discovery and Efficient Requesting. Among these capabilities, large-scale data mining is perceived by many in the community to be an area of technical risk. One of the main reasons for this is that standard data mining research and algorithms operate on datasets that are several orders of magnitude smaller than the actual sizes of datasets maintained by realistic earth science data archives. Therefore, we defined a test-bed activity to implement a large-scale data mining algorithm in a pseudo-operational scale environment and to examine any issues involved. The application chosen for applying the data mining algorithm is wildfire prediction over the continental U.S. This paper reports a number of observations based on our experience with this test-bed. While proof-of-concept for data mining scalability and utility has been a major goal for the research reported here, it was not the only one. The other five capabilities of an WKBS named above have been considered as well, and an assessment of the implications of our experience for these other areas will also be presented. The lessons learned through the testbed effort and presented in this paper will benefit technologists, scientists, and system operators as they consider introducing IA/KBS capabilities into production systems.

Ramapriyan, Hampapuram↗
NASA NTRS2000

KDD Services at the Goddard Earth Sciences Distributed Active Archive Center

NASA's Goddard Earth Sciences Distributed Active Archive Center (GES DAAC) processes, stores and distributes earth science data from a variety of remote sensing satellites. End users of the data range from instrument scientists to global change and climate researchers to federal agencies and foreign governments. Many of these users apply data mining techniques to large volumes of data (up to 1 TB) received from the GES DAAC. However, rapid advances in processing power are enabling increases in data processing that are outpacing tape drive performance and network capacity. As a result, the proportion of data that can be distributed to users continues to decrease. As mitigation, we are migrating more data mining and mining preparation activities into the data center in order to reduce the data volume that needs to be distributed and to offer the users a more useful and manageable product. This migration of activities faces a number of technical and human-factor challenges. As data reduction and mining algorithms are normally quite specific to the user's research needs, the user's algorithm must be integrated virtually unchanged into the archive environment. Also, the archive itself is busy with everyday data archive and distribution activities and cannot be dedicated to, or even impacted by, the mining activities. Therefore, we schedule KDD 'campaigns' (similar to reprocessing campaigns), during which we schedule a wholesale retrieval of specific data products, offering users the opportunity to extract information from the data being retrieved during the campaign.

Lynnes, Christopher↗