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Results for “Knowledge bases”
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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Coherent Blending of Biophysics-Based Knowledge with Bayesian Neural Networks for Robust Protein Property Prediction
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Structuring superconductor data with ontology: reproducing historical datasets as knowledge bases
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Knowledge-Based Fault Diagnosis for a Distribution System with High PV Penetration.
Abstract not provided.
iPNHOT: a knowledge-based approach for identifying protein-nucleic acid interaction hot spots
The interaction between proteins and nucleic acids plays pivotal roles in various biological processes such as transcription, translation, and gene regulation. Hot spots are a small set of residues that contribute most to the binding affinity of a protein-nucleic acid interaction. Compared to the extensive studies of the hot spots on protein-protein interfaces, the hot spot residues within protein-nucleic acids interfaces remain less well-studied, in part because mutagenesis data for protein-nucleic acids interaction are not as abundant as that for protein-protein interactions. In this study, we built a new computational model, iPNHOT, to effectively predict hot spot residues on protein-nucleic acids interfaces. One training data set and an independent test set were collected from dbAMEPNI and some recent literature, respectively. To build our model, we generated 97 different sequential and structural features and used a two-step strategy to select the relevant features. The final model was built based only on 7 features using a support vector machine (SVM). The features include two unique features such as ΔSASsa 1/2 and esp3, which are newly proposed in this study. Based on the cross validation results, our model gave F1 score and AUROC as 0.725 and 0.807 on the subset collected from ProNIT, respectively, compared to 0.407 and 0.670 of mCSM-NA, a state-of-the art model to predict the thermodynamic effects of protein-nucleic acid interaction. The iPNHOT model was further tested on the independent test set, which showed that our model outperformed other methods. Here, by collecting data from a recently published database dbAMEPNI, we proposed a new model, iPNHOT, to predict hotspots on both protein-DNA and protein-RNA interfaces. The results show that our model outperforms the existing state-of-art models. Our model is available for users through a webserver: http://zhulab.ahu.edu.cn/iPNHOT/.
Knowledge-Based Fault Diagnosis for a Distribution System with High PV Penetration.
Abstract not provided.
Knowledge-based Expert Systems for Crop Identification
The development of an improved understanding of the interactive man machine environment is investigated. In such an environment, as many feature inputs as practical would be automatically derived from a data base and input into an expert system decision making procedure. This procedure could then provide expert assistance to a trained image analyst to upgrade and improve the quantity and accuracy of the information extracted from the input data. A comparison of the similarities and differences between manual and automated image interpretation techniques is also examined.
A knowledge based expert system for propellant system monitoring at the Kennedy Space Center
The Lox Expert System (LES) is the first attempt to build a realtime expert system capable of simulating the thought processes of NASA system engineers, with regard to fluids systems analysis and troubleshooting. An overview of the hardware and software describes the techniques used, and possible applications to other process control systems. LES is now in the advanced development stage, with a full implementation planned for late 1985.
A knowledge-based system for monitoring the electrical power system of the Hubble Space Telescope
The design and the prototype for the expert system for the Hubble Space Telescope's electrical power system are discussed. This prototype demonstrated the capability to use real time data from a 32k telemetry stream and to perform operational health and safety status monitoring, detect trends such as battery degradation, and detect anomalies such as solar array failures. This prototype, along with the pointing control system and data management system expert systems, forms the initial Telemetry Analysis for Lockheed Operated Spacecraft (TALOS) capability.
FMEAssist: A knowledge-based approach to Failure Modes and Effects Analysis
A Failure Modes and Effects Analysis workstation (FMESassist) was designed for use during the development of the space station. It assists engineers in the complex task of tracking failures and their effects on the system. Engineers experience increased productivity through reduced clerical loads, reduced data inconsistency, and significantly reduced analysis time. System developments benefit from a more thorough analysis than was available using previous methods.
Hologram representation of design data in an expert system knowledge base
A novel representational scheme for design object descriptions is presented. An abstract notion of modules and signals is developed as a conceptual foundation for the scheme. This abstraction relates the objects to the meaning of system descriptions. Anchored on this abstraction, a representational model which incorporates dynamic semantics for these objects is presented. This representational model is called a hologram scheme since it represents dual level information, namely, structural and semantic. The benefits of this scheme are presented.
Knowledge based tools for Hubble Space Telescope planning and scheduling: Constraints and strategies
The Hubble Space Telescope (HST) presents an especially challenging scheduling problem since a year's observing program encompasses tens of thousands of exposures facing numerous coupled constraints. Recent progress in the development of planning and scheduling tools is discussed which augment the existing HST ground system. General methods for representing activities, constraints, and constraint satisfaction, and time segmentation were implemented in a scheduling testbed. The testbed permits planners to evaluate optimal scheduling time intervals, calculate resource usage, and to generate long and medium range plans. Graphical displays of activities, constraints, and plans are an important feature of the system. High-level scheduling strategies using rule based and neural net approaches were implemented.
A knowledge-based tool for multilevel decomposition of a complex design problem
Although much work has been done in applying artificial intelligence (AI) tools and techniques to problems in different engineering disciplines, only recently has the application of these tools begun to spread to the decomposition of complex design problems. A new tool based on AI techniques has been developed to implement a decomposition scheme suitable for multilevel optimization and display of data in an N x N matrix format.
Knowledge based and interactive control for the Superfluid Helium On-orbit Transfer Project
NASA's Superfluid Helium On-Orbit Transfer (SHOOT) project is a Shuttle-based experiment designed to acquire data on the properties of superfluid helium in micro-gravity. Aft Flight Deck Computer Software for the SHOOT experiment is comprised of several monitoring programs which give the astronaut crew visibility into SHOOT systems and a rule based system which will provide process control, diagnosis and error recovery for a helium transfer without ground intervention. Given present Shuttle manifests, this software will become the first expert system to be used in space. The SHOOT Command and Monitoring System (CMS) software will provide a near real time highly interactive interface for the SHOOT principal investigator to control the experiment and to analyze and display its telemetry. The CMS software is targeted for all phases of the SHOOT project: hardware development, pre-flight pad servicing, in-flight operations, and post-flight data analysis.
A user interface for a knowledge-based planning and scheduling system
The objective of EMPRESS (Expert Mission Planning and Replanning Scheduling System) is to support the planning and scheduling required to prepare science and application payloads for flight aboard the US Space Shuttle. EMPRESS was designed and implemented in Zetalisp on a 3600 series Symbolics Lisp machine. Initially, EMPRESS was built as a concept demonstration system. The system has since been modified and expanded to ensure that the data have integrity. Issues underlying the design and development of the EMPRESS-I interface, results from a system usability assessment, and consequent modifications are described.
Knowledge-based low-level image analysis for computer vision systems
Two algorithms for entry-level image analysis and preliminary segmentation are proposed which are flexible enough to incorporate local properties of the image. The first algorithm involves pyramid-based multiresolution processing and a strategy to define and use interlevel and intralevel link strengths. The second algorithm, which is designed for selected window processing, extracts regions adaptively using local histograms. The preliminary segmentation and a set of features are employed as the input to an efficient rule-based low-level analysis system, resulting in suboptimal meaningful segmentation.
Knowledge-based network operations
An expert system for enhancing the operability of the ground communication element of the Jet Propulsion Laboratory's Deep Space Network is described. The system performs network fault management, configuration management, and performance management in real time. Extracted management information serves as input to the expert system and is used to update a management information data base. The monitor and control activities involve dividing software for each processor into layers which are each modeled as a finite state machine.