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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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At least 253 records · Page 14
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NASA NTRS2018

Tropical Cyclone Intensity Estimation Using Deep Convolutional Neural Networks

Estimating tropical cyclone intensity by just using satellite image is a challenging problem. With successful application of the Dvorak technique for more than 30 years along with some modifications and improvements, it is still used worldwide for tropical cyclone intensity estimation. A number of semi-automated techniques have been derived using the original Dvorak technique. However, these techniques suffer from subjective bias as evident from the most recent estimations on October 10, 2017 at 1500 UTC for Tropical Storm Ophelia: The Dvorak intensity estimates ranged from T2.3/33 kt (Tropical Cyclone Number 2.3/33 knots) from UW-CIMSS (University of Wisconsin-Madison - Cooperative Institute for Meteorological Satellite Studies) to T3.0/45 kt from TAFB (the National Hurricane Center's Tropical Analysis and Forecast Branch) to T4.0/65 kt from SAB (NOAA/NESDIS Satellite Analysis Branch). In this particular case, two human experts at TAFB and SAB differed by 20 knots in their Dvorak analyses, and the automated version at the University of Wisconsin was 12 knots lower than either of them. The National Hurricane Center (NHC) estimates about 10-20 percent uncertainty in its post analysis when only satellite based estimates are available. The success of the Dvorak technique proves that spatial patterns in infrared (IR) imagery strongly relate to tropical cyclone intensity. This study aims to utilize deep learning, the current state of the art in pattern recognition and image recognition, to address the need for an automated and objective tropical cyclone intensity estimation. Deep learning is a multi-layer neural network consisting of several layers of simple computational units. It learns discriminative features without relying on a human expert to identify which features are important. Our study mainly focuses on convolutional neural network (CNN), a deep learning algorithm, to develop an objective tropical cyclone intensity estimation. CNN is a supervised learning algorithm requiring a large number of training data. Since the archives of intensity data and tropical cyclone centric satellite images is openly available for use, the training data is easily created by combining the two. Results, case studies, prototypes, and advantages of this approach will be discussed.

tropical cyclone intensity↗
NASA NTRS1993

PILOT: An intelligent distributed operations support system

The Real-Time Data System (RTDS) project is exploring the application of advanced technologies to the real-time flight operations environment of the Mission Control Centers at NASA's Johnson Space Center. The system, based on a network of engineering workstations, provides services such as delivery of real time telemetry data to flight control applications. To automate the operation of this complex distributed environment, a facility called PILOT (Process Integrity Level and Operation Tracker) is being developed. PILOT comprises a set of distributed agents cooperating with a rule-based expert system; together they monitor process operation and data flows throughout the RTDS network. The goal of PILOT is to provide unattended management and automated operation under user control.

Rasmussen, Arthur N.↗
NASA NTRS2005

Three-Dimensional High-Resolution Optical/X-Ray Stereoscopic Tracking Velocimetry

Measurement of three-dimensional (3-D) three-component velocity fields is of great importance in a variety of research and industrial applications for understanding materials processing, fluid physics, and strain/displacement measurements. The 3-D experiments in these fields most likely inhibit the use of conventional techniques, which are based only on planar and optically-transparent-field observation. Here, we briefly review the current status of 3-D diagnostics for motion/velocity detection, for both optical and x-ray systems. As an initial step for providing 3-D capabilities, we have developed stereoscopic tracking velocimetry (STV) to measure 3-D flow/deformation through optical observation. The STV is advantageous in system simplicity, for continually observing 3-D phenomena in near real-time. In an effort to enhance the data processing through automation and to avoid the confusion in tracking numerous markers or particles, artificial neural networks are employed to incorporate human intelligence. Our initial optical investigations have proven the STV to be a very viable candidate for reliably measuring 3-D flow motions. With previous activities focused on improving the processing efficiency, overall accuracy, and automation based on the optical system, the current efforts is directed to the concurrent expansion to the x-ray system for broader experimental applications.

Cha, Soyoung S.↗
NASA NTRS1998

Ensembles of radial basis function networks for spectroscopic detection of cervical precancer

The mortality related to cervical cancer can be substantially reduced through early detection and treatment. However, current detection techniques, such as Pap smear and colposcopy, fail to achieve a concurrently high sensitivity and specificity. In vivo fluorescence spectroscopy is a technique which quickly, noninvasively and quantitatively probes the biochemical and morphological changes that occur in precancerous tissue. A multivariate statistical algorithm was used to extract clinically useful information from tissue spectra acquired from 361 cervical sites from 95 patients at 337-, 380-, and 460-nm excitation wavelengths. The multivariate statistical analysis was also employed to reduce the number of fluorescence excitation-emission wavelength pairs required to discriminate healthy tissue samples from precancerous tissue samples. The use of connectionist methods such as multilayered perceptrons, radial basis function (RBF) networks, and ensembles of such networks was investigated. RBF ensemble algorithms based on fluorescence spectra potentially provide automated and near real-time implementation of precancer detection in the hands of nonexperts. The results are more reliable, direct, and accurate than those achieved by either human experts or multivariate statistical algorithms.

Cervix Neoplasms/pathology↗
NASA NTRS2011

Identifying Functional Requirements for Flexible Airspace Management Concept Using Human-In-The-Loop Simulations

Flexible Airspace Management (FAM) is a mid- term Next Generation Air Transportation System (NextGen) concept that allows dynamic changes to airspace configurations to meet the changes in the traffic demand. A series of human-in-the-loop (HITL) studies have identified procedures and decision support requirements needed to implement FAM. This paper outlines a suggested FAM procedure and associated decision support functionality based on these HITL studies. A description of both the tools used to support the HITLs and the planned NextGen technologies available in the mid-term are presented and compared. The mid-term implementation of several NextGen capabilities, specifically, upgrades to the Traffic Management Unit (TMU), the initial release of an en route automation system, the deployment of a digital data communication system, a more flexible voice communications network, and the introduction of a tool envisioned to manage and coordinate networked ground systems can support the implementation of the FAM concept. Because of the variability in the overall deployment schedule of the mid-term NextGen capabilities, the dependency of the individual NextGen capabilities are examined to determine their impact on a mid-term implementation of FAM. A cursory review of the different technologies suggests that new functionality slated for the new en route automation system is a critical enabling technology for FAM, as well as the functionality to manage and coordinate networked ground systems. Upgrades to the TMU are less critical but important nonetheless for FAM to be fully realized. Flexible voice communications network and digital data communication system could allow more flexible FAM operations but they are not as essential.

dynamic airspace configuration↗
NASA NTRS1996

A Mission Operations Architecture for the 21st Century

A new operations architecture for low cost missions is proposed, to build a framework to enable NASA to face its major challenges beond 2000. This architecture is composed of three elements: Service Based Architecture, Demand Access Automation, Distributed Science Hubs.

operations↗
NASA NTRS2004

Three-Dimensional High-Resolution Optical/X-Ray Stereoscopic Tracking Velocimetry

Measurement of three-dimensional (3-D) three-component velocity fields is of great importance in a variety of research and industrial applications for understanding materials processing, fluid physics, and strain/displacement measurements. The 3-D experiments in these fields most likely inhibit the use of conventional techniques, which are based only on planar and optically-transparent-field observation. Here, we briefly review the current status of 3-D diagnostics for motion/velocity detection, for both optical and x-ray systems. As an initial step for providing 3-D capabilities, we nave developed stereoscopic tracking velocimetry (STV) to measure 3-D flow/deformation through optical observation. The STV is advantageous in system simplicity, for continually observing 3- D phenomena in near real-time. In an effort to enhance the data processing through automation and to avoid the confusion in tracking numerous markers or particles, artificial neural networks are employed to incorporate human intelligence. Our initial optical investigations have proven the STV to be a very viable candidate for reliably measuring 3-D flow motions. With previous activities are focused on improving the processing efficiency, overall accuracy, and automation based on the optical system, the current efforts is directed to the concurrent expansion to the x-ray system for broader experimental applications.

Cha, Soyoung S.↗
NASA NTRS1999

A Neural Network Aero Design System for Advanced Turbo-Engines

An inverse design method calculates the blade shape that produces a prescribed input pressure distribution. By controlling this input pressure distribution the aerodynamic design objectives can easily be met. Because of the intrinsic relationship between pressure distribution and airfoil physical properties, a Neural Network can be trained to choose the optimal pressure distribution that would meet a set of physical requirements. Neural network systems have been attempted in the context of direct design methods. From properties ascribed to a set of blades the neural network is trained to infer the properties of an 'interpolated' blade shape. The problem is that, especially in transonic regimes where we deal with intrinsically non linear and ill posed problems, small perturbations of the blade shape can produce very large variations of the flow parameters. It is very unlikely that, under these circumstances, a neural network will be able to find the proper solution. The unique situation in the present method is that the neural network can be trained to extract the required input pressure distribution from a database of pressure distributions while the inverse method will still compute the exact blade shape that corresponds to this 'interpolated' input pressure distribution. In other words, the interpolation process is transferred to a smoother problem, namely, finding what pressure distribution would produce the required flow conditions and, once this is done, the inverse method will compute the exact solution for this problem. The use of neural network is, in this context, highly related to the use of proper optimization techniques. The optimization is used essentially as an automation procedure to force the input pressure distributions to achieve the required aero and structural design parameters. A multilayered feed forward network with back-propagation is used to train the system for pattern association and classification.

Sanz, Jose M.↗
NASA NTRS2003

The Logical Extension

The same software controlling autonomous and crew-assisted operations for the International Space Station (ISS) is enabling commercial enterprises to integrate and automate manual operations, also known as decision logic, in real time across complex and disparate networked applications, databases, servers, and other devices, all with quantifiable business benefits. Auspice Corporation, of Framingham, Massachusetts, developed the Auspice TLX (The Logical Extension) software platform to effectively mimic the human decision-making process. Auspice TLX automates operations across extended enterprise systems, where any given infrastructure can include thousands of computers, servers, switches, and modems that are connected, and therefore, dependent upon each other. The concept behind the Auspice software spawned from a computer program originally developed in 1981 by Cambridge, Massachusetts-based Draper Laboratory for simulating tasks performed by astronauts aboard the Space Shuttle. At the time, the Space Shuttle Program was dependent upon paper-based procedures for its manned space missions, which typically averaged 2 weeks in duration. As the Shuttle Program progressed, NASA began increasing the length of manned missions in preparation for a more permanent space habitat. Acknowledging the need to relinquish paper-based procedures in favor of an electronic processing format to properly monitor and manage the complexities of these longer missions, NASA realized that Draper's task simulation software could be applied to its vision of year-round space occupancy. In 1992, Draper was awarded a NASA contract to build User Interface Language software to enable autonomous operations of a multitude of functions on Space Station Freedom (the station was redesigned in 1993 and converted into the international venture known today as the ISS)

Source record↗
NASA NTRS1990

Automated Power-Distribution System

Automated power-distribution system monitors and controls electrical power to modules in network. Handles both 208-V, 20-kHz single-phase alternating current and 120- to 150-V direct current. Power distributed to load modules from power-distribution control units (PDCU's) via subsystem distributors. Ring busses carry power to PDCU's from power source. Needs minimal attention. Detects faults and also protects against them. Potential applications include autonomous land vehicles and automated industrial process systems.

Thomason, Cindy↗
NASA NTRS1993

Scheduling the future NASA Space Network: Experiences with a flexible scheduling prototype

NASA's Space Network (SN) provides telecommunications and tracking services to low earth orbiting spacecraft. One proposal for improving resource allocation and automating conflict resolution for the SN is the concept of flexible scheduling. In this concept, each Payload Operations Control Center (POCC) will possess a Space Network User POCC Interface (SNUPI) to support the development and management of flexible requests. Flexible requests express the flexibility, constraints, and repetitious nature of the user's communications requirements. Flexible scheduling is expected to improve SN resource utilization and user satisfaction, as well as reduce the effort to produce and maintain a schedule. A prototype testbed has been developed to better understand flexible scheduling as it applies to the SN. This testbed consists of a SNUPI workstation, an SN scheduler, and a flexible request language that conveys information between the two systems. All three are being evaluated by operations personnel. Benchmark testing is being conducted on the scheduler to quantify the productivity improvements achieved with flexible requests.

Happell, Nadine↗
NASA NTRS2005

Software for Automation of Real-Time Agents, Version 2

Version 2 of Closed Loop Execution and Recovery (CLEaR) has been developed. CLEaR is an artificial intelligence computer program for use in planning and execution of actions of autonomous agents, including, for example, Deep Space Network (DSN) antenna ground stations, robotic exploratory ground vehicles (rovers), robotic aircraft (UAVs), and robotic spacecraft. CLEaR automates the generation and execution of command sequences, monitoring the sequence execution, and modifying the command sequence in response to execution deviations and failures as well as new goals for the agent to achieve. The development of CLEaR has focused on the unification of planning and execution to increase the ability of the autonomous agent to perform under tight resource and time constraints coupled with uncertainty in how much of resources and time will be required to perform a task. This unification is realized by extending the traditional three-tier robotic control architecture by increasing the interaction between the software components that perform deliberation and reactive functions. The increase in interaction reduces the need to replan, enables earlier detection of the need to replan, and enables replanning to occur before an agent enters a state of failure.

Fisher, Forest↗
NASA NTRS1982

NASA Deep Space Network operating control

The primary function of the Deep Space Network (DSN) is to provide effective and reliable tracking and data acquisition for planetary and interplanetary space flight missions. This involves providing data to flight project mission operations, accepting commands from mission operations and transmitting the commands to stations and spacecraft, and providing a record of telemetry and command data to mission operations. Also included are network performance monitoring, the generation of predictions for antenna pointing and signal acquisition, network scheduling, and network validation tests. Descriptions are given of the three facilities and six systems of the DSN. Also described are interfaces, automation and standardized procedures, and discrepancy reporting. It is pointed out that the greatest challenge facing the DSN is the implementation of NASA's Network Consolidation Program, which is scheduled to be completed in 1986. The objectives of this program are enumerated.

Weisman, W. D.↗
NASA NTRS1990

Ames vision group research overview

A major goal of the reseach group is to develop mathematical and computational models of early human vision. These models are valuable in the prediction of human performance, in the design of visual coding schemes and displays, and in robotic vision. To date researchers have models of retinal sampling, spatial processing in visual cortex, contrast sensitivity, and motion processing. Based on their models of early human vision, researchers developed several schemes for efficient coding and compression of monochrome and color images. These are pyramid schemes that decompose the image into features that vary in location, size, orientation, and phase. To determine the perceptual fidelity of these codes, researchers developed novel human testing methods that have received considerable attention in the research community. Researchers constructed models of human visual motion processing based on physiological and psychophysical data, and have tested these models through simulation and human experiments. They also explored the application of these biological algorithms to applications in automated guidance of rotorcraft and autonomous landing of spacecraft. Researchers developed networks for inhomogeneous image sampling, for pyramid coding of images, for automatic geometrical correction of disordered samples, and for removal of motion artifacts from unstable cameras.

Watson, Andrew B.↗
NASA NTRS1991

Standard formatted data units-control authority procedures

The purpose of this document is to establish a set of minimum and optional requirements for the implementation of Control Authority (CA) organizations within and among the Agencies participating in the Consultative Committee for Space Data Systems (CCSDS). By satisfying these requirements, the resultant cooperating set of CA organizations will produce a global CA service supporting information transfer with digital data under the Standard Formatted Data Unit (SFDU) concept. This service is primarily accomplished through the registration, permanent archiving, and dissemination of metadata in the form of Metadata Objects (MDO) that assist in the interpretation of data objects received in SFDU form. This Recommendation addresses the responsibilities, services, and interface protocols for a hierarchy of CA organizations. The top level, consisting of the CCSDS Secretariat and its operational agent, is unique and primarily provides a global coordination function. The lower levels are Agency CA organizations that have primary responsibility for the registration, archiving, and dissemination of MDOs. As experience is gained and technology evolves, the CA Procedures will be extended to include enhanced services and their supporting protocols. In particular, it is anticipated that eventually CA organizations will be linked via networks on a global basis, and will provide requestors with online automated access to CA services. While this Recommendation does not preclude such operations, it also does not recommend the specific protocols to be used to ensure global compatibility of these services. These recommendations will be generated as experience is gained.

Source record↗
NASA NTRS1992

Second tracking and data relay satellite system (TDRSS) ground terminal - STGT

The STGT will provide high availability command and control and improved services to users of NASA's Space Network in the 1990s and beyond. The Space Network, comprising the TDRSS, will be the primary communications gateway for Space Station Freedom and other user's spacecraft and their ground support elements. The STGT will contain a redundant, distributed computer system providing configuration and control of redundant RF to baseband equipment chains for throughput of user data, for user tracking services and for control and monitoring of the TDR Satellites. An interface with NASA's Network Control Center, located at the Goddard Space Flight Center, provides automated scheduling and control of the STGT. A local TDRSS Operations Control Center for local monitoring and back-up control and an interface with the Domestic Satellite for data distribution will be provided by the STGT. This paper describes the STGT, with emphasis on configuration, control and monitoring of those elements providing TDRSS services to user spacecraft.

Berndt, Allen K.↗
NASA NTRS1993

A strip chart recorder pattern recognition tool kit for Shuttle operations

During Space Shuttle operations, Mission Control personnel monitor numerous mission-critical systems such as electrical power; guidance, navigation, and control; and propulsion by means of paper strip chart recorders. For example, electrical power controllers monitor strip chart recorder pen traces to identify onboard electrical equipment activations and deactivations. Recent developments in pattern recognition technologies coupled with new capabilities that distribute real-time Shuttle telemetry data to engineering workstations make it possible to develop computer applications that perform some of the low-level monitoring now performed by controllers. The number of opportunities for such applications suggests a need to build a pattern recognition tool kit to reduce software development effort through software reuse. We are building pattern recognition applications while keeping such a tool kit in mind. We demonstrated the initial prototype application, which identifies electrical equipment activations, during three recent Shuttle flights. This prototype was developed to test the viability of the basic system architecture, to evaluate the performance of several pattern recognition techniques including those based on cross-correlation, neural networks, and statistical methods, to understand the interplay between an advanced automation application and human controllers to enhance utility, and to identify capabilities needed in a more general-purpose tool kit.

Hammen, David G.↗
NASA NTRS1998

RIACS

The Research Institute for Advanced Computer Science (RIACS) was established by the Universities Space Research Association (USRA) at the NASA Ames Research Center (ARC) on June 6, 1983. RIACS is privately operated by USRA, a consortium of universities that serves as a bridge between NASA and the academic community. Under a five-year co-operative agreement with NASA, research at RIACS is focused on areas that are strategically enabling to the Ames Research Center's role as NASA's Center of Excellence for Information Technology. Research is carried out by a staff of full-time scientist,augmented by visitors, students, post doctoral candidates and visiting university faculty. The primary mission of RIACS is charted to carry out research and development in computer science. This work is devoted in the main to tasks that are strategically enabling with respect to NASA's bold mission in space exploration and aeronautics. There are three foci for this work: Automated Reasoning. Human-Centered Computing. and High Performance Computing and Networking. RIACS has the additional goal of broadening the base of researcher in these areas of importance to the nation's space and aeronautics enterprises. Through its visiting scientist program, RIACS facilitates the participation of university-based researchers, including both faculty and students, in the research activities of NASA and RIACS. RIACS researchers work in close collaboration with NASA computer scientists on projects such as the Remote Agent Experiment on Deep Space One mission, and Super-Resolution Surface Modeling.

Moore, Robert C.↗