Networks of Gaussian channels with applications to feedback systems.
Networks of channels with additive Gaussian noise, with applications to feedback systems
Elias, P.↗
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Using CLIPS in a distributed system: The Network Control Center (NCC) expert system
This paper describes an intelligent troubleshooting system for the Help Desk domain. It was developed on an IBM-compatible 80286 PC using Microsoft C and CLIPS and an AT&T 3B2 minicomputer using the UNIFY database and a combination of shell script, C programs and SQL queries. The two computers are linked by a lan. The functions of this system are to help non-technical NCC personnel handle trouble calls, to keep a log of problem calls with complete, concise information, and to keep a historical database of problems. The database helps identify hardware and software problem areas and provides a source of new rules for the troubleshooting knowledge base.
Wannemacher, Tom↗
Zebra: A striped network file system
The design of Zebra, a striped network file system, is presented. Zebra applies ideas from log-structured file system (LFS) and RAID research to network file systems, resulting in a network file system that has scalable performance, uses its servers efficiently even when its applications are using small files, and provides high availability. Zebra stripes file data across multiple servers, so that the file transfer rate is not limited by the performance of a single server. High availability is achieved by maintaining parity information for the file system. If a server fails its contents can be reconstructed using the contents of the remaining servers and the parity information. Zebra differs from existing striped file systems in the way it stripes file data: Zebra does not stripe on a per-file basis; instead it stripes the stream of bytes written by each client. Clients write to the servers in units called stripe fragments, which are analogous to segments in an LFS. Stripe fragments contain file blocks that were written recently, without regard to which file they belong. This method of striping has numerous advantages over per-file striping, including increased server efficiency, efficient parity computation, and elimination of parity update.
Hartman, John H.↗
Controllable Buoys and Networked Buoy Systems
Buoyant sensor networks are described, comprising floating buoys with sensors and energy harvesting capabilities. The buoys can control their buoyancy and motion, and can organize communication in a distributed fashion. Some buoys may have tethered underwater vehicles with a smart spooling system that allows the vehicles to dive deep underwater while remaining in communication and connection with the buoys.
Davoodi, Faranak↗
Controllable Buoys and Networked Buoy Systems
Buoyant sensor networks are described, comprising floating buoys with sensors and energy harvesting capabilities. The buoys can control their buoyancy and motion, and can organize communication in a distributed fashion. Some buoys may have tethered underwater vehicles with a smart spooling system that allows the vehicles to dive deep underwater while remaining in communication and connection with the buoys.
Davoodi, Faranak↗
Manned Space Flight Network Telemetry System
Manned space flight network telemetry system modification for Skylab, ERTS and Apollo J missions, giving data flow diagrams and equipment electrical characteristics
Underwood, T. C., Jr.↗
Kennedy Space Center network documentation system
The Kennedy Space Center Network Documentation System (KSC NDS) is being designed and implemented by NASA and the KSC contractor organizations to provide a means of network tracking, configuration, and control. Currently, a variety of host and client platforms are in use as a result of each organization having established its own network documentation system. The solution is to incorporate as many existing 'systems' as possible in the effort to consolidate and standardize KSC-wide documentation.
Lohne, William E.↗
Dan Goldin Presentation: Pathway to the Future
In the "Path to the Future" presentation held at NASA's Langley Center on March 31, 1999, NASA's Administrator Daniel S. Goldin outlined the future direction and strategies of NASA in relation to the general space exploration enterprise. NASA's Vision, Future System Characteristics, Evolutions of Engineering, and Revolutionary Changes are the four main topics of the presentation. In part one, the Administrator talks in detail about NASA's vision in relation to the NASA Strategic Activities that are Space Science, Earth Science, Human Exploration, and Aeronautics & Space Transportation. Topics discussed in this section include: space science for the 21st century, flying in mars atmosphere (mars plane), exploring new worlds, interplanetary internets, earth observation and measurements, distributed information-system-in-the-sky, science enabling understanding and application, space station, microgravity, science and exploration strategies, human mars mission, advance space transportation program, general aviation revitalization, and reusable launch vehicles. In part two, he briefly talks about the future system characteristics. He discusses major system characteristics like resiliencey, self-sufficiency, high distribution, ultra-efficiency, and autonomy and the necessity to overcome any distance, time, and extreme environment barriers. Part three of Mr. Goldin's talk deals with engineering evolution, mainly evolution in the Computer Aided Design (CAD)/Computer Aided Engineering (CAE) systems. These systems include computer aided drafting, computerized solid models, virtual product development (VPD) systems, networked VPD systems, and knowledge enriched networked VPD systems. In part four, the last part, the Administrator talks about the need for revolutionary changes in communication and networking areas of a system. According to the administrator, the four major areas that need cultural changes in the creativity process are human-centered computing, an infrastructure for distributed collaboration, rapid synthesis and simulation tools, and life-cycle integration and validation. Mr. Goldin concludes his presentation with the following maxim "Collaborate, Integrate, Innovate or Stagnate and Evaporate." He also answers some questions after the presentation.
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Physics-Informed Graph Neural Networks for Collaborative Dynamic Reconfiguration and Voltage Regulation in Unbalanced Distribution Systems
Network reconfiguration has long been employed as a strategic approach to minimize power distribution system losses and effectively regulate voltage levels. Tap-changing voltage regulators are also critical for controlling bus voltages, especially in accommodating the increasing integration of distributed energy resources (DERs) with intermittent outputs. This paper introduces novel methodologies to address the challenges of dynamic reconfiguration and optimal tap setting in unbalanced three-phase distribution systems. We propose an approximated mixed-integer quadratically constrained program (MIQCP) to model dynamic reconfiguration, along with a pioneering formulation for voltage regulator (VR) tap-setting based on Special Ordered Set type 1 (SOS1). To mitigate computational complexity, we propose a physics-informed spatial-temporal graph convolutional network (STGCN) with an integrated link classifier. The proposed approach enables efficient solution generation by fixing specific variables in the MIQCP instance and solving the simplified sub-MIP using an MIP solver. Numerical studies demonstrate the superior prediction accuracy of our STGCN model compared to baseline neural network models, resulting in reduced DER curtailment and voltage deviation with shorter computation time.
dynamic reconfiguration↗
Deep Space Network information system architecture study
The purpose of this article is to describe an architecture for the Deep Space Network (DSN) information system in the years 2000-2010 and to provide guidelines for its evolution during the 1990s. The study scope is defined to be from the front-end areas at the antennas to the end users (spacecraft teams, principal investigators, archival storage systems, and non-NASA partners). The architectural vision provides guidance for major DSN implementation efforts during the next decade. A strong motivation for the study is an expected dramatic improvement in information-systems technologies, such as the following: computer processing, automation technology (including knowledge-based systems), networking and data transport, software and hardware engineering, and human-interface technology. The proposed Ground Information System has the following major features: unified architecture from the front-end area to the end user; open-systems standards to achieve interoperability; DSN production of level 0 data; delivery of level 0 data from the Deep Space Communications Complex, if desired; dedicated telemetry processors for each receiver; security against unauthorized access and errors; and highly automated monitor and control.
Beswick, C. A.↗
Software For Graphical Representation Of A Network
System Visualization Tool (SVT) computer program developed to provide systems engineers with means of graphically representing networks. Generates diagrams illustrating structures and states of networks defined by users. Provides systems engineers powerful tool simplifing analysis of requirements and testing and maintenance of complex software-controlled systems. Employs visual models supporting analysis of chronological sequences of requirements, simulation data, and related software functions. Applied to pneumatic, hydraulic, and propellant-distribution networks. Used to define and view arbitrary configurations of such major hardware components of system as propellant tanks, valves, propellant lines, and engines. Also graphically displays status of each component. Advantage of SVT: utilizes visual cues to represent configuration of each component within network. Written in Turbo Pascal(R), version 5.0.
Mcallister, R. William↗
A Gamma Memory Neural Network for System Identification
A gamma neural network topology is investigated for a system identification application. A discrete gamma memory structure is used in the input layer, providing delayed values of both the control inputs and the network output to the input layer. The discrete gamma memory structure implements a tapped dispersive delay line, with the amount of dispersion regulated by a single, adaptable parameter. The network is trained using static back propagation, but captures significant features of the system dynamics. The system dynamics identified with the network are the Mach number dynamics of the 16 Foot Transonic Tunnel at NASA Langley Research Center, Hampton, Virginia. The training data spans an operating range of Mach numbers from 0.4 to 1.3.
Motter, Mark A.↗
Interference susceptibility of a typical deep space network receiving system
The Deep Space Network (DSN), operated by the Jet Propulsion Laboratory for NASA, is used primarily for communication with interplanetary spacecraft. The high sensitivity required to achieve communication with distant spacecraft makes the DSN very susceptible to radio frequency interference (RFI). In this article a comprehensive description of the interference susceptibility of a typical DSN receiving system is given. Specifically, the effects of interference on the carrier tracking loop, the telemetry receiving subsystem, and the saturation effect on the maser, are presented.
Sue, M. K.↗
Reliability Modeling of Microelectromechanical Systems Using Neural Networks
Microelectromechanical systems (MEMS) are a broad and rapidly expanding field that is currently receiving a great deal of attention because of the potential to significantly improve the ability to sense, analyze, and control a variety of processes, such as heating and ventilation systems, automobiles, medicine, aeronautical flight, military surveillance, weather forecasting, and space exploration. MEMS are very small and are a blend of electrical and mechanical components, with electrical and mechanical systems on one chip. This research establishes reliability estimation and prediction for MEMS devices at the conceptual design phase using neural networks. At the conceptual design phase, before devices are built and tested, traditional methods of quantifying reliability are inadequate because the device is not in existence and cannot be tested to establish the reliability distributions. A novel approach using neural networks is created to predict the overall reliability of a MEMS device based on its components and each component's attributes. The methodology begins with collecting attribute data (fabrication process, physical specifications, operating environment, property characteristics, packaging, etc.) and reliability data for many types of microengines. The data are partitioned into training data (the majority) and validation data (the remainder). A neural network is applied to the training data (both attribute and reliability); the attributes become the system inputs and reliability data (cycles to failure), the system output. After the neural network is trained with sufficient data. the validation data are used to verify the neural networks provided accurate reliability estimates. Now, the reliability of a new proposed MEMS device can be estimated by using the appropriate trained neural networks developed in this work.
Perera. J. Sebastian↗
Apollo network pcm decommutation systems
Apollo network pulse code modulation decommutation system
PULSE CODE MODULATION /PCM/↗
Airport Remote Tower Sensor Systems
Networks of video cameras, meteorological sensors, and ancillary electronic equipment are under development in collaboration among NASA Ames Research Center, the Federal Aviation Administration (FAA), and the National Oceanic Atmospheric Administration (NOAA). These networks are to be established at and near airports to provide real-time information on local weather conditions that affect aircraft approaches and landings. The prototype network is an airport-approach-zone camera system (AAZCS), which has been deployed at San Francisco International Airport (SFO) and San Carlos Airport (SQL). The AAZCS includes remotely controlled color video cameras located on top of SFO and SQL air-traffic control towers. The cameras are controlled by the NOAA Center Weather Service Unit located at the Oakland Air Route Traffic Control Center and are accessible via a secure Web site. The AAZCS cameras can be zoomed and can be panned and tilted to cover a field of view 220 wide. The NOAA observer can see the sky condition as it is changing, thereby making possible a real-time evaluation of the conditions along the approach zones of SFO and SQL. The next-generation network, denoted a remote tower sensor system (RTSS), will soon be deployed at the Half Moon Bay Airport and a version of it will eventually be deployed at Los Angeles International Airport. In addition to remote control of video cameras via secure Web links, the RTSS offers realtime weather observations, remote sensing, portability, and a capability for deployment at remote and uninhabited sites. The RTSS can be used at airports that lack control towers, as well as at major airport hubs, to provide synthetic augmentation of vision for both local and remote operations under what would otherwise be conditions of low or even zero visibility.
Maluf, David A.↗
The Deep Space Network Advanced Systems Program
The deep space network (DSN)--with its three complexes in Goldstone, California, Madrid, Spain, and Canberra, Australia--provides the resources to track and communicate with planetary and deep space missions. Each complex consists of an array of capabilities for tracking probes almost anywhere in the solar system. A number of innovative hardware, software and procedural tools are used for day-to-day operations at DSN complexes as well as at the network control at the Jet Propulsion Laboratory (JPL). Systems and technologies employed by the network include large-aperture antennas (34-m and 70-m), cryogenically cooled receivers, high-power transmitters, stable frequency and timing distribution assemblies, modulation and coding schemes, spacecraft transponders, radiometric tracking techniques, etc. The DSN operates at multiple frequencies, including the 2-GHz band, the 7/8-GHz band, and the 32/34-GHz band.
tracking↗
Network operating system
Long-term and short-term objectives for the development of a network operating system for the Space Station are stated. The short-term objective is to develop a prototype network operating system for a 100 megabit/second fiber optic data bus. The long-term objective is to establish guidelines for writing a detailed specification for a Space Station network operating system. Major milestones are noted. Information is given in outline form.
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