Results for “COUPLING NETWORK” — Page 11

NASA NTRS2022

Exploring Applications of Machine Learning for Wildfire Monitoring and Detection using Unmanned Aerial Vehicles

Wildfires are increasing in frequency and severity around the world, including the United States. The losses caused by wildfires could be mitigated if high-risk areas, hotspots, and flare-ups could be monitored continuously, such as through the use of Unmanned Aerial Vehicles (UAVs). This paper documents exploratory efforts using machine learning to determine efficient flight paths for UAVs and to detect wildfires using image classification. On path planning, three machine learning techniques—Genetic Algorithm, Simulated Annealing, and Dynamic Programming—were explored. Genetic Algorithm was found to be an effective approach for path planning for wildfire monitoring and surveillance by UAVs. For a scenario of 25 locations in a circular arrangement, the algorithm was able to return the optimal path. The accuracy and execution time was found to be sensitive to the algorithm hyperparameters selected, which was especially evident in scenarios with hundreds or thousands of locations. Simulated Annealing was also found to be an effective approach for UAV path planning, with a major benefit of avoiding getting trapped in local minima and being straightforward to implement. Like Genetic Algorithm, the performance of Simulated Annealing was also found to be sensitive to the algorithm hyperparameters selected. By comparison, Dynamic Programming guarantees optimality for any number of locations, but it was found to be less practical in terms of execution time for scenarios with more than about a couple dozen locations. On wildfire detection, image classification using deep learning with a convolutional neural network was explored. Transfer learning was found to be a useful technique to efficiently train deep learning models. Also, it was determined that GPU processing can increase training speed by an order of magnitude, which enables significantly faster development. For a validation test set of 500 images, there were only two false negatives and zero false positives. These results demonstrate that detecting wildfires in static cameras using machine learning is feasible and establish a baseline for using images captured by UAVs in flight for wildfire detection.

Wildfire management↗

NASA NTRS1983

Proceedings of the 1st Space Plasma Computer Analysis Network (SCAN) Workshop

The purpose of the workshop was to identify specific cooperative scientific study topics within the discipline of Ionosphere Magnetosphere Coupling processes and to develop methods and procedures to accomplish this cooperative research using SCAN facilities. Cooperative scientific research was initiated in the areas of polar cusp composition, O+ polar outflow, and magnetospheric boundary morphology studies and an approach using a common metafile structure was adopted to facilitate the exchange of data and plots between the various workshop participants. The advantages of in person versus remote workshops were discussed also.

Green, J. L.↗

NASA NTRS2020

Convolutional Neural Network for Transition Modeling Based on Linear Stability Theory

Transition prediction is an important aspect of aerodynamic design because of its impact on skin friction and potential coupling with flow separation characteristics. Traditionally, the modeling of transition has relied on correlation-based empirical formulas based on integral quantities such as the shape factor of the boundary layer. However, in many applications of computational fluid dynamics, the shape factor is not straightforwardly available or not well-defined. We propose using the complete velocity profile along with other quantities (e.g., frequency, Reynolds number) to predict the perturbation amplification factor. While this can be achieved with regression models based on a classical fully connected neural network, such a model can be computationally more demanding. We propose a novel convolutional neural network inspired by the underlying physics as described by the stability equations. Specifically, convolutional layers are first used to extract integral quantities from the velocity profiles, and then fully connected layers are used to map the extracted integral quantities, along with frequency and Reynolds number, to the output (amplification ratio). Numerical tests on classical boundary layers clearly demonstrate the merits of the proposed method. More importantly, we demonstrate that, for Tollmien-Schlichting instabilities in two-dimensional, low-speed boundary layers, the proposed network encodes information in the boundary layer profiles into an integral quantity that is strongly correlated to a well-known, physically defined parameter – the shape factor.

Laminar-turbulent transition↗

NASA NTRS2006

Reconfigurable Control with Neural Network Augmentation for a Modified F-15 Aircraft

Description of the performance of a simplified dynamic inversion controller with neural network augmentation follows. Simulation studies focus on the results with and without neural network adaptation through the use of an F-15 aircraft simulator that has been modified to include canards. Simulated control law performance with a surface failure, in addition to an aerodynamic failure, is presented. The aircraft, with adaptation, attempts to minimize the inertial cross-coupling effect of the failure (a control derivative anomaly associated with a jammed control surface). The dynamic inversion controller calculates necessary surface commands to achieve desired rates. The dynamic inversion controller uses approximate short period and roll axis dynamics. The yaw axis controller is a sideslip rate command system. Methods are described to reduce the cross-coupling effect and maintain adequate tracking errors for control surface failures. The aerodynamic failure destabilizes the pitching moment due to angle of attack. The results show that control of the aircraft with the neural networks is easier (more damped) than without the neural networks. Simulation results show neural network augmentation of the controller improves performance with aerodynamic and control surface failures in terms of tracking error and cross-coupling reduction.

Burken, John J.↗

NASA NTRS2006

Adaptive Control Using Neural Network Augmentation for a Modified F-15 Aircraft

Description of the performance of a simplified dynamic inversion controller with neural network augmentation follows. Simulation studies focus on the results with and without neural network adaptation through the use of an F-15 aircraft simulator that has been modified to include canards. Simulated control law performance with a surface failure, in addition to an aerodynamic failure, is presented. The aircraft, with adaptation, attempts to minimize the inertial cross-coupling effect of the failure (a control derivative anomaly associated with a jammed control surface). The dynamic inversion controller calculates necessary surface commands to achieve desired rates. The dynamic inversion controller uses approximate short period and roll axis dynamics. The yaw axis controller is a sideslip rate command system. Methods are described to reduce the cross-coupling effect and maintain adequate tracking errors for control surface failures. The aerodynamic failure destabilizes the pitching moment due to angle of attack. The results show that control of the aircraft with the neural networks is easier (more damped) than without the neural networks. Simulation results show neural network augmentation of the controller improves performance with aerodynamic and control surface failures in terms of tracking error and cross-coupling reduction.

Burken, John J.↗

NASA NTRS1984

An evaluation of the directed flow graph methodology

The applicability of the Directed Graph Methodology (DGM) to the design and analysis of special purpose image and signal processing hardware was evaluated. A special purpose image processing system was designed and described using DGM. The design, suitable for very large scale integration (VLSI) implements a region labeling technique. Two computer chips were designed, both using metal-nitride-oxide-silicon (MNOS) technology, as well as a functional system utilizing those chips to perform real time region labeling. The system is described in terms of DGM primitives. As it is currently implemented, DGM is inappropriate for describing synchronous, tightly coupled, special purpose systems. The nature of the DGM formalism lends itself more readily to modeling networks of general purpose processors.

Snyder, W. E.↗

NASA NTRS1988

Autonomous image data reduction by analysis and interpretation

Image data is a critical component of the scientific information acquired by space missions. Compression of image data is required due to the limited bandwidth of the data transmission channel and limited memory space on the acquisition vehicle. This need becomes more pressing when dealing with multispectral data where each pixel may comprise 300 or more bytes. An autonomous, real time, on-board image analysis system for an exploratory vehicle such as a Mars Rover is developed. The completed system will be capable of interpreting image data to produce reduced representations of the image, and of making decisions regarding the importance of data based on current scientific goals. Data from multiple sources, including stereo images, color images, and multispectral data, are fused into single image representations. Analysis techniques emphasize artificial neural networks. Clusters are described by their outlines and class values. These analysis and compression techniques are coupled with decision making capacity for determining importance of each image region. Areas determined to be noise or uninteresting can be discarded in favor of more important areas. Thus limited resources for data storage and transmission are allocated to the most significant images.

Eberlein, Susan↗

NASA NTRS1988

Autonomous image data reduction by analysis and interpretation

Image data is a critical component of the scientific information acquired by space missions. Compression of image data is required due to the limited bandwidth of the data transmission channel and limited memory space on the acquisition vehicle. This need becomes more pressing when dealing with multispectral data where each pixel may comprise 300 or more bytes. An autonomous, real time, on-board image analysis system for an exploratory vehicle such as a Mars Rover is developed. The completed system will be capable of interpreting image data to produce reduced representations of the image, and of making decisions regarding the importance of data based on current scientific goals. Data from multiple sources, including stereo images, color images, and multispectral data, are fused into single image representations. Analysis techniques emphasize artificial neural networks. Clusters are described by their outlines and class values. These analysis and compression techniques are coupled with decision-making capacity for determining importance of each image region. Areas determined to be noise or uninteresting can be discarded in favor of more important areas. Thus limited resources for data storage and transmission are allocated to the most significant images.

Eberlein, Susan↗

NASA NTRS1996

Southern Hemisphere In Situ Observations of OH, HO2, CIO and BrO from the ER-2 Aircraft for the 1994 ASHOE Mission

A summary of the first order scientific conclusions that emerged from the research done under this grant are as follows: (1) For the first time, the concentration of the key hydrogen and halogen radicals OH, H02, ClO and BrO were determined on a global scale extending from the arctic circle to the antarctic circle, over the altitude domain of the ER-2. That domain extends from 15-20 km altitude, covering a critical part of the lower stratosphere; (2) Simultaneous, in situ measurements of the concentrations of OH, H02, ClO, BrO, NO and NO2 demonstrate the predominance of odd-hydrogen and halogen free radical catalysis in determining the rate of removal of ozone in the lower stratosphere over the complete ASHOE mission. This extends to the global scale the "first look" data obtained during the NASA Stratospheric Photochemistry and Dynamics Experiment (SPADE), executed out of Ames Research Center in June 1993. This represents a major rearrangement of our understanding with respect to the hierarchy of dominant catalytic cycles controlling ozone loss in the lower stratosphere. For the past twenty years, it has been assumed that nitrogen radicals dominate the destruction rate of ozone in the lower stratosphere; (3) Throughout the altitude and latitude range covered by ASHOE, it was determined that a single catalytic cycle, HO2 + O3 yields OH + 2O2, accounted for one half of the total O3 removal in this region of the atmosphere. Halogen radical catalytic cycles were found to account for one third of the ozone loss, and nitrogen radicals were found to account for 20% of the loss; (4) Simultaneous observations of the full complement of radicals, tracers, ozone, and water vapor during ASHOE demonstrated quantitatively the coupling that exists between the rate limiting radicals and other reactive species in the photochemical reaction network. Specifically, the concentrations of ClO and HO2 are inversely correlated with the concentration of NOx. This carries the implication that the NOx effluent from the proposed High Speed Civil Transport may be less destructive to stratospheric ozone than had previously been thought. ASHOE brought this conclusion forward for the first time on a global basis; and (5) The density of BrO was measured on a global scale during ASHOE in the lower stratosphere. It was found that bromine is responsible for 55-65% of the local rate of catalytic destruction of ozone by reactions involving bromine and chlorine. Normalizing calculated loss rates to total available inorganic bromine and chlorine explicitly demonstrates that bromine is 60-80 times more efficient than chlorine in removing ozone in the lower stratosphere. An inferred value of total inorganic bromine is in excellent agreement with measurements of their source species, organic bromine compounds in the troposphere.

Anderson, James G.↗

NASA NTRS1996

(abstract) Using GPS Measurements to Identify Global Ionospheric Storms in Near Real-Time

The solar wind interacts with the Earth's magnetosphere, eventually dissipating energy into the ionosphere and atmosphere. As a terminator, the ionosphere responds to magnetic storms, which is very important in understanding the energy coupling process between the Sun and the Earth and in forecasting space weather changes.The worldwide GPS network, for the first time, makes near real-time global ionospheric TEC measurements a possibility. Based on these measurements, global ionospheric TEC maps are generated with time resolution of from 5 minutes to hours. Using these maps, we can analyze the global evolution of ionospheric storms on temporal and spatial scales, which have been dificult to study before. We find that for certain types of storms (such as TID-driven), it is possible to identify them near onset and issue warning signals during the early stages. Main attention has been paid on northern hemispheric winter storms. Their common features and physical mechanisms are being investigated.

solar wind magnetospheres GPS ionosphere atmospher↗

NASA NTRS2003

Radiation tolerant back biased CMOS VLSI

A CMOS circuit formed in a semiconductor substrate having improved immunity to total ionizing dose radiation, improved immunity to radiation induced latch up, and improved immunity to a single event upset. The architecture of the present invention can be utilized with the n-well, p-well, or dual-well processes. For example, a preferred embodiment of the present invention is described relative to a p-well process wherein the p-well is formed in an n-type substrate. A network of NMOS transistors is formed in the p-well, and a network of PMOS transistors is formed in the n-type substrate. A contact is electrically coupled to the p-well region and is coupled to first means for independently controlling the voltage in the p-well region. Another contact is electrically coupled to the n-type substrate and is coupled to second means for independently controlling the voltage in the n-type substrate. By controlling the p-well voltage, the effective threshold voltages of the n-channel transistors both drawn and parasitic can be dynamically tuned. Likewise, by controlling the n-type substrate, the effective threshold voltages of the p-channel transistors both drawn and parasitic can also be dynamically tuned. Preferably, by optimizing the threshold voltages of the n-channel and p-channel transistors, the total ionizing dose radiation effect will be neutralized and lower supply voltages can be utilized for the circuit which would result in the circuit requiring less power.

Maki, Gary K.↗

NASA NTRS2005

Coupled Gravity and Elevation Measurements of Ice Sheet Mass Change

We measured surface gravity and position at ten locations about two glaciological measurement networks located on the South-central Greenland Ice during June 2004. Six of the individual sites of the first network were occupied the previous year. At the repeat sites we were able to measure annual accumulation rate and surface displacement by referencing measurements to aluminum poles left in the firn the previous year. We occupied 4 additional sites at a second measurement network for the first time since initial observations were last made at the network in 1981. At each individual site, we operated a GPS unit for 90 minutes - the unit was operated simultaneously with a base station unit in Sondrestrom Fjord so as to enable differential, post-processing of the data. We installed an aluminum, accumulation-rate-pole at each site. The base section of the pole also served as the mount for the GPS antenna. A new, Scintrex gravimeter was used at each site and relative gravity measurements were tied to the network of absolute gravity stations in Sondrestrom. We measured snow physical properties in two shallow pits. This report summarizes our observations and data analysis.

Jezek, K. C.↗

NASA NTRS1989

Type of service wide area networking

A variety of long-distance types of circuits can be used for data communication. A description is given of an experiment which optimizes the simultaneous use of terrestrial and satellite circuits in a TCP/IP network. Traditionally, it has been difficult to use effectively the full capacity of high-bandwidth satellite circuits, due to long round-trip times coupled with TCP window-size limitations. The experiment significantly reduced the problem, and the result was effective use of all the available bandwidth and a satellite circuit for a single data transfer. This experiment also demonstrates the use of multiple T1 communication lines and type of service networking over these lines.

Lekashman, John↗

NASA NTRS1972

Optimal design of a class of nonlinear networks.

The problem of synthesizing nth order nonlinear nonautonomous networks with a prescribed small signal behavior is considered. It is shown that, in the absence of coupling elements, the solution of this problem reduces to synthesizing a set of first order nonlinear characteristics. These characteristics can then be determined using a recently developed generalized steepest descent criterion.

Peikari, B.↗

NASA NTRSDate not supplied

Dynamic Spectrum Allocation in Urban Air Transportation System via Deep Reinforcement Learning

The emerging concepts of Urban Air Mobility (UAM) and Advanced Air Mobility (AAM) open a new paradigm for urban air transportation. A big challenge is that these new aerial vehicles will quickly saturate the already crowded aviation spectrum, which is an essential resource to ensure reliable communications for safe operations. In this paper, we consider an air transportation system where multiple aerial vehicles are operated to transport passengers or cargo from different sources to destinations along their pre-defined paths. During the flight, the minimum communication Quality of Service (QoS) requirement must be achieved to ensure flight safety. Our objective is to minimize the average mission completion time by jointly optimizing the velocity selection and spectrum allocation for all aerial vehicles. We formulate the optimization problem as a multi-stage Markov Decision Process (MDP) where the optimization variables are coupled together. A multi-agent Deep Reinforcement Learning (DRL) based solution is proposed where Value Decomposition Networks (VDN) algorithm is utilized to take discrete actions. Additionally, we propose a heuristic greedy algorithm as a baseline solution. Simulation results show that our learning based solution outperforms the heuristic greedy algorithm and another Orthogonal Multiple Access (OMA) solution in minimizing the mission completion time.

Ruixuan Han↗

NASA NTRSDate not supplied

Dynamic Spectrum Allocation in Urban Air Transportation System via Deep Reinforcement Learning

The emerging concepts of Urban Air Mobility (UAM) and Advanced Air Mobility (AAM) open a new paradigm for urban air transportation. A big challenge is that these new aerial vehicles will quickly saturate the already crowded aviation spectrum, which is an essential resource to ensure reliable communications for safe operations. In this paper, we consider an air transportation system where multiple aerial vehicles are operated to transport passengers or cargo from different sources to destinations along their pre-defined paths. During the flight, the minimum communication Quality of Service (QoS) requirement must be achieved to ensure flight safety. Our objective is to minimize the average mission completion time by jointly optimizing the velocity selection and spectrum allocation for all aerial vehicles. We formulate the optimization problem as a multi-stage Markov Decision Process (MDP) where the optimization variables are coupled together. A multi-agent Deep Reinforcement Learning (DRL) based solution is proposed where Value Decomposition Networks (VDN) algorithm is utilized to take discrete actions. Additionally, we propose a heuristic greedy algorithm as a baseline solution. Simulation results show that our learning based solution outperforms the heuristic greedy algorithm and another Orthogonal Multiple Access (OMA) solution in minimizing the mission completion time.

Ruixuan Han↗

NASA NTRS1998

Electrical signal transmission in a bone cell network: the influence of a discrete gap junction

A refined electrical cable model is formulated to investigate the role of a discrete gap junction in the intracellular transmission of electrical signals in an electrically coupled system of osteocytes and osteoblasts in an osteon. The model also examines the influence of the ratio q between the membrane's electrical time constant and the characteristic time of pore fluid pressure, the circular, cylindrical geometry of the osteon, and key simplifying assumptions in our earlier continuous cable model (see Zhang, D., S. C. Cowin, and S. Weinbaum. Electrical signal transmission and gap junction regulation in a bone cell network: A cable model for an osteon. Ann. Biomed. Eng. 25:379-396, 1997). Using this refined model, it is shown that (1) the intracellular potential amplitude at the osteoblastic end of the osteonal cable retains the character of a combination of a low-pass and a high-pass filter as the corner frequency varies in the physiological range; (2) the presence of a discrete gap junction near a resting osteoblast can lead to significant modulation of the intracellular potential and current in the osteoblast for measured values of the gap junction coupling strength; and (3) the circular, cylindrical geometry of the osteon is well simulated by the beam analogy used in Zhang et al.

Non-NASA Center↗

NASA NTRS1994

Embedded parallel processing based ground control systems for small satellite telemetry

The use of networked terminals which utilize embedded processing techniques results in totally integrated, flexible, high speed, reliable, and scalable systems suitable for telemetry and data processing applications such as mission operations centers (MOC). Synergies of these terminals, coupled with the capability of terminal to receive incoming data, allow the viewing of any defined display by any terminal from the start of data acquisition. There is no single point of failure (other than with network input) such as exists with configurations where all input data goes through a single front end processor and then to a serial string of workstations. Missions dedicated to NASA's ozone measurements program utilize the methodologies which are discussed, and result in a multimission configuration of low cost, scalable hardware and software which can be run by one flight operations team with low risk.

Forman, Michael L.↗