Distributed ground systems for a large multi-instrument space mission: lessons learned from the Cassini Huygens Program
Explore the source record for details and available documents.
Cassini↗
SEARCH · Search NASA
Results for “distributed learning”
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Distributed operations as applied in a large multi-instrument space mission: lessons learned from the Cassini-Huygens Program
Launched in 1997, the Cassini-Huygens Mission sent the largest interplanetary spacecraft ever built in the service of science. Carrying a suite of 12 scientific instruments and an atmospheric entry probe, this complex spacecraft to explore the Saturn system may not have gotten off the ground without undergoing significant design changes and cost reductions.
distributed↗
Decision-making and problem-solving methods in automation technology
The state of the art in the automation of decision making and problem solving is reviewed. The information upon which the report is based was derived from literature searches, visits to university and government laboratories performing basic research in the area, and a 1980 Langley Research Center sponsored conferences on the subject. It is the contention of the authors that the technology in this area is being generated by research primarily in the three disciplines of Artificial Intelligence, Control Theory, and Operations Research. Under the assumption that the state of the art in decision making and problem solving is reflected in the problems being solved, specific problems and methods of their solution are often discussed to elucidate particular aspects of the subject. Synopses of the following major topic areas comprise most of the report: (1) detection and recognition; (2) planning; and scheduling; (3) learning; (4) theorem proving; (5) distributed systems; (6) knowledge bases; (7) search; (8) heuristics; and (9) evolutionary programming.
Hankins, W. W.↗
On the dependence of solar flare X-ray spectral line intensity ratios of highly ionized sulfur, calcium, and iron on electron temperature, differential emission measure, and atomic physics
This paper focuses on what can be learned about the emission measure distribution and certain atomic physics parameters from spectral lines of highly ionized ions of sulfur, calcium, and iron that appear in solar flare spectra. The particular lines chosen for analysis allow the electron temperature to be determined independently of the assumption of ionization equilibrium. An attempt is made to find emission measure models based on selected functional dependences of emission measure on temperature that reproduce the observed temperatures deduced from spectral line ratios as well as the relative intensities of resonance lines of different elements.
Doschek, G. A.↗
Lecture on Thermal Radiation
This lecture will cover solar thermal radiation, particularly as it relates to the high energy solar processes that are the subject of this summer school. After a general review of thermal radiation from the Sun and a discussion of basic definitions, the various emission and absorption mechanisms will be described including black-body emission, bremsstrahlung, free-bound, and atomic line emissions of all kinds. The bulk of the time will be spent discussing the observational characteristics of thermal flare plasma and what can be learned about the flare energy release process from observations of the thermal radiation at all wavelengths. Information that has been learned about the morphology, temperature distribution, and composition of the flare plasma will be presented. The energetics of the thermal flare plasma will be discussed in relation to the nonthermal energy of the particles accelerated during the flare. This includes the total energy, the radiated and conductive cooling processes, and the total irradiated energy.
Dennis, Brian R.↗
DCS-Neural-Network Program for Aircraft Control and Testing
A computer program implements a dynamic-cell-structure (DCS) artificial neural network that can perform such tasks as learning selected aerodynamic characteristics of an airplane from wind-tunnel test data and computing real-time stability and control derivatives of the airplane for use in feedback linearized control. A DCS neural network is one of several types of neural networks that can incorporate additional nodes in order to rapidly learn increasingly complex relationships between inputs and outputs. In the DCS neural network implemented by the present program, the insertion of nodes is based on accumulated error. A competitive Hebbian learning rule (a supervised-learning rule in which connection weights are adjusted to minimize differences between actual and desired outputs for training examples) is used. A Kohonen-style learning rule (derived from a relatively simple training algorithm, implements a Delaunay triangulation layout of neurons) is used to adjust node positions during training. Neighborhood topology determines which nodes are used to estimate new values. The network learns, starting with two nodes, and adds new nodes sequentially in locations chosen to maximize reductions in global error. At any given time during learning, the error becomes homogeneously distributed over all nodes.
Jorgensen, Charles C.↗
Multiple-Instance Regression with Structured Data
We present a multiple-instance regression algorithm that models internal bag structure to identify the items most relevant to the bag labels. Multiple-instance regression (MIR) operates on a set of bags with real-valued labels, each containing a set of unlabeled items, in which the relevance of each item to its bag label is unknown. The goal is to predict the labels of new bags from their contents. Unlike previous MIR methods, MI-ClusterRegress can operate on bags that are structured in that they contain items drawn from a number of distinct (but unknown) distributions. MI-ClusterRegress simultaneously learns a model of the bag's internal structure, the relevance of each item, and a regression model that accurately predicts labels for new bags. We evaluated this approach on the challenging MIR problem of crop yield prediction from remote sensing data. MI-ClusterRegress provided predictions that were more accurate than those obtained with non-multiple-instance approaches or MIR methods that do not model the bag structure.
learning↗
A Census of Young Stellar Objects in Two Line-of-Sight Star-Forming Regions Toward IRAS 22147+5948 in the Outer Galaxy
Context. Star formation in the outer Galaxy, namely, outside of the Solar circle, has not been extensively studied in part due to the low CO brightness of the molecular clouds linked with the negative metallicity gradient. Recent infrared surveys provide an overview of dust emission in large sections of the Galaxy, but they suffer from cloud confusion and poor spatial resolution at far-infrared wavelengths. Aims. We aim to develop a methodology to identify and classify young stellar objects (YSOs) in star-forming regions in the outer Galaxy and use it to resolve a long-standing disparity in terms of the distance and evolutionary status of IRAS 22147+5948. Methods. We used a support vector machine learning algorithm to complement standard color–color and color–magnitude diagrams in our search for YSOs in the IRAS 22147 region, based on publicly available data from the Spitzer Mapping of the Outer Galaxy survey. The agglomerative hierarchical clustering algorithm was used to identify clusters. Then the physical properties of individual YSOs were calculated. The distances were determined using CO 1–0 from the Five College Radio Astronomy Observatory survey. Results. We identified 13 Class I and 13 Class II YSO candidates using the color–color diagrams, along with an additional 2 and 21 sources, respectively, using the applied machine learning techniques. The spectral energy distributions of 23 sources were modeled with a star and a passive disk, corresponding to Class II objects. The models of three sources include envelopes that are typical for Class I objects. The objects were grouped into two clusters located at a distance of 2:2 kpc and 5 clusters at 5:6 kpc. The spatial extent of CO, radio continuum, and dust emission confirms the origin of YSOs in two distinct star-forming regions along a similar line of sight. Conclusions. The outer Galaxy may serve as a unique laboratory for exploring star formation across environments, on the condition that complementary methods and ancillary data are used to properly account for cloud confusion and distance uncertainties.
Agata Karska↗
Wildfire Segmentation From Remotely Sensed Data Using Quantum-Compatible Conditional Vector Quantized-Variational Autoencoders
Wildfires represent a critical environmental hazard with multifaceted implications for ecosystems, communities, and public health [1]. The escalating frequency and intensity of wildfires globally have intensified the urgency for robust segmentation methodologies to facilitate effective mitigation, response, and recovery strategies [2]. Accurate wildfire segmentation is pivotal for delineating fire boundaries, assessing progression patterns, and prioritizing resource allocation during emergency scenarios. Furthermore, precise segmentation enables stakeholders, including policymakers, environmental scientists, and emergency responders, to formulate evidence-based strategies, thereby minimizing socio-economic disruptions and ecological degradation. Consequently, advancing wildfire segmentation techniques through innovative technological interventions remains a paramount research imperative. Although foundational in wildfire segmentation, traditional deterministic models exhibit inherent limitations that compromise their efficacy in dynamic and uncertain environments. These models often operate on rigid algorithms prioritizing deterministic classifications, thereby overlooking the inherent complexities and uncertainties associated with wildfire behavior and satellite data variability. Such deterministic frameworks tend to produce oversimplified representations that fail to capture the intricate nuances of evolving fire dynamics, spatial heterogeneity, and environmental interactions [1]. Consequently, the deterministic approach’s propensity for uncertainty collapsing [1, 3] hampers the accuracy, reliability, and applicability of segmentation outcomes in real-world scenarios. Contrastingly, stochastic models offer a more nuanced and adaptable framework for wildfire segmentation. By integrating probabilistic elements into the modeling paradigm, stochastic approaches, particularly probabilistic approaches such as variational auto encoders (VAEs) [4], facilitate comprehensive uncertainty assessment, enabling researchers to quantify and incorporate uncertainties into segmentation outcomes effectively. This probabilistic nature empowers stochastic models to encapsulate variability, account for data inconsistencies, and adapt to evolving environmental conditions, enhancing segmentation accuracy, reliability, and robustness. Embracing stochastic methodologies thus catalyzes advancements in wildfire science by fostering a more holistic, adaptive, and resilient segmentation framework. Despite VAEs demonstrating significant promise in various applications, they come with inherent limitations that have garnered attention within the machine learning community. One of the primary drawbacks lies in their reliance on static priors, which essentially assume a fixed distribution for latent variables, thereby limiting the model’s flexibility to capture complex data structures effectively [5]. This static nature leads to suboptimal representations, especially when dealing with complex and high-dimensional data. Additionally, VAEs often struggle with generating sharp and realistic samples, a phenomenon commonly referred to as mode collapse [5, 7, 6]. Furthermore, the optimization process in VAEs, which involves balancing the reconstruction loss and the regularization term, can sometimes be challenging to fine-tune [7]. In recent efforts to address these shortcomings, alternative approaches like Vector Quantized Variational Auto encoders(VQ-VAEs) [7], address the challenges by incorporating discrete latent variables and leveraging techniques that enhance the quality and diversity of generated samples while maintaining efficient training dynamics. VQ-VAEs propose a dynamic prior distribution generation mechanism that diverges from the static priors commonly associated with traditional VAEs. This dynamic approach allows for more adaptive and context-aware latent variable representations, thereby potentially capturing complex data structures more effectively. Unlike autoregressive prior models such as PixelCNN, which, despite their ability to model dependencies across data dimensions, suffer from significant computational inefficiencies and lack flexibility in handling diverse datasets. In our work, we propose to use a generative quantum-compatible approach to help alleviate the shortcomings of autoregressive prior model in VQ-VAEs. Restricted Boltzmann Machines (RBMs) are a viable alternative prior model that can learn prior distributions in a faster and more flexible manner. In this research endeavor, we meticulously curate a state-of-the-art dataset leveraging satellite MODIS data in conjunction with VIIRS fire masks, derived from Fire Radiative Power (FRP), thereby encapsulating diverse wildfire scenarios and environmental contexts. We developed a conditional VQ-VAE architecture with the RBM prior model that is trained in a supervised manner for segmenting wildfire masks. This innovative approach synergistically harnesses deep learning capabilities, enabling the generation of segmentation maps characterized by heightened precision, granularity, and contextual relevance. Furthermore, replacing the autoregressive prior learning method proposed by the original VQ-VAE with a prior density approximation via quantum-compatible RBM facilitates expedited inference processes, augments flexibility in prior sampling, optimizes computational efficiency and establishes a groundbreaking benchmark in wildfire segmentation methodologies.
quantum machine learning↗
Joint Spectrum Access and Power Control in Air-Air Communications - A Deep Reinforcement Learning Based Approach
This paper considers the dynamic spectrum access and power control problem in a single-hop point-to-point Air-Air Communication Network (AACN). Due to spectrum scarcity, we assume the number of Aircraft-to-Aircraft (A2A) communication links is greater than that of the available channels, such that some communication links need to share the same channel, causing co-channel interference. We formulate the joint channel selection and power control optimization problem to maximize the Weighted Sum Spectral Efficiency (WSSE). A distributed and dynamic deep Q learning-based algorithm is proposed to find the optimal solution. Specifically, we design two different policies that are trained by conducting a trial-and-error scheme. Each communication link can achieve the optimal policy by exploiting the local information from its neighbors, and this distributive approach make it scalable to large networks. Finally, our experimental results demonstrate the effectiveness of the proposed solution in various AACN scenarios.
Zhe Wang↗
Joint Spectrum Access and Power Control in Air-Air Communications - A Deep Reinforcement Learning Based Approach
This paper considers the dynamic spectrum access and power control problem in a single-hop point-to-point Air-Air Communication Network (AACN). Due to spectrum scarcity, we assume the number of Aircraft-to-Aircraft (A2A) communication links is greater than that of the available channels, such that some communication links need to share the same channel, causing co-channel interference. We formulate the joint channel selection and power control optimization problem to maximize the Weighted Sum Spectral Efficiency (WSSE). A distributed and dynamic deep Q learning-based algorithm is proposed to find the optimal solution. Specifically, we design two different policies that are trained by conducting a trial-and-error scheme. Each communication link can achieve the optimal policy by exploiting the local information from its neighbors, and this distributive approach make it scalable to large networks. Finally, our experimental results demonstrate the effectiveness of the proposed solution in various AACN scenarios.
Zhe Wang↗
Automated Decomposition of Model-based Learning Problems
A new generation of sensor rich, massively distributed autonomous systems is being developed that has the potential for unprecedented performance, such as smart buildings, reconfigurable factories, adaptive traffic systems and remote earth ecosystem monitoring. To achieve high performance these massive systems will need to accurately model themselves and their environment from sensor information. Accomplishing this on a grand scale requires automating the art of large-scale modeling. This paper presents a formalization of [\em decompositional model-based learning (DML)], a method developed by observing a modeler's expertise at decomposing large scale model estimation tasks. The method exploits a striking analogy between learning and consistency-based diagnosis. Moriarty, an implementation of DML, has been applied to thermal modeling of a smart building, demonstrating a significant improvement in learning rate.
Williams, Brian C.↗
Support for joint infrared and Copernicus X-Ray observations of Cygnus X-3
Simultaneous X-ray and infrared measurements were carried out of the flares from Cygnus X-3 from the Copernicus spacecraft observatory. The detectors, InSb, were arranged so that 1.65 and 2.2 micrometer broadbend photometry was performed through a common diaphragm. The measurements were used to determine the energy distribution during a flare and thus learn about the infrared spectrum and its changes during the flare.
Source record↗
Origin and propagation of galactic cosmic rays
The study of systematic trends in elemental abundances is important for unfolding the nuclear and/or atomic effects that should govern the shaping of source abundances and in constraining the parameters of cosmic ray acceleration models. In principle, much can be learned about the large-scale distributions of cosmic rays in the galaxy from all-sky gamma ray surveys such as COS-B and SAS-2. Because of the uncertainties in the matter distribution which come from the inability to measure the abundance of molecular hydrogen, the results are somewhat controversial. The leaky-box model accounts for a surprising amount of the data on heavy nuclei. However, a growing body of data indicates that the simple picture may have to be abandoned in favor of more complex models which contain additional parameters. Future experiments on the Spacelab and space station will hopefully be made of the spectra of individual nuclei at high energy. Antiprotons must be studied in the background free environment above the atmosphere with much higher reliability and presion to obtain spectral information.
Cesarsky, Catherine J.↗
Availability of hydrogen for lunar base activities
Hydrogen will be needed on a lunar base to make water for consumables, to provide fuel, and to serve as a reducing agent in the extraction of oxygen from lunar minerals. This study was undertaken in order to learn more about the abundance and distribution of solar-wind-implanted hydrogen. Hydrogen was found in all samples studied, with concentrations, varying widely depending on soil maturity, grain size, and mineral composition. Seven cores returned from the Moon were studied. Although hydrogen was implanted in the upper surface layer of the regolith, it was found throughout the cores due to micrometeorite reworking of the soil.
Bustin, Roberta↗
A data distribution strategy for the 1990s (files are not enough)
Virtually all of the data distribution strategies being contemplated for the EOSDIS era revolve around the use of files. Most, if not all, mass storage technologies are based around the file model. However, files may be the wrong primary abstraction for supporting scientific users in the 1990s and beyond. Other abstractions more closely matching the respective scientific discipline of the end user may be more appropriate. JPL has built a unique multimission data distribution system based on a strategy of telemetry stream emulation to match the responsibilities of spacecraft team and ground data system operators supporting our nations suite of planetary probes. The current system, operational since 1989 and the launch of the Magellan spacecraft, is supporting over 200 users at 15 remote sites. This stream-oriented data distribution model can provide important lessons learned to builders of future data systems.
Tankenson, Mike↗