Analysis of General Structural Networks by Matrix Methods
Transfer matrix method applied in analysis of complex structural networks
COMPOSITE STRUCTURE↗
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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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Measuring the Energy Consumption and Efficiency of Deep Neural Networks: An Empirical Analysis and Design Recommendations
Addressing the "Red-AI" trend of rising energy consumption by large-scale neural networks, this study investigates the measured energy consumption of training various fully connected neural network architectures. We introduce the BUTTER-E dataset, an augmentation to the BUTTER Empirical Deep Learning dataset, containing energy consumption and performance data from 41,129 individual experimental runs spanning 30,582 distinct configurations: 13 datasets, 20 sizes (trainable parameters), 8 "shapes", and 14 depths on both CPUs and GPUs using node-level watt-meters. This dataset reveals the complex relationship between dataset size, network structure, and energy use. Our analysis uncovers a surprising, hardware-mediated non-linear relationship between energy efficiency and network design, challenging the assumption that reducing the number of parameters or FLOPs is the best way to achieve greater energy efficiency. We propose a straightforward and effective energy model that accounts for network size, computing, and memory hierarchy. Highlighting the need for cache-considerate algorithm development, we suggest a codesign approach to energy efficient network, algorithm, and hardware design. This work contributes to the fields of sustainable computing and Green AI, offering practical guidance for creating more energy-efficient neural networks and promoting sustainable AI.
97 MATHEMATICS AND COMPUTING↗
Enhancing Fault Isolation for Health Monitoring of Electric Aircraft Propulsion by Embedding Failure Mode and Effect Analysis into Bayesian Networks
This paper describes a fault isolation approach for electric powertrains of unmanned aerial vehicles. The approach leverages the combination of failure mode and effect analysis (FMEA) and Bayesian networks, thus introducing depend-ability structures into a diagnostic framework. Faults and failure events from the FMEA are mapped within a Bayesian network, where network edges replicate the links embedded within FMEAs. This framework helps the fault isolation process by identifying the probability of occurrence of specific faults or root causes given evidence observed through sensor signals. The framework is applied to an electric power-train system of a small, rotary-wing unmanned aerial vehicle, demonstrating how a Bayesian network enhanced by FMEA helps disambiguate between root causes of incipient failures, which would otherwise be considered as equally probable.
Fault Isolation↗
Social Network and Content Analysis of the North American Carbon Program as a Scientific Community of Practice
The North American Carbon Program (NACP) was formed to further the scientific understanding of sources, sinks, and stocks of carbon in Earth's environment. Carbon cycle science integrates multidisciplinary research, providing decision-support information for managing climate and carbon-related change across multiple sectors of society. This investigation uses the conceptual framework of com-munities of practice (CoP) to explore the role that the NACP has played in connecting researchers into a carbon cycle knowledge network, and in enabling them to conduct physical science that includes ideas from social science. A CoP describes the communities formed when people consistently engage in shared communication and activities toward a common passion or learning goal. We apply the CoP model by using keyword analysis of abstracts from scientific publications to analyze the research outputs of the NACP in terms of its knowledge domain. We also construct a co-authorship network from the publications of core NACP members, describe the structure and social pathways within the community. Results of the content analysis indicate that the NACP community of practice has substantially expanded its research on human and social impacts on the carbon cycle, contributing to a better understanding of how human and physical processes interact with one another. Results of the co-authorship social network analysis demonstrate that the NACP has formed a tightly connected community with many social pathways through which knowledge may flow, and that it has also expanded its network of institutions involved in carbon cycle research over the past seven years.
Knowledge domain↗
Near-Earth asteroids: Observer alert network and database analysis
The Planetary Science Institute (PSI) was funded by SERCulpr to develop a communication network to alert observers of newly discovered near-earth asteroids (NEA's). This network is intended to encourage observers to obtain physical observations of NEA's, which are needed in order to characterize and assess the resource potential of these bodies. This network was declared operational in October 1990 via an announcement to the asteroid observing community. The PSI is also supported to develop the Near-Earth Asteroid Database (NEAD), a comprehensive database of physical and dynamical data on NEA's. In the past year, the database was updated on newly discovered NEA's during 1990, and new data on radar observations and dynamical classifications were added.
Davis, Donald R.↗
Large-scale compute-intensive analysis via a combined in-situ and co-scheduling workflow approach, In: SC '15 Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis
Large-scale simulations can produce hundreds of terabytes to petabytes of data, complicating and limiting the efficiency of workflows. Traditionally, outputs are stored on the file system and analyzed in post-processing. With the rapidly increasing size and complexity of simulations, this approach faces an uncertain future. Trending techniques consist of performing the analysis in-situ, utilizing the same resources as the simulation, and/or off-loading subsets of the data to a compute-intensive analysis system. We introduce an analysis framework developed for HACC, a cosmological N-body code, that uses both in-situ and co-scheduling approaches for handling petabyte-scale outputs. We compare different analysis set-ups ranging from purely off-line, to purely in-situ to in-situ/co-scheduling. The analysis routines are implemented using the PISTON/VTK-m framework, allowing a single implementation of an algorithm that simultaneously targets a variety of GPU, multi-core, and many-core architectures.
Computer Science↗
Statistical analysis of geodetic networks for detecting regional events
We present an application of hidden Markov models (HMMs) to analysis of geodetic time series in Southern California. Our model fitting method uses a regularized version of the deterministic annealing expectation-maximization algorithm to ensure that model solutions are both robust and of high quality.
geodetic networks↗
Simplified Pore Network Model for Analysis of Flow Capacity and Residual Oil Saturation Distribution Based on Computerized Tomography
The pore network (connected tunnels) can be used not only to describe pore geometry inside a rock but also to calculate various fluid flow properties on a micro scale. Based on a “digital core” extracted from micro computerized tomography (μ-CT), we have developed a simplified pore network model. This study simplifies the pore structure as a lattice of cubes. Pore size, pore distribution, tunnel connectivity, fluid viscosity, interfacial tension, and the external driving pressure gradient are considered for each adjacent cube to evaluate the flow capacity of the pore network. We calculated the permeability, effective permeability, and effective sweep efficiency. The results show that fluid flow initially starts in the largest tunnel, which corresponds to the threshold pressure of the rock sample (fluid flow onset pressure). Permeability changes nonlinearly before all the tunnels are involved in the flow. Changes in the microscopic residual oil saturation and the sweep efficiency were visualized based on the three-dimensional pore distribution. Visualization shows that more oil remains in the formation, the heterogeneity is greater, and the oil viscosity is higher.
58 GEOSCIENCES↗
Emulation Platform for Cyber Analysis of Wireless Communication Network Protocols
Wireless networking and mobile communications is increasing around the world and in all sectors of our lives. With increasing use, the density and complexity of the systems increase with more base stations and advanced protocols to enable higher data throughputs. The security of data transported over wireless networks must also evolve with the advances in technologies enabling more capable wireless networks. However, means for analysis of the effectiveness of security approaches and implementations used on wireless networks are lacking. More specifically a capability to analyze the lower-layer protocols (i.e., Link and Physical layers) is a major challenge. An analysis approach that incorporates protocol implementations without the need for RF emissions is necessary. In this research paper several emulation tools and custom extensions that enable an analysis platform to perform cyber security analysis of lower layer wireless networks is presented. A use case of a published exploit in the 802.11 (i.e., WiFi) protocol family is provided to demonstrate the effectiveness of the described emulation platform.
97 MATHEMATICS AND COMPUTING↗
Enhancing Network Visibility and Security through Tensor Analysis
The increasing size, variety, rate of growth and change, and complexity of network data has warranted advanced network analysis and services. Tools that provide automated analysis through traditional or advanced signature-based systems or machine learning classifiers suffer from practical difficulties. These tools fail to provide comprehensive and contextual insights into the network when put to practical use in operational cyber security. In this paper, we present an effective tool for network security and traffic analysis that uses high-performance data analytics based on a class of unsupervised learning algorithms called tensor decompositions. The tool aims to provide a scalable analysis of the network traffic data and also reduce the cognitive load of network analysts and be network-expert-friendly by presenting clear and actionable insights into the network. With this being said, In this paper, we demonstrate the successful use of the tool in two completely diverse operational cyber security environments, namely, (1) security operations center (SOC) for the SCinet network at the SuperComputing (SC) Conference in 2016 and 2017 and (2) Reservoir Labs’ Local Area Network (LAN). In each of these environments, we produce actionable results for cyber security specialists including (but not limited to) (1) finding malicious network traffic involving internal and external attackers using port scans, SSH brute forcing, and NTP amplification attacks, (2) uncovering obfuscated network threats such as data exfiltration using DNS port and using ICMP traffic, and (3) finding network misconfiguration and performance degradation patterns.
97 MATHEMATICS AND COMPUTING↗
C-band radar network data evaluation and analysis
C-band radar network calibration, using GEOS 2 satellite worldwide tracking data
Brooks, R. L.↗
Topology design and performance analysis of an integrated communication network
A research study on the topology design and performance analysis for the Space Station Information System (SSIS) network is conducted. It is begun with a survey of existing research efforts in network topology design. Then a new approach for topology design is presented. It uses an efficient algorithm to generate candidate network designs (consisting of subsets of the set of all network components) in increasing order of their total costs, and checks each design to see if it forms an acceptable network. This technique gives the true cost-optimal network, and is particularly useful when the network has many constraints and not too many components. The algorithm for generating subsets is described in detail, and various aspects of the overall design procedure are discussed. Two more efficient versions of this algorithm (applicable in specific situations) are also given. Next, two important aspects of network performance analysis: network reliability and message delays are discussed. A new model is introduced to study the reliability of a network with dependent failures. For message delays, a collection of formulas from existing research results is given to compute or estimate the delays of messages in a communication network without making the independence assumption. The design algorithm coded in PASCAL is included as an appendix.
Li, V. O. K.↗
Throughput Measurements and Profile Analysis of Cloud Networks
Cloud networks utilize virtual connections to connect virtual machines distributed across cloud sites. They are increasingly deployed due to flexible provisioning using software and cost-effectiveness in not requiring to build physical network infrastructure. However, their extensive virtualization makes it unclear how well the established practices of conventional networks translate to them. Here, we study throughput measurements over a Google Cloud network using a matching hardware emulated conventional network, which provide production and exploratory conditions, respectively. The measurements span connections representing local, cross-continental and around the Earth distances. We study the effects of parallel flows, congestion control algorithms and retransmissions on the network throughput profile expressed as a function of RTT. We compare the throughput profile of Google Cloud network with those of emulated network under various loss conditions, including those too disruptive or expensive in the former. Our analysis based on the concave-convex shape and utilization-concavity coefficients of throughput profiles indicates an overall agreement of performance between the two networks, thereby justifying the use of conventional network emulations to analyze cloud networks. In terms of practical use, our study establishes that BBR and BBRv2 alpha TCP achieve higher throughput compared to loss-based congestion control algorithms under most network configurations, especially, under losses at large RTT.
Phanekham, Derek [Southern Methodist Univ., Dallas↗
Modeling MTS pyrolysis and SiC deposition kinetics using principal component analysis and neural networks
Accurate chemical kinetics modeling is crucial for improving the efficiency of chemical processing and synthesis of ceramic matrix composites. Detailed kinetic models are computationally expensive due to the large number of transported chemical species, while the simplified physics-based models, such as single-step global mechanisms, are efficient but often overlook key chemical intermediates and pathways. Recent deep learning approaches promise accurate and cost-effective models. Yet, they require additional closures for the transported nonlinear latent variables, complicating integration with existing solvers. In this work, we develop a hybrid linear—nonlinear reduced model for silicon carbide deposition from methyltrichlorosilane precursor by combining principal component analysis (PCA) and autoencoder (AE) neural network (NN) approaches. PCA is used to identify a smaller set of linear transport variables, enabling direct reuse of conventional transport solvers. NNs then reconstruct the full chemical state from these reduced variables. We demonstrate the method on a chemical vapor deposition reactor—comprising a gas-phase pyrolysis plug flow reactor and a heterogeneous surface reactor—over a wide range of temperatures, pressures, and residence times. Our PCA–AE model achieves high accuracy with only five transported scalars, achieving an eightfold cost reduction compared to detailed mechanisms, in both a priori (using data from the test set only) and a posteriori (coupled with a differential equation solver). In conclusion, notable errors arise primarily near training domain boundaries and for long residence times, indicating the need for domain shift indicators and better long-horizon predictions in future reduced chemistry model development.
autoencoder neural networks↗
On the Probabilistic Analysis of Neural Networks
Neural networks are powerful tools for automated decision-making,seeing increased application in safety-critical domains, such as autonomous driving. Due to their black-box nature and large scale,reasoning about their behavior is challenging. Statistical analysis is often used to infer probabilistic properties of a network, such as its robustness to noise and inaccurate inputs. While scalable, statistical methods can only provide probabilistic guarantees on the quality of their results and may underestimate the impact of low probability inputs leading to undesired behavior of the network.We investigate here the use of symbolic analysis and constraint solution space quantification to precisely quantify probabilistic properties in neural networks. We demonstrate the potential of the proposed technique in a case study involving the analysis of ACAS-Xu, a collision avoidance system for unmanned aircraft control.
neural nets↗
Design and Analysis of Convolutional Neural Network for RF Signal Modulation Classification for In-Orbit Deployment
To effectively transmit data to and from satellites requires a complex and robust RF communication system. Commonly, several different types of signal modulations may be required to maximize satellite efficiency depending on a variety of unexpected channel impairments. We propose a neural network algorithm capable of learning these RF signal modulations using a supervised learning technique designed for low power, high-efficiency in-orbit deployment. The work presented demonstrates a convolutional neural network (CNN) capable of learning and recognizing a set of modulation schemes commonly used to transmit RF information. We are capable of recognizing the modulation scheme from the I and Q data channels directly, with no preprocessing or data conversion required other than breaking the incoming signal into a set of uniform normalized samples. We perform a network design and size analysis, showing that reasonably high accuracy can be obtained using networks with a relatively low number of trainable parameters. Given that a user of a system such as this may wish to receive a signal using a modulation scheme that the network has not previously learned, we demonstrate that transfer learning can learn new modulation schemes by retraining only the fully connected layers in the CNN. Thus, this type of network would excel in outer space deployment using high-efficiency transfer learning hardware. Modulation recognition can be performed through rapid feedforward computation, and the CNN training process is significantly simplified when learning new modulations is required.
CNN↗
THE TEMPERATURE EFFECT IN SECONDARY COSMIC RAYS (MUONS) OBSERVED AT THE GROUND: ANALYSIS OF THE GLOBAL MUON DETECTOR NETWORK DATA
The analysis of cosmic ray intensity variation seen by muon detectors at Earth's surface can help us to understand astrophysical, solar, interplanetary and geomagnetic phenomena. However, before comparing cosmic ray intensity variations with extraterrestrial phenomena, it is necessary to take into account atmospheric effects such as the temperature effect. In this work, we analyzed this effect on the Global Muon Detector Network (GMDN), which is composed of four ground-based detectors, two in the northern hemisphere and two in the southern hemisphere. In general, we found a higher temperature influence on detectors located in the northern hemisphere. Besides that, we noticed that the seasonal temperature variation observed at the ground and at the altitude of maximum muon production are in antiphase for all GMDN locations (low-latitude regions). In this way, contrary to what is expected in high-latitude regions, the ground muon intensity decrease occurring during summertime would be related to both parts of the temperature effect (the negative and the positive). We analyzed several methods to describe the temperature effect on cosmic ray intensity. We found that the mass weighted method is the one that best reproduces the seasonal cosmic ray variation observed by the GMDN detectors and allows the highest correlation with long-term variation of the cosmic ray intensity seen by neutron monitors.
79 ASTRONOMY AND ASTROPHYSICS↗
Measuring Aqueous Humor Glucose Across Physiological Levels: NIR Raman Spectroscopy, Multivariate Analysis, Artificial Neural Networks, and Bayesian Probabilities
We have elicited a reliable Raman spectral signature for glucose in rabbit aqueous humor across mammalian physiological ranges in a rabbit model stressed by recent myocardial infarction.
NIR↗