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At least 1,045 records · Page 58
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Lunar accelerometer network gravitational observatory (LANGO)

With ground-based interferometers detecting hundreds of gravitational-wave (GW) events, GW astronomy has continued to blossom. U.S. and European scientists are developing plans to construct third-generation ground-based interferometers. ESA has proceeded through the mission formulation phase of space-based interferometer in a lower-frequency band, 10 –4 –0.1 Hz. Despite all these exciting developments, there is still a missing frequency band, 0.1–10 Hz. This mid-frequency band is rich with interesting astrophysical events. Coalescence and merger of intermediate-mass black holes (IMBHs) will occur in this frequency band. Coalescing stellar-mass BHs will pass through this frequency band days before they reach the frequency band of LIGO and Virgo. Detection of such signals would enable a mid-frequency detector to issue an advance notice to the high-frequency GW detectors, as well as to optical, x-ray and γ-ray telescopes. We propose Lunar Accelerometer Network Gravitational Observatory (LANGO) to detect GWs in this frequency band. In the first phase (LANGO 1), we propose to deploy four ambient-temperature (250 K) accelerometers in the tetrahedral or in a square configuration on the hemisphere facing the Earth. After successful operation at 250 K, LANGO would be upgraded to a cryogenic (4 K) version with over two orders of magnitude increased sensitivity (LANGO 2), with coherent rejection of seismic noise implemented. LANGO is a full-tensor detector, capable of determining the source direction and wave polarization. Each test mass (TM) is suspended as a pendulum with resonance frequency ∼ 0.01 Hz, thus is only weakly coupled to the lunar surface horizontally. LANGO is designed to detect the relative motion of globally separated, nearly free, TMs by using the Moon as a large quiet platform. LANGO 1 and 2 accelerometers aim at sensitivities ⩽ 10 –11 m s –2 Hz –1/2 and ⩽ 10 –13 m s –2 Hz –1/2 in the horizontal axes over the frequency band of 1 mHz–10 Hz, which yield GW sensitivities 1.1 x 10 -21 Hz -1/2 and 3.8 x 10 -24 Hz -1/2 at 1 Hz, respectively. The LANGO accelerometers will be 10 3 –10 5 times more sensitive than Apollo seismometers. With such sensitivity, LANGO will also make great contribution to the advancement of lunar geophysics.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Structural Health Monitoring of Microreactor Safety Systems Using Convolutional Neural Networks

Microreactors, a class of modular reactors with net power output of less than 20 MWth, have innovative applications in nuclear and nonnuclear industries due to their portability, reliability, resilience, and high capacity factors. In order to operate microreactors on a wider scale, it is essential to bring down maintenance life-cycle costs while ensuring the integrity of operating such systems. Autonomous operations in microreactors using augmented digital-twin (DT) technology can serve as a cost-effective solution by increasing awareness about the system’s health. Structural health monitoring (SHM) is a key component of nuclear DT frameworks. Artificial neural networks can be beneficial to detect degradation in the nuclear safety systems, such as piping equipment systems, by monitoring the sensor data obtained from the plant and its corresponding structures, systems and components. In this report, an SHM methodology is presented which uses convolutional neural networks to determine degraded locations and their corresponding degradation-severity levels at various locations of nuclear piping equipment systems. A simple pipe system, subjected to seismic loads, is selected to design the post-hazard SHM framework. The effectiveness of the proposed SHM methodology is demonstrated by obtaining high accuracy in detecting degraded locations as well as the severity levels.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2016

Stability-driven nonnegative matrix factorization to interpret spatial gene expression and build local gene networks

Spatial gene expression patterns enable the detection of local covariability and are extremely useful for identifying local gene interactions during normal development. The abundance of spatial expression data in recent years has led to the modeling and analysis of regulatory networks. The inherent complexity of such data makes it a challenge to extract biological information. We developed staNMF, a method that combines a scalable implementation of nonnegative matrix factorization (NMF) with a new stability-driven model selection criterion. When applied to a set of Drosophila early embryonic spatial gene expression images, one of the largest datasets of its kind, staNMF identified 21 principal patterns (PP). Providing a compact yet biologically interpretable representation of Drosophila expression patterns, PP are comparable to a fate map generated experimentally by laser ablation and show exceptional promise as a data-driven alternative to manual annotations. Our analysis mapped genes to cell-fate programs and assigned putative biological roles to uncharacterized genes. Finally, we used the PP to generate local transcription factor regulatory networks. Spatially local correlation networks were constructed for six PP that span along the embryonic anterior-posterior axis. Using a two-tail 5% cutoff on correlation, we reproduced 10 of the 11 links in the well-studied gap gene network. In conclusion, the performance of PP with the Drosophila data suggests that staNMF provides informative decompositions and constitutes a useful computational lens through which to extract biological insight from complex and often noisy gene expression data.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

CSK hopping pattern model for visible light communication networks

This paper proposes a new channel access method for visible light communications. The transmitter uses the CSK hopping pattern mode to load data to the R, G, and B light-emitting diodes of LEDs and establishes the network access point (AP) of the LEDs for the illumination coverage area. The coordinator controls the work of each AP to ensure that it simultaneously uses different color patterns to distinguish its respective networks. When the receiver (mobile terminal) detects a particular color pattern in the illumination coverage area, it establishes a communication access network process with the AP that is transmitting the particular color pattern. The advantage of this method is that interference between adjacent APs can be avoided. In addition, due to the simplification of the access network, the proposed CSK hopping model can shorten delays in the terminal’s access network. Through modeling and simulation, it is found that this method can effectively support network power and reduce network delay. Moreover, the fairness and stability of terminals that access the network are improved to a certain extent.

42 ENGINEERING↗
NASA NTRS1985

Limits to the burster repetition rate as deduced from the 2nd catalog of the interplanetary network

The burster repetition rate is an important parameter in many gamma ray burst models. The localizations of the interplanetary network, which have a relatively small combined surface area, may be used to estimate the average repetition rate. The method consists of (1) estimating the number of random overlaps between error boxes expected in the catalog and comparing this number to that actually observed; (2) modeling the response of the detectors in the network, so that the probability of detecting a burst can be estimated; and (3) simulating the arrival of bursts at the network assuming that burster repetition is governed by a Poisson process. The application of this method for many different burster luminosity functions shows that (1) the lower limit to the burster repetition rate depends strongly upon the assumed luminosity function; (2) the best lower limit to the repetition period obtainable from the data of the network is about 100 months; and (3) that a luminosity function for all bursters similar to that of the 1979 Mar 5 burster is inconsistent with the data.

Atteia, J. L.↗
DOE OSTI2016

Universal Collaboration Strategies for Signal Detection: A Sparse Learning Approach

Here, this paper considers the problem of high-dimensional signal detection in a large distributed network whose nodes can collaborate with their one-hop neighboring nodes (spatial collaboration). We assume that only a small subset of nodes communicate with the fusion center (FC). We design optimal collaboration strategies which are universal for a class of deterministic signals. By establishing the equivalence between the collaboration strategy design problem and sparse principal component analysis (PCA), we solve the problem efficiently and evaluate the impact of collaboration on detection performance.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Ensemble models for circuit topology estimation, fault detection and classification in distribution systems

This paper presents a methodology for simultaneous fault detection, classification, and topology estimation for adaptive protection of distribution systems. The methodology estimates the probability of the occurrence of each one of these events by using a hybrid structure that combines three sub-systems, a convolutional neural network for topology estimation, a fault detection based on predictive residual analysis, and a standard support vector machine with probabilistic output for fault classification. The input to all these sub-systems is the local voltage and current measurements. A convolutional neural network uses these local measurements in the form of sequential data to extract features and estimate the topology conditions. The fault detector is constructed with a Bayesian stage (a multitask Gaussian process) that computes a predictive distribution (assumed to be Gaussian) of the residuals using the input. Since the distribution is known, these residuals can be transformed into a Standard distribution, whose values are then introduced into a one-class support vector machine. The structure allows using a one-class support vector machine without parameter cross-validation, so the fault detector is fully unsupervised. Finally, a support vector machine uses the input to perform the classification of the fault types. All three sub-systems can work in a parallel setup for both performance and computation efficiency. In conclusion, we test all three sub-systems included in the structure on a modified IEEE123 bus system, and we compare and evaluate the results with standard approaches.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2016

Clustering VoIP caller for SPIT identification

Abstract The number of unsolicited and advertisement telephony calls over traditional and Internet telephony has rapidly increased over recent few years. Every year, the telecommunication regulators, law enforcement agencies and telecommunication operators receive a very large number of complaints against these unsolicited, unwanted calls. These unwanted calls not only bring financial loss to the users of the telephony but also annoy them with unwanted ringing alerts. Therefore, it is important for the operators to block telephony spammers at the edge of the network so to gain trust of their customers. In this paper, we propose a novel spam detection system by incorporating different social network features for combating unwanted callers at the edge of the network. To this extent the reputation of each caller is computed by processing call detailed records of user using three social network features that are the frequency of the calls between caller and the callee, the duration between caller and the callee and the number of outgoing partners associated with the caller. Once the reputation of the caller is computed, the caller is then places in a spam and non‐spam clusters using unsupervised machine learning. The performance of the proposed approach is evaluated using a synthetic dataset generated by simulating the social behaviour of the spammers and the non‐spammers. The evaluation results reveal that the proposed approach is highly effective in blocking spammer with 2% false positive rate under a large number of spammers. Moreover, the proposed approach does not require any change in the underlying VoIP network architecture, and also does not introduce any additional signalling delay in a call set‐up phase. Copyright © 2016 John Wiley & Sons, Ltd.

Azad, Muhammad Ajmal↗
DOE OSTI2013

Streaming Solutions for Fine-Grained Network Traffic Measurements and Analysis

Online network traffic measurements and analysis is critical for detecting and preventing any real-time anomalies in the network. We propose, implement, and evaluate an online, adaptive measurement platform, which utilizes real-time traffic analysis results to refine subsequent traffic measurements. Central to our solution is the concept of Multi-Resolution Tiling (MRT), a heuristic approach that performs sequential analysis of traffic data to zoom into traffic subregions of interest. However, MRT is sensitive to transient traffic spikes. In this paper, we propose three novel traffic streaming algorithms that overcome the limitations of MRT and can cater to varying degrees of computational and storage budgets, detection latency, and accuracy of query response. We evaluate our streaming algorithms on a highly parallel and programmable hardware as well as a traditional software-based platforms. The algorithms demonstrate significant accuracy improvement over MRT in detecting anomalies consisting of synthetic hard-to-track elephant flows and global icebergs. Our proposed algorithms maintain the worst-case complexities of the MRT while incurring only a moderate increase in average resource utilization.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Detecting Quantum Critical Points of Correlated Systems by Quantum Convolutional Neural Network Using Data from Variational Quantum Eigensolver

Machine learning has been applied to a wide variety of models, from classical statistical mechanics to quantum strongly correlated systems, for classifying phase transitions. The recently proposed quantum convolutional neural network (QCNN) provides a new framework for using quantum circuits instead of classical neural networks as the backbone of classification methods. We present the results from training the QCNN by the wavefunctions of the variational quantum eigensolver for the one-dimensional transverse field Ising model (TFIM). We demonstrate that the QCNN identifies wavefunctions corresponding to the paramagnetic and ferromagnetic phases of the TFIM with reasonable accuracy. The QCNN can be trained to predict the corresponding ‘phase’ of wavefunctions around the putative quantum critical point even though it is trained by wavefunctions far away. The paper provides a basis for exploiting the QCNN to identify the quantum critical point.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2014

Forensic Signature Detection of Yersinia Pestis Culturing Practices Across Institutions Using a Bayesian Network

The field of bioforensics is focused on the analysis of evidence from a biocrime. Existing laboratory analyses can identify the specific strain of an organism in the evidence, as well signatures of the specific culture batch of organisms, such as low-frequency contaminants or indicators of growth and processing methods. To link these disparate types of physical data to potential suspects, investigators may need to identify institutions or individuals whose access to strains and culturing practices match those identified from the evidence. In this work we present a Bayesian statistical network to fuse different types of analytical measurements that predict the production environment of a Yersinia pestis sample under investigation with automated test processing of scientific publications to identify institutions with a history of growing Y. pestis under similar conditions. Furthermore, the textual and experimental signatures were evaluated recursively to determine the overall sensitivity of the network across all levels of false positives. We illustrate that institutions associated with several specific culturing practices can be accurately selected based on the experimental signature from only a few analytical measurements. These findings demonstrate that similar Bayesian networks can be generated generically for many organisms of interest and their deployment is not prohibitive due to either computational or experimental factors.

Bayesian networks↗
NASA NTRS1984

Structure function analyses of mesoscale VAS retrievals

Structure function analyses are used for a comparison of two VAS retrieval procedures, one obtained using a physical inversion technique and the other from a linear regression technique. Results from both procedures are evaluated and compared with those from a special mesoscale network of radiosonde stations, operated during March 6, 7, 1982 as part of the AVE-VAS field experiment. It is shown that both types of VAS retrievals underestimate the magnitudes of mean nondirectional gradients that are detected by the mesoscale radiosonde network. This result is expected since the VAS provides a volumetric sample of the atmosphere.

Meyer, P. J.↗
DOE OSTI2025

Analysis and optimization of seismic monitoring networks with Bayesian optimal experimental design

SUMMARY Monitoring networks increasingly aim to assimilate data from a large number of diverse sensors covering many sensing modalities. Bayesian optimal experimental design (OED) seeks to identify data, sensor configurations or experiments which can optimally reduce uncertainty and hence increase the performance of a monitoring network. Information theory guides OED by formulating the choice of experiment or sensor placement as an optimization problem that maximizes the expected information gain (EIG) about quantities of interest given prior knowledge and models of expected observation data. Therefore, within the context of seismo-acoustic monitoring, we can use Bayesian OED to configure sensor networks by choosing sensor locations, types and fidelity in order to improve our ability to identify and locate seismic sources. In this work, we develop the framework necessary to use Bayesian OED to optimize a sensor network’s ability to locate seismic events from arrival time data of detected seismic phases at the regional-scale. This framework requires five elements: (i) A likelihood function that describes the distribution of detection and traveltime data from the sensor network, (ii) A prior distribution that describes a priori belief about seismic events, (iii) A Bayesian solver that uses a prior and likelihood to identify the posterior distribution of seismic events given the data, (iv) An algorithm to compute EIG about seismic events over a data set of hypothetical prior events, (v) An optimizer that finds a sensor network which maximizes EIG. Once we have developed this framework, we explore many relevant questions to monitoring such as: how to trade off sensor fidelity and earth model uncertainty; how sensor types, number and locations influence uncertainty; and how prior models and constraints influence sensor placement.

58 GEOSCIENCES↗
DOE OSTI2017

Situational Awareness of Network System Roles (SANSR)

In a large enterprise it is difficult for cyber security analysts to know what services and roles every machine on the network is performing (e.g., file server, domain name server, email server). Using network flow data, already collected by most enterprises, we developed a proof-of-concept tool that discovers the roles of a system using both clustering and categorization techniques. The tool's role information would allow cyber analysts to detect consequential changes in the network, initiate incident response plans, and optimize their security posture. The results of this proof-of-concept tool proved to be quite accurate on three real data sets. We will present the algorithms used in the tool, describe the results of preliminary testing, provide visualizations of the results, and discuss areas for future work. Without this kind of situational awareness, cyber analysts cannot quickly diagnose an attack or prioritize remedial actions.

cyber security↗