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At least 559 records · Page 31

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DOE OSTI2025

Conflict Detection in Open RAN with Recurrent Neural Networks Using Geometric Manifolds

Allowing third-party applications on Radio Access Network (RAN) Intelligent Controllers (RICs) within the OpenRAN (O-RAN) framework introduces conflicting interactions that are often difficult to detect in advance. These conflicts, occurring between third-party applications in the Near RealTime RIC (Near-RT RIC), known as xApps, can lead to performance degradation and instability in O-RAN if not identified early. Existing conflict detection and mitigation solutions in the literature assume that the conflicts are known beforehand, which is not always accurate due to the complex and often hidden relationships between control parameters and Key Performance Indicators (KPIs). In this paper, we propose a novel Recurrent Neural Network (RNN) to detect both known and unknown conflicts in O-RAN xApps as specified in the O-RAN standards. We model the xApps, control parameters, and KPIs with nodes and edges to create graph structures and use the hidden nonEuclidean geometric properties of the Riemannian manifold to train the RNN model. The performance of this proposed model is validated using evaluation metrics and compared with benchmarks. Results demonstrate that the proposed RNN model, leveraging Riemannian geometric properties, can achieve 100% of the F1-score provided by an optimal solution in just 20 iterations.

5G↗

DOE OSTI2025

Conflict Detection in Open RAN with Recurrent Neural Networks Using Geometric Manifolds

5G↗

DOE OSTI2022

A Realistic Theory of Quantum Measurement

Abstract We propose that the ontic understanding of quantum mechanics can be extended to a fully realistic theory that describes the evolution of the wavefunction at all times, including during a measurement. In such an approach the wave equation should reduce to the standard wave equation when there is no measurement, and describe state reduction when the system is measured. The general wave equation must be nonlinear and nonlocal, and we require it to be time-symmetric; consequently, this approach is not a new interpretation but a new theory. The wave equation is an integrodifferential equation (IDE). The time symmetry requirement leads to a retrocausal approach, in which the wave equation is solved subject to initial and final conditions to determine history at intermediate times. We propose that different outcomes from (apparently) identically prepared experiments may result from uncontrolled parameters; both the nonlocality and the retrocausality of the theory imply that Bell’s Theorem cannot rule out such “hidden variables.” Beginning with Hamilton’s principle, we demonstrate the construction of such a theory by replacing the action with a functional designed to give rise to a nonlinear, nonlocal IDE as the wave equation. This IDE reduces to the standard wave equation (a differential equation) in the absence of a measurement, but exhibits state reduction to a single eigenvalue when the system interacts with another system with the properties of a measurement apparatus. We demonstrate several desirable features of this theory; for other properties we indicate their plausibility and possible avenues to a proof.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

Information-theoretic aspects of Werner states

Highlights: • The rich structures of Werner states are reviewed. • Werner states are characterized in terms of quantum uncertainty. • Information-theoretic aspects of Werner states are revealed. In a seminal study of quantum states with Einstein–Podolsky–Rosen correlations (entanglement) admitting a hidden-variable model (Werner, 1989), Werner introduced the dichotomy of entanglement/separability and devised a family of highly symmetric states, now termed the Werner states, some of which exhibit entanglement but no Bell nonlocality. It turns out that the Werner states have a rich structure of correlations and constitute a paradigm which has played an innovative role in both theoretical and experimental explorations of quantum information. Given the theoretical significance and wide applications of the Werner states, here we first give a concise review of information contents of the Werner states, and then present an information-theoretic characterization of them in terms of the Wigner–Yanase skew information: The Werner states are identified as the states with the minimum quantum uncertainty with respect to a natural family of observables (i.e., the generators of the diagonal unitary group). For this purpose, we introduce a measure of quantum uncertainty which is of independent interest in studying asymmetry, coherence, and uncertainty, and reveal its fundamental properties. We further identify the Bell triplet states as the opposite states of the Werner states in the sense that they have the maximal amount of quantum uncertainty. Analogously, we provide a similar characterization of the isotropic states as the minimum quantum uncertainty states with respect to a closely related family of operators.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

Machine learning to discover mineral trapping signatures due to CO 2 injection

Mineral trapping is pursued as a geological CO 2 sequestration (GCS) mechanism because it permanently stores CO 2 in solid phases or minerals. However, CO 2 mineral-trapping mechanisms are poorly understood due to (1) lack of sufficient field and laboratory data characterizing these complex processes, and (2) challenges to develop site-specific reactive-transport models coupling fluid flow and geochemical reactions occurring at various temporal (from milliseconds to years) and spatial (from pore (millimeters) to field (kilometers)) scales. Reactive transport with additional complexities such as heterogeneity can make the simulation outputs even more difficult to interpret because of complex nonlinearity and multi-scale interdependencies. Furthermore, the values of model outputs such as concentrations can vary by several orders of magnitude, making it harder to correlate and characterize the impact of the variables via traditional data interpretation techniques such as exploratory data analyses. Recently, machine learning (ML) has shown promise in feature discovery and in highlighting hidden mechanisms that cannot be obtained by existing data-analytics and statistical methods. In this study, we applied an unsupervised ML approach, non-negative matrix factorization with custom -means clustering (NMF) to the data generated by reactive-transport simulations of GCS. The reactive-transport data consisted of 19 attributes, including four physio-chemical variables (pH, porosity, aqueous CO 2 , and sequestered CO 2 ), six chemical species (K + , Na + , HCO, Ca 2+ , Mg 2+ , Fe 2+ ), and four carbonate minerals (calcite, dolomite, siderite, and ankerite), a feldspar mineral (albite), and four clay minerals (illite, clinochlore, kaolinite, and smectite) over a period of 200 years of simulation time. Furthermore, the simulation data used was for Morrow B sandstone at the Farnsworth hydrocarbon unit in Texas. Data are sampled at two locations within the model domain: (1) at the injection well and (2) 200 m west of the injection well. The injection was performed for a period of 10 years. Using NMF, we estimated the temporal interdependencies among the 19 attributes over a span of 200 years. We found that NMF was able to identify four reaction stages and their dominant attributes; these cannot be directly discerned through traditional visualization (e.g., line plots, Pareto analysis, Glyph-based visualization methods) or exploratory data analysis tools of the simulation data. The four stages were: reactions in the injection phase followed by short-, mid-, and long-term reactions. The NMF analysis also revealed that 10 among the 19 attributes are dominant. These dominant attributes for mineral trapping include calcite, dolomite at injection well, siderite at 200 m away from the injection well, clinochlore, kaolinite, Na + , K + , Ca 2+ , Mg 2+ , pH, and aqeuous CO 2 . Finally, at late times (65–200 years), our results showed that calcite plays a major role in mineral trapping with insignificant contribution from siderite, ankerite, and clay minerals. These findings make the proposed unsupervised ML-model attractive for reactive-transport sensing towards real-time GCS monitoring.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Precision tests of fundamental physics with η and η ' mesons

Decays of the neutral and long-lived $\eta$ and $\eta'$ mesons provide a unique, flavor-conserving laboratory to test low-energy Quantum Chromodynamics and search for new physics beyond the Standard Model. They have drawn world-wide attention in recent years and have inspired broad experimental programs in different high-intensity-frontier centers. Additionally, new experimental data will offer critical inputs to precisely determine the light quark mass ratios, $\eta$-$\eta'$ mixing parameters, and hadronic contributions to the anomalous magnetic moment of the muon. At the same time, it will provide a sensitive probe to test potential new physics. This includes searches for hidden photons, light Higgs scalars, and axion-like particles that are complementary to worldwide efforts to detect new light particles below the GeV mass scale, as well as tests of discrete symmetry violation. In this review, we give an update on theoretical developments, discuss the experimental opportunities, and identify future research needed in this field.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2020

Data-driven Detection and Early Prediction of Thermoacoustic Instability in a Multi-nozzle Combustor

Thermoacoustic instability (TAI) is a critical issue in modern lean-burn gas-turbine combustors, which is induced by a strong coupling between the resonant combustor acoustics and fluctuations in the heat release rate. This instability may lead to high-amplitude pressure waves that generate undesirable noise levels as well as fatigue stresses in mechanical structures of the combustor. The intense pressure fluctuations due to TAI may also cause large flow perturbations and possibly flow reversal that may lead to flame oscillations, flame liftoff, and even flame blow-out. Hence, there is a strong need for exercising control actions in a timely fashion to mitigate the TAI phenomena. Anomaly detection is an essential prerequisite to the design of a good controller and such a detector must be able to reliably predict a forthcoming TAI. To detect and predict the onset of a TAI from an ensemble of pressure time series, this paper investigates three data-driven methods: Fast Fourier transform (FFT), symbolic time series analysis (STSA), and hidden Markov modeling (HMM). The main focus of the paper is to make a comparative evaluation of these three anomaly detection methods for classification of the current regime of operation into stable and unstable categories as well as for real-time identification of precursors to impending instabilities with short-length time series of measured variables (e.g., pressure oscillations). Finally, the results, generated on experimental data from a multi-nozzle combustor apparatus, have been compared to evaluate the performance of FFT, STSA, and HMM methods for TAI analysis.

42 ENGINEERING↗

DOE OSTI2022

Temporal Characterization and Filtering of Sensor Data to Support Anomaly Detection

Here, we present an approach for characterizing complex temporal behavior in the sensor measurements of a system in order to support detection of anomalies in that system. We first characterize typical behavior by extending a hidden Markov model-based approach to time series alignment. We then use a trace of that learned behavior to develop a particle filter that enables efficient estimation of the filtering distribution on the state space. This produces filtered residuals that can then be used in an anomaly detection framework. Our motivating example is the daily behavior of a building’s heating, ventilation, and air conditioning (HVAC) system, using sensor measurements that arrive every minute and induce a state space with 15,120 states. We provide an end-to-end demonstration of our approach showing improved performance of anomaly detection after application of alignment and filtering compared to the unaligned data. The proposed model is implemented as a computationally efficient R package alignts (align time series) built with R and Fortran 95 with OpenMP support.

47 OTHER INSTRUMENTATION↗

DOE OSTI2022

Viterbi decoding of CRES signals in Project 8

Abstract Cyclotron radiation emission spectroscopy (CRES) is a modern approach for determining charged particle energies via high-precision frequency measurements of the emitted cyclotron radiation. For CRES experiments with gas within the fiducial volume, signal and noise dynamics can be modelled by a hidden Markov model. We introduce a novel application of the Viterbi algorithm in order to derive informational limits on the optimal detection of cyclotron radiation signals in this class of gas-filled CRES experiments, thereby providing concrete limits from which future reconstruction algorithms, as well as detector designs, can be constrained. The validity of the resultant decision rules is confirmed using both Monte Carlo and Project 8 data.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2022

Global perspectives of the bulk electronic structure of URu 2 Si 2 from angle-resolved photoemission

Previous high-resolution angle-resolved photoemission (ARPES) studies of URu 2 Si 2 have characterized the temperature-dependent behavior of narrow-band states close to the Fermi level ( E F ) at low photon energies near the zone center, with an emphasis on electronic reconstruction due to Brillouin zone folding. A substantial challenge to a proper description is that these states interact with other hole-band states that are generally absent from bulk-sensitive soft x-ray ARPES measurements. Here we provide a more global k -space context for the presence of such states and their relation to the bulk Fermi surface (FS) topology using synchrotron-based wide-angle and photon energy-dependent ARPES mapping of the electronic structure using photon energies intermediate between the low-energy regime and the high-energy soft x-ray regime. Small-spot spatial dependence, f -resonant photoemission, Si 2 p core-levels, x-ray polarization, surface-dosing modification, and theoretical surface slab calculations are employed to assist identification of bulk versus surface state character of the E F -crossing bands and their relation to specific U- or Si-terminations of the cleaved surface. The bulk FS topology is critically compared to density functional theory (DFT) and to dynamical mean field theory calculations. In addition to clarifying some aspects of the previously measured high symmetry Γ, Z and X points, incommensurate 0.6 a * nested Fermi-edge states located along Z – N – Z are found to be distinctly different from the DFT FS prediction. The temperature evolution of these states above T HO , combined with a more detailed theoretical investigation of this region, suggests a key role of the N -point in the hidden order transition.

36 MATERIALS SCIENCE↗

DOE OSTI2020

A Deep Generative Model for Non-Intrusive Identification of EV Charging Profiles

The proliferation of electric vehicles (EVs) brings environmental benefits and technical challenges to power grids. An identification algorithm which can accurately extract individual EV charging profiles out of widely available smart meter measurements has attracted great interests. This paper proposes a non-intrusive identification framework for EV charging profile extraction, which is driven by deep generative models (DGM). First, the proposed DGM is designed as a representation layer embedded into the Markov process and used to model the joint probability distribution of available time-series data. A novel contribution is to approximate posterior distributions by neural networks whose parameters are obtained by variational inference and supervised learning. Second, the EV charging status is inferred from the DGM via dynamic programming. Lastly, the desired EV charging profile can be reconstructed by the rated power of EV models and inferred status. Compared with the benchmark Hidden Markov Models, the proposed framework can better handle noise in data with less computational complexity and better overall accuracy performances with smaller recall. The proposed framework is validated by numerical experiments on the Pecan Street dataset.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2020

FeGenie: a comprehensive tool for the identification of iron genes and iron gene neighborhoods in genomes and metagenome assemblies

Iron is a micronutrient for nearly all life on Earth. It can be used as an electron donor and electron acceptor by iron-oxidizing and iron-reducing microorganisms and is used in a variety of biological processes, including photosynthesis and respiration. While it is the fourth most abundant metal in the Earth’s crust, iron is often limiting for growth in oxic environments because it is readily oxidized and precipitated. Much of our understanding of how microorganisms compete for and utilize iron is based on laboratory experiments. However, the advent of next-generation sequencing and surge in publicly available sequence data has made it possible to probe the structure and function of microbial communities in the environment. To bridge the gap between our understanding of iron acquisition, iron redox cycling, iron storage, and magnetosome formation in model microorganisms and the plethora of sequence data available from environmental studies, we have created a comprehensive database of hidden Markov models (HMMs) based on genes related to iron acquisition, storage, and reduction/oxidation in Bacteria and Archaea. Along with this database, we present FeGenie, a bioinformatics tool that accepts genome and metagenome assemblies as input and uses our comprehensive HMM database to annotate provided datasets with respect to iron-related genes and gene neighborhood. An important contribution of this tool is the efficient identification of genes involved in iron oxidation and dissimilatory iron reduction, which have been largely overlooked by standard annotation pipelines. We validated FeGenie against a selected set of 28 isolate genomes and showcase its utility in exploring iron genes present in 27 metagenomes, 4 isolate genomes from human oral biofilms, and 17 genomes from candidate organisms, including members of the candidate phyla radiation. We show that FeGenie accurately identifies iron genes in isolates. Furthermore, analysis of metagenomes using FeGenie demonstrates that the iron gene repertoire and abundance of each environment is correlated with iron richness. While this tool will not replace the reliability of culture-dependent analyses of microbial physiology, it provides reliable predictions derived from the most up-to-date genetic markers. FeGenie’s database will be maintained and continually updated as new genes are discovered.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Synthetic Streamflow Datasets Derived from DOE 9505 for Select Texas Basins

This dataset is generated using a Bayesian Hidden Markov Model trained on the DOE 9505 streamflow projection ensemble. A set of 21,000 streamflow realizations are generated for the Colorado, Sabine, and Trinity river basins in Texas. Separate models are trained either using the full 9505 ensemble or a subset based on three hyperparameters: bias correction, downscaling, and hydrological model.

drought↗

NASA NTRS1994

Hideen Markov Models and Neural Networks for Fault Detection in Dynamic Systems

None given. (From conclusion): Neural networks plus Hidden Markov Models(HMM)can provide excellene detection and false alarm rate performance in fault detection applications. Modified models allow for novelty detection. Also covers some key contributions of neural network model, and application status.

space network↗

NASA NTRS2004

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↗

DOE OSTI2021

Simultaneously Blocking Chemical Crosstalk and Internal Short Circuit via Gel-Stretching Derived Nanoporous Non-Shrinkage Separator for Safe Lithium-Ion Batteries

The separator, an ionic permeable and electronic insulating membrane between cathode and anode, plays a crucial role in the electrochemical and safety performance of batteries. However, the commercial polyolefin separators not only suffer from inevitable thermal shrinkage at elevated temperature, but also fail to inhibit the hidden chemical crosstalk of reactive gases such as O 2 , leading to often reported thermal runaway (TR) and hence preventing large-scale implementation of high-energy-density lithium-ion batteries. Herein, a nanoporous non-shrinkage separator (GS-PI) was fabricated via a novel gel-stretching orientation approach to eliminate TR. In situ synchrotron small angle X-ray scattering during heating clearly showed that the as-prepared thin GS-PI separator exhibits superior mechanical tolerance at high temperature, thus effectively preventing internal short circuit. Meanwhile, the unique nanoporous structure design further blocks chemical crosstalk and the associated exothermic reactions. Accelerating Rate Calorimetry test revealed that the practical 1 Ah LiNi 0.6 Co 0.2 Mn 0.2 O 2 (NCM622)/graphite pouch cell using GS-PI nanoporous separator show a maximum temperature rise (dT/dt max ) of only 3.7 °C s -1 compared to 131.6 °C s -1 in the case of Al 2 O 3 @PE macroporous separator. Furthermore, despite the reduced pore size, the GS-PI separator demonstrates better cycling stability than conventional Al 2 O 3 @PE separator at high temperature without sacrificing specific capacity and rate capability.

25 ENERGY STORAGE↗

DOE OSTI2023

Anisotropic Excitons Reveal Local Spin Chain Directions in a van der Waals Antiferromagnet

A long‐standing pursuit in materials science is to identify suitable magnetic semiconductors for integrated information storage, processing, and transfer. Van der Waals magnets have brought forth new material candidates for this purpose. Recently, sharp exciton resonances in antiferromagnet NiPS 3 have been reported to correlate with magnetic order, that is, the exciton photoluminescence intensity diminishes above the Néel temperature. Here, it is found that the polarization of maximal exciton emission rotates locally, revealing three possible spin chain directions. This discovery establishes a new understanding of the antiferromagnet order hidden in previous neutron scattering and optical experiments. Furthermore, defect‐bound states are suggested as an alternative exciton formation mechanism that has yet to be explored in NiPS 3 . The supporting evidence includes chemical analysis, excitation power, and thickness dependent photoluminescence and first‐principles calculations. This mechanism for exciton formation is also consistent with the presence of strong phonon side bands. This study shows that anisotropic exciton photoluminescence can be used to read out local spin chain directions in antiferromagnets and realize multi‐functional devices via spin‐photon transduction.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Hole‐Carrier‐Dominant Transport in 2D Single‐Crystal Copper

Abstract In 2D noble metals like copper, the carrier scattering at grain boundaries has obscured the intrinsic nature of electronic transport. However, it is demonstrated that the intrinsic nature of transport by hole carriers in 2D copper can be revealed by growing thin films without grain boundaries. As even a slight deviation from the twin boundary is perceived as grain boundaries by electrons, it is only through the thorough elimination of grain boundaries that the hidden hole‐like attribute of 2D single‐crystal copper can be unmasked. Two types of Fermi surfaces, a large hexagonal Fermi surface centered at the zone center and the triangular Fermi surface around the zone corner, tightly matching to the calculated Fermi surface topology, confirmed by angle‐resolved photoemission spectroscopy (ARPES) measurements and vivid nonlinear Hall effects of the 2D single‐crystal copper account for the presence of hole carriers experimentally. This breakthrough suggests the potential to manipulate the majority carrier polarity in metals by means of grain boundary engineering in a 2D geometry.

36 MATERIALS SCIENCE↗