Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “SIGNALS”

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.

At least 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Search for multimessenger signals in NOvA coincident with LIGO/Virgo detections

Using the NOvA neutrino detectors, a broad search has been performed for any signal coincident with 28 gravitational wave events detected by the LIGO/Virgo Collaboration between September 2015 and July 2019. For all of these events, NOvA is sensitive to possible arrival of neutrinos and cosmic rays of GeV and higher energies. For five (seven) events in the NOvA Far (Near) Detector, timely public alerts from the LIGO/Virgo Collaboration allowed recording of MeV-scale events. No signal candidates were found.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Signal processing and spectral modeling for the BeEST experiment

The Beryllium Electron capture in Superconducting Tunnel junctions (BeEST) experiment searches for evidence of heavy neutrino mass eigenstates in the nuclear electron capture decay of 7 Be by precisely measuring the recoil energy of the 7 Li daughter. In Phase III, the BeEST experiment has been scaled from a singl superconducting tunnel junction (STJ) sensor to a 36-pixel array to increase sensitivity and mitigate gamma-induced backgrounds. Phase III also uses a new continuous data acquisition system that greatly increases the flexibility for signal processing and data cleaning. Here, we have developed procedures for signal processing and spectral fitting that are sufficiently robust to be automated for large datasets. Furthermore, this article presents the optimized procedures before unblinding the majority of the Phase III dataset to search for physics beyond the standard model.

6 ≤ A ≤ 19↗
DOE OSTI2023

First Observation of Λπ + and Λπ - Signals near the $\bar{K}$N⁡(I=1) Mass Threshold in Λ$^+_c$ → Λ⁢π + ⁢π + π - Decay

Using the data sample of 980 fb -1 collected with the Belle detector operating at the KEKB asymmetric-energy e + ⁢e - collider, we present the results of an investigation of the Λ⁢π + and Λ⁢π - invariant mass distributions looking for substructure in the decay Λ$^+_c$ → Λ⁢π + π + π - . We find a significant signal in each mass distribution. When interpreted as resonances, we find for the Λπ + (Λ⁢π - ) combination a mass of 1434.3 ± 0.6⁢(stat) ± 0.9⁢(syst) MeV/c 2 [1438.5 ± 0.9⁢(stat) ± 2.5⁢(syst) MeV/c 2 ], an intrinsic width of 11.5 ± 2.8⁢(stat) ± 5.3⁢(syst) MeV/c 2 [33.0 ± 7.5⁢(stat) ± 23.6⁢(syst) MeV/c 2 ] with a significance of 7.5⁢σ (6.2⁢σ). As these two signals are very close to the $\bar{K}N$ threshold, we also investigate the possibility of a $\bar{K}N$ cusp, and find that we cannot discriminate between these two interpretations due to the limited size of the data sample.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Stimulated Emission of Signal Photons from Dark Matter Waves

The manipulation of quantum states of light has resulted in significant advancements in both dark matter searches and gravitational wave detectors. Current dark matter searches operating in the microwave frequency range use nearly quantum-limited amplifiers. Future high frequency searches will use photon counting techniques to evade the standard quantum limit. We present a signal enhancement technique that utilizes a superconducting qubit to prepare a superconducting microwave cavity in a nonclassical Fock state and stimulate the emission of a photon from a dark matter wave. By initializing the cavity in an | n = 4 ⟩ Fock state, we demonstrate a quantum enhancement technique that increases the signal photon rate and hence also the dark matter scan rate each by a factor of 2.78. Using this technique, we conduct a dark photon search in a band around 5.965 GHz ( 24.67 μ eV ), where the kinetic mixing angle ε ≥ 4.35 × 10 − 13 is excluded at the 90% confidence level. Published by the American Physical Society 2024

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Nodal Admittance Matrix Based Area Partition Method for Small-Signal Stability Analysis of Large-Scale Power Electronics Based Power Systems

In power electronics-based power systems (PEPSs), small-signal stability is an important factor for system design and operation, where the impedance-based approach is often used. However, unlike small-scale PEPSs with simple and straightforward impedance models, it would take much more efforts to derive the large-scale PEPSs impedance model, which is very complicated and sometimes may get wrong results due to the elimination of right-half plane (RHP) poles during the impedance aggregation process. To simplify the derivation procedure and analyze the small-signal stability of large-scale PEPSs, this paper proposes a nodal admittance matrix (NAM) based area partition method. In this method, the large-scale PEPS is divided into several sub-areas, and the stability is analyzed within the sub-area first, and then the interconnection stability among these sub-areas is analyzed. The proposed method is scalable and can help to locate the weakest areas/converters that may cause instability in the whole system. In this paper, the concept of the proposed method and its application to an example system are introduced. Experimental results are also given to validate the effectiveness of the proposed method.

Qiao, Liang↗
DOE OSTI2023

Uncertainty Aware Deep Learning for Fault Prediction Using Multivariate Time Series Signals

The superconducting radio-frequency cavities are a crucial component of the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab. When a cavity faults, beam delivery to experimental end users is disrupted. Prediction of cavity faults prior to onset is essential to reduce operation and maintenance costs. In this work, a parallel long short-term memory (LSTM)-convolution neural network (CNN)-based deep learning (DL) model is proposed to predict impending faults using pre-fault signals. Further, we introduce an uncertainty quantification approach using Monte Carlo dropout with the LSTM-CNN model to ascertain confidence in the prediction. The model was tested using multivariate time series signals from stable cavity operations and before faults. Initial results show that on the test dataset, the model can identify impending faults before their onset with an average 10-fold cross validation accuracy of 97.39% and a standard deviation of 0.12% using a 100-ms time window. It is also observed that the model performs better as the prediction time moves closer to the fault onset. For additional context, we compare the performance of the model with three machine-learning-based (ML) fault prediction models. Our proposed parallel LSTM-CNN-based DL method shows better performance than the ML-based methods.

Rahman, Md Monibor↗
DOE OSTI2021

In Situ Transmission Electron Microscopy: Signal processing challenges and examples

Transmission electron microscopy (TEM) is a powerful tool for imaging material structure and characterizing material chemistry. Recent advances in data collection technology for TEM have enabled high-volume and high-resolution data collection at a microsecond frame rate. Here, taking advantage of these advances in data collection rates requires the development and application of data processing tools, including image analysis, feature extraction, and streaming data processing techniques. In this article, we highlight a few areas in materials science that have benefited from combining signal processing and statistical analysis with data collection capabilities in TEM and present a future outlook on opportunities of integrating signal processing with automated TEM data analysis.

36 MATERIALS SCIENCE↗
DOE OSTI2023

On the Investigation of Phase Fault Classification in Power Grid Signals: A Case Study for Support Vector Machines, Decision Tree and Random Forest

In monitoring the power grid, an ability to differentiate between fault types is essential to ensuring electrical safety. Accordingly, this study introduces a fault detection and classification method by considering different machine learning (ML) and feature extraction (FE) methods combinations. Specifically, the proposed method is established in two classification layers; the first layer determines the fault, and the second layer distinguishes the type of fault. Based on the proposed system model, this study seeks to determine the influential data attributes in a power grid signal using FE methods, including fast Fourier transform, power spectral density (PSD), auto-correlation, and wavelet transform (WT). A cross-comparison of the effectiveness of the Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF) is also performed to accomplish the classification layers of the proposed method. The designed algorithm is analyzed under the various combinations of FE and ML methods, and outcomes are presented by considering the trade-off between computational complexity and prediction accuracy. The results reveal that the RF-based ML algorithm shows the most accurate classification performance with PSD, and the most time-saving of the models is the DT WT. Also, SVM emerges superior on a subsequent test of the simulated models on real-world signals.

Galbraith, Kelli↗
DOE OSTI2024

Probing Signal-Based Inertia and Frequency Response Estimation for Power Systems With High Levels of Inverter-Based Resources

Power system inertia is the inherent capability of a power system to resist changes in its frequency during disturbances. Real-time inertia estimation technology has become more important due to the low-inertia issues caused by the increasing integration levels of inverter-based resources (IBRs) from renewable energy; however, existing inertia estimation methods hardly consider multiple frequency response controls that act within the same time frame as conventional inertial response, thus making measured inertia values vary under different testing conditions. To resolve this issue, this paper proposes a novel real-time estimation method to simultaneously estimate a power system's inertia constant and frequency response droop constant using a well-designed probing signal. First, we formulate the inertia and frequency response model of a power system with IBRs. Second, through the integration and manipulation of the developed model, we propose a multivariate linear regression-based estimation method that is resilient to measurement noise. Third, we design a probing signal that can be injected by IBRs to incite the required transients for estimation. Finally, we validate the proposed estimation method through comprehensive power-hardware-in-the-loop experiments using inverter hardware and a realistic island power system model. The results demonstrate that the proposed method can accurately estimate the inertia and droop value of the power system with grid-following IBRs and grid-forming IBRs with virtual synchronous machine control.

frequency response↗
DOE OSTI2020

Minimizing Timing Jitter’s Impact on Ground-Penetrating Radar Array Coupling Signals

This article presents a novel investigation into the effect of receiver timing jitter on the quality of imaging of ground-penetrating radar systems. We explicitly show the impact of timing jitter throughout an entire example processing pipeline. The process of coupling removal in systems whose receivers have a random Gaussian-distributed jitter with a high standard deviation leaves randomly distributed residue in the radar image. This residue, called jitter-induced coupling noise, extends even into the expected target domain which may cause false alarms. This residue approximately follows a sum of Gaussian and Gamma distributions based on the higher order derivatives of the coupling signal. Once migrated to create an image, the coupling residue is best described by a Weibull distribution. We show that simple filtering does not remove enough residue and propose two preventative design specifications called the linear slope and migrated probability specifications. Antenna and system designers should use these specifications to assess the true severity of coupling signals in a given jitter environment.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Signal-Compensation-Based Adaptive PID Control for Fused Magnesia Smelting Processes

For fused magnesia smelting processes, the proportional-integral-derivative (PID) controller is difficult to regulate the smelting currents within desired ranges due to unexpected variations of the dynamics caused by material feeding operations. To solve this problem, we first proposed its dynamic model composed of a linear model and an uncertain nonlinear dynamic system that describes the unknown time-varying dynamics of nonlinearity and strong couplings among controlled variables. Then, using the accurately calculated value of the uncertain part at the previous sampling time instant and the tracking error that reflects the influence of its change rate, we developed a feedforward and a one-step optimal control to obtain two signal compensators to eliminate the impact of frequent variations of the uncertainties. For the compensators, we proposed a weight selection method to minimize the sum of the squared tracking error with ensured closed-loop stability. Based on the abovementioned modeling and control structure, we finally developed a novel adaptive PID controller augmented with signal compensators to control the whole process. Industrial applications have shown that the proposed method can always keep the tracking error and the control input within their targeted ranges, leading to a significantly reduced energy consumption per ton and increased fused magnesia production.

Wang, Weizhou↗
DOE OSTI2021

Nonmagnetized Collisional Plasma Parameter Estimation From Two Frequency Signal Interrogation Attenuation

A nonmagnetized collisional plasma parameter estimator from two frequency signal interrogation attenuation is developed. The plasma parameters that are estimated are the plasma frequency, electron neutral momentum collision frequency, and the plasma thickness. The plasma frequency and electron neutral momentum collision frequency are considered uniform across the plasma thickness. The relative permittivity is defined, and the complex index of refraction is developed. Using this definition and applying the plasma frequency, electron neutral momentum collision frequency, the radial propagation frequency, and plasma thickness, an attenuation is determined for known cases. The development of the estimator is discussed. The estimator uses a performance index where the minimum difference between the plasma frequencies and electron neutral momentum collision frequencies is determined for the two signal interrogation frequencies under the constraint of the same plasma thickness. The estimator was developed in three stages which include iterative, sequential, and adaptive. The setups of the iterative, sequential, and adaptive approaches are discussed. The impact of the interrogation frequency and the estimator setup is investigated. The estimator in the three development stages is compared with known cases and the plasma parameter estimator performance is quantified.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

A Double-Signal Retail Pricing Scheme for Acquiring Operational Flexibility from Batteries

Batteries can provide valuable operational flexibility to facilitate the system efficiency. However, there lacks market environments to effectively harness and monetize the value of these assets. Most existing works apply a profit-oriented single-signal pricing scheme, which mixes the value of energy and flexibility together and may ultimately raise the electricity bill. Therefore, this paper proposes a profit-neutral double-signal retail pricing scheme that distinguishes elastic market players (i.e., batteries) from inelastic market players (i.e., inflexible loads) and quantifies the value of energy and flexibility separately. Experimental results indicate that under the incentive provided by the proposed retail pricing scheme: 1) The system efficiency benefit aligns with benefits to both batteries and inflexible loads; 2) Value of operational flexibility, contributing to improve the energy efficiency, can be transparently priced and fairly allocated among batteries; and 3) The dominant role in which inflexible loads play on determining the market price is avoided.

25 ENERGY STORAGE↗
DOE OSTI2020

Cross talk between RNA N6‐methyladenosine methyltransferase‐like 3 and miR‐186 regulates hepatoblastoma progression through Wnt/β‐catenin signalling pathway

Abstract Objectives N6‐methyladenosine (m6A) is a ubiquitous epigenetic RNA modification that plays a pivotal role in tumour development and metastasis. In this study, we aimed to investigate the expression profiling, clinical significance, biological function and the regulation of m6A‐related genes in hepatoblastoma (HB). Materials and Methods The mRNA and protein expression levels of m6A‐related genes were analysed using Gene Expression Omnibus (GEO) and tissue microarray (TMA) cohort. Kaplan‐Meier analysis was performed to evaluate the prognostic value of m6A‐related genes in HB. Knockdown of m6A‐related genes was conducted to analyse its function on cell proliferation, migration and invasion. Furthermore, bioinformatics analysis and experimental verification were used to explore the potential molecular mechanism and signalling pathway. Results We found that most m6A‐related genes were significantly upregulated in HB tumour tissues. High levels of methyltransferase‐like 3 (METTL3, P = .013), YTHDF2 ( P = .037) and FTO ( P = .032) indicated poor clinical outcomes, and the upregulation of METTL3 was an independent prognostic factor in HB patients. Functional assays showed that knockdown of METTL3 could dramatically suppress the proliferation, migration and invasion of HB cells. In addition, METTL3 was identified to be a direct target of microRNA‐186 (miR‐186). Consistently, miR‐186 was low expressed in HB tumour tissues. Moreover, overexpression of miR‐186 significantly inhibited cell aggressive phenotype both in vitro and in vivo, while the inhibitory effect could be reversed by METTL3 overexpression. Mechanism study indicated that miR‐186/METTL3 axis contributed to the progression of HB via the Wnt/β‐catenin signalling pathway. Conclusions M6A‐related genes were frequently dysregulated in HB. miR‐186/METTL3/Wnt/β‐catenin axis might serve as novel therapeutic targets and prognostic biomarkers in HB.

Cui, Xichun↗
DOE OSTI2020

Knockout of AKAP150 improves impaired BK channel‐mediated vascular dysfunction through the Akt/GSK3β signalling pathway in diabetes mellitus

Abstract Vascular dysfunction resulting from diabetes is an important factor in arteriosclerosis. Previous studies have shown that during hyperglycaemia and diabetes, AKAP150 promotes vascular tone enhancement by intensifying the remodelling of the BK channel. However, the interaction between AKAP150 and the BK channel remains open to discussion. In this study, we investigated the regulation of impaired BK channel‐mediated vascular dysfunction in diabetes mellitus. Using AKAP150 null mice (AKAP150 −/− ) and wild‐type (WT) control mice (C57BL/6J), diabetes was induced by intraperitoneal injection of streptozotocin. We found that knockout of AKAP150 reversed vascular remodelling and fibrosis in mice with diabetes and in AKAP150 −/− diabetic mice. Impaired Akt/GSK3β signalling contributed to decreased BK‐β 1 expression in aortas from diabetic mice, and the silencing of AKAP150 increased Akt phosphorylation and BK‐β 1 expression in MOVAS cells treated with HG medium. The inhibition of Akt activity caused a decrease in BK‐β 1 expression, and treatment with AKAP150 siRNA suppressed GSK3β expression in the nuclei of MOVAS cells treated with HG. Knockout of AKAP150 reverses impaired BK channel‐mediated vascular dysfunction through the Akt/GSK3β signalling pathway in diabetes mellitus.

Zhu, Yan‐Rong↗
DOE OSTI2021

A highly efficient, 10-J output signal amplifier for ultra-intense all-OPCPA systems

Optical parametric chirped-pulse amplification (OPCPA) using high-energy Nd:glass lasers has the potential to produce ultra-intense pulses (>10 23 W/cm 2 ). We report on the performance of the final high-efficiency amplifier in an OPCPA system based on large-aperture (63 x 63-mm 2 ) partially deuterated potassium dihydrogen phosphate (DKDP) crystals. Here, the seed beam (170 nm, 240 mJ) was provided by the preceding OPCPA stages. The maximum pump-to-signal conversion efficiency of 37% was achieved with a 52-mm-long DKDP crystal and 40-J pump energy at 527 nm due to the flattop super-Gaussian pump beam profile and flat-in-time pulse shape. The 11.8-J output signal was compressed to 19 fs.

42 ENGINEERING↗
DOE OSTI2020

Application of entropy and signal energy for ultrasound-based classification of three-dimensional printed polyetherketoneketone components

This paper describes a preliminary method for the classification of annealed and unannealed polyetherketoneketone (PEKK) components manufactured using a material extrusion three-dimensional (3D) printing process. PEKK is representative of a class of high-performance thermoplastics that are increasingly employed as feedstocks for use in 3D printing. PEKK components may be used continuously at elevated temperatures, are chemically resistant, and able to withstand large mechanical loads. These properties render PEKK suitable as a metal component replacement in aerospace applications, high-temperature industrial applications, and surgical implants. The structure of PEKK is semi-crystalline with the specific crystallinity correlating to the final properties during application, making determination of this property crucial. This study compares three different signal processing techniques intended to distinguish annealed (high crystallinity) from unannealed (low crystallinity) components using backscattered ultrasound. The first is energy-based and is unable to detect annealing. The second two are based on different entropies of the backscattered signal: a limiting form of Renyi's entropy and a limiting form of joint entropy. The joint entropy values for the annealed and unannealed specimens fall into two non-overlapping intervals and have a statistical separation of two standard deviations.

36 MATERIALS SCIENCE↗
DOE OSTI2021

The Azospirillum brasilense Core Chemotaxis Proteins CheA1 and CheA4 Link Chemotaxis Signaling with Nitrogen Metabolism

Bacterial chemotaxis affords motile bacteria the ability to navigate the environment to locate niches for growth and survival. At the molecular level, chemotaxis depends on chemoreceptor signaling arrays that interact with cytoplasmic proteins to control the direction of movement. In Azospirillum brasilense , chemotaxis is mediated by two distinct chemotaxis pathways: Che1 and Che4. Both Che1 and Che4 are critical in the A. brasilense free-living and plant-associated lifestyles. Here, we use whole-cell proteomics and metabolomics to characterize the role of chemotaxis in A. brasilense physiology. We found that mutants lacking CheA1 or CheA4 or both are affected in nonchemotaxis functions, including major changes in transcription, signaling transport, and cell metabolism. We identify specific effects of CheA1 and CheA4 on nitrogen metabolism, including nitrate assimilation and nitrogen fixation, that may depend, at least, on the transcriptional control of rpoN , which encodes RpoN, a global regulator of metabolism, including nitrogen. Consistent with proteomics, the abundance of several nitrogenous compounds (purines, pyrimidines, and amino acids) changed in the metabolomes of the chemotaxis mutants relative to the parental strain. Further, we uncover novel, and yet uncharacterized, layers of transcriptional and posttranscriptional control of nitrogen metabolism regulators. Together, our data reveal roles for CheA1 and CheA4 in linking chemotaxis and nitrogen metabolism, likely through control of global regulatory networks.

59 BASIC BIOLOGICAL SCIENCES↗