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At least 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
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 OSTI2013

DETECTING ACTIVE GALACTIC NUCLEI USING MULTI-FILTER IMAGING DATA. II. INCORPORATING ARTIFICIAL NEURAL NETWORKS

This is the second paper of the series Detecting Active Galactic Nuclei Using Multi-filter Imaging Data. In this paper we review shapelets, an image manipulation algorithm, which we employ to adjust the point-spread function (PSF) of galaxy images. This technique is used to ensure the image in each filter has the same and sharpest PSF, which is the preferred condition for detecting AGNs using multi-filter imaging data as we demonstrated in Paper I of this series. We apply shapelets on Canada-France-Hawaii Telescope Legacy Survey Wide Survey ugriz images. Photometric parameters such as effective radii, integrated fluxes within certain radii, and color gradients are measured on the shapelets-reconstructed images. These parameters are used by artificial neural networks (ANNs) which yield: photometric redshift with an rms of 0.026 and a regression R-value of 0.92; galaxy morphological types with an uncertainty less than 2 T types for z ≤ 0.1; and identification of galaxies as AGNs with 70% confidence, star-forming/starburst (SF/SB) galaxies with 90% confidence, and passive galaxies with 70% confidence for z ≤ 0.1. The incorporation of ANNs provides a more reliable technique for identifying AGN or SF/SB candidates, which could be very useful for large-scale multi-filter optical surveys that also include a modest set of spectroscopic data sufficient to train neural networks.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Optimal Modulation and DM Filter Design for a High Switching Frequency Single-Stage Microinverter

This paper presents an optimal modulation and systematic filter design approach for a single-stage dual-active-bridge (DAB) based dc-ac microinverter to achieve improved differential-mode (DM) noise performance for electromagnetic interference (EMI) tests. As DM filters contribute significantly to the overall converter volume, the main objective of this work is to leverage the degrees of freedom in the DAB converters to effectively attenuate the EMI noise. In addition, the DM filter design method needs to ensure near unity power factor converter operation. To achieve these targets, this paper analyzes three modulation strategies based on fixed or variable switching frequency operation where the different control modulation variables are varied to find the simulated DM noise spectrum. Based on the required DM attenuation, a constrained optimization problem is formulated to determine minimal DM filter parameters. Simulation results show that a spread spectrum approach with variable switching frequency is shown to minimize the DM EMI attenuation effort by spreading the noise profile. A fully GaN 400 W hardware prototype demonstrated the spread-sprectrum approach.

14 SOLAR ENERGY↗
DOE OSTI2018

Super Subwavelength Guiding and Rejecting of Terahertz Spoof SPPs Enabled by Planar Plasmonic Waveguides and Notch Filters Based on Spiral-Shaped Units

In this study, we numerically simulate novel planar plasmonic waveguides and notch filters with excellent guiding and rejection of terahertz (THz) waves with super subwavelength confinement. Our design is based on spoof surface plasmon polaritons-surface plasmon polaritons with a frequency that has been tuned using patterned conductive surfaces. We find that by using patterns of periodically arranged spiral-shaped units, the dispersion characteristics can be engineered at will by tuning the parameters of the spirals. We find that the resulting plasmonic waveguides have much lower asymptotic frequencies and much tighter terahertz field confinement when compared with conventional rectangular-grooved plasmonic waveguides. We show it is possible to design a structure with lateral dimensions that are only 25% the size of the conventional spoof surface plasmon polariton waveguides but with the same asymptotic frequency. Finally, we combined this architecture with broadband couplers to design an ultrawideband low-pass filter with sharp roll-off (cut-off frequency at 1.29 THz) and low insertion loss (<;3 dB). Furthermore, by introducing double ring resonators based on spiral-shaped units, a planar plasmonic notch filter with rejection of more than 17 dB between 0.97 and 0.99 THz is demonstrated. Finally, the proposed waveguides and notch filters may have great potential applications in the promising terahertz integrated plasmonic circuits and systems.

42 ENGINEERING↗
DOE OSTI2019

Unscented Kalman Filter-based Unbiased Minimum-Variance Estimation for Nonlinear Systems with Unknown Inputs

This paper proposes an unscented Kalman filter (UKF)-based unbiased minimum-variance estimation (UMV) method for the nonlinear system with unknown inputs. By utilizing the statistical linerization, the nonlinear system and measurement functions are transformed into a “linear-like” regression form. The latter preserves the nonlinearity of the system and the measurement models. To this end, the unknown inputs can be estimated by the weighted least-squares. This “linear-like” regression form also allows us to resort to the UMV state estimation framework for the development of new nonlinear filter to handle unknown inputs. Specifically, two approaches have been developed: 1) given the estimated inputs, we derive a filter by minimizing the trace of the state error covariance matrix; 2) without input estimation, we derive the filter by minimizing the trace of the state error covariance matrix subject to a constraint imposed on the gain matrix. We prove that these two approaches provide the same results. Numerical results validate the effectiveness of the proposed method.

Unscented Kalman filter, unbiased minimumvariance ↗
DOE OSTI2016

Joint Data Filtering and Labeling Using Gaussian Processes and Alternating Direction Method of Multipliers

Here, sequence labeling aims at assigning a label to every sample of a signal (or pixel of an image) while considering the sequentiality (or vicinity) of the samples. To perform this task, many works in the literature first filter and then label the data. Unfortunately, the filtering, which is performed independently from the labeling, is far from optimal and frequently makes the latter task harder. In this paper, a novel approach that trains a Gaussian process classifier and estimates the coefficients of an optimal filter jointly is presented. The new approach, based on Bayesian modeling and alternating direction method of multipliers (ADMMs) optimization, performs both tasks simultaneously. All unknowns are treated as stochastic variables, which are estimated using variational inference and filtering and labeling are linked with the use of ADMM. In the experimental section, synthetic and real experiments are presented to compare the proposed method with other existing approaches.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Radiological Source Term Estimation and Isotopic Identification with Parallel Log Domain Particle Filters

This paper presents a parallel log-domain particle filtering algorithm combined with gamma spectrum unfolding to perform localization, identification, and evaluation of multiple point sources of various isotopes in an environment with attenuating obstacles. The method uses sets of precomputed attenuation kernels that map the attenuation characteristics of the environment. These kernels are specific to the energy level of a photopeak of interest. The spectral measurements are deconvolved into count measurements of each photopeak. These count measurements are fed into a set of parallel particle filters using attenuation kernels computed for that photopeak’s energy level. The individual regularized particle filters perform all likelihood calculations in the logarithmic domain to mitigate the effects of particle degeneracy. The output of each particle filter is combined to estimate which isotopes are present as well as their positions and strengths. The performance of the algorithm is characterized in a lab-scale environment using a mobile robot equipped with a gamma ray spectrometer in the presence of up to three different radioactive isotopes simultaneously. The sources were localized to within 10 cm, and their strengths were estimated within 10% of their true values. Furthermore, the isotopes were all correctly identified, and no spurious sources were reported.

42 ENGINEERING↗
DOE OSTI2016

SU-F-T-283: A Novel Device to Enable Portal Dosimetry for Flattening Filter Free Beams

Purpose: Varian’s electronic portal imaging device (EPID) based portal dosimetry tool is a popular and effective means of performing IMRT QA. EPIDs for older models of the TrueBeam accelerator utilize a 40cmx30cm Image Detection Unit (IDU) that saturates at the center for standard source to imager distances with high dose rate flattening filter free (FFF) beams. This makes portal dosimetry not possible and an alternative means of IMRT QA necessary. We developed a filter that would attenuate the beam to a dose rate measureable by the IDU for portal dosimetry IMRT QA. Methods: Multipurpose 304 stainless steel plates were placed on an accessory tray to attenuate the beam. Profiles of an open field measured on the IDU were acquired with varying number of plates to assess the thickness needed to reduce the maximum dose rates of 6XFFF and 10XFFF beams to measurable levels. A new portal dose image prediction (PDIP) model was commissioned based on open field measurements with plates in position, and a modified beam profile was input to portal dosimetry calibration at the console to empirically correct for attenuation and scatter. The portal dosimetry tool was used to assess agreement between predicted and measured doses for open 25×25cm{sup 2} fields and intensity modulated fields using 6XFFF and 10XFFF beams. Results: Thicknesses of 2.5cm and 3.8cm of steel were required to reduce the highest dose rates to a measureable level for 6XFFF and 10XFFF, respectively. Gamma analysis using a 3%/3mm relative criterion with the filter in place and using the new PDIP model resulted in 98.2% and 93.6% of pixels passing while intensity modulated fields showed passing rates of 98.2% and 99.0%. Conclusion: Use of the filter allows for portal dosimetry to be used for IMRT QA of FFF plans in place of purchasing a second option for IMRT QA.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-G-IeP4-15: Ultrasound Imaging of Absorbable Inferior Vena Cava Filters for Proper Placement

Purpose: Inferior vena cava filters (IVCFs) are used in patients with a high risk of pulmonary embolism in situations when the use of blood thinning drugs would be inappropriate. These filters are implanted under x-ray guidance; however, this provides a dose of ionizing radiation to both patient and physician. B-mode ultrasound (US) imaging allows for localization of certain implanted devices without radiation dose concerns. The goal of this study was to investigate the feasibility of imaging the placement of absorbable IVCFs using US imaging to alleviate the dosage concern inherent to fluoroscopy. Methods: A phantom was constructed to mimic a human IVC using tissue-mimicking material with 0.5 dB/cm/MHz acoustic attenuation, while agar inclusions were used to model acoustic mismatch at the venous interface. Absorbable IVCF’s were imaged at 15 cm depth using B-mode US at 2, 3, 5, and 7 MHz transmit frequencies. Then, to determine temporal stability, the IVCF was left in the phantom for 10 weeks; during this time, the IVCF was imaged using the same techniques as above, while the integrity of the filter was analyzed by inspecting for fiber discontinuities. Results: Visualization of the inferior vena cava filter was possible at 5, 7.5, and 15 cm depth at US central frequencies of 2, 3, 5, and 7 MHz. Imaging the IVCF at 5 MHz yielded the clearest images while maintaining acceptable spatial resolution for identifying the IVCF’s, while lower frequencies provided noticeably worse image quality. No obvious degradation was observed over the course of the 10 weeks in a static phantom environment. Conclusion: Biodegradable IVCF localization was possible up to 15 cm in depth using conventional B-mode US in a tissue-mimicking phantom. This leads to the potential for using B-mode US to guide the placement of the IVCF upon deployment by the interventional radiologist. Mitch Eggers is an owner of Adient Medical Technologies. There are no other conflicts of interest to disclose.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

TH-AB-207A-07: Radiation Dose Simulation for a Newly Proposed Dynamic Bowtie Filters for CT Using Fast Monte Carlo Methods

Purpose: Dynamic bowtie filter is an innovative design capable of modulating the X-ray and balancing the flux in the detectors, and it introduces a new way of patient-specific CT scan optimizations. This study demonstrates the feasibility of performing fast Monte Carlo dose calculation for a type of dynamic bowtie filter for cone-beam CT (Liu et al. 2014 9(7) PloS one) using MIC coprocessors. Methods: The dynamic bowtie filter in question consists of a highly attenuating bowtie component (HB) and a weakly attenuating bowtie (WB). The HB is filled with CeCl3 solution and its surface is defined by a transcendental equation. The WB is an elliptical cylinder filled with air and immersed in the HB. As the scanner rotates, the orientation of WB remains the same with the static patient. In our Monte Carlo simulation, the HB was approximated by 576 boxes. The phantom was a voxelized elliptical cylinder composed of PMMA and surrounded by air (44cm×44cm×40cm, 1000×1000×1 voxels). The dose to the PMMA phantom was tallied with 0.15% statistical uncertainty under 100 kVp source. Two Monte Carlo codes ARCHER and MCNP-6.1 were compared. Both used double-precision. Compiler flags that may trade accuracy for speed were avoided. Results: The wall time of the simulation was 25.4 seconds by ARCHER on a 5110P MIC, 40 seconds on a X5650 CPU, and 523 seconds by the multithreaded MCNP on the same CPU. The high performance of ARCHER is attributed to the parameterized geometry and vectorization of the program hotspots. Conclusion: The dynamic bowtie filter modeled in this study is able to effectively reduce the dynamic range of the detected signals for the photon-counting detectors. With appropriate software optimization methods, the accelerator-based (MIC and GPU) Monte Carlo dose engines have shown good performance and can contribute to patient-specific CT scan optimizations.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Spin filtering at COSY and perspectives for PAX

The high physics potential of experiments with stored polarized antiprotons led to the proposal of the PAX [1] experiment (Polarized Antiproton eXperiment) for the High Energy Storage Ring (HESR) of the new FAIR facility at GSI (Darmstadt/Germany). It is proposed to polarize a stored antiproton beam by means of spin filtering by a polarized hydrogen gas target. The feasibility of spin filtering with protons using a transversely polarized hydrogen gas target has been demonstrated in the FILTEX experiment [2] and recently confirmed at the COSY ring in Jülich. A spin filtering experiment using a longitudinally polarized hydrogen gas target is planned after the installation of a siberian snake in 2014. Further measurements including the investigation of pd-breakup reactions [3] and a test of the time reversal (TRIC) [4] are planned using the developed experimental setup. A proposal for a spin-filtering experiment with antiprotons at AD/CERN [5] has been submitted to the CERN SPS comitee to investigate the spin-dependence of the pbar-p and pbar-d interaction. This knowledge is necessary to properly design a dedicated Antiproton-Polarizer Ring.

43 PARTICLE ACCELERATORS↗
DOE OSTI2016

Deterministic Mean-Field Ensemble Kalman Filtering

The proof of convergence of the standard ensemble Kalman filter (EnKF) from Le Gland, Monbet, and Tran [Large sample asymptotics for the ensemble Kalman filter, in The Oxford Handbook of Nonlinear Filtering, Oxford University Press, Oxford, UK, 2011, pp. 598--631] is extended to non-Gaussian state-space models. In this paper, a density-based deterministic approximation of the mean-field limit EnKF (DMFEnKF) is proposed, consisting of a PDE solver and a quadrature rule. Given a certain minimal order of convergence κ between the two, this extends to the deterministic filter approximation, which is therefore asymptotically superior to standard EnKF for dimension d < 2κ. The fidelity of approximation of the true distribution is also established using an extension of the total variation metric to random measures. Lastly, this is limited by a Gaussian bias term arising from nonlinearity/non-Gaussianity of the model, which arises in both deterministic and standard EnKF. Numerical results support and extend the theory.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Filters for Improvement of Multiscale Data from Atomistic Simulations

Multiscale computational models strive to produce accurate and efficient numerical simulations of systems involving interactions across multiple spatial and temporal scales that typically differ by several orders of magnitude. Some such models utilize a hybrid continuum-atomistic approach combining continuum approximations with first-principles-based atomistic models to capture multiscale behavior. By following the heterogeneous multiscale method framework for developing multiscale computational models, unknown continuum scale data can be computed from an atomistic model. Concurrently coupling the two models requires performing numerous atomistic simulations which can dominate the computational cost of the method. Furthermore, when the resulting continuum data is noisy due to sampling error, stochasticity in the model, or randomness in the initial conditions, filtering can result in significant accuracy gains in the computed multiscale data without increasing the size or duration of the atomistic simulations. In this work, we demonstrate the effectiveness of spectral filtering for increasing the accuracy of noisy multiscale data obtained from atomistic simulations. Moreover, we present a robust and automatic method for closely approximating the optimum level of filtering in the case of additive white noise. By improving the accuracy of this filtered simulation data, it leads to a dramatic computational savings by allowing for shorter and smaller atomistic simulations to achieve the same desired multiscale simulation precision.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Autodifferentiable Ensemble Kalman Filters

Data assimilation is concerned with sequentially estimating a temporally evolving state. This task, which arises in a wide range of scientific and engineering applications, is particularly challenging when the state is high-dimensional and the state-space dynamics are unknown. This paper introduces a machine learning framework for learning dynamical systems in data assimilation. Here, our auto-differentiable ensemble Kalman filters (AD-EnKFs) blend ensemble Kalman filters for state recovery with machine learning tools for learning the dynamics. In doing so, AD-EnKFs leverage the ability of ensemble Kalman filters to scale to high-dimensional states and the power of automatic differentiation to train high-dimensional surrogate models for the dynamics. Numerical results using the Lorenz-96 model show that AD-EnKFs outperform existing methods that use expectation-maximization or particle filters to merge data assimilation and machine learning. In addition, AD-EnKFs are easy to implement and require minimal tuning.

autodifferentiation↗
DOE OSTI2016

Wavelength Scanning with a Tilting Interference Filter for Glow-Discharge Elemental Imaging

Glow discharges have long been used for depth profiling and bulk analysis of solid samples. In addition, over the past decade, several methods of obtaining lateral surface elemental distributions have been introduced, each with its own strengths and weaknesses. Challenges for each of these techniques are acceptable optical throughput and added instrumental complexity. Here, these problems are addressed with a tilting-filter instrument. A pulsed glow discharge is coupled to an optical system comprising an adjustable-angle tilting filter, collimating and imaging lenses, and a gated, intensified charge-coupled device (CCD) camera, which together provide surface elemental mapping of solid samples. The tilting-filter spectrometer is instrumentally simpler, produces less image distortion, and achieves higher optical throughput than a monochromator-based instrument, but has a much more limited tunable spectral range and poorer spectral resolution. As a result, the tilting-filter spectrometer is limited to single-element or two-element determinations, and only when the target spectral lines fall within an appropriate spectral range and can be spectrally discerned. Spectral interferences that result from heterogeneous impurities can be flagged and overcome by observing the spatially resolved signal response across the available tunable spectral range. Lastly, the instrument has been characterized and evaluated for the spatially resolved analysis of glow-discharge emission from selected but representative samples.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Bloom filters for molecules

Abstract Ultra-large chemical libraries are reaching 10s to 100s of billions of molecules. A challenge for these libraries is to efficiently check if a proposed molecule is present. Here we propose and study Bloom filters for testing if a molecule is present in a set using either string or fingerprint representations. Bloom filters are small enough to hold billions of molecules in just a few GB of memory and check membership in sub milliseconds. We found string representations can have a false positive rate below 1% and require significantly less storage than using fingerprints. Canonical SMILES with Bloom filters with the simple FNV (Fowler-Noll-Voll) hashing function provide fast and accurate membership tests with small memory requirements. We provide a general implementation and specific filters for detecting if a molecule is purchasable, patented, or a natural product according to existing databases at https://github.com/whitead/molbloom .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

A copula-based rank histogram ensemble filter

Serial ensemble filters implement triangular probability transport maps to reduce high-dimensional inference problems to sequences of state-by-state univariate inference problems. The univariate inference problems are solved by sampling posterior probability densities obtained by combining constructed prior densities with observational likelihoods according to Bayes' rule. Many serial filters in the literature focus on representing the marginal posterior densities of each state. However, rigorously capturing the conditional dependencies between the different univariate inferences is crucial to correctly sampling multidimensional posteriors. This work proposes a new serial ensemble filter, called the copula rank histogram filter (CoRHF), that seeks to capture the conditional dependency structure between variables via empirical copula estimates; these estimates are used to rigorously implement the triangular (state-by-state univariate) Bayesian inference. The success of the CoRHF is demonstrated on two-dimensional examples and the Lorenz'63 problem. A practical extension to the high-dimensional setting is developed by localizing the empirical copula estimation, and is demonstrated on the Lorenz'96 problem.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Filter Measurement System for Nuclear Material Storage Canisters. End of Year Report FY 2013

A test system has been developed at Los Alamos National Laboratory to measure the aerosol collection efficiency of filters in the lids of storage canisters for special nuclear materials. Two FTS (filter test system) devices have been constructed; one will be used in the LANL TA-55 facility with lids from canisters that have stored nuclear material. The other FTS device will be used in TA-3 at the Radiation Protection Division’s Aerosol Engineering Facility. The TA-3 system will have an expanded analytical capability, compared to the TA-55 system that will be used for operational performance testing. The LANL FTS is intended to be automatic in operation, with independent instrument checks for each system component. The FTS has been described in a complete P&ID (piping and instrumentation diagram) sketch, included in this report. The TA-3 FTS system is currently in a proof-of-concept status, and TA-55 FTS is a production-quality prototype. The LANL specification for (Hagan and SAVY) storage canisters requires the filter shall “capture greater than 99.97% of 0.45-micron mean diameter dioctyl phthalate (DOP) aerosol at the rated flow with a DOP concentration of 65±15 micrograms per liter”. The percent penetration (PEN%) and pressure drop (DP) of fifteen (15) Hagan canister lids were measured by NFT Inc. (Golden, CO) over a period of time, starting in the year 2002. The Los Alamos FTS measured these quantities on June 21, 2013 and on Oct. 30, 2013. The LANL(6-21-2013) results did not statistically match the NFT Inc. data, and the LANL FTS system was re-evaluated, and the aerosol generator was replaced and the air flow measurement method was corrected. The subsequent LANL(10-30-2013) tests indicate that the PEN% results are statistically identical to the NFT Inc. results. The LANL(10-30-2013) pressure drop measurements are closer to the NFT Inc. data, but future work will be investigated. An operating procedure for the FTS (filter test system) was written, and future project milestones are on track for completion

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗