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Results for “sensor noise reduction”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1995

Combustion Technology Outreach

Lewis' High Speed Research (HSR) Propulsion Project Office initiated a targeted outreach effort to market combustion-related technologies developed at Lewis for the next generation of supersonic civil transport vehicles. These combustion-related innovations range from emissions measurement and reduction technologies, to diagnostics, spray technologies, NOx and SOx reduction of burners, noise reduction, sensors, and fuel-injection technologies. The Ohio Aerospace Institute and the Great Lakes Industrial Technology Center joined forces to assist Lewis' HSR Office in this outreach activity. From a database of thousands of nonaerospace firms considered likely to be interested in Lewis' combustion and emission-related technologies, the outreach team selected 41 companies to contact. The selected companies represent oil-gas refineries, vehicle/parts suppliers, and manufacturers of residential furnaces, power turbines, nonautomobile engines, and diesel internal combustion engines.

Source record↗
NASA NTRSDate not supplied

Gust Load Alleviation Control and Gust Estimation for a High Aspect Ratio Wing Wind Tunnel Model

This paper presents the gust load alleviation (GLA) study of the aspect ratio 13.5 Common Research Model (CRM) wind-tunnel model. This study details the design of the GLA controller in preparation for wind-tunnel testing in the Transonic Dynamics Tunnel at NASA Langley Research Center. An aeroservolastic (ASE) model is first reduced using a model reduction method that takes advantage of the sinusoidal steady-state response. Then, the reduced model is used to design an extended-state Kalman filter which estimates the states and the sinusoidal gust input. The GLA control is then derived using the optimal control solution to a multi-objective cost function. The results of the GLA controller indicate a 69.07% reduction in wing-root strain without sensor noise and 68.45% reduction with sensor noise, while maintaining robust stability margins. An adaptive GLA controller is developed for uncertain gust frequency and shows a 71.03% reduction in wing root strain compared to the non-adaptive control reduction of just 39.19%.

Christopher Forte↗
NASA NTRS2010

Emergent Adaptive Noise Reduction from Communal Cooperation of Sensor Grid

In the last decade, the realization of small, inexpensive, and powerful devices with sensors, computers, and wireless communication has promised the development of massive sized sensor networks with dense deployments over large areas capable of high fidelity situational assessments. However, most management models have been based on centralized control and research has concentrated on methods for passing data from sensor devices to the central controller. Most implementations have been small but, as it is not scalable, this methodology is insufficient for massive deployments. Here, a specific application of a large sensor network for adaptive noise reduction demonstrates a new paradigm where communities of sensor/computer devices assess local conditions and make local decisions from which emerges a global behaviour. This approach obviates many of the problems of centralized control as it is not prone to single point of failure and is more scalable, efficient, robust, and fault tolerant

Jones, Kennie H.↗
NASA NTRS2011

Communal Sensor Network for Adaptive Noise Reduction in Aircraft Engine Nacelles

Emergent behavior, a subject of much research in biology, sociology, and economics, is a foundational element of Complex Systems Science and is apropos in the design of sensor network systems. To demonstrate engineering for emergent behavior, a novel approach in the design of a sensor/actuator network is presented maintaining optimal noise attenuation as an adaptation to changing acoustic conditions. Rather than use the conventional approach where sensors are managed by a central controller, this new paradigm uses a biomimetic model where sensor/actuators cooperate as a community of autonomous organisms, sharing with neighbors to control impedance based on local information. From the combination of all individual actions, an optimal attenuation emerges for the global system.

Jones, Kennie H.↗
DOE OSTI2025

Denoising Autoencoder for Reconstructing Sensor Observation Data and Predicting Evapotranspiration: Noisy and Missing Values Repair and Uncertainty Quantification

Abstract Machine learning (ML) methods applied in scientific research often deal with interrelated features in high‐dimensional data. Reducing data noise and redundancy is needed to increase prediction accuracy and efficiency especially when dealing with data from field sensors. We explored an unsupervised learning method, the denoising autoencoder (DAE), to extract the underlying data structure from noisy raw data in the context of predicting hydrologic quantities from multiple field sensors. These sensors have intrinsic instrumental noise and occasional malfunctions that cause missing values. Our DAE neural network reconstructed meteorological sensor data containing noise and missing values to predict evapotranspiration in a mountainous watershed. The DAE reconstructed the sensor variables with a mean coefficient of determination value of 0.77 across 15 dimensions representing individual sensors. It reduced variance and bias uncertainties compared to a classical autoencoder model. The reconstruction quality varied across dimensions depending on their cross‐correlation and alignment with the underlying data structure. Uncertainties arising from the model structure were overall higher than those resulting from data corruption. We attached the DAE structure to a downstream ET‐prediction neural network in three formats and achieved reasonably accurate ET predictions . The use of the DAE notably reduced variance uncertainty in ET prediction. However, excessive variance reduction may be accompanied by an increase in bias due to the intrinsic bias‐variance tradeoff. Our method of evaluating and reducing uncertainties in aggregated data from different sources can be used to improve predictive models, process understanding, and uncertainty quantification for better water resource management. Plain Language Summary We present a machine learning method, namely the denoising autoencoder, which reduces the effects of data noise and missing values typically present in scientific data sets collected through sensor measurements. This method selects the most relevant information from noisy raw data collected by the instruments and fills in missing values. To demonstrate the effectiveness of our method, we applied it to predict evapotranspiration, a hydrologic variable that represents the water moved from the land surface to the atmosphere through a combination of evaporation and plant water use (transpiration). We also used a random sampling technique (the Monte Carlo method) to compare the uncertainty in the predictions when using the raw and noisy data versus the reconstructed data. The denoising process produced more accurate predictions of evapotranspiration with less uncertainty. Improved predictions of evapotranspiration can lead to a better understanding and accounting of water budgets. This ML approach is broadly suitable for a wide variety of applications that involve noisy sensor data with missing values. Key Points We used a denoising autoencoder (DAE) neural network to reduce noise in meteorological and soil sensor observations by on average We used Monte Carlo sampling to estimate the bias and variance of all model outputs, including uncertainty sources from data and the model We attached the DAE component to a downstream neural network to predict ET with the variance reduced by , compared to that without the DAE

denoising autoencoder↗
NASA NTRS2011

Chlorophyll-a Algorithms for Oligotrophic Oceans: A Novel Approach Based on Three-Band Reflectance Difference

A new empirical algorithm is proposed to estimate surface chlorophyll-a concentrations (Chl) in the global ocean for Chl less than or equal to 0.25 milligrams per cubic meters (approximately 77% of the global ocean area). The algorithm is based on a color index (CI), defined as the difference between remote sensing reflectance (R(sub rs), sr(sup -1) in the green and a reference formed linearly between R(sub rs) in the blue and red. For low Chl waters, in situ data showed a tighter (and therefore better) relationship between CI and Chl than between traditional band-ratios and Chl, which was further validated using global data collected concurrently by ship-borne and SeaWiFS satellite instruments. Model simulations showed that for low Chl waters, compared with the band-ratio algorithm, the CI-based algorithm (CIA) was more tolerant to changes in chlorophyll-specific backscattering coefficient, and performed similarly for different relative contributions of non-phytoplankton absorption. Simulations using existing atmospheric correction approaches further demonstrated that the CIA was much less sensitive than band-ratio algorithms to various errors induced by instrument noise and imperfect atmospheric correction (including sun glint and whitecap corrections). Image and time-series analyses of SeaWiFS and MODIS/Aqua data also showed improved performance in terms of reduced image noise, more coherent spatial and temporal patterns, and consistency between the two sensors. The reduction in noise and other errors is particularly useful to improve the detection of various ocean features such as eddies. Preliminary tests over MERIS and CZCS data indicate that the new approach should be generally applicable to all existing and future ocean color instruments.

Hu, Chuanmin↗
DOE OSTI2025

Combining quantum noise reduction resources: A practical approach

Optomechanical sensors are capable of transducing external perturbations to resolvable optical signals. A particular regime of interest is that of high-bandwidth force detection, where an impulse is delivered to the system over a short period of time. Exceedingly sensitive impulse detection has been proposed to observe very weak signals like those due to long-range interactions with dark matter that require much higher sensitivities than current sensors can provide. Quantum resources to go beyond the traditional standard quantum limit of these sensors include squeezing of the light used to transduce the signal, backaction evasion by measuring the optimal quadrature, and quantum nondemolition (QND) measurements that reduce backaction directly. These methods have been developed in the context of gravitational wave detection for target frequencies in the audio band range. Here, we provide the theoretical limits to quantum noise reduction for higher and broader frequency targets, such as those from dark matter signals, while combining quantum-enhanced readout techniques based on squeezed light and QND measurements with optomechanical sensors. Here, we demonstrate that backaction evasion through QND techniques dramatically reduces the technical challenges presented when using squeezed light for broadband force detection, paving the way for combining multiple quantum noise reduction techniques for enhanced sensitivity in the context of impulse metrology.

Optomechanics↗
NASA NTRS2002

A Comparison Study of Magnetic Bearing Controllers for a Fully Suspended Dynamic Spin Rig

NASA Glenn Research Center (GRC) has developed a fully suspended magnetic bearing system for the Dynamic Spin Rig (DSR) that is used to perform vibration tests of turbomachinery blades and components under spinning conditions in a vacuum. Two heteropolar radial magnetic bearings and a thrust bearing and the associated control system were integrated into the DSR to provide noncontact magnetic suspension and mechanical excitation of the 35 lb vertical rotor with blades to induce turbomachinery blade vibration. A simple proportional-integral-derivative (PID) controller with a special feature for multidirectional radial excitation worked very well to both support and shake the shaft with blades. However, more advanced controllers were developed and successfully tested to determine the optimal controller in terms of sensor and processing noise reduction, smaller rotor orbits, and energy savings for the system. The test results of a variety of controllers we demonstrated up to the rig's maximum allowable speed of 10,000 rpm are shown.

Choi, Benjamin↗
NASA NTRS2003

Control Study for Five-axis Dynamic Spin Rig Using Magnetic Bearings

The NASA Glenn Research Center (GRC) has developed a magnetic bearing system for the Dynamic Spin Rig (DSR) with a fully suspended shaft that is used to perform vibration tests of turbomachinery blades and components under spinning conditions in a vacuum. Two heteropolar radial magnetic bearings and a thrust magnetic bearing and the associated control system were integrated into the DSR to provide magnetic excitation as well as non-contact mag- netic suspension of a 15.88 kg (35 lb) vertical rotor with blades to induce turbomachinery blade vibration. For rotor levitation, a proportional-integral-derivative (PID) controller with a special feature for multidirectional radial excitation worked well to both support and shake the shaft with blades. However, more advanced controllers were developed and successfully tested to determine the optimal controller in terms of sensor and processing noise reduction, smaller rotor orbits, more blade vibration amplitude, and energy savings for the system. The test results of a variety of controllers that were demonstrated up to 10.000 rpm are shown. Furthermore, rotor excitation operation and conceptual study of active blade vibration control are addressed.

Choi, Benjamin↗
NASA NTRS2001

Evaluation and Improvement of Eddy Current Position Sensors in Magnetically Suspended Flywheel Systems

Eddy current position sensor performance is evaluated for use in a high-speed flywheel development system. The flywheel utilizes a five axis active magnetic bearing system. The eddy current sensors are used for position feedback for the bearing controller. Measured characteristics include sensitivity to multiple target materials and susceptibility to noise from the magnetic bearings and from sensor-to-sensor crosstalk. Improvements in axial sensor configuration and techniques for noise reduction are described.

Dever, Timothy P.↗
NASA NTRS2019

Enhancement of the AC RPA

The overall goal of this research project is to improve the response and sensitivity of the AC Retarding Potential Analyzer (RPA). The AC RPA can accurately measure the flux, energy, and energy distribution of charged particles in a space environment. The enhancement of the sensor derives from changes that increase sensitivity of flux measurements through reduction of the baseline noise. The enhanced AC RPA sensor allows diagnosis of required charge particle beams necessary for tests of materials, instruments and subsystems, for future exploration missions.

Israel, Mordecai Ben↗
NASA NTRS1995

Signal processing for ION mobility spectrometers

Signal processing techniques for systems based upon Ion Mobility Spectrometry will be discussed in the light of 10 years of experience in the design of real-time IMS. Among the topics to be covered are compensation techniques for variations in the number density of the gas - the use of an internal standard (a reference peak) or pressure and temperature sensors. Sources of noise and methods for noise reduction will be discussed together with resolution limitations and the ability of deconvolution techniques to improve resolving power. The use of neural networks (either by themselves or as a component part of a processing system) will be reviewed.

Taylor, S.↗
DOE OSTI2026

Robustness of the smartpixels classifier for different simulated sensor geometries and non-ideal detector conditions

Pixel tracking detectors at upcoming collider experiments will see unprecedented charged-particle densities. Real-time data reduction on the detector will enable higher granularity and faster readout, possibly enabling the use of the pixel detector in high-rate online event selection, such as the ATLAS or CMS first-level trigger systems. This data reduction can be accomplished with a neural network (NN) in the readout chip bonded with the sensor that recognizes and rejects tracks with low transverse momentum (p T ) based on the geometrical shape of the charge deposition (“cluster”). To design viable detectors for deployment, the dependence of the NN as a function of the sensor geometry, external magnetic field, irradiation, and noise must be understood. In this paper, we present first studies of the efficiency and data reduction for planar pixel sensors exploring these parameters. For the CMS HL-LHC sensor geometry, we obtain a signal efficiency of (91.9 ± 0.7)% and a data reduction of (29.7 ± 1.0)%. A smaller sensor pitch in the bending direction improves the p T discrimination, but a larger pitch can be partially compensated with detector thickness. Any accumulated radiation damage also changes the cluster shape, reducing the signal efficiency compared to the baseline by approximately 30–60% in absolute terms, but nearly all of the performance can be recovered through retraining of the network and updating the weights. Finally, the impact of noise was investigated, and retraining the network on noise-injected datasets was found to maintain performance within 6% of the baseline network trained and evaluated on noiseless data. •ASIC-compatible track-momentum classifier is robust in realistic detector conditions.•About 90% signal efficiency and 30% data reduction per layer for CMS HL-LHC geometry.•Single-layer signal efficiency increases for smaller pixel pitch or thicker sensors.•Performance with noise or after radiation damage mostly recovered by retraining.

Shekar, Danush [Illinois U., Chicago] (ORCID:00000↗
DOE OSTI2024

Skipper-in-CMOS: Nondestructive Readout With Subelectron Noise Performance for Pixel Detectors

The Skipper-in-CMOS image sensor integrates the nondestructive readout capability of skipper charge coupled devices (Skipper-CCDs) with the high conversion gain of a pinned photodiode (PPD) in a CMOS imaging process while taking advantage of in-pixel signal processing. This allows both single photon counting as well as high frame rate readout through highly parallel processing. The first results obtained from a ${15} \times {15}~\mu $ m2 pixel cell of a Skipper-in-CMOS sensor fabricated in Tower Semiconductor’s commercial 180-nm CMOS image sensor process are presented. Measurements confirm the expected reduction of the readout noise with the number of samples down to deep subelectron noise of $0.15\text {e}^ - $ , demonstrating the charge transfer operation from the PPD and the single photon counting operation when the sensor is exposed to light. This article also discusses new testing strategies employed for its operation and characterization.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS2024

Noise Reduction for Solar-Induced Fluorescence Retrievals Using Machine Learning and Principal Component Analysis: Simulations and Applications to GOME-2 Satellite Retrievals

We use a spectral-based approach that employs principal component analysis along with a relatively shallow artificial neural network (NN) to substantially reduce noise and other artifacts in terrestrial chlorophyll solar-induced fluorescence (SIF) retrievals. SIF is a very small emission at red and far-red wavelengths that is difficult to measure and is highly sensitive to random errors and systematic artifacts. Our approach relies upon an assumption that a trained NN can effectively reconstruct the total SIF signal from a relatively small number of leading principal components of the satellite-observed far-red radiance spectra without using information from the trailing modes that contain most of the random errors. We test the approach with simulated reflectance spectra produced with a full atmospheric and surface radiative transfer model using different observing and geophysical parameters and various noise levels. Resulting noisy and noise-reduced retrieved SIF values are compared with true values to assess performance. We then apply our noise reduction approach to SIF derived from two different satellite spectrometers. For evaluation, since the truth in this case is unknown, we compare SIF retrievals from two independent sensors with each other. We also compare the noise-reduced SIF temporal variations with those from an independent gross primary product (GPP) product that should display similar variations. Results show that our noise reduction approach improves capture of SIF seasonal and inter-annual variability. Our approach should be applicable to many noisy data products derived from spectral measurements. Our methodology does not replace the original retrieval algorithms; rather the original noisy retrievals are needed as the target for the NN training process.

noise reduction, solar-induced fluorescence, SIF, ↗
NASA NTRSDate not supplied

Applicability of Loads Estimation Techniques Using Sparse Acceleration Sensor Data to Spacecraft Structural Health Monitoring

The use of structural health monitoring systems on spacecraft structures can play a crucial role in ensuring the safety, reliability, and longevity of the structure by gathering and analyzing onboard sensor data. Of specific importance is monitoring for excessive loading at critical interfaces as any off-nominal structural excitations experienced by spacecraft structures can cause early unpredicted high structural life consumption or damage. The availability and cost of flight-certified sensors along with the size of spacecraft structures and allowable payload mass drives the need for a method to estimate loads using sparsely-located sensors. Numerous approaches such as physics-based, statistical learning, and physics-enhanced statistical learning algorithms have gained popularity among structural prognostics applications. However, developing noise-robust prediction models to assess loads and structural life predictions from a sparse multi-sensor data acquisition system can be a challenging task. This paper discusses the evaluation of physics-based versus machine-learning algorithms for predicting loads and structural life at mission critical locations on the spacecraft structure using a finite element loads analysis with the application of simulated noise and noise reduction techniques. To estimate the loads from accelerations, the physics-based algorithm leverages a loads transformation matrix from a Craig-Bampton reduced finite element model. A System Equivalent Reduction Expansion Process (SEREP) and a pseudo-inverse approach are considered to expand from the onboard sensor degrees of freedom to the Craig-Bampton model degrees of freedom. The machine learning algorithm provides a data driven solution/mapping of the sensor accelerations to the loads at the mission critical locations using a high dimensionality analysis. Although these strategies produce comparable loads prediction without noise, the limitations of these strategies with incorporating simulated noise and noise reduction techniques with low signal to noise ratio signals are evaluated. The study demonstrates the immense potential of statistical learning algorithms for sparse structural prognostic models and enhancing signal denoising techniques. These findings also highlight the need for noise-resilient prognostic models and low-noise data acquisition systems onboard spacecraft structures.

Spacecraft Structural Health Monitoring↗