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At least 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Stochastic Virtual Battery Modeling of Uncertain Electrical Loads using Variational Autoencoder

Effective utilization of flexible loads for grid services, while satisfying end-user preferences and constraints, requires an accurate estimation of the aggregated predictive flexibility offered by the electrical loads. Recently, there have been efforts to quantify the predictive flexibility of thermostatic loads (e.g. residential air-conditioners, electric water-heaters) using the notion of virtual battery (VB), whose state evolution is governed by a first order dynamics including self-dissipation rate, and power and energy capacities. Identifying the VB model parameters for a collection of thermostatic loads, however, is challenging primarily due to uncertainties and lack of information regarding the end-user behavior, underlying device models and parameters. In this paper, we propose a \textit{variational autoencoder}-based deep learning algorithm to identify the parameters of the VB model. Using available sensors and meters data, the proposed algorithm generates not only point estimates of the VB parameters, but also confidence intervals around those values. Effectiveness of the proposed frameworks is demonstrated on a collection of electric water-heater loads, whose operation is driven by uncertain water usage profiles.

virtual battery, deep learning algorithms↗
DOE OSTI2021

Hardware-Based Emulator with Deep Learning Model for Building Energy Control and Prediction Based on Occupancy Sensors’ Data

Heating, ventilation, and air conditioning (HVAC) is the largest source of residential energy consumption. Occupancy sensors’ data can be used for HVAC control since it indicates the number of people in the building. HVAC and sensors form a typical cyber-physical system (CPS). In this paper, we aim to build a hardware-based emulation platform to study the occupancy data’s features, which can be further extracted by using machine learning models. In particular, we propose two hardware-based emulators to investigate the use of wired/wireless communication interfaces for occupancy sensor-based building CPS control, and the use of deep learning to predict the building energy consumption with the sensor data. We hypothesize is that the building energy consumption may be predicted by using the occupancy data collected by the sensors, and question what type of prediction model should be used to accurately predict the energy load. Another hypothesis is that an in-lab hardware/software platform could be built to emulate the occupancy sensing process. The machine learning algorithms can then be used to analyze the energy load based on the sensing data. To test the emulator, the occupancy data from the sensors is used to predict energy consumption. The synchronization scheme between sensors and the HVAC server will be discussed. We have built two hardware/software emulation platforms to investigate the sensor/HVAC integration strategies, and used an enhanced deep learning model—which has sequence-to-sequence long short-term memory (Seq2Seq LSTM)—with an attention model to predict the building energy consumption with the preservation of the intrinsic patterns. Because the long-range temporal dependencies are captured, the Seq2Seq models may provide a higher accuracy by using LSTM architectures with encoder and decoder. Meanwhile, LSTMs can capture the temporal and spatial patterns of time series data. The attention model can highlight the most relevant input information in the energy prediction by allocating the attention weights. The communication overhead between the sensors and the HVAC control server can also be alleviated via the attention mechanism, which can automatically ignore the irrelevant information and amplify the relevant information during CNN training. Our experiments and performance analysis show that, compared with the traditional LSTM neural network, the performance of the proposed method has a 30% higher prediction accuracy.

Ye, Zhijing↗
NASA NTRS1993

Kalman filter estimation of attitude and gyro bias with the QUEST observation model

The loss function for the Wahba attitude estimation problem employs unit vector observations with scalar weights. It is usually associated with the QUEST observation model, where the actual sensor noise is assumed the same for all components of the observed vector, regardless of the position in the field of view. The QUEST model has the great advantage of being sensor-independent except for the scalar parameter that characterizes the sensor errors. Although efficient algorithms for solving the Wahba problem exist, extending these algorithms to estimate gyro biases or sensor alignments has had mixed success. However, it is straightforward to estimate bias and alignment parameters with a Kalman filter. This paper investigates the use of an extended Kalman filter for the attitude and gyro bias that incorporates the QUEST observation model, to be referred to as the Unit Vector Filter (UVF). The UVF results are compared with those from a more conventional filter, the Real-Time Sequential Filter (RTSF), for which the residual is the two-dimensional projection of the unit vector onto the plane perpendicular to the sensor boresight. The RTSF is similar in design to that used by Multimission Modular Spacecraft for onboard attitude determination. An apparent obstacle to the use of unit vectors as measurements is their singular noise covariance matrix. Shuster has shown that this problem should not affect filter performance. The UVF and RTSF are tested using actual flight data from the Extreme Ultraviolet Explorer (EUVE). It is found that these filters generate nearly identical attitude and gyro bias estimates, thus validating the use of unit vectors and the QUEST noise model.

Sedlak, J.↗
NASA NTRS1980

A model of plant canopy polarization response

Sensors to remotely measure the linear polarization of ground scenes have been proposed for the Multispectral Resource Sampler (MRS), a satellite sensor system proposed to complement the Thematic Mapper. At present justification for a sensor on MRS to measure scene polarization is limited. This paper discusses a model for the amount of linearly polarized light reflected by the shiny leaves of such crops as wheat, corn, and sorghum. The theory demonstrates that, potentially, measurements of the linearly polarized light from a crop canopy may be used as an additional feature to discriminate between crops. Examination of the model suggests that, potentially, satellite polarization measurements may be used to monitor crop development stage, leaf water content, leaf area index, hail damage, and certain plant diseases. The model adds to the understanding of the potential information content of scene polarization measurements acquired by future satellite sensor systems such as MRS.

Vanderbilt, V. C.↗
DOE OSTI2015

Novel Sensor for the In Situ Measurement of Uranium Fluxes

The goal of this project was to develop a sensor that incorporates the field-tested concepts of the passive flux meter to provide direct in situ measures of flux for uranium and groundwater in porous media. Measurable contaminant fluxes [J] are essentially the product of concentration [C] and groundwater flux or specific discharge [q ]. The sensor measures [J] and [q] by changes in contaminant and tracer amounts respectively on a sorbent. By using measurement rather than inference from static parameters, the sensor can directly advance conceptual and computational models for field scale simulations. The sensor was deployed in conjunction with DOE in obtaining field-scale quantification of subsurface processes affecting uranium transport (e.g., advection) and transformation (e.g., uranium attenuation) at the Rifle IFRC Site in Rifle, Colorado. Project results have expanded our current understanding of how field-scale spatial variations in fluxes of uranium, groundwater and salient electron donor/acceptors are coupled to spatial variations in measured microbial biomass/community composition, effective field-scale uranium mass balances, attenuation, and stability. The coupling between uranium, various nutrients and micro flora can be used to estimate field-scale rates of uranium attenuation and field-scale transitions in microbial communities. This research focuses on uranium (VI), but the sensor principles and design are applicable to field-scale fate and transport of other radionuclides. Laboratory studies focused on sorbent selection and calibration, along with sensor development and validation under controlled conditions. Field studies were conducted at the Rifle IFRC Site in Rifle, Colorado. These studies were closely coordinated with existing SBR (formerly ERSP) projects to complement data collection. Small field tests were conducted during the first two years that focused on evaluating field-scale deployment procedures and validating sensor performance under controlled field conditions. In the third and fourth year a suite of larger field studies were conducted. For these studies, the uranium flux sensor was used with uranium speciation measurements and molecular-biological tools to characterize microbial community and active biomass at synonymous wells distributed in a large grid. These field efforts quantified spatial changes in uranium flux and field-scale rates of uranium attenuation (ambient and stimulated), uranium stability, and quantitatively assessed how fluxes and effective reaction rates were coupled to spatial variations in microbial community and active biomass. Analyses of data from these field experiments were used to generate estimates of Monod kinetic parameters that are ‘effective’ in nature and optimal for modeling uranium fate and transport at the field-scale. This project provided the opportunity to develop the first sensor that provides direct measures of both uranium (VI) and groundwater flux. A multidisciplinary team was assembled to include two geochemists, a microbiologist, and two quantitative contaminant hydrologists. Now that the project is complete, the sensor can be deployed at DOE sites to evaluate field-scale uranium attenuation, source behavior, the efficacy of remediation, and off-site risk. Because the sensor requires no power, it can be deployed at remote sites for periods of days to months. The fundamental science derived from this project can be used to advance the development of predictive models for various transport and attenuation processes in aquifers. Proper development of these models is critical for long-term stewardship of contaminated sites in the context of predicting uranium source behavior, remediation performance, and off-site risk.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Large area CMOS active pixel sensor x-ray imager for digital breast tomosynthesis: Analysis, modeling, and characterization

Purpose: Large area x-ray imagers based on complementary metal-oxide-semiconductor (CMOS) active pixel sensor (APS) technology have been proposed for various medical imaging applications including digital breast tomosynthesis (DBT). The low electronic noise (50–300 e{sup −}) of CMOS APS x-ray imagers provides a possible route to shrink the pixel pitch to smaller than 75 μm for microcalcification detection and possible reduction of the DBT mean glandular dose (MGD). Methods: In this study, imaging performance of a large area (29 × 23 cm{sup 2}) CMOS APS x-ray imager [Dexela 2923 MAM (PerkinElmer, London)] with a pixel pitch of 75 μm was characterized and modeled. The authors developed a cascaded system model for CMOS APS x-ray imagers using both a broadband x-ray radiation and monochromatic synchrotron radiation. The experimental data including modulation transfer function, noise power spectrum, and detective quantum efficiency (DQE) were theoretically described using the proposed cascaded system model with satisfactory consistency to experimental results. Both high full well and low full well (LFW) modes of the Dexela 2923 MAM CMOS APS x-ray imager were characterized and modeled. The cascaded system analysis results were further used to extract the contrast-to-noise ratio (CNR) for microcalcifications with sizes of 165–400 μm at various MGDs. The impact of electronic noise on CNR was also evaluated. Results: The LFW mode shows better DQE at low air kerma (K{sub a} < 10 μGy) and should be used for DBT. At current DBT applications, air kerma (K{sub a} ∼ 10 μGy, broadband radiation of 28 kVp), DQE of more than 0.7 and ∼0.3 was achieved using the LFW mode at spatial frequency of 0.5 line pairs per millimeter (lp/mm) and Nyquist frequency ∼6.7 lp/mm, respectively. It is shown that microcalcifications of 165–400 μm in size can be resolved using a MGD range of 0.3–1 mGy, respectively. In comparison to a General Electric GEN2 prototype DBT system (at MGD of 2.5 mGy), an increased CNR (by ∼10) for microcalcifications was observed using the Dexela 2923 MAM CMOS APS x-ray imager at a lower MGD (2.0 mGy). Conclusions: The Dexela 2923 MAM CMOS APS x-ray imager is capable to achieve a high imaging performance at spatial frequencies up to 6.7 lp/mm. Microcalcifications of 165 μm are distinguishable based on reported data and their modeling results due to the small pixel pitch of 75 μm. At the same time, potential dose reduction is expected using the studied CMOS APS x-ray imager.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2006

Performance of Dispersed Fringe Sensor in the Presence of Segmented Mirror Aberrations: Modeling and Simulation

Dispersed Fringe Sensing (DFS) is an efficient and robust method for coarse phasing of a segmented primary mirror such as the James Webb Space Telescope (JWST). In this paper, modeling and simulations are used to study the effect of segmented mirror aberrations on the fringe image, DFS signals and DFS detection accuracy. The study has shown due to the pixilation spatial filter effect from DFS signal extraction the effect of wavefront error is reduced and DFS algorithm will be more robust against wavefront aberration by using multi-trace DFS approach. We also studied the JWST Dispersed Hartmann Sensor (DHS) performance in presence of wavefront aberrations caused by the gravity sag and we use the scaled gravity sag to explore the JWST DHS performance relationship with the level of the wavefront aberration. This also includes the effect from line-of-sight jitter.

segmented mirrors↗
NASA NTRS1987

Development of response models for the Earth Radiation Budget Experiment (ERBE) sensors. Part 3: ERBE scanner measurement accuracy analysis due to reduced housekeeping data

The accuracy of scanner measurements was evaluated when the sampling frequency of sensor housekeeping (HK) data was reduced from once every scan to once every eight scans. The resulting increase in uncertainty was greatest for sources with rapid or extreme temperature changes. This analysis focused on the mirror attenuator mosaic (MAM) baffle and plate and scanner radiometer baffle due to their relatively high temperature changes during solar calibrations. Since only solar simulator data were available, the solar temperatures were approximated on these components and the radiative and thermal gradients in the MAM baffle due to reflected sunlight. Of the two cases considered for the MAM plate and baffle temperatures, one uses temperatures obtained from the ground calibration. The other attempt uses temperatures computed from the MAM baffle model. This analysis shows that the heat input variations due largely to the solar radiance and irradiance during a scan cycle are small. It also demonstrates that reasonable intervals longer than the current HK data acquisition interval should not significantly affect the estimation of a radiation field in the sensor field-of-view.

Choi, Sang H.↗
NASA NTRS1994

From Antarctica to space: Use of telepresence and virtual reality in control of remote vehicles

In the Fall of 1993, NASA Ames deployed a modified Phantom S2 Remotely-Operated underwater Vehicle (ROV) into an ice-covered sea environment near McMurdo Science Station, Antarctica. This deployment was part of the antarctic Space Analog Program, a joint program between NASA and the National Science Foundation to demonstrate technologies relevant for space exploration in realistic field setting in the Antarctic. The goal of the mission was to operationally test the use of telepresence and virtual reality technology in the operator interface to a remote vehicle, while performing a benthic ecology study. The vehicle was operated both locally, from above a dive hole in the ice through which it was launched, and remotely over a satellite communications link from a control room at NASA's Ames Research Center. Local control of the vehicle was accomplished using the standard Phantom control box containing joysticks and switches, with the operator viewing stereo video camera images on a stereo display monitor. Remote control of the vehicle over the satellite link was accomplished using the Virtual Environment Vehicle Interface (VEVI) control software developed at NASA Ames. The remote operator interface included either a stereo display monitor similar to that used locally or a stereo head-mounted head-tracked display. The compressed video signal from the vehicle was transmitted to NASA Ames over a 768 Kbps satellite channel. Another channel was used to provide a bi-directional Internet link to the vehicle control computer through which the command and telemetry signals traveled, along with a bi-directional telephone service. In addition to the live stereo video from the satellite link, the operator could view a computer-generated graphic representation of the underwater terrain, modeled from the vehicle's sensors. The virtual environment contained an animate graphic model of the vehicle which reflected the state of the actual vehicle, along with ancillary information such as the vehicle track, science markers, and locations of video snapshots. The actual vehicle was driven either from within the virtual environment or through a telepresence interface. All vehicle functions could be controlled remotely over the satellite link.

Stoker, Carol↗
NASA NTRS2021

Estimating Terrestrial Snow Mass via Multi-sensor Assimilation of Synthetic AMSR-E Brightness Temperature Spectral Differences and Synthetic GRACE Terrestrial Water Storage Retrievals

This study explores multi-sensor data assimilation (DA) using synthetic Advanced Microwave Scanning Radiometer (AMSR-E) passive microwave brightness temperature spectral differences (∆T(B)) and synthetic Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage (TWS) retrievals to improve estimates of snow water equivalent (SWE), subsurface water storage, and TWS across snow-covered terrain. Results show that multi-sensor DA improves SWE estimates by reducing the RMSE by 14.1% relative to a model-only simulation. Multi-sensor assimilation also yields the smallest TWS RMSE (reduced by 13.0% relative to a model-only simulation). However, multi-sensor DA does not always yield complementary updates, and can sometimes lead to conflicting changes to SWE, where the assimilation of synthetic ∆T(b) generates positive SWE increments while the assimilation of synthetic TWS removes SWE, which can ultimately degrade the posterior SWE estimates. This synthetic experiment provides useful insight for future DA experiments using real-world AMSR-E/AMSR-2 ∆T(b) observations and GRACE/GRACE-FO TWS retrievals to better characterize terrestrial freshwater storage across regional scales.

Jing Wang↗
DOE OSTI2019

Comparative performance analysis of relative humidity sensor based on intermodal interference using tapered square no-core optical fiber and tapered cylindrical optical fiber

This work presents the computational evaluation of a new model for relative humidity sensor based on the principle of intermodal interference using a square-section no-core tapered optical fiber compared to a classical cylindrical tapered optical fiber sensor. Attempting at a new application of this type of sensor, simulations were carried out in the range of up to 60% of relative humidity, the main interest range of the pharmaceutical industry. Several materials were used in its modeling and an analysis was developed in its constructive dimensions, in order to verify the influence of each of them on the sensor performance.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS2015

The Parameterization of Top-Hat Particle Sensors with Microchannel-Plate-Based Detection Systems and its Application to the Fast Plasma Investigation on NASA's Magnetospheric MultiScale Mission

The most common instrument for low energy plasmas consists of a top-hat electrostatic analyzer geometry coupled with a microchannel-plate (MCP)-based detection system. While the electrostatic optics for such sensors are readily simulated and parameterized during the laboratory calibration process, the detection system is often less well characterized. Furthermore, due to finite resources, for large sensor suites such as the Fast Plasma Investigation (FPI) on NASA's Magnetospheric Multiscale (MMS) mission, calibration data are increasingly sparse. Measurements must be interpolated and extrapolated to understand instrument behavior for untestable operating modes and yet sensor inter-calibration is critical to mission success. To characterize instruments from a minimal set of parameters we have developed the first comprehensive mathematical description of both sensor electrostatic optics and particle detection systems. We include effects of MCP efficiency, gain, scattering, capacitive crosstalk, and charge cloud spreading at the detector output. Our parameterization enables the interpolation and extrapolation of instrument response to all relevant particle energies, detector high voltage settings, and polar angles from a small set of calibration data. We apply this model to the 32 sensor heads in the Dual Electron Sensor (DES) and 32 sensor heads in the Dual Ion Sensor (DIS) instruments on the 4 MMS observatories and use least squares fitting of calibration data to extract all key instrument parameters. Parameters that will evolve in flight, namely MCP gain, will be determined daily through application of this model to specifically tailored in-flight calibration activities, providing a robust characterization of sensor suite performance throughout mission lifetime. Beyond FPI, our model provides a valuable framework for the simulation and evaluation of future detection system designs and can be used to maximize instrument understanding with minimal calibration resources.

Instrument-model↗
DOE OSTI2017

Exploiting Cross-sensitivity by Bayesian Decoding of Mixed Potential Sensor Arrays

LANL mixed-potential electrochemical sensor (MPES) device arrays were coupled with advanced Bayesian inference treatment of the physical model of relevant sensor-analyte interactions. We demonstrated that our approach could be used to uniquely discriminate the composition of ternary gas sensors with three discreet MPES sensors with an average error of less than 2%. We also observed that the MPES exhibited excellent stability over a year of operation at elevated temperatures in the presence of test gases.

47 OTHER INSTRUMENTATION↗
NASA NTRS1989

Microwave remote sensing and radar polarization signatures of natural fields

Theoretical models developed for simulation of microwave remote sensing of the Earth surface from airborne/spaceborne sensors are described. Theoretical model calculations were performed and the results were compared with data of field measurements. Data studied included polarimetric images at the frequencies of P band, L band, and C band, acquired with airborne polarimeters over a agricultural field test site. Radar polarization signatures from bare soil surfaces and from tree covered fields were obtained from the data. The models developed in this report include: (1) Small perturbation model of wave scatterings from randomly rough surfaces, (2) Physical optics model, (3) Geometrical optics model, and (4) Electromagnetic wave scattering from dielectric cylinders of finite lengths, which replace the trees and branches in the modeling of tree covered field. Additionally, a three-layer emissivity model for passive sensing of a vegetation covered soil surface is also developed. The effects of surface roughness, soil moisture contents, and tree parameters on the polarization signatures were investigated.

Mo, Tsan↗
DOE OSTI2016

TH-CD-207B-07: Noise Modeling of Single Photon Avalanche Diode (SPAD) for Photon Counting CT Applications

Purpose: Silicon photomultiplier (SiPM) has recently emerged as a promising photodetector for biomedical imaging applications. Due to its high multiplication gain (comparable to PMT), fast timing, low cost and compactness, it is considered a good candidate for photon counting CT. Dark noise is a limiting factor which impacts both energy resolution and detection dynamic range. Our goal is to develop a comprehensive model for noise sources for SiPM sensors. Methods: The physical parameters used in this work were based upon a test SPAD fabricated in 130nm CMOS process. The SPAD uses an n+/p-well junction, which is isolated from the p-substrate by a deep n-well junction. Inter-avalanche time measurement was used to record the time interval between two adjacent avalanche pulses. After collecting 1×106 counts, the histogram was obtained and multiple exponential fitting process was used to extract the lifetime associated with the traps within the bandgap. Results: At room temperature, the breakdown voltage of the SPAD is ∼11.4V and shows a temperature coefficient of 7.7mV/°C. The dark noise of SPAD increases with both the excess biasing voltage and temperature. The primary dark counts from the model were validated against the measurement results. A maximum relative error of 8.7% is observed at 20 °C with an excess voltage of 0.5V. The probabilities of after-pulsing are found to be dependent of both temperature and excess voltage. With 0.5V excess voltage, the after-pulsing probability is 63.5% at - 30 °C and drops to ∼6.6% at 40 °C. Conclusion: A comprehensive noise model for SPAD sensor was proposed. The model takes into account of static, dynamic and statistical behavior of SPADs. We believe that this is the first SPAD circuit simulation model that includes the band-to-band tunneling dark noise contribution and temporal dependence of the after-pulsing probability.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1979

Low-speed wind-tunnel parametric investigation of flight spoilers as trailing-vortex-alleviation devices on a transport aircraft model

The trailing-wing sensor technique was used in the Langley V/STOL tunnel to determine the effectiveness of 11 combinations of the existing flight-spoiler segments on a jumbo-jet transport aircraft model when they were deflected as trailing-vortex-alleviation devices. All 11 of the flight-spoiler configurations investigated were effective in reducing the induced rolling moment on the trailing model. This investigation is an extension of earlier wind-tunnel and flight tests which showed that the existing flight spoilers on the jumbo-jet aircraft can be used as effective trailing-vortex-alleviation devices. Essentially, all of the reduction in induced rolling moment on the trailing-wing model was realized at a spoiler deflection of 45 deg for single-spoiler configurations, 30 for two-spoiler configurations, and 15 deg for both the three- and four-spoiler configurations. Of the 11 flight-spoiler configurations investigated, the most promising configuration for trailing-vortex abatement on the jumbo-jet aircraft appears to be the three inboard flight spoilers deflected 15 deg.

Croom, D. R.↗
NASA NTRS2014

Calibrating Historical IR Sensors Using GEO, and AVHRR Infrared Tropical Mean Calibration Models

Long-term, remote-sensing-based climate data records (CDRs) are highly dependent on having consistent, wellcalibrated satellite instrument measurements of the Earth's radiant energy. Therefore, by making historical satellite calibrations consistent with those of today's imagers, the Earth-observing community can benefit from a CDR that spans a minimum of 30 years. Most operational meteorological satellites rely on an onboard blackbody and space looks to provide on-orbit IR calibration, but neither target is traceable to absolute standards. The IR channels can also be affected by ice on the detector window, angle dependency of the scan mirror emissivity, stray-light, and detector-to-detector striping. Being able to quantify and correct such degradations would mean IR data from any satellite imager could contribute to a CDR. Recent efforts have focused on utilizing well-calibrated modern hyper-spectral sensors to intercalibrate concurrent operational IR imagers to a single reference. In order to consistently calibrate both historical and current IR imagers to the same reference, however, another strategy is needed. Large, well-characterized tropical-domain Earth targets have the potential of providing an Earth-view reference accuracy of within 0.5 K. To that effort, NASA Langley is developing an IR tropical mean calibration model in order to calibrate historical Advanced Very High Resolution Radiometer (AVHRR) instruments. Using Meteosat-9 (Met-9) as a reference, empirical models are built based on spatially/temporally binned Met-9 and AVHRR tropical IR brightness temperatures. By demonstrating the stability of the Met-9 tropical models, NOAA-18 AVHRR can be calibrated to Met-9 by matching the AVHRR monthly histogram averages with the Met-9 model. This method is validated with ray-matched AVHRR and Met-9 biasdifference time series. Establishing the validity of this empirical model will allow for the calibration of historical AVHRR sensors to within 0.5 K, and thereby establish a climate-quality IR data record.

Scarino, Benjamin↗
NASA NTRS1990

Modal domain fiber optic sensor for closed loop vibration control of a flexible beam

The use of a modal domain sensor in a vibration control experiment is described. An optical fiber is bonded along the length of a flexible beam. A control signal derived from the output of the modal domain sensor is used to suppress vibrations induced in the beam. A distributed effect model for the modal domain sensor is developed and combined with models of the beam and actuator dynamics to produce a system suitable for control design.

Cox, D.↗