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Circuit using zero-order light is reflected to a photomultiplier in the incoming radiation of a spectrograph monitor to provide an error signal which controls the advancing and driving rate of the film through the camera.
A revised Bayesian algorithm for estimating surface rain rate, convective rain proportion, and latent heating profiles from satellite-borne passive microwave radiometer observations over ocean backgrounds is described. The algorithm searches a large database of cloud-radiative model simulations to find cloud profiles that are radiatively consistent with a given set of microwave radiance measurements. The properties of these radiatively consistent profiles are then composited to obtain best estimates of the observed properties. The revised algorithm is supported by an expanded and more physically consistent database of cloud-radiative model simulations. The algorithm also features a better quantification of the convective and nonconvective contributions to total rainfall, a new geographic database, and an improved representation of background radiances in rain-free regions. Bias and random error estimates are derived from applications of the algorithm to synthetic radiance data, based upon a subset of cloud-resolving model simulations, and from the Bayesian formulation itself. Synthetic rain-rate and latent heating estimates exhibit a trend of high (low) bias for low (high) retrieved values. The Bayesian estimates of random error are propagated to represent errors at coarser time and space resolutions, based upon applications of the algorithm to TRMM Microwave Imager (TMI) data. Errors in TMI instantaneous rain-rate estimates at 0.5 -resolution range from approximately 50% at 1 mm/h to 20% at 14 mm/h. Errors in collocated spaceborne radar rain-rate estimates are roughly 50%-80% of the TMI errors at this resolution. The estimated algorithm random error in TMI rain rates at monthly, 2.5deg resolution is relatively small (less than 6% at 5 mm day.1) in comparison with the random error resulting from infrequent satellite temporal sampling (8%-35% at the same rain rate). Percentage errors resulting from sampling decrease with increasing rain rate, and sampling errors in latent heating rates follow the same trend. Averaging over 3 months reduces sampling errors in rain rates to 6%-15% at 5 mm day.1, with proportionate reductions in latent heating sampling errors.
To demodulate a communication signal, a receiver must recover and synchronize to the symbol timing of a received waveform. In a system that utilizes digital sampling, the fidelity of synchronization is limited by the time between the symbol boundary and closest sample time location. To reduce this error, one typically uses a sample clock in excess of the symbol rate in order to provide multiple samples per symbol, thereby lowering the error limit to a fraction of a symbol time. For systems with a large modulation bandwidth, the required sample clock rate is prohibitive due to current technological barriers and processing complexity. With precise control of the phase of the sample clock, one can sample the received signal at times arbitrarily close to the symbol boundary, thus obviating the need, from a synchronization perspective, for multiple samples per symbol. Sample-clock phase-control feedback was developed for use in the demodulation of an optical communication signal, where multi-GHz modulation bandwidths would require prohibitively large sample clock frequencies for rates in excess of the symbol rate. A custom mixedsignal (RF/digital) offset phase-locked loop circuit was developed to control the phase of the 6.4-GHz clock that samples the photon-counting detector output. The offset phase-locked loop is driven by a feedback mechanism that continuously corrects for variation in the symbol time due to motion between the transmitter and receiver as well as oscillator instability. This innovation will allow significant improvements in receiver throughput; for example, the throughput of a pulse-position modulation (PPM) with 16 slots can increase from 188 Mb/s to 1.5 Gb/s.
Human/process error is potentially a large contributor to failures of aerospace systems. For some systems, such as the space shuttle main engine, large amounts of data are available, and accurate failure rates can be determined empirically. For such systems, the human error is implicit in the empirical failure rate and does not need to be quantified separately. However, for other systems, such as the solid rocket boosters, large amounts of data are not available and the failure rates must be determined by more theoretical means. When empirical data is lacking, structural models and engineering judgment must be employed. In this case, the human/process error must be modeled explicitly. An extensive literature review was performed to determine what methods already exist for modeling human error for aerospace systems. No methods that apply directly were found, although there are a number of methods that have been developed specifically for the nuclear power industry that could probably be modified for space applications. The results of the literature review are presented, as well as recommendations for future research.
Purpose: To explore vulnerabilities in the gamma index metric that undermine its wide use as a radiation therapy quality assurance tool. Methods: 2D test field pairs (images) are created specifically to achieve high gamma passing rates, but to also include gross errors by exploiting the distance-to-agreement and percent-passing components of the metric. The first set has no requirement of clinical practicality, but is intended to expose vulnerabilities. The second set exposes clinically realistic vulnerabilities. To circumvent limitations inherent to user-specific tuning of prediction algorithms to match measurements, digital test cases are manually constructed, thereby mimicking high-quality image prediction. Results: With a 3 mm distance-to-agreement metric, changing field size by ±6 mm results in a gamma passing rate over 99%. For a uniform field, a lattice of passing points spaced 5 mm apart results in a passing rate of 100%. Exploiting the percent-passing component, a 10×10 cm{sup 2} field can have a 95% passing rate when an 8 cm{sup 2}=2.8×2.8 cm{sup 2} highly out-of-tolerance (e.g. zero dose) square is missing from the comparison image. For clinically realistic vulnerabilities, an arc plan for which a 2D image is created can have a >95% passing rate solely due to agreement in the lateral spillage, with the failing 5% in the critical target region. A field with an integrated boost (e.g whole brain plus small metastases) could neglect the metastases entirely, yet still pass with a 95% threshold. All the failure modes described would be visually apparent on a gamma-map image. Conclusion: The %gamma<1 metric has significant vulnerabilities. High passing rates can obscure critical faults in hypothetical and delivered radiation doses. Great caution should be used with gamma as a QA metric; users should inspect the gamma-map. Visual analysis of gamma-maps may be impractical for cine acquisition.
The feasibility of using artificial neural networks as control systems for modern, complex aerospace vehicles is investigated via an example aircraft control design study. The problem considered is that of designing a controller for an integrated airframe/propulsion longitudinal dynamics model of a modern fighter aircraft to provide independent control of pitch rate and airspeed responses to pilot command inputs. An explicit model following controller using H infinity control design techniques is first designed to gain insight into the control problem as well as to provide a baseline for evaluation of the neurocontroller. Using the model of the desired dynamics as a command generator, a multilayer feedforward neural network is trained to control the vehicle model within the physical limitations of the actuator dynamics. This is achieved by minimizing an objective function which is a weighted sum of tracking errors and control input commands and rates. To gain insight in the neurocontrol, linearized representations of the nonlinear neurocontroller are analyzed along a commanded trajectory. Linear robustness analysis tools are then applied to the linearized neurocontroller models and to the baseline H infinity based controller. Future areas of research are identified to enhance the practical applicability of neural networks to flight control design.
The feasibility of using artificial neural network as control systems for modern, complex aerospace vehicles is investigated via an example aircraft control design study. The problem considered is that of designing a controller for an integrated airframe/propulsion longitudinal dynamics model of a modern fighter aircraft to provide independent control of pitch rate and airspeed responses to pilot command inputs. An explicit model following controller using H infinity control design techniques is first designed to gain insight into the control problem as well as to provide a baseline for evaluation of the neurocontroller. Using the model of the desired dynamics as a command generator, a multilayer feedforward neural network is trained to control the vehicle model within the physical limitations of the actuator dynamics. This is achieved by minimizing an objective function which is a weighted sum of tracking errors and control input commands and rates. To gain insight in the neurocontrol, linearized representations of the nonlinear neurocontroller are analyzed along a commanded trajectory. Linear robustness analysis tools are then applied to the linearized neurocontroller models and to the baseline H infinity based controller. Future areas of research identified to enhance the practical applicability of neural networks to flight control design.
Phasor measurement units (PMUs), a type of synchrophasor, are powerful diagnostic tools that can help avert catastrophic failures in the power grid. Because of this, PMU measurement errors are particularly worrisome. This report examines the internal and external factors contributing to PMU phase angle and frequency measurement errors and gives a reasonable explanation for them. It also analyzes the impact of those measurement errors on several synchrophasor applications: event location detection, oscillation detection, islanding detection, and dynamic line rating. The primary finding is that dynamic line rating is more likely to be influenced by measurement error. Other findings include the possibility of reporting nonoscillatory activity as an oscillation as the result of error, failing to detect oscillations submerged by error, and the unlikely impact of error on event location and islanding detection.
The use of Modelica for simulating the dynamic behaviors of building heating, ventilation, and air conditioning (HVAC) systems has gained popularity. Calibration of a model that represents large and complex HVAC systems in Modelica involves the determination of hundreds of parameters using real-world operation data. Considering the coupling effects among various components, such a calibration process is complex and time-consuming. In this study, we propose a systematic framework to efficiently calibrate and validate a complex HVAC system model in Modelica. The framework includes strategies to solicit real-system operation data and to decouple the model. The goal of this calibration framework is to accurately and efficiently determine a set of Modelica model parameters that provide a good match between the simulated and real system behaviors. To demonstrate the validity of the framework, it was applied to calibrate the parameters of a Modelica model of the air loop subsystem of a real AHU-VAV system. The results show that the calibrated model can generate simulated results, including VAV air flow rate, outdoor air flow, and fan power consumption, that match well with the operational data of the real system. The coefficient of the variation of the root mean square error, CV(RMSE), of the air flow rate and power consumption are 14.6 % and 11.5 %, respectively. The results prove that the framework is valid and effective, and it can be used to calibrate other complex HVAC system Modelica models in the future.
Characterization of the error associated to satellite rainfall estimates is a necessary component of deterministic and probabilistic frameworks involving spaceborne passive and active microwave measurements for applications ranging from water budget studies to forecasting natural hazards related to extreme rainfall events. We focus here on the error structure of Tropical Rainfall Measurement Mission (TRMM) Precipitation Radar (PR) quantitative precipitation estimation (QPE) at ground. The problem was addressed in a previous paper by comparison of 2A25 version 6 (V6) product with reference values derived from NOAA/NSSL's ground radar-based National Mosaic and QPE system (NMQ/Q2). The primary contribution of this study is to compare the new 2A25 version 7 (V7) products that were recently released as a replacement of V6. This new version is considered superior over land areas. Several aspects of the two versions are compared and quantified including rainfall rate distributions, systematic biases, and random errors. All analyses indicate V7 is an improvement over V6.
Long-term changes in the thickness of the polar ice sheets are important indicators of climate change. Understanding the contributions to the global water mass balance from the accumulation or ablation of grounded ice in Greenland and Antarctica is considered crucial for determining the source of the about 2 mm/yr sea-level rise in the last century. Though the Antarctic ice sheet is much larger than its northern counterpart, the Greenland ice sheet is more likely to undergo dramatic changes in response to a warming trend. This can be attributed to the warmer Greenland climate, as well as a potential for amplification of a global warming trend in the polar regions of the Northern Hemisphere. In collaboration with Drs. Curt Davis and Craig Kluever of the University of Missouri, we are using data from satellite radar altimeters to measure changes in the elevation of the Southern Greenland ice sheet from 1978 to the present. Difficulties with systematic altimeter measurement errors, particularly in intersatellite comparisons, beset earlier studies of the Greenland ice sheet thickness. We use altimeter data collected contemporaneously over the global ocean to establish a reference for correcting ice-sheet data. In addition, the waveform data from the ice-sheet radar returns are reprocessed to better determine the range from the satellite to the ice surface. At JPL, we are focusing our efforts principally on the reduction of orbit errors and range biases in the measurement systems on the various altimeter missions. Our approach emphasizes global characterization and reduction of the long-period orbit errors and range biases using altimeter data from NASA's Ocean Pathfinder program. Along-track sea-height residuals are sequentially filtered and backwards smoothed, and the radial orbit errors are modeled as sinusoids with a wavelength equal to one revolution of the satellite. The amplitudes of the sinusoids are treated as exponentially-correlated noise processes with a time-constant of six days. Measurement errors (e.g., altimeter range bias) are simultaneously recovered as constant parameters. The corrections derived from the global ocean analysis are then applied over the Greenland ice sheet. The orbit error and measurement bias corrections for different missions are developed in a single framework to enable robust linkage of ice-sheet measurements from 1978 to the present. In 1998, we completed our re-evaluation of the 1978 Seasat and 1985-1989 Geosat Exact Repeat Mission data. The estimates of ice thickness over Southern Greenland (south of 72N and above 2000 m) from 1978 to 1988 show large regional variations (+/-18 cm/yr), but yield an overall rate of +1.5 +/- 0.5 cm/yr (one standard error). Accounting for systematic errors, the estimate may not be significantly different from the null growth rate. The average elevation change from 1978 to 1988 is too small to assess whether the Greenland ice sheet is undergoing a long-term change.
The software of a commercially available digital radio receiver has been modified to make the receiver function as a two-channel low-noise phase meter. This phase meter is a prototype in the continuing development of a phase meter for a system in which radiofrequency (RF) signals in the two channels would be outputs of a spaceborne heterodyne laser interferometer for detecting gravitational waves. The frequencies of the signals could include a common Doppler-shift component of as much as 15 MHz. The phase meter is required to measure the relative phases of the signals in the two channels at a sampling rate of 10 Hz at a root power spectral density <5 microcycle/(Hz)1/2 and to be capable of determining the power spectral density of the phase difference over the frequency range from 1 mHz to 1 Hz. Such a phase meter could also be used on Earth to perform similar measurements in laser metrology of moving bodies. To illustrate part of the principle of operation of the phase meter, the figure includes a simplified block diagram of a basic singlechannel digital receiver. The input RF signal is first fed to the input terminal of an analog-to-digital converter (ADC). To prevent aliasing errors in the ADC, the sampling rate must be at least twice the input signal frequency. The sampling rate of the ADC is governed by a sampling clock, which also drives a digital local oscillator (DLO), which is a direct digital frequency synthesizer. The DLO produces samples of sine and cosine signals at a programmed tuning frequency. The sine and cosine samples are mixed with (that is, multiplied by) the samples from the ADC, then low-pass filtered to obtain in-phase (I) and quadrature (Q) signal components. A digital signal processor (DSP) computes the ratio between the Q and I components, computes the phase of the RF signal (relative to that of the DLO signal) as the arctangent of this ratio, and then averages successive such phase values over a time interval specified by the user.
Wire and arc additive manufacturing (WAAM) has shown promise in recent years for producing large-scale parts with higher deposition rates than other additive processes. WAAM is often combined with subtractive machining to form a hybrid manufacturing process. This hybrid process can be realized by retrofitting computer numerical control (CNC) machines with deposition heads, adding spindles and deposition heads to robots, or developing part localization methods to transfer parts from an additive cell to a CNC machine. Here, a novel, robot-CNC hybrid configuration is introduced where a maneuverable robot is placed in front of a CNC machine to deposit material within the machine envelop. Furthermore, this method removes the need for part localization and the extensive machine modifications required for retrofitting; however, the problem of robot localization is also added. In this work, the effects of error in vision-based, contactless robot localization on machining parameters in a robot-machine hybrid process were studied. Performance was characterized on an implementation of this system using classical computer vision techniques. In addition, machining simulations were conducted to evaluate the effects of image-induced error on chip thickness, material removal rate, and machining allowance. Initial tests show that computer vision could adequately locate a robot for the hybrid WAAM process within .5 mm.
Short–term forecasting of wind gusts, particularly those of higher intensity, is of great societal importance but is challenging due to the presence of multiple gust generation mechanisms. Wind gust observations from eight high–passenger–volume airports across the continental United States (CONUS) are summarized and used to develop predictive models of wind gust occurrence and magnitude. These short–term (same hour) forecast models are built using multiple logistic and linear regression, as well as artificial neural networks (ANNs) of varying complexity. A suite of 19 upper–air predictors drawn from the ERA5 reanalysis and an autoregressive (AR) term are used. Stepwise procedures instruct predictor selection, and resampling is used to quantify model stability. All models are developed separately for the warm (April–September) and cold (October–March) seasons. Results show that ANNs of 3–5 hidden layers (HLs) generally exhibit higher hit rates than logistic regression models and also improve skill with respect to wind gust magnitudes. However, deeper networks with more HLs increase false alarm rates in occurrence models and mean absolute error in magnitude models due to model overfitting. For model skill, inclusion of the AR term is critical while the majority of the remaining skill derives from wind speeds and lapse rates. A predictive ceiling is also clearly demonstrated, particularly for the strong and damaging gust magnitudes, which appears to be partially due to ERA5 predictor characteristics and the presence of mixed wind climates.
Fault tolerance in future processing and switching communication satellites is addressed by showing new methods for detecting hardware failures in the first major subsystem, the multichannel demultiplexer. An efficient method for demultiplexing frequency slotted channels uses multirate filter banks which contain fast Fourier transform processing. All numerical processing is performed at a lower rate commensurate with the small bandwidth of each bandbase channel. The integrity of the demultiplexing operations is protected by using real number convolutional codes to compute comparable parity values which detect errors at the data sample level. High rate, systematic convolutional codes produce parity values at a much reduced rate, and protection is achieved by generating parity values in two ways and comparing them. Parity values corresponding to each output channel are generated in parallel by a subsystem, operating even slower and in parallel with the demultiplexer that is virtually identical to the original structure. These parity calculations may be time shared with the same processing resources because they are so similar.
The role of error fields in stellarators is explored in this work through simulations of, and experiments performed on, the Wendelstein 7-X (W7-X) stellarator. Previous experimental results, based on limiter plasmas in W7-X, suggest that error fields are small in the experiment based on limiter plasmas. In this work, we document results from the first divertor campaign on W7-X with a focus on error fields resonant with the edge island divertor structure. Measurements obtained using divertor thermocouples corroborate earlier limiter results that the error fields are small and correctable, requiring only 5% of the total rated current of the trim coil system. The effect of these error fields on magnetic geometry and topology are investigated using the flux surface measurement technique. The resulting divertor thermal load asymmetry is modeled via field line diffusion and compared to experimental data.
The aim of this study was to analyze the feasibility of designing comprehensive QA plans using iComCAT for Elekta machines equipped with Agility multileaf collimator and continuously variable dose rate. Test plans with varying MLC speed, gantry speed, and dose rate were created and delivered in a controlled manner. A strip test was designed with three 1 cm MLC positions and delivered using dynamic, StepNShoot and VMAT techniques. Plans were also designed to test error in MLC position with various gantry speeds and various MLC speeds. The delivery fluence was captured using the electronic portal-imaging device. Gantry speed was found to be within tolerance as per the Canadian standards. MLC positioning errors at higher MLC speed with gravity effects does add more than 2 mm discrepancy. More tests need to be performed to evaluate MLC performance using independent measurement systems. The treatment planning system with end-to-end testing necessary for commissioning was also investigated and found to have >95% passing rates within 3%/3mm gamma criteria. Future studies involve performing off-axis gantry starshot pattern and repeating the tests on three matched Elekta linear accelerators.
The purpose of this paper is to present a model and associated analysis of the deleterious effects which both transmitter and receiver data clock time-base instability have on bit error probability. The types of time-base instability modeled and analyzed include bit jitter (epoch jitter), bit jitter rate (time interval jitter) and data asymmetry. The bit error probability is determined as a function of the statistical characteristics of the time-base instability, the data transition density, the signal-to-noise ratio in the bit rate bandwidth and the mechanizations of both the data bit detector and bit synchronizer. Numerical results are provided.