Data-driven estimation of energy consumption for electric bus under real-world driving conditions
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Exemplary embodiments provide wear spreading among die regions (i.e., one or more circuits) in an integrated circuit or among dies by using operating condition data in addition to or instead of environmental data such as temperature data, from each of a plurality of die regions. Control logic produces a cumulative amount of time each of the plurality of die regions has spent at an operating condition based on operating condition data wherein the operating condition data is based on at least one of the following operating characteristics: frequency of operation of the plurality of die regions, an operating voltage of the plurality of die regions, an activity level of the plurality of die regions, a timing margin of the plurality of die regions, and a number of detected faults of the plurality of die regions. The method and apparatus spreads wear among the plurality of same type of die regions by controlling task execution among the plurality of die regions using the die wear-out data.
Data were taken with a film-cooled test plate placed in the exhaust stream of a rectangular combustor. Results showed that in a fuel-rich zone, fuel entrained into the film-cooling air would burn at the conditions tested. Test conditions were cooling-gas flow rates, 9.5 and 5 percent of total gas flow; cooling-gas velocities, 23 and 12 m/sec; ambient-temperature cooling gas; hot-gas velocity, nominally 215 m/sec; fuel-air ratio to stoichiometric fuel-air ratio values of 0.1, 0.8, 1.0, 1.2, and 1.35, resulting in hot-gas temperatures from 590 to 2100 K; and atmospheric pressure. Analytical prediction of wall temperatures agreed reasonably well with experimental results.
There is provided a digitally controlled first order hold circuit and waveform synthesizer for digitally controlling the representation of a function over an approximation interval. In accordance with the operation of the invention, the first order hold circuit and waveform generator receives a digital data input signal which contains initial condition data, up/down data, and slope data for the approximation interval. The initial condition data is loaded into an up/down counter which is incremented using counting data at a rate depending on the value of the slope data and in a direction depending on the value of the up-down data. In order to minimize delays arising from data acquistion, two frequency synthesizer circuits are provided such that one frequency synthesizer provides counting data while the other frequency synthesizer receives slope data. During alternating intervals, the other frequency synthesizer circuit provides counting data while the other circuit receives slope data. In addition, long length data input signals covering a plurality of approximation intervals are provided to reduce the demands on a main system central processing unit.
Data of electron microscopic analysis of meristem and root cap of pea seedlings grown aboard the Salyut-6 orbital research station in the Oazis apparatus and in the laboratory are presented. The main morphological and anatomical characteristics of the test and control plants are shown to be similar. At the same time, some differences are found in the structural and functional organization of the experimental cells as compared to the controls. They concern first of all the plastic apparatus, mitochondria and Golgi apparatus. It is assumed that cell function for certain periods of weightlessness on the whole ensures execution of the cytodifferentiation programs genetically determined on the Earth. Biochemical and physiological processes vary rather markedly due to lack of initially rigorous determination.
Conditions under which a computational procedure can be applied to arbitrary camera sensors to permit estimation of the human photoreceptor response are considered. The adopted procedures recover the effective surface reflectance at the time of measurement, and the reflectance estimates depend not only on the surface, but upon the viewing geometry. The present method for color rendering assumes that the observer's state of adaptation at the time of viewing the original and the rendered images is the same. The analysis aids in specifying which classes of surfaces are required to be accurately rendered, and for which surfaces some error can be tolerated.
The Experiment Support System is a switchboard system with displays and controls. It routes electrical power to experiments M092, M093, and M171 equipment; gaseous nitrogen to the Blood Pressure Measurement System; receives biomedical data from all related equipment; routes the conditioned data signals to the Airlock Module Telemetry System and also displays (in digital or analog from) portions of that data which the crewmen must see to complete the experiment successfully. The Experiment Support System is interfaced to the M131 control panel to transfer conditioned data to the Airlock Module Telemetry System.
Quantifying the ability of a transiting planet survey to recover transit signals has commonly been accomplished through Monte-Carlo injection of transit signals into the observed data and subsequent running of the signal search algorithm (Gilliland et al., 2000; Weldrake et al., 2005; Burke et al., 2006). In order to characterize the performance of the Kepler pipeline (Twicken et al., 2016; Jenkins et al., 2017) on a sample of over 200,000 stars, two complementary injection and recovery tests are utilized:1. Injection of a single transit signal per target into the image or pixel-level data, hereafter referred to as pixel-level transit injection (PLTI), with subsequent processing through the Photometric Analysis (PA), Presearch Data Conditioning (PDC), Transiting Planet Search (TPS), and Data Validation (DV) modules of the Kepler pipeline. The PLTI quantification of the Kepler pipeline's completeness has been described previously by Christiansen et al. (2015, 2016); the completeness of the final SOC 9.3 Kepler pipeline acting on the Data Release 25 (DR25) light curves is described by Christiansen (2017).2. Injection of multiple transit signals per target into the normalized flux time series data with a subsequent transit search using a stream-lined version of the Transiting Planet Search (TPS) module. This test, hereafter referred to as flux-level transit injection (FLTI), is the subject of this document. By running a heavily modified version of TPS, FLTI is able to perform many injections on selected targets and determine in some detail which injected signals are recoverable. Significant numerical efficiency gains are enabled by precomputing the data conditioning steps at the onset of TPS and limiting the search parameter space (i.e., orbital period, transit duration, and ephemeris zero-point) to a small region around each injected transit signal.The PLTI test has the advantage that it follows transit signals through all processing steps of the Kepler pipeline, and the recovered signals can be further classified as planet candidates or false positives in the exact same manner as detections from the nominal (i.e., observed) pipeline run (Twicken et al., 2016, Thompson et al., in preparation). To date, the PLTI test has been the standard means of measuring pipeline completeness averaged over large samples of targets (Christiansen et al., 2015, 2016; Christiansen, 2017). However, since the PLTI test uses only one injection per target, it does not elucidate individual-target variations in pipeline completeness due to differences in stellar properties or astrophysical variability. Thus, we developed the FLTI test to provide a numerically efficient way to fully map individual targets and explore the performance of the pipeline in greater detail. The FLTI tests thereby allow a thorough validation of the pipeline completeness models (such as window function (Burke and Catanzarite, 2017a), detection efficiency (Burke Catanzarite, 2017b), etc.) across the spectrum of Kepler targets (i.e., various astrophysical phenomena and differences in instrumental noise). Tests during development of the FLTI capability revealed that there are significant target-to-target variations in the detection efficiency.
This paper presents an algorithm that automatically identifies and extracts steady-state engine operating points from engine flight data. It calculates the mean and standard deviation of select parameters contained in the incoming flight data stream. If the standard deviation of the data falls below defined constraints, the engine is assumed to be at a steady-state operating point, and the mean measurement data at that point are archived for subsequent condition monitoring purposes. The fundamental design of the steady-state data filter is completely generic and applicable for any dynamic system. Additional domain-specific logic constraints are applied to reduce data outliers and variance within the collected steady-state data. The filter is designed for on-line real-time processing of streaming data as opposed to post-processing of the data in batch mode. Results of applying the steady-state data filter to recorded helicopter engine flight data are shown, demonstrating its utility for engine condition monitoring applications.
The existence of large scale coherent structures in turbulent shear flows has been well documented. Discrepancies between experimental and computational data suggest a necessity to understand the roles they play in mass and momentum transport. Using conditional sampling and averaging on coincident two component velocity and concentration velocity experimental data for swirling and nonswirling coaxial jets, triggers for identifying the structures were examined. Concentration fluctuation was found to be an adequate trigger or indicator for the concentration-velocity data, but no suitable detector was located for the two component velocity data. The large scale structures are found in the region where the largest discrepancies exist between model and experiment. The traditional gradient transport model does not fit in this region as a result of these structures. The large scale motion was found to be responsible for a large percentage downstream at approximately the mean velocity of the overall flow in the axial direction. The radial mean velocity of the structures was found to be substantially greater than that of the overall flow.
Integrating photovoltaics (PV) into electricity grids is challenged by potentially large fluctuations in power generation. In recent years, sky image-based PV output prediction using convolutional neural networks (CNNs) has emerged as a promising approach to forecasting fluctuations. A key challenge is imbalanced sky image datasets: because of the geography of solar PV system installations, sky image datasets are often rich in sunny condition data but deficient in cloudy condition data. This imbalance contrasts with the fact that model errors are dominated by cloudy condition performance. In this study, we attempt to remedy this by exploring the enrichment and augmentation of an imbalanced sky images dataset for two PV output prediction tasks: nowcasting (predicting concurrent PV output) and forecasting (predicting 15-minute-ahead future PV output). We empirically examine the efficacy of using different resampling and data augmentation approaches to create a rebalanced dataset for model development. A three-stage greedy search is used to determine the optimal resampling approach, data augmentation techniques and over-sampling rate. The results show that for the nowcast problem, resampling and data augmentation can effectively enhance the model performance, reducing overall root mean squared error (RMSE) by an average of 4%, or a 15 std. (standard deviation) of improvement compared to the variability of the baseline model. In contrast, the treatment RMSE for the forecast problem nearly always overlaps the baseline performance at the ± 2 std. level. The optimal resampling approach expands on the original dataset by over-sampling the minority cloudy data, with the best results from large over-sampling rate (e.g., 4 ~ 6 times over-sampling of cloudy images).
A wave energy capture system deployed in water converts mechanical motion induced by waves in the water to electrical energy. A controller of the wave energy capture system receives input regarding real-time wave conditions in a vicinity of the wave energy capture system. The controller applies a control model to the received input to select a value of a control parameter for the wave energy capture system, where the control model includes a model that has been trained using machine learning to take wave condition data as input and to output control parameter values selected based on the wave condition data in order to increase an amount of energy captured by the wave energy capture system. The controller implements the selected value of the control parameter on the wave energy capture system.
A graphical presentation of the aerodynamic data acquired during coannular nozzle performance wind tunnel tests is given. The graphical data consist of plots of nozzle gross thrust coefficient, fan nozzle discharge coefficient, and primary nozzle discharge coefficient. Normalized model component static pressure distributions are presented as a function of primary total pressure, fan total pressure, and ambient static pressure for selected operating conditions. In addition, the supersonic cruise configuration data include plots of nozzle efficiency and secondary-to-fan total pressure pumping characteristics. Supersonic and subsonic cruise data are given.
The studies conducted of the potential use of SEASAT ocean condition data and resulting forecasts by dry cargo ships and tankers reached the following conclusions. The SEASAT ocean condition data and resulting forecasts could be usefully employed to route ships around storms, thereby resulting in reduced adverse weather damage, time loss and the related operating costs, and occasional catastrophic losses. These benefits are incremental benefits beyond those which present and future conventional ship routing procedures can supply. The values of the benefits are listed.
Generative Adversarial Network (GAN) – based models have been successfully applied in generating different geological models in the literature. However, it is still challenging to use GAN to generate geological realizations with extremely sparse conditioning data (e.g. several well data), which may be regarded as local noise by GAN during the training process. In this work, we propose a novel conditional Generative Adversarial Neural Operator (cGANO) to tackle this challenge. In cGANO, the mapping between conditioning data and output is established through the U-shaped neural operators (UNO), which better preserves local information. Another advantage of using UNO comes from its grid-independent property, which makes the generation of downscaling stochastic geologic realizations possible. We tested the model performance on the IBDP geostatistical dataset with 100 realizations.
The ability to accurately set repeatable total temperature conditions is critical for collecting quality icing condition data, particularly near freezing conditions. As part of efforts to continually improve data quality in the NASA Glenn Icing Research Tunnel (IRT), new facility instrumentation and new calibration hardware for total temperature measurement were installed and new operational techniques were developed and implemented. This paper focuses on the improvements made in the calibration of total temperature in the IRT.
When parallel algorithms for simulation were introduced in the 1970s, their development and use interested only experts in parallel computation. This circumstance changed as multi-core processors became commonplace, putting a parallel computer into the hands of every modeler. A natural outcome is growing interest in parallel simulation among persons not intimately familiar with parallel computing. At the same time, parallel simulation tools continue to be developed with the implicit assumption that the modeler is knowledgeable about parallel programming. The unintended consequence is a rapidly growing number of users of parallel simulation tools that are unlikely to recognize when the interaction of race conditions, partitioning strategies, and simultaneous action in their simulation models make results non-reproducible, thereby calling into question the validity of conclusions drawn from the simulation data. Here, we illustrate the potential dangers of exposing parallel algorithms to users who are not experts in parallel computation with example models constructed using existing parallel simulation tools. By doing so, we hope to refocus tool developers on usability, even if this new focus incurs loss of some performance.
In the constant drive to further the safety and efficiency of air travel, the complexity of avionics-related systems and of the procedures for interacting with them appear to be on an ever-increasing trend. While this growing complexity often yields productive results with respect to system capabilities and flight efficiency, it typically places a larger burden on pilots to manage increasing amounts of information and to understand intricate system designs. This can be problematic as too much information and/or ineffective provisions of information can potentially overwhelm and/or confuse pilots, and as a result, increase the likelihood of loss of airplane state awareness (ASA). One way to gain more insight into this issue is through experimentation using more objective measures. This study summarizes an analysis of eye-tracking data obtained during a high-fidelity flight simulation study that included most of the complexities of current flight decks, as well as several planned for the next generation air transportation system. Multiple analyses were performed to understand how the 22 participating airline pilots were observing ASA-related information provided during different stages of flights and in response to specific events within these stages. Also, study findings are compared to data presented in similar previous studies to assess trends or common themes regarding how airline crews apply visual attention in complex flight deck and operational environments.