AQMEII3 evaluation of regional NA/EU simulations and ...

EPA Pesticide Factsheets

Through the comparison of several regional-scale chemistry transport modelling systems that simulate meteorology and air quality over the European and American continents, this study aims at i) apportioning the error to the responsible processes using time-scale analysis, ii) helping to detect causes of models error, and iii) identifying the processes and scales most urgently requiring dedicated investigations. The analysis is conducted within the framework of the third phase of the Air Quality Model Evaluation International Initiative (AQMEII) and tackles model performance gauging through measurement-to-model comparison, error decomposition and time series analysis of the models biases for several fields (ozone, CO, SO2, NO, NO2, PM10, PM2.5, wind speed, and temperature). The operational metrics (magnitude of the error, sign of the bias, associativity) provide an overall sense of model strengths and deficiencies, while apportioning the error to its constituent parts (bias, variance and covariance) can help to assess the nature and quality of the error. Each of the error components is analysed independently and apportioned to specific processes based on the corresponding timescale (long scale, synoptic, diurnal, and intra-day) using the error apportionment technique devised in the former phases of AQMEII. The application of the error apportionment method to the AQMEII Phase 3 simulations provides several key insights. In addition to reaffirming the strong impac

Experimental study of overland flow resistance coefficient model of grassland based on BP neural network

NASA Astrophysics Data System (ADS)

Jiao, Peng; Yang, Er; Ni, Yong Xin

2018-06-01

The overland flow resistance on grassland slope of 20° was studied by using simulated rainfall experiments. Model of overland flow resistance coefficient was established based on BP neural network. The input variations of model were rainfall intensity, flow velocity, water depth, and roughness of slope surface, and the output variations was overland flow resistance coefficient. Model was optimized by Genetic Algorithm. The results show that the model can be used to calculate overland flow resistance coefficient, and has high simulation accuracy. The average prediction error of the optimized model of test set is 8.02%, and the maximum prediction error was 18.34%.

Difference-based ridge-type estimator of parameters in restricted partial linear model with correlated errors.

PubMed

Wu, Jibo

2016-01-01

In this article, a generalized difference-based ridge estimator is proposed for the vector parameter in a partial linear model when the errors are dependent. It is supposed that some additional linear constraints may hold to the whole parameter space. Its mean-squared error matrix is compared with the generalized restricted difference-based estimator. Finally, the performance of the new estimator is explained by a simulation study and a numerical example.

Five-equation and robust three-equation methods for solution verification of large eddy simulation

NASA Astrophysics Data System (ADS)

Dutta, Rabijit; Xing, Tao

2018-02-01

This study evaluates the recently developed general framework for solution verification methods for large eddy simulation (LES) using implicitly filtered LES of periodic channel flows at friction Reynolds number of 395 on eight systematically refined grids. The seven-equation method shows that the coupling error based on Hypothesis I is much smaller as compared with the numerical and modeling errors and therefore can be neglected. The authors recommend five-equation method based on Hypothesis II, which shows a monotonic convergence behavior of the predicted numerical benchmark ( S C ), and provides realistic error estimates without the need of fixing the orders of accuracy for either numerical or modeling errors. Based on the results from seven-equation and five-equation methods, less expensive three and four-equation methods for practical LES applications were derived. It was found that the new three-equation method is robust as it can be applied to any convergence types and reasonably predict the error trends. It was also observed that the numerical and modeling errors usually have opposite signs, which suggests error cancellation play an essential role in LES. When Reynolds averaged Navier-Stokes (RANS) based error estimation method is applied, it shows significant error in the prediction of S C on coarse meshes. However, it predicts reasonable S C when the grids resolve at least 80% of the total turbulent kinetic energy.

Modelling and Simulation on Multibody Dynamics for Vehicular Cold Launch Systems Based on Subsystem Synthesis Method

NASA Astrophysics Data System (ADS)

Panyun, YAN; Guozhu, LIANG; Yongzhi, LU; Zhihui, QI; Xingdou, GAO

2017-12-01

The fast simulation of the vehicular cold launch system (VCLS) in the launch process is an essential requirement for practical engineering applications. In particular, a general and fast simulation model of the VCLS will help the designer to obtain the optimum scheme in the initial design phase. For these purposes, a system-level fast simulation model was established for the VCLS based on the subsystem synthesis method. Moreover, a comparison of the load of a seven-axis VCLS on the rigid ground through both theoretical calculations and experiments was carried out. It was found that the error of the load of the rear left outrigger is less than 7.1%, and the error of the total load of all the outriggers is less than 2.8%. Moreover, time taken for completion of the simulation model is only 9.5 min, which is 5% of the time taken by conventional algorithms.

Modeling human response errors in synthetic flight simulator domain

NASA Technical Reports Server (NTRS)

Ntuen, Celestine A.

1992-01-01

This paper presents a control theoretic approach to modeling human response errors (HRE) in the flight simulation domain. The human pilot is modeled as a supervisor of a highly automated system. The synthesis uses the theory of optimal control pilot modeling for integrating the pilot's observation error and the error due to the simulation model (experimental error). Methods for solving the HRE problem are suggested. Experimental verification of the models will be tested in a flight quality handling simulation.

Propagation of uncertainty in nasal spray in vitro performance models using Monte Carlo simulation: Part II. Error propagation during product performance modeling.

PubMed

Guo, Changning; Doub, William H; Kauffman, John F

2010-08-01

Monte Carlo simulations were applied to investigate the propagation of uncertainty in both input variables and response measurements on model prediction for nasal spray product performance design of experiment (DOE) models in the first part of this study, with an initial assumption that the models perfectly represent the relationship between input variables and the measured responses. In this article, we discard the initial assumption, and extended the Monte Carlo simulation study to examine the influence of both input variable variation and product performance measurement variation on the uncertainty in DOE model coefficients. The Monte Carlo simulations presented in this article illustrate the importance of careful error propagation during product performance modeling. Our results show that the error estimates based on Monte Carlo simulation result in smaller model coefficient standard deviations than those from regression methods. This suggests that the estimated standard deviations from regression may overestimate the uncertainties in the model coefficients. Monte Carlo simulations provide a simple software solution to understand the propagation of uncertainty in complex DOE models so that design space can be specified with statistically meaningful confidence levels. (c) 2010 Wiley-Liss, Inc. and the American Pharmacists Association

Infrared Retrievals of Ice Cloud Properties and Uncertainties with an Optimal Estimation Retrieval Method

NASA Astrophysics Data System (ADS)

Wang, C.; Platnick, S. E.; Meyer, K.; Zhang, Z.

2014-12-01

We developed an optimal estimation (OE)-based method using infrared (IR) observations to retrieve ice cloud optical thickness (COT), cloud effective radius (CER), and cloud top height (CTH) simultaneously. The OE-based retrieval is coupled with a fast IR radiative transfer model (RTM) that simulates observations of different sensors, and corresponding Jacobians in cloudy atmospheres. Ice cloud optical properties are calculated using the MODIS Collection 6 (C6) ice crystal habit (severely roughened hexagonal column aggregates). The OE-based method can be applied to various IR space-borne and airborne sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and the enhanced MODIS Airborne Simulator (eMAS), by optimally selecting IR bands with high information content. Four major error sources (i.e., the measurement error, fast RTM error, model input error, and pre-assumed ice crystal habit error) are taken into account in our OE retrieval method. We show that measurement error and fast RTM error have little impact on cloud retrievals, whereas errors from the model input and pre-assumed ice crystal habit significantly increase retrieval uncertainties when the cloud is optically thin. Comparisons between the OE-retrieved ice cloud properties and other operational cloud products (e.g., the MODIS C6 and CALIOP cloud products) are shown.

A simulation study to quantify the impacts of exposure measurement error on air pollution health risk estimates in copollutant time-series models.

PubMed

Dionisio, Kathie L; Chang, Howard H; Baxter, Lisa K

2016-11-25

Exposure measurement error in copollutant epidemiologic models has the potential to introduce bias in relative risk (RR) estimates. A simulation study was conducted using empirical data to quantify the impact of correlated measurement errors in time-series analyses of air pollution and health. ZIP-code level estimates of exposure for six pollutants (CO, NO x , EC, PM 2.5 , SO 4 , O 3 ) from 1999 to 2002 in the Atlanta metropolitan area were used to calculate spatial, population (i.e. ambient versus personal), and total exposure measurement error. Empirically determined covariance of pollutant concentration pairs and the associated measurement errors were used to simulate true exposure (exposure without error) from observed exposure. Daily emergency department visits for respiratory diseases were simulated using a Poisson time-series model with a main pollutant RR = 1.05 per interquartile range, and a null association for the copollutant (RR = 1). Monte Carlo experiments were used to evaluate the impacts of correlated exposure errors of different copollutant pairs. Substantial attenuation of RRs due to exposure error was evident in nearly all copollutant pairs studied, ranging from 10 to 40% attenuation for spatial error, 3-85% for population error, and 31-85% for total error. When CO, NO x or EC is the main pollutant, we demonstrated the possibility of false positives, specifically identifying significant, positive associations for copollutants based on the estimated type I error rate. The impact of exposure error must be considered when interpreting results of copollutant epidemiologic models, due to the possibility of attenuation of main pollutant RRs and the increased probability of false positives when measurement error is present.

DEPEND: A simulation-based environment for system level dependability analysis

NASA Technical Reports Server (NTRS)

Goswami, Kumar; Iyer, Ravishankar K.

1992-01-01

The design and evaluation of highly reliable computer systems is a complex issue. Designers mostly develop such systems based on prior knowledge and experience and occasionally from analytical evaluations of simplified designs. A simulation-based environment called DEPEND which is especially geared for the design and evaluation of fault-tolerant architectures is presented. DEPEND is unique in that it exploits the properties of object-oriented programming to provide a flexible framework with which a user can rapidly model and evaluate various fault-tolerant systems. The key features of the DEPEND environment are described, and its capabilities are illustrated with a detailed analysis of a real design. In particular, DEPEND is used to simulate the Unix based Tandem Integrity fault-tolerance and evaluate how well it handles near-coincident errors caused by correlated and latent faults. Issues such as memory scrubbing, re-integration policies, and workload dependent repair times which affect how the system handles near-coincident errors are also evaluated. Issues such as the method used by DEPEND to simulate error latency and the time acceleration technique that provides enormous simulation speed up are also discussed. Unlike any other simulation-based dependability studies, the use of these approaches and the accuracy of the simulation model are validated by comparing the results of the simulations, with measurements obtained from fault injection experiments conducted on a production Tandem Integrity machine.

Quantitative modeling of the accuracy in registering preoperative patient-specific anatomic models into left atrial cardiac ablation procedures

PubMed Central

Rettmann, Maryam E.; Holmes, David R.; Kwartowitz, David M.; Gunawan, Mia; Johnson, Susan B.; Camp, Jon J.; Cameron, Bruce M.; Dalegrave, Charles; Kolasa, Mark W.; Packer, Douglas L.; Robb, Richard A.

2014-01-01

Purpose: In cardiac ablation therapy, accurate anatomic guidance is necessary to create effective tissue lesions for elimination of left atrial fibrillation. While fluoroscopy, ultrasound, and electroanatomic maps are important guidance tools, they lack information regarding detailed patient anatomy which can be obtained from high resolution imaging techniques. For this reason, there has been significant effort in incorporating detailed, patient-specific models generated from preoperative imaging datasets into the procedure. Both clinical and animal studies have investigated registration and targeting accuracy when using preoperative models; however, the effect of various error sources on registration accuracy has not been quantitatively evaluated. Methods: Data from phantom, canine, and patient studies are used to model and evaluate registration accuracy. In the phantom studies, data are collected using a magnetically tracked catheter on a static phantom model. Monte Carlo simulation studies were run to evaluate both baseline errors as well as the effect of different sources of error that would be present in a dynamic in vivo setting. Error is simulated by varying the variance parameters on the landmark fiducial, physical target, and surface point locations in the phantom simulation studies. In vivo validation studies were undertaken in six canines in which metal clips were placed in the left atrium to serve as ground truth points. A small clinical evaluation was completed in three patients. Landmark-based and combined landmark and surface-based registration algorithms were evaluated in all studies. In the phantom and canine studies, both target registration error and point-to-surface error are used to assess accuracy. In the patient studies, no ground truth is available and registration accuracy is quantified using point-to-surface error only. Results: The phantom simulation studies demonstrated that combined landmark and surface-based registration improved landmark-only registration provided the noise in the surface points is not excessively high. Increased variability on the landmark fiducials resulted in increased registration errors; however, refinement of the initial landmark registration by the surface-based algorithm can compensate for small initial misalignments. The surface-based registration algorithm is quite robust to noise on the surface points and continues to improve landmark registration even at high levels of noise on the surface points. Both the canine and patient studies also demonstrate that combined landmark and surface registration has lower errors than landmark registration alone. Conclusions: In this work, we describe a model for evaluating the impact of noise variability on the input parameters of a registration algorithm in the context of cardiac ablation therapy. The model can be used to predict both registration error as well as assess which inputs have the largest effect on registration accuracy. PMID:24506630

Quantitative modeling of the accuracy in registering preoperative patient-specific anatomic models into left atrial cardiac ablation procedures

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rettmann, Maryam E., E-mail: rettmann.maryam@mayo.edu; Holmes, David R.; Camp, Jon J.

2014-02-15

Purpose: In cardiac ablation therapy, accurate anatomic guidance is necessary to create effective tissue lesions for elimination of left atrial fibrillation. While fluoroscopy, ultrasound, and electroanatomic maps are important guidance tools, they lack information regarding detailed patient anatomy which can be obtained from high resolution imaging techniques. For this reason, there has been significant effort in incorporating detailed, patient-specific models generated from preoperative imaging datasets into the procedure. Both clinical and animal studies have investigated registration and targeting accuracy when using preoperative models; however, the effect of various error sources on registration accuracy has not been quantitatively evaluated. Methods: Datamore » from phantom, canine, and patient studies are used to model and evaluate registration accuracy. In the phantom studies, data are collected using a magnetically tracked catheter on a static phantom model. Monte Carlo simulation studies were run to evaluate both baseline errors as well as the effect of different sources of error that would be present in a dynamicin vivo setting. Error is simulated by varying the variance parameters on the landmark fiducial, physical target, and surface point locations in the phantom simulation studies. In vivo validation studies were undertaken in six canines in which metal clips were placed in the left atrium to serve as ground truth points. A small clinical evaluation was completed in three patients. Landmark-based and combined landmark and surface-based registration algorithms were evaluated in all studies. In the phantom and canine studies, both target registration error and point-to-surface error are used to assess accuracy. In the patient studies, no ground truth is available and registration accuracy is quantified using point-to-surface error only. Results: The phantom simulation studies demonstrated that combined landmark and surface-based registration improved landmark-only registration provided the noise in the surface points is not excessively high. Increased variability on the landmark fiducials resulted in increased registration errors; however, refinement of the initial landmark registration by the surface-based algorithm can compensate for small initial misalignments. The surface-based registration algorithm is quite robust to noise on the surface points and continues to improve landmark registration even at high levels of noise on the surface points. Both the canine and patient studies also demonstrate that combined landmark and surface registration has lower errors than landmark registration alone. Conclusions: In this work, we describe a model for evaluating the impact of noise variability on the input parameters of a registration algorithm in the context of cardiac ablation therapy. The model can be used to predict both registration error as well as assess which inputs have the largest effect on registration accuracy.« less

Two States Mapping Based Time Series Neural Network Model for Compensation Prediction Residual Error

NASA Astrophysics Data System (ADS)

Jung, Insung; Koo, Lockjo; Wang, Gi-Nam

2008-11-01

The objective of this paper was to design a model of human bio signal data prediction system for decreasing of prediction error using two states mapping based time series neural network BP (back-propagation) model. Normally, a lot of the industry has been applied neural network model by training them in a supervised manner with the error back-propagation algorithm for time series prediction systems. However, it still has got a residual error between real value and prediction result. Therefore, we designed two states of neural network model for compensation residual error which is possible to use in the prevention of sudden death and metabolic syndrome disease such as hypertension disease and obesity. We determined that most of the simulation cases were satisfied by the two states mapping based time series prediction model. In particular, small sample size of times series were more accurate than the standard MLP model.

Research on key technologies of LADAR echo signal simulator

NASA Astrophysics Data System (ADS)

Xu, Rui; Shi, Rui; Ye, Jiansen; Wang, Xin; Li, Zhuo

2015-10-01

LADAR echo signal simulator is one of the most significant components of hardware-in-the-loop (HWIL) simulation systems for LADAR, which is designed to simulate the LADAR return signal in laboratory conditions. The device can provide the laser echo signal of target and background for imaging LADAR systems to test whether it is of good performance. Some key technologies are investigated in this paper. Firstly, the 3D model of typical target is built, and transformed to the data of the target echo signal based on ranging equation and targets reflection characteristics. Then, system model and time series model of LADAR echo signal simulator are established. Some influential factors which could induce fixed delay error and random delay error on the simulated return signals are analyzed. In the simulation system, the signal propagating delay of circuits and the response time of pulsed lasers are belong to fixed delay error. The counting error of digital delay generator, the jitter of system clock and the desynchronized between trigger signal and clock signal are a part of random delay error. Furthermore, these system insertion delays are analyzed quantitatively, and the noisy data are obtained. The target echo signals are got by superimposing of the noisy data and the pure target echo signal. In order to overcome these disadvantageous factors, a method of adjusting the timing diagram of the simulation system is proposed. Finally, the simulated echo signals are processed by using a detection algorithm to complete the 3D model reconstruction of object. The simulation results reveal that the range resolution can be better than 8 cm.

Uncertainty Propagation of Non-Parametric-Derived Precipitation Estimates into Multi-Hydrologic Model Simulations

NASA Astrophysics Data System (ADS)

Bhuiyan, M. A. E.; Nikolopoulos, E. I.; Anagnostou, E. N.

2017-12-01

Quantifying the uncertainty of global precipitation datasets is beneficial when using these precipitation products in hydrological applications, because precipitation uncertainty propagation through hydrologic modeling can significantly affect the accuracy of the simulated hydrologic variables. In this research the Iberian Peninsula has been used as the study area with a study period spanning eleven years (2000-2010). This study evaluates the performance of multiple hydrologic models forced with combined global rainfall estimates derived based on a Quantile Regression Forests (QRF) technique. In QRF technique three satellite precipitation products (CMORPH, PERSIANN, and 3B42 (V7)); an atmospheric reanalysis precipitation and air temperature dataset; satellite-derived near-surface daily soil moisture data; and a terrain elevation dataset are being utilized in this study. A high-resolution, ground-based observations driven precipitation dataset (named SAFRAN) available at 5 km/1 h resolution is used as reference. Through the QRF blending framework the stochastic error model produces error-adjusted ensemble precipitation realizations, which are used to force four global hydrological models (JULES (Joint UK Land Environment Simulator), WaterGAP3 (Water-Global Assessment and Prognosis), ORCHIDEE (Organizing Carbon and Hydrology in Dynamic Ecosystems) and SURFEX (Stands for Surface Externalisée) ) to simulate three hydrologic variables (surface runoff, subsurface runoff and evapotranspiration). The models are forced with the reference precipitation to generate reference-based hydrologic simulations. This study presents a comparative analysis of multiple hydrologic model simulations for different hydrologic variables and the impact of the blending algorithm on the simulated hydrologic variables. Results show how precipitation uncertainty propagates through the different hydrologic model structures to manifest in reduction of error in hydrologic variables.

Effects of motion base and g-seat cueing of simulator pilot performance

NASA Technical Reports Server (NTRS)

Ashworth, B. R.; Mckissick, B. T.; Parrish, R. V.

1984-01-01

In order to measure and analyze the effects of a motion plus g-seat cueing system, a manned-flight-simulation experiment was conducted utilizing a pursuit tracking task and an F-16 simulation model in the NASA Langley visual/motion simulator. This experiment provided the information necessary to determine whether motion and g-seat cues have an additive effect on the performance of this task. With respect to the lateral tracking error and roll-control stick force, the answer is affirmative. It is shown that presenting the two cues simultaneously caused significant reductions in lateral tracking error and that using the g-seat and motion base separately provided essentially equal reductions in the pilot's lateral tracking error.

Error modeling for surrogates of dynamical systems using machine learning: Machine-learning-based error model for surrogates of dynamical systems

DOE PAGES

Trehan, Sumeet; Carlberg, Kevin T.; Durlofsky, Louis J.

2017-07-14

A machine learning–based framework for modeling the error introduced by surrogate models of parameterized dynamical systems is proposed. The framework entails the use of high-dimensional regression techniques (eg, random forests, and LASSO) to map a large set of inexpensively computed “error indicators” (ie, features) produced by the surrogate model at a given time instance to a prediction of the surrogate-model error in a quantity of interest (QoI). This eliminates the need for the user to hand-select a small number of informative features. The methodology requires a training set of parameter instances at which the time-dependent surrogate-model error is computed bymore » simulating both the high-fidelity and surrogate models. Using these training data, the method first determines regression-model locality (via classification or clustering) and subsequently constructs a “local” regression model to predict the time-instantaneous error within each identified region of feature space. We consider 2 uses for the resulting error model: (1) as a correction to the surrogate-model QoI prediction at each time instance and (2) as a way to statistically model arbitrary functions of the time-dependent surrogate-model error (eg, time-integrated errors). We then apply the proposed framework to model errors in reduced-order models of nonlinear oil-water subsurface flow simulations, with time-varying well-control (bottom-hole pressure) parameters. The reduced-order models used in this work entail application of trajectory piecewise linearization in conjunction with proper orthogonal decomposition. Moreover, when the first use of the method is considered, numerical experiments demonstrate consistent improvement in accuracy in the time-instantaneous QoI prediction relative to the original surrogate model, across a large number of test cases. When the second use is considered, results show that the proposed method provides accurate statistical predictions of the time- and well-averaged errors.« less

Error modeling for surrogates of dynamical systems using machine learning: Machine-learning-based error model for surrogates of dynamical systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Trehan, Sumeet; Carlberg, Kevin T.; Durlofsky, Louis J.

A machine learning–based framework for modeling the error introduced by surrogate models of parameterized dynamical systems is proposed. The framework entails the use of high-dimensional regression techniques (eg, random forests, and LASSO) to map a large set of inexpensively computed “error indicators” (ie, features) produced by the surrogate model at a given time instance to a prediction of the surrogate-model error in a quantity of interest (QoI). This eliminates the need for the user to hand-select a small number of informative features. The methodology requires a training set of parameter instances at which the time-dependent surrogate-model error is computed bymore » simulating both the high-fidelity and surrogate models. Using these training data, the method first determines regression-model locality (via classification or clustering) and subsequently constructs a “local” regression model to predict the time-instantaneous error within each identified region of feature space. We consider 2 uses for the resulting error model: (1) as a correction to the surrogate-model QoI prediction at each time instance and (2) as a way to statistically model arbitrary functions of the time-dependent surrogate-model error (eg, time-integrated errors). We then apply the proposed framework to model errors in reduced-order models of nonlinear oil-water subsurface flow simulations, with time-varying well-control (bottom-hole pressure) parameters. The reduced-order models used in this work entail application of trajectory piecewise linearization in conjunction with proper orthogonal decomposition. Moreover, when the first use of the method is considered, numerical experiments demonstrate consistent improvement in accuracy in the time-instantaneous QoI prediction relative to the original surrogate model, across a large number of test cases. When the second use is considered, results show that the proposed method provides accurate statistical predictions of the time- and well-averaged errors.« less

Computation of misalignment and primary mirror astigmatism figure error of two-mirror telescopes

NASA Astrophysics Data System (ADS)

Gu, Zhiyuan; Wang, Yang; Ju, Guohao; Yan, Changxiang

2018-01-01

Active optics usually uses the computation models based on numerical methods to correct misalignments and figure errors at present. These methods can hardly lead to any insight into the aberration field dependencies that arise in the presence of the misalignments. An analytical alignment model based on third-order nodal aberration theory is presented for this problem, which can be utilized to compute the primary mirror astigmatic figure error and misalignments for two-mirror telescopes. Alignment simulations are conducted for an R-C telescope based on this analytical alignment model. It is shown that in the absence of wavefront measurement errors, wavefront measurements at only two field points are enough, and the correction process can be completed with only one alignment action. In the presence of wavefront measurement errors, increasing the number of field points for wavefront measurements can enhance the robustness of the alignment model. Monte Carlo simulation shows that, when -2 mm ≤ linear misalignment ≤ 2 mm, -0.1 deg ≤ angular misalignment ≤ 0.1 deg, and -0.2 λ ≤ astigmatism figure error (expressed as fringe Zernike coefficients C5 / C6, λ = 632.8 nm) ≤0.2 λ, the misaligned systems can be corrected to be close to nominal state without wavefront testing error. In addition, the root mean square deviation of RMS wavefront error of all the misaligned samples after being corrected is linearly related to wavefront testing error.

Experimental Errors in QSAR Modeling Sets: What We Can Do and What We Cannot Do.

PubMed

Zhao, Linlin; Wang, Wenyi; Sedykh, Alexander; Zhu, Hao

2017-06-30

Numerous chemical data sets have become available for quantitative structure-activity relationship (QSAR) modeling studies. However, the quality of different data sources may be different based on the nature of experimental protocols. Therefore, potential experimental errors in the modeling sets may lead to the development of poor QSAR models and further affect the predictions of new compounds. In this study, we explored the relationship between the ratio of questionable data in the modeling sets, which was obtained by simulating experimental errors, and the QSAR modeling performance. To this end, we used eight data sets (four continuous endpoints and four categorical endpoints) that have been extensively curated both in-house and by our collaborators to create over 1800 various QSAR models. Each data set was duplicated to create several new modeling sets with different ratios of simulated experimental errors (i.e., randomizing the activities of part of the compounds) in the modeling process. A fivefold cross-validation process was used to evaluate the modeling performance, which deteriorates when the ratio of experimental errors increases. All of the resulting models were also used to predict external sets of new compounds, which were excluded at the beginning of the modeling process. The modeling results showed that the compounds with relatively large prediction errors in cross-validation processes are likely to be those with simulated experimental errors. However, after removing a certain number of compounds with large prediction errors in the cross-validation process, the external predictions of new compounds did not show improvement. Our conclusion is that the QSAR predictions, especially consensus predictions, can identify compounds with potential experimental errors. But removing those compounds by the cross-validation procedure is not a reasonable means to improve model predictivity due to overfitting.

Experimental Errors in QSAR Modeling Sets: What We Can Do and What We Cannot Do

PubMed Central

2017-01-01

Numerous chemical data sets have become available for quantitative structure–activity relationship (QSAR) modeling studies. However, the quality of different data sources may be different based on the nature of experimental protocols. Therefore, potential experimental errors in the modeling sets may lead to the development of poor QSAR models and further affect the predictions of new compounds. In this study, we explored the relationship between the ratio of questionable data in the modeling sets, which was obtained by simulating experimental errors, and the QSAR modeling performance. To this end, we used eight data sets (four continuous endpoints and four categorical endpoints) that have been extensively curated both in-house and by our collaborators to create over 1800 various QSAR models. Each data set was duplicated to create several new modeling sets with different ratios of simulated experimental errors (i.e., randomizing the activities of part of the compounds) in the modeling process. A fivefold cross-validation process was used to evaluate the modeling performance, which deteriorates when the ratio of experimental errors increases. All of the resulting models were also used to predict external sets of new compounds, which were excluded at the beginning of the modeling process. The modeling results showed that the compounds with relatively large prediction errors in cross-validation processes are likely to be those with simulated experimental errors. However, after removing a certain number of compounds with large prediction errors in the cross-validation process, the external predictions of new compounds did not show improvement. Our conclusion is that the QSAR predictions, especially consensus predictions, can identify compounds with potential experimental errors. But removing those compounds by the cross-validation procedure is not a reasonable means to improve model predictivity due to overfitting. PMID:28691113

The impact of 14nm photomask variability and uncertainty on computational lithography solutions

NASA Astrophysics Data System (ADS)

Sturtevant, John; Tejnil, Edita; Buck, Peter D.; Schulze, Steffen; Kalk, Franklin; Nakagawa, Kent; Ning, Guoxiang; Ackmann, Paul; Gans, Fritz; Buergel, Christian

2013-09-01

Computational lithography solutions rely upon accurate process models to faithfully represent the imaging system output for a defined set of process and design inputs. These models rely upon the accurate representation of multiple parameters associated with the scanner and the photomask. Many input variables for simulation are based upon designed or recipe-requested values or independent measurements. It is known, however, that certain measurement methodologies, while precise, can have significant inaccuracies. Additionally, there are known errors associated with the representation of certain system parameters. With shrinking total CD control budgets, appropriate accounting for all sources of error becomes more important, and the cumulative consequence of input errors to the computational lithography model can become significant. In this work, we examine via simulation, the impact of errors in the representation of photomask properties including CD bias, corner rounding, refractive index, thickness, and sidewall angle. The factors that are most critical to be accurately represented in the model are cataloged. CD bias values are based on state of the art mask manufacturing data and other variables changes are speculated, highlighting the need for improved metrology and communication between mask and OPC model experts. The simulations are done by ignoring the wafer photoresist model, and show the sensitivity of predictions to various model inputs associated with the mask. It is shown that the wafer simulations are very dependent upon the 1D/2D representation of the mask and for 3D, that the mask sidewall angle is a very sensitive factor influencing simulated wafer CD results.

Evaluation and error apportionment of an ensemble of ...

EPA Pesticide Factsheets

Through the comparison of several regional-scale chemistry transport modelling systems that simulate meteorology and air quality over the European and American continents, this study aims at i) apportioning the error to the responsible processes using time-scale analysis, ii) helping to detect causes of models error, and iii) identifying the processes and scales most urgently requiring dedicated investigations. The analysis is conducted within the framework of the third phase of the Air Quality Model Evaluation International Initiative (AQMEII) and tackles model performance gauging through measurement-to-model comparison, error decomposition and time series analysis of the models biases for several fields (ozone, CO, SO2, NO, NO2, PM10, PM2.5, wind speed, and temperature). The operational metrics (magnitude of the error, sign of the bias, associativity) provide an overall sense of model strengths and deficiencies, while apportioning the error to its constituent parts (bias, variance and covariance) can help to assess the nature and quality of the error. Each of the error components is analysed independently and apportioned to specific processes based on the corresponding timescale (long scale, synoptic, diurnal, and intra-day) using the error apportionment technique devised in the former phases of AQMEII.The application of the error apportionment method to the AQMEII Phase 3 simulations provides several key insights. In addition to reaffirming the strong impact

Estimation of open water evaporation using land-based meteorological data

NASA Astrophysics Data System (ADS)

Li, Fawen; Zhao, Yong

2017-10-01

Water surface evaporation is an important process in the hydrologic and energy cycles. Accurate simulation of water evaporation is important for the evaluation of water resources. In this paper, using meteorological data from the Aixinzhuang reservoir, the main factors affecting water surface evaporation were determined by the principal component analysis method. To illustrate the influence of these factors on water surface evaporation, the paper first adopted the Dalton model to simulate water surface evaporation. The results showed that the simulation precision was poor for the peak value zone. To improve the model simulation's precision, a modified Dalton model considering relative humidity was proposed. The results show that the 10-day average relative error is 17.2%, assessed as qualified; the monthly average relative error is 12.5%, assessed as qualified; and the yearly average relative error is 3.4%, assessed as excellent. To validate its applicability, the meteorological data of Kuancheng station in the Luan River basin were selected to test the modified model. The results show that the 10-day average relative error is 15.4%, assessed as qualified; the monthly average relative error is 13.3%, assessed as qualified; and the yearly average relative error is 6.0%, assessed as good. These results showed that the modified model had good applicability and versatility. The research results can provide technical support for the calculation of water surface evaporation in northern China or similar regions.

A novel diagnosis method for a Hall plates-based rotary encoder with a magnetic concentrator.

PubMed

Meng, Bumin; Wang, Yaonan; Sun, Wei; Yuan, Xiaofang

2014-07-31

In the last few years, rotary encoders based on two-dimensional complementary metal oxide semiconductors (CMOS) Hall plates with a magnetic concentrator have been developed to measure contactless absolute angle. There are various error factors influencing the measuring accuracy, which are difficult to locate after the assembly of encoder. In this paper, a model-based rapid diagnosis method is presented. Based on an analysis of the error mechanism, an error model is built to compare minimum residual angle error and to quantify the error factors. Additionally, a modified particle swarm optimization (PSO) algorithm is used to reduce the calculated amount. The simulation and experimental results show that this diagnosis method is feasible to quantify the causes of the error and to reduce iteration significantly.

Dynamic Modeling Accuracy Dependence on Errors in Sensor Measurements, Mass Properties, and Aircraft Geometry

NASA Technical Reports Server (NTRS)

Grauer, Jared A.; Morelli, Eugene A.

2013-01-01

A nonlinear simulation of the NASA Generic Transport Model was used to investigate the effects of errors in sensor measurements, mass properties, and aircraft geometry on the accuracy of dynamic models identified from flight data. Measurements from a typical system identification maneuver were systematically and progressively deteriorated and then used to estimate stability and control derivatives within a Monte Carlo analysis. Based on the results, recommendations were provided for maximum allowable errors in sensor measurements, mass properties, and aircraft geometry to achieve desired levels of dynamic modeling accuracy. Results using other flight conditions, parameter estimation methods, and a full-scale F-16 nonlinear aircraft simulation were compared with these recommendations.

Research of laser echo signal simulator

NASA Astrophysics Data System (ADS)

Xu, Rui; Shi, Rui; Wang, Xin; Li, Zhou

2015-11-01

Laser echo signal simulator is one of the most significant components of hardware-in-the-loop (HWIL) simulation systems for LADAR. System model and time series model of laser echo signal simulator are established. Some influential factors which could induce fixed error and random error on the simulated return signals are analyzed, and then these system insertion errors are analyzed quantitatively. Using this theoretical model, the simulation system is investigated experimentally. The results corrected by subtracting fixed error indicate that the range error of the simulated laser return signal is less than 0.25m, and the distance range that the system can simulate is from 50m to 20km.

Driving-forces model on individual behavior in scenarios considering moving threat agents

NASA Astrophysics Data System (ADS)

Li, Shuying; Zhuang, Jun; Shen, Shifei; Wang, Jia

2017-09-01

The individual behavior model is a contributory factor to improve the accuracy of agent-based simulation in different scenarios. However, few studies have considered moving threat agents, which often occur in terrorist attacks caused by attackers with close-range weapons (e.g., sword, stick). At the same time, many existing behavior models lack validation from cases or experiments. This paper builds a new individual behavior model based on seven behavioral hypotheses. The driving-forces model is an extension of the classical social force model considering scenarios including moving threat agents. An experiment was conducted to validate the key components of the model. Then the model is compared with an advanced Elliptical Specification II social force model, by calculating the fitting errors between the simulated and experimental trajectories, and being applied to simulate a specific circumstance. Our results show that the driving-forces model reduced the fitting error by an average of 33.9% and the standard deviation by an average of 44.5%, which indicates the accuracy and stability of the model in the studied situation. The new driving-forces model could be used to simulate individual behavior when analyzing the risk of specific scenarios using agent-based simulation methods, such as risk analysis of close-range terrorist attacks in public places.

Effects of distribution density and cell dimension of 3D vegetation model on canopy NDVI simulation base on DART

NASA Astrophysics Data System (ADS)

Tao, Zhu; Shi, Runhe; Zeng, Yuyan; Gao, Wei

2017-09-01

The 3D model is an important part of simulated remote sensing for earth observation. Regarding the small-scale spatial extent of DART software, both the details of the model itself and the number of models of the distribution have an important impact on the scene canopy Normalized Difference Vegetation Index (NDVI).Taking the phragmitesaustralis in the Yangtze Estuary as an example, this paper studied the effect of the P.australias model on the canopy NDVI, based on the previous studies of the model precision, mainly from the cell dimension of the DART software and the density distribution of the P.australias model in the scene, As well as the choice of the density of the P.australiass model under the cost of computer running time in the actual simulation. The DART Cell dimensions and the density of the scene model were set by using the optimal precision model from the existing research results. The simulation results of NDVI with different model densities under different cell dimensions were analyzed by error analysis. By studying the relationship between relative error, absolute error and time costs, we have mastered the density selection method of P.australias model in the simulation of small-scale spatial scale scene. Experiments showed that the number of P.australias in the simulated scene need not be the same as those in the real environment due to the difference between the 3D model and the real scenarios. The best simulation results could be obtained by keeping the density ratio of about 40 trees per square meter, simultaneously, of the visual effects.

3D fluoroscopic image estimation using patient-specific 4DCBCT-based motion models

PubMed Central

Dhou, Salam; Hurwitz, Martina; Mishra, Pankaj; Cai, Weixing; Rottmann, Joerg; Li, Ruijiang; Williams, Christopher; Wagar, Matthew; Berbeco, Ross; Ionascu, Dan; Lewis, John H.

2015-01-01

3D fluoroscopic images represent volumetric patient anatomy during treatment with high spatial and temporal resolution. 3D fluoroscopic images estimated using motion models built using 4DCT images, taken days or weeks prior to treatment, do not reliably represent patient anatomy during treatment. In this study we develop and perform initial evaluation of techniques to develop patient-specific motion models from 4D cone-beam CT (4DCBCT) images, taken immediately before treatment, and use these models to estimate 3D fluoroscopic images based on 2D kV projections captured during treatment. We evaluate the accuracy of 3D fluoroscopic images by comparing to ground truth digital and physical phantom images. The performance of 4DCBCT- and 4DCT- based motion models are compared in simulated clinical situations representing tumor baseline shift or initial patient positioning errors. The results of this study demonstrate the ability for 4DCBCT imaging to generate motion models that can account for changes that cannot be accounted for with 4DCT-based motion models. When simulating tumor baseline shift and patient positioning errors of up to 5 mm, the average tumor localization error and the 95th percentile error in six datasets were 1.20 and 2.2 mm, respectively, for 4DCBCT-based motion models. 4DCT-based motion models applied to the same six datasets resulted in average tumor localization error and the 95th percentile error of 4.18 and 5.4 mm, respectively. Analysis of voxel-wise intensity differences was also conducted for all experiments. In summary, this study demonstrates the feasibility of 4DCBCT-based 3D fluoroscopic image generation in digital and physical phantoms, and shows the potential advantage of 4DCBCT-based 3D fluoroscopic image estimation when there are changes in anatomy between the time of 4DCT imaging and the time of treatment delivery. PMID:25905722

Convergence of methods for coupling of microscopic and mesoscopic reaction-diffusion simulations

NASA Astrophysics Data System (ADS)

Flegg, Mark B.; Hellander, Stefan; Erban, Radek

2015-05-01

In this paper, three multiscale methods for coupling of mesoscopic (compartment-based) and microscopic (molecular-based) stochastic reaction-diffusion simulations are investigated. Two of the three methods that will be discussed in detail have been previously reported in the literature; the two-regime method (TRM) and the compartment-placement method (CPM). The third method that is introduced and analysed in this paper is called the ghost cell method (GCM), since it works by constructing a "ghost cell" in which molecules can disappear and jump into the compartment-based simulation. Presented is a comparison of sources of error. The convergent properties of this error are studied as the time step Δt (for updating the molecular-based part of the model) approaches zero. It is found that the error behaviour depends on another fundamental computational parameter h, the compartment size in the mesoscopic part of the model. Two important limiting cases, which appear in applications, are considered: Δt → 0 and h is fixed; Δt → 0 and h → 0 such that √{ Δt } / h is fixed. The error for previously developed approaches (the TRM and CPM) converges to zero only in the limiting case (ii), but not in case (i). It is shown that the error of the GCM converges in the limiting case (i). Thus the GCM is superior to previous coupling techniques if the mesoscopic description is much coarser than the microscopic part of the model.

Three-parameter error analysis method based on rotating coordinates in rotating birefringent polarizer system

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cao, Junjie; Jia, Hongzhi, E-mail: hzjia@usst.edu.cn

2015-11-15

We propose error analysis using a rotating coordinate system with three parameters of linearly polarized light—incidence angle, azimuth angle on the front surface, and angle between the incidence and vibration planes—and demonstrate the method on a rotating birefringent prism system. The transmittance and angles are calculated plane-by-plane using a birefringence ellipsoid model and the final transmitted intensity equation is deduced. The effects of oblique incidence, light interference, beam convergence, and misalignment of the rotation and prism axes are discussed. We simulate the entire error model using MATLAB and conduct experiments based on a built polarimeter. The simulation and experimental resultsmore » are consistent and demonstrate the rationality and validity of this method.« less

Characterizing Satellite Rainfall Errors based on Land Use and Land Cover and Tracing Error Source in Hydrologic Model Simulation

NASA Astrophysics Data System (ADS)

Gebregiorgis, A. S.; Peters-Lidard, C. D.; Tian, Y.; Hossain, F.

2011-12-01

Hydrologic modeling has benefited from operational production of high resolution satellite rainfall products. The global coverage, near-real time availability, spatial and temporal sampling resolutions have advanced the application of physically based semi-distributed and distributed hydrologic models for wide range of environmental decision making processes. Despite these successes, the existence of uncertainties due to indirect way of satellite rainfall estimates and hydrologic models themselves remain a challenge in making meaningful and more evocative predictions. This study comprises breaking down of total satellite rainfall error into three independent components (hit bias, missed precipitation and false alarm), characterizing them as function of land use and land cover (LULC), and tracing back the source of simulated soil moisture and runoff error in physically based distributed hydrologic model. Here, we asked "on what way the three independent total bias components, hit bias, missed, and false precipitation, affect the estimation of soil moisture and runoff in physically based hydrologic models?" To understand the clear picture of the outlined question above, we implemented a systematic approach by characterizing and decomposing the total satellite rainfall error as a function of land use and land cover in Mississippi basin. This will help us to understand the major source of soil moisture and runoff errors in hydrologic model simulation and trace back the information to algorithm development and sensor type which ultimately helps to improve algorithms better and will improve application and data assimilation in future for GPM. For forest and woodland and human land use system, the soil moisture was mainly dictated by the total bias for 3B42-RT, CMORPH, and PERSIANN products. On the other side, runoff error was largely dominated by hit bias than the total bias. This difference occurred due to the presence of missed precipitation which is a major contributor to the total bias both during the summer and winter seasons. Missed precipitation, most likely light rain and rain over snow cover, has significant effect on soil moisture and are less capable of producing runoff that results runoff dependency on the hit bias only.

BEATBOX v1.0: Background Error Analysis Testbed with Box Models

NASA Astrophysics Data System (ADS)

Knote, Christoph; Barré, Jérôme; Eckl, Max

2018-02-01

The Background Error Analysis Testbed (BEATBOX) is a new data assimilation framework for box models. Based on the BOX Model eXtension (BOXMOX) to the Kinetic Pre-Processor (KPP), this framework allows users to conduct performance evaluations of data assimilation experiments, sensitivity analyses, and detailed chemical scheme diagnostics from an observation simulation system experiment (OSSE) point of view. The BEATBOX framework incorporates an observation simulator and a data assimilation system with the possibility of choosing ensemble, adjoint, or combined sensitivities. A user-friendly, Python-based interface allows for the tuning of many parameters for atmospheric chemistry and data assimilation research as well as for educational purposes, for example observation error, model covariances, ensemble size, perturbation distribution in the initial conditions, and so on. In this work, the testbed is described and two case studies are presented to illustrate the design of a typical OSSE experiment, data assimilation experiments, a sensitivity analysis, and a method for diagnosing model errors. BEATBOX is released as an open source tool for the atmospheric chemistry and data assimilation communities.

Bootstrap-based methods for estimating standard errors in Cox's regression analyses of clustered event times.

PubMed

Xiao, Yongling; Abrahamowicz, Michal

2010-03-30

We propose two bootstrap-based methods to correct the standard errors (SEs) from Cox's model for within-cluster correlation of right-censored event times. The cluster-bootstrap method resamples, with replacement, only the clusters, whereas the two-step bootstrap method resamples (i) the clusters, and (ii) individuals within each selected cluster, with replacement. In simulations, we evaluate both methods and compare them with the existing robust variance estimator and the shared gamma frailty model, which are available in statistical software packages. We simulate clustered event time data, with latent cluster-level random effects, which are ignored in the conventional Cox's model. For cluster-level covariates, both proposed bootstrap methods yield accurate SEs, and type I error rates, and acceptable coverage rates, regardless of the true random effects distribution, and avoid serious variance under-estimation by conventional Cox-based standard errors. However, the two-step bootstrap method over-estimates the variance for individual-level covariates. We also apply the proposed bootstrap methods to obtain confidence bands around flexible estimates of time-dependent effects in a real-life analysis of cluster event times.

Utility of a novel error-stepping method to improve gradient-based parameter identification by increasing the smoothness of the local objective surface: a case-study of pulmonary mechanics.

PubMed

Docherty, Paul D; Schranz, Christoph; Chase, J Geoffrey; Chiew, Yeong Shiong; Möller, Knut

2014-05-01

Accurate model parameter identification relies on accurate forward model simulations to guide convergence. However, some forward simulation methodologies lack the precision required to properly define the local objective surface and can cause failed parameter identification. The role of objective surface smoothness in identification of a pulmonary mechanics model was assessed using forward simulation from a novel error-stepping method and a proprietary Runge-Kutta method. The objective surfaces were compared via the identified parameter discrepancy generated in a Monte Carlo simulation and the local smoothness of the objective surfaces they generate. The error-stepping method generated significantly smoother error surfaces in each of the cases tested (p<0.0001) and more accurate model parameter estimates than the Runge-Kutta method in three of the four cases tested (p<0.0001) despite a 75% reduction in computational cost. Of note, parameter discrepancy in most cases was limited to a particular oblique plane, indicating a non-intuitive multi-parameter trade-off was occurring. The error-stepping method consistently improved or equalled the outcomes of the Runge-Kutta time-integration method for forward simulations of the pulmonary mechanics model. This study indicates that accurate parameter identification relies on accurate definition of the local objective function, and that parameter trade-off can occur on oblique planes resulting prematurely halted parameter convergence. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

QUANTIFYING AN UNCERTAIN FUTURE: HYDROLOGIC MODEL PERFORMANCE FOR A SERIES OF REALIZED "/FUTURE" CONDITIONS

EPA Science Inventory

A systematic analysis of model performance during simulations based on observed landcover/use change is used to quantify errors associated with simulations of known "future" conditions. Calibrated and uncalibrated assessments of relative change over different lengths of...

An Overview of Atmospheric Composition OSSE Activities at NASA's Global Modeling and Assimilation Office

NASA Technical Reports Server (NTRS)

daSilva, Arlinda

2012-01-01

A model-based Observing System Simulation Experiment (OSSE) is a framework for numerical experimentation in which observables are simulated from fields generated by an earth system model, including a parameterized description of observational error characteristics. Simulated observations can be used for sampling studies, quantifying errors in analysis or retrieval algorithms, and ultimately being a planning tool for designing new observing missions. While this framework has traditionally been used to assess the impact of observations on numerical weather prediction, it has a much broader applicability, in particular to aerosols and chemical constituents. In this talk we will give a general overview of Observing System Simulation Experiments (OSSE) activities at NASA's Global Modeling and Assimilation Office, with focus on its emerging atmospheric composition component.

Drought Persistence Errors in Global Climate Models

NASA Astrophysics Data System (ADS)

Moon, H.; Gudmundsson, L.; Seneviratne, S. I.

2018-04-01

The persistence of drought events largely determines the severity of socioeconomic and ecological impacts, but the capability of current global climate models (GCMs) to simulate such events is subject to large uncertainties. In this study, the representation of drought persistence in GCMs is assessed by comparing state-of-the-art GCM model simulations to observation-based data sets. For doing so, we consider dry-to-dry transition probabilities at monthly and annual scales as estimates for drought persistence, where a dry status is defined as negative precipitation anomaly. Though there is a substantial spread in the drought persistence bias, most of the simulations show systematic underestimation of drought persistence at global scale. Subsequently, we analyzed to which degree (i) inaccurate observations, (ii) differences among models, (iii) internal climate variability, and (iv) uncertainty of the employed statistical methods contribute to the spread in drought persistence errors using an analysis of variance approach. The results show that at monthly scale, model uncertainty and observational uncertainty dominate, while the contribution from internal variability is small in most cases. At annual scale, the spread of the drought persistence error is dominated by the statistical estimation error of drought persistence, indicating that the partitioning of the error is impaired by the limited number of considered time steps. These findings reveal systematic errors in the representation of drought persistence in current GCMs and suggest directions for further model improvement.

Modeling error PDF optimization based wavelet neural network modeling of dynamic system and its application in blast furnace ironmaking

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhou, Ping; Wang, Chenyu; Li, Mingjie

In general, the modeling errors of dynamic system model are a set of random variables. The traditional performance index of modeling such as means square error (MSE) and root means square error (RMSE) can not fully express the connotation of modeling errors with stochastic characteristics both in the dimension of time domain and space domain. Therefore, the probability density function (PDF) is introduced to completely describe the modeling errors in both time scales and space scales. Based on it, a novel wavelet neural network (WNN) modeling method is proposed by minimizing the two-dimensional (2D) PDF shaping of modeling errors. First,more » the modeling error PDF by the tradional WNN is estimated using data-driven kernel density estimation (KDE) technique. Then, the quadratic sum of 2D deviation between the modeling error PDF and the target PDF is utilized as performance index to optimize the WNN model parameters by gradient descent method. Since the WNN has strong nonlinear approximation and adaptive capability, and all the parameters are well optimized by the proposed method, the developed WNN model can make the modeling error PDF track the target PDF, eventually. Simulation example and application in a blast furnace ironmaking process show that the proposed method has a higher modeling precision and better generalization ability compared with the conventional WNN modeling based on MSE criteria. Furthermore, the proposed method has more desirable estimation for modeling error PDF that approximates to a Gaussian distribution whose shape is high and narrow.« less

Modeling error PDF optimization based wavelet neural network modeling of dynamic system and its application in blast furnace ironmaking

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhou, Ping; Wang, Chenyu; Li, Mingjie

In general, the modeling errors of dynamic system model are a set of random variables. The traditional performance index of modeling such as means square error (MSE) and root means square error (RMSE) cannot fully express the connotation of modeling errors with stochastic characteristics both in the dimension of time domain and space domain. Therefore, the probability density function (PDF) is introduced to completely describe the modeling errors in both time scales and space scales. Based on it, a novel wavelet neural network (WNN) modeling method is proposed by minimizing the two-dimensional (2D) PDF shaping of modeling errors. First, themore » modeling error PDF by the traditional WNN is estimated using data-driven kernel density estimation (KDE) technique. Then, the quadratic sum of 2D deviation between the modeling error PDF and the target PDF is utilized as performance index to optimize the WNN model parameters by gradient descent method. Since the WNN has strong nonlinear approximation and adaptive capability, and all the parameters are well optimized by the proposed method, the developed WNN model can make the modeling error PDF track the target PDF, eventually. Simulation example and application in a blast furnace ironmaking process show that the proposed method has a higher modeling precision and better generalization ability compared with the conventional WNN modeling based on MSE criteria. However, the proposed method has more desirable estimation for modeling error PDF that approximates to a Gaussian distribution whose shape is high and narrow.« less

Modeling error PDF optimization based wavelet neural network modeling of dynamic system and its application in blast furnace ironmaking

DOE PAGES

Zhou, Ping; Wang, Chenyu; Li, Mingjie; ...

2018-01-31

In general, the modeling errors of dynamic system model are a set of random variables. The traditional performance index of modeling such as means square error (MSE) and root means square error (RMSE) cannot fully express the connotation of modeling errors with stochastic characteristics both in the dimension of time domain and space domain. Therefore, the probability density function (PDF) is introduced to completely describe the modeling errors in both time scales and space scales. Based on it, a novel wavelet neural network (WNN) modeling method is proposed by minimizing the two-dimensional (2D) PDF shaping of modeling errors. First, themore » modeling error PDF by the traditional WNN is estimated using data-driven kernel density estimation (KDE) technique. Then, the quadratic sum of 2D deviation between the modeling error PDF and the target PDF is utilized as performance index to optimize the WNN model parameters by gradient descent method. Since the WNN has strong nonlinear approximation and adaptive capability, and all the parameters are well optimized by the proposed method, the developed WNN model can make the modeling error PDF track the target PDF, eventually. Simulation example and application in a blast furnace ironmaking process show that the proposed method has a higher modeling precision and better generalization ability compared with the conventional WNN modeling based on MSE criteria. However, the proposed method has more desirable estimation for modeling error PDF that approximates to a Gaussian distribution whose shape is high and narrow.« less

Lunar gravitational field estimation and the effects of mismodeling upon lunar satellite orbit prediction. M.S. Thesis

NASA Technical Reports Server (NTRS)

Davis, John H.

1993-01-01

Lunar spherical harmonic gravity coefficients are estimated from simulated observations of a near-circular low altitude polar orbiter disturbed by lunar mascons. Lunar gravity sensing missions using earth-based nearside observations with and without satellite-based far-side observations are simulated and least squares maximum likelihood estimates are developed for spherical harmonic expansion fit models. Simulations and parameter estimations are performed by a modified version of the Smithsonian Astrophysical Observatory's Planetary Ephemeris Program. Two different lunar spacecraft mission phases are simulated to evaluate the estimated fit models. Results for predicting state covariances one orbit ahead are presented along with the state errors resulting from the mismodeled gravity field. The position errors from planning a lunar landing maneuver with a mismodeled gravity field are also presented. These simulations clearly demonstrate the need to include observations of satellite motion over the far side in estimating the lunar gravity field. The simulations also illustrate that the eighth degree and order expansions used in the simulated fits were unable to adequately model lunar mascons.

Soft tissue deformation for surgical simulation: a position-based dynamics approach.

PubMed

Camara, Mafalda; Mayer, Erik; Darzi, Ara; Pratt, Philip

2016-06-01

To assist the rehearsal and planning of robot-assisted partial nephrectomy, a real-time simulation platform is presented that allows surgeons to visualise and interact with rapidly constructed patient-specific biomechanical models of the anatomical regions of interest. Coupled to a framework for volumetric deformation, the platform furthermore simulates intracorporeal 2D ultrasound image acquisition, using preoperative imaging as the data source. This not only facilitates the planning of optimal transducer trajectories and viewpoints, but can also act as a validation context for manually operated freehand 3D acquisitions and reconstructions. The simulation platform was implemented within the GPU-accelerated NVIDIA FleX position-based dynamics framework. In order to validate the model and determine material properties and other simulation parameter values, a porcine kidney with embedded fiducial beads was CT-scanned and segmented. Acquisitions for the rest position and three different levels of probe-induced deformation were collected. Optimal values of the cluster stiffness coefficients were determined for a range of different particle radii, where the objective function comprised the mean distance error between real and simulated fiducial positions over the sequence of deformations. The mean fiducial error at each deformation stage was found to be compatible with the level of ultrasound probe calibration error typically observed in clinical practice. Furthermore, the simulation exhibited unconditional stability on account of its use of clustered shape-matching constraints. A novel position-based dynamics implementation of soft tissue deformation has been shown to facilitate several desirable simulation characteristics: real-time performance, unconditional stability, rapid model construction enabling patient-specific behaviour and accuracy with respect to reference CT images.

LACIE performance predictor FOC users manual

NASA Technical Reports Server (NTRS)

1976-01-01

The LACIE Performance Predictor (LPP) is a computer simulation of the LACIE process for predicting worldwide wheat production. The simulation provides for the introduction of various errors into the system and provides estimates based on these errors, thus allowing the user to determine the impact of selected error sources. The FOC LPP simulates the acquisition of the sample segment data by the LANDSAT Satellite (DAPTS), the classification of the agricultural area within the sample segment (CAMS), the estimation of the wheat yield (YES), and the production estimation and aggregation (CAS). These elements include data acquisition characteristics, environmental conditions, classification algorithms, the LACIE aggregation and data adjustment procedures. The operational structure for simulating these elements consists of the following key programs: (1) LACIE Utility Maintenance Process, (2) System Error Executive, (3) Ephemeris Generator, (4) Access Generator, (5) Acquisition Selector, (6) LACIE Error Model (LEM), and (7) Post Processor.

Measurement error in time-series analysis: a simulation study comparing modelled and monitored data.

PubMed

Butland, Barbara K; Armstrong, Ben; Atkinson, Richard W; Wilkinson, Paul; Heal, Mathew R; Doherty, Ruth M; Vieno, Massimo

2013-11-13

Assessing health effects from background exposure to air pollution is often hampered by the sparseness of pollution monitoring networks. However, regional atmospheric chemistry-transport models (CTMs) can provide pollution data with national coverage at fine geographical and temporal resolution. We used statistical simulation to compare the impact on epidemiological time-series analysis of additive measurement error in sparse monitor data as opposed to geographically and temporally complete model data. Statistical simulations were based on a theoretical area of 4 regions each consisting of twenty-five 5 km × 5 km grid-squares. In the context of a 3-year Poisson regression time-series analysis of the association between mortality and a single pollutant, we compared the error impact of using daily grid-specific model data as opposed to daily regional average monitor data. We investigated how this comparison was affected if we changed the number of grids per region containing a monitor. To inform simulations, estimates (e.g. of pollutant means) were obtained from observed monitor data for 2003-2006 for national network sites across the UK and corresponding model data that were generated by the EMEP-WRF CTM. Average within-site correlations between observed monitor and model data were 0.73 and 0.76 for rural and urban daily maximum 8-hour ozone respectively, and 0.67 and 0.61 for rural and urban loge(daily 1-hour maximum NO2). When regional averages were based on 5 or 10 monitors per region, health effect estimates exhibited little bias. However, with only 1 monitor per region, the regression coefficient in our time-series analysis was attenuated by an estimated 6% for urban background ozone, 13% for rural ozone, 29% for urban background loge(NO2) and 38% for rural loge(NO2). For grid-specific model data the corresponding figures were 19%, 22%, 54% and 44% respectively, i.e. similar for rural loge(NO2) but more marked for urban loge(NO2). Even if correlations between model and monitor data appear reasonably strong, additive classical measurement error in model data may lead to appreciable bias in health effect estimates. As process-based air pollution models become more widely used in epidemiological time-series analysis, assessments of error impact that include statistical simulation may be useful.

An Application of Semi-parametric Estimator with Weighted Matrix of Data Depth in Variance Component Estimation

NASA Astrophysics Data System (ADS)

Pan, X. G.; Wang, J. Q.; Zhou, H. Y.

2013-05-01

The variance component estimation (VCE) based on semi-parametric estimator with weighted matrix of data depth has been proposed, because the coupling system model error and gross error exist in the multi-source heterogeneous measurement data of space and ground combined TT&C (Telemetry, Tracking and Command) technology. The uncertain model error has been estimated with the semi-parametric estimator model, and the outlier has been restrained with the weighted matrix of data depth. On the basis of the restriction of the model error and outlier, the VCE can be improved and used to estimate weighted matrix for the observation data with uncertain model error or outlier. Simulation experiment has been carried out under the circumstance of space and ground combined TT&C. The results show that the new VCE based on the model error compensation can determine the rational weight of the multi-source heterogeneous data, and restrain the outlier data.

Impact of Spatial Soil and Climate Input Data Aggregation on Regional Yield Simulations

PubMed Central

Hoffmann, Holger; Zhao, Gang; Asseng, Senthold; Bindi, Marco; Biernath, Christian; Constantin, Julie; Coucheney, Elsa; Dechow, Rene; Doro, Luca; Eckersten, Henrik; Gaiser, Thomas; Grosz, Balázs; Heinlein, Florian; Kassie, Belay T.; Kersebaum, Kurt-Christian; Klein, Christian; Kuhnert, Matthias; Lewan, Elisabet; Moriondo, Marco; Nendel, Claas; Priesack, Eckart; Raynal, Helene; Roggero, Pier P.; Rötter, Reimund P.; Siebert, Stefan; Specka, Xenia; Tao, Fulu; Teixeira, Edmar; Trombi, Giacomo; Wallach, Daniel; Weihermüller, Lutz; Yeluripati, Jagadeesh; Ewert, Frank

2016-01-01

We show the error in water-limited yields simulated by crop models which is associated with spatially aggregated soil and climate input data. Crop simulations at large scales (regional, national, continental) frequently use input data of low resolution. Therefore, climate and soil data are often generated via averaging and sampling by area majority. This may bias simulated yields at large scales, varying largely across models. Thus, we evaluated the error associated with spatially aggregated soil and climate data for 14 crop models. Yields of winter wheat and silage maize were simulated under water-limited production conditions. We calculated this error from crop yields simulated at spatial resolutions from 1 to 100 km for the state of North Rhine-Westphalia, Germany. Most models showed yields biased by <15% when aggregating only soil data. The relative mean absolute error (rMAE) of most models using aggregated soil data was in the range or larger than the inter-annual or inter-model variability in yields. This error increased further when both climate and soil data were aggregated. Distinct error patterns indicate that the rMAE may be estimated from few soil variables. Illustrating the range of these aggregation effects across models, this study is a first step towards an ex-ante assessment of aggregation errors in large-scale simulations. PMID:27055028

Impact of Spatial Soil and Climate Input Data Aggregation on Regional Yield Simulations.

PubMed

Hoffmann, Holger; Zhao, Gang; Asseng, Senthold; Bindi, Marco; Biernath, Christian; Constantin, Julie; Coucheney, Elsa; Dechow, Rene; Doro, Luca; Eckersten, Henrik; Gaiser, Thomas; Grosz, Balázs; Heinlein, Florian; Kassie, Belay T; Kersebaum, Kurt-Christian; Klein, Christian; Kuhnert, Matthias; Lewan, Elisabet; Moriondo, Marco; Nendel, Claas; Priesack, Eckart; Raynal, Helene; Roggero, Pier P; Rötter, Reimund P; Siebert, Stefan; Specka, Xenia; Tao, Fulu; Teixeira, Edmar; Trombi, Giacomo; Wallach, Daniel; Weihermüller, Lutz; Yeluripati, Jagadeesh; Ewert, Frank

2016-01-01

We show the error in water-limited yields simulated by crop models which is associated with spatially aggregated soil and climate input data. Crop simulations at large scales (regional, national, continental) frequently use input data of low resolution. Therefore, climate and soil data are often generated via averaging and sampling by area majority. This may bias simulated yields at large scales, varying largely across models. Thus, we evaluated the error associated with spatially aggregated soil and climate data for 14 crop models. Yields of winter wheat and silage maize were simulated under water-limited production conditions. We calculated this error from crop yields simulated at spatial resolutions from 1 to 100 km for the state of North Rhine-Westphalia, Germany. Most models showed yields biased by <15% when aggregating only soil data. The relative mean absolute error (rMAE) of most models using aggregated soil data was in the range or larger than the inter-annual or inter-model variability in yields. This error increased further when both climate and soil data were aggregated. Distinct error patterns indicate that the rMAE may be estimated from few soil variables. Illustrating the range of these aggregation effects across models, this study is a first step towards an ex-ante assessment of aggregation errors in large-scale simulations.

Full-envelope aerodynamic modeling of the Harrier aircraft

NASA Technical Reports Server (NTRS)

Mcnally, B. David

1986-01-01

A project to identify a full-envelope model of the YAV-8B Harrier using flight-test and parameter identification techniques is described. As part of the research in advanced control and display concepts for V/STOL aircraft, a full-envelope aerodynamic model of the Harrier is identified, using mathematical model structures and parameter identification methods. A global-polynomial model structure is also used as a basis for the identification of the YAV-8B aerodynamic model. State estimation methods are used to ensure flight data consistency prior to parameter identification.Equation-error methods are used to identify model parameters. A fixed-base simulator is used extensively to develop flight test procedures and to validate parameter identification software. Using simple flight maneuvers, a simulated data set was created covering the YAV-8B flight envelope from about 0.3 to 0.7 Mach and about -5 to 15 deg angle of attack. A singular value decomposition implementation of the equation-error approach produced good parameter estimates based on this simulated data set.

Diffuse-flow conceptualization and simulation of the Edwards aquifer, San Antonio region, Texas

USGS Publications Warehouse

Lindgren, R.J.

2006-01-01

A numerical ground-water-flow model (hereinafter, the conduit-flow Edwards aquifer model) of the karstic Edwards aquifer in south-central Texas was developed for a previous study on the basis of a conceptualization emphasizing conduit development and conduit flow, and included simulating conduits as one-cell-wide, continuously connected features. Uncertainties regarding the degree to which conduits pervade the Edwards aquifer and influence ground-water flow, as well as other uncertainties inherent in simulating conduits, raised the question of whether a model based on the conduit-flow conceptualization was the optimum model for the Edwards aquifer. Accordingly, a model with an alternative hydraulic conductivity distribution without conduits was developed in a study conducted during 2004-05 by the U.S. Geological Survey, in cooperation with the San Antonio Water System. The hydraulic conductivity distribution for the modified Edwards aquifer model (hereinafter, the diffuse-flow Edwards aquifer model), based primarily on a conceptualization in which flow in the aquifer predominantly is through a network of numerous small fractures and openings, includes 38 zones, with hydraulic conductivities ranging from 3 to 50,000 feet per day. Revision of model input data for the diffuse-flow Edwards aquifer model was limited to changes in the simulated hydraulic conductivity distribution. The root-mean-square error for 144 target wells for the calibrated steady-state simulation for the diffuse-flow Edwards aquifer model is 20.9 feet. This error represents about 3 percent of the total head difference across the model area. The simulated springflows for Comal and San Marcos Springs for the calibrated steady-state simulation were within 2.4 and 15 percent of the median springflows for the two springs, respectively. The transient calibration period for the diffuse-flow Edwards aquifer model was 1947-2000, with 648 monthly stress periods, the same as for the conduit-flow Edwards aquifer model. The root-mean-square error for a period of drought (May-November 1956) for the calibrated transient simulation for 171 target wells is 33.4 feet, which represents about 5 percent of the total head difference across the model area. The root-mean-square error for a period of above-normal rainfall (November 1974-July 1975) for the calibrated transient simulation for 169 target wells is 25.8 feet, which represents about 4 percent of the total head difference across the model area. The root-mean-square error ranged from 6.3 to 30.4 feet in 12 target wells with long-term water-level measurements for varying periods during 1947-2000 for the calibrated transient simulation for the diffuse-flow Edwards aquifer model, and these errors represent 5.0 to 31.3 percent of the range in water-level fluctuations of each of those wells. The root-mean-square errors for the five major springs in the San Antonio segment of the aquifer for the calibrated transient simulation, as a percentage of the range of discharge fluctuations measured at the springs, varied from 7.2 percent for San Marcos Springs and 8.1 percent for Comal Springs to 28.8 percent for Leona Springs. The root-mean-square errors for hydraulic heads for the conduit-flow Edwards aquifer model are 27, 76, and 30 percent greater than those for the diffuse-flow Edwards aquifer model for the steady-state, drought, and above-normal rainfall synoptic time periods, respectively. The goodness-of-fit between measured and simulated springflows is similar for Comal, San Marcos, and Leona Springs for the diffuse-flow Edwards aquifer model and the conduit-flow Edwards aquifer model. The root-mean-square errors for Comal and Leona Springs were 15.6 and 21.3 percent less, respectively, whereas the root-mean-square error for San Marcos Springs was 3.3 percent greater for the diffuse-flow Edwards aquifer model compared to the conduit-flow Edwards aquifer model. The root-mean-square errors for San Antonio and San Pedro Springs were appreciably greater, 80.2 and 51.0 percent, respectively, for the diffuse-flow Edwards aquifer model. The simulated water budgets for the diffuse-flow Edwards aquifer model are similar to those for the conduit-flow Edwards aquifer model. Differences in percentage of total sources or discharges for a budget component are 2.0 percent or less for all budget components for the steady-state and transient simulations. The largest difference in terms of the magnitude of water budget components for the transient simulation for 1956 was a decrease of about 10,730 acre-feet per year (about 2 per-cent) in springflow for the diffuse-flow Edwards aquifer model compared to the conduit-flow Edwards aquifer model. This decrease in springflow (a water budget discharge) was largely offset by the decreased net loss of water from storage (a water budget source) of about 10,500 acre-feet per year.

Torus Approach in Gravity Field Determination from Simulated GOCE Gravity Gradients

NASA Astrophysics Data System (ADS)

Liu, Huanling; Wen, Hanjiang; Xu, Xinyu; Zhu, Guangbin

2016-08-01

In Torus approach, observations are projected to the nominal orbits with constant radius and inclination, lumped coefficients provides a linear relationship between observations and spherical harmonic coefficients. Based on the relationship, two-dimensional FFT and block-diagonal least-squares adjustment are used to recover Earth's gravity field model. The Earth's gravity field model complete to degree and order 200 is recovered using simulated satellite gravity gradients on a torus grid, and the degree median error is smaller than 10-18, which shows the effectiveness of Torus approach. EGM2008 is employed as a reference model and the gravity field model is resolved using the simulated observations without noise given on GOCE orbits of 61 days. The error from reduction and interpolation can be mitigated by iterations. Due to polar gap, the precision of low-order coefficients is lower. Without considering these coefficients the maximum geoid degree error and cumulative error are 0.022mm and 0.099mm, respectively. The Earth's gravity field model is also recovered from simulated observations with white noise 5mE/Hz1/2, which is compared to that from direct method. In conclusion, it is demonstrated that Torus approach is a valid method for processing massive amount of GOCE gravity gradients.

A Modeling Framework for Optimal Computational Resource Allocation Estimation: Considering the Trade-offs between Physical Resolutions, Uncertainty and Computational Costs

NASA Astrophysics Data System (ADS)

Moslehi, M.; de Barros, F.; Rajagopal, R.

2014-12-01

Hydrogeological models that represent flow and transport in subsurface domains are usually large-scale with excessive computational complexity and uncertain characteristics. Uncertainty quantification for predicting flow and transport in heterogeneous formations often entails utilizing a numerical Monte Carlo framework, which repeatedly simulates the model according to a random field representing hydrogeological characteristics of the field. The physical resolution (e.g. grid resolution associated with the physical space) for the simulation is customarily chosen based on recommendations in the literature, independent of the number of Monte Carlo realizations. This practice may lead to either excessive computational burden or inaccurate solutions. We propose an optimization-based methodology that considers the trade-off between the following conflicting objectives: time associated with computational costs, statistical convergence of the model predictions and physical errors corresponding to numerical grid resolution. In this research, we optimally allocate computational resources by developing a modeling framework for the overall error based on a joint statistical and numerical analysis and optimizing the error model subject to a given computational constraint. The derived expression for the overall error explicitly takes into account the joint dependence between the discretization error of the physical space and the statistical error associated with Monte Carlo realizations. The accuracy of the proposed framework is verified in this study by applying it to several computationally extensive examples. Having this framework at hand aims hydrogeologists to achieve the optimum physical and statistical resolutions to minimize the error with a given computational budget. Moreover, the influence of the available computational resources and the geometric properties of the contaminant source zone on the optimum resolutions are investigated. We conclude that the computational cost associated with optimal allocation can be substantially reduced compared with prevalent recommendations in the literature.

Incorporating approximation error in surrogate based Bayesian inversion

NASA Astrophysics Data System (ADS)

Zhang, J.; Zeng, L.; Li, W.; Wu, L.

2015-12-01

There are increasing interests in applying surrogates for inverse Bayesian modeling to reduce repetitive evaluations of original model. In this way, the computational cost is expected to be saved. However, the approximation error of surrogate model is usually overlooked. This is partly because that it is difficult to evaluate the approximation error for many surrogates. Previous studies have shown that, the direct combination of surrogates and Bayesian methods (e.g., Markov Chain Monte Carlo, MCMC) may lead to biased estimations when the surrogate cannot emulate the highly nonlinear original system. This problem can be alleviated by implementing MCMC in a two-stage manner. However, the computational cost is still high since a relatively large number of original model simulations are required. In this study, we illustrate the importance of incorporating approximation error in inverse Bayesian modeling. Gaussian process (GP) is chosen to construct the surrogate for its convenience in approximation error evaluation. Numerical cases of Bayesian experimental design and parameter estimation for contaminant source identification are used to illustrate this idea. It is shown that, once the surrogate approximation error is well incorporated into Bayesian framework, promising results can be obtained even when the surrogate is directly used, and no further original model simulations are required.

Error Covariance Penalized Regression: A novel multivariate model combining penalized regression with multivariate error structure.

PubMed

Allegrini, Franco; Braga, Jez W B; Moreira, Alessandro C O; Olivieri, Alejandro C

2018-06-29

A new multivariate regression model, named Error Covariance Penalized Regression (ECPR) is presented. Following a penalized regression strategy, the proposed model incorporates information about the measurement error structure of the system, using the error covariance matrix (ECM) as a penalization term. Results are reported from both simulations and experimental data based on replicate mid and near infrared (MIR and NIR) spectral measurements. The results for ECPR are better under non-iid conditions when compared with traditional first-order multivariate methods such as ridge regression (RR), principal component regression (PCR) and partial least-squares regression (PLS). Copyright © 2018 Elsevier B.V. All rights reserved.

Correcting for Measurement Error in Time-Varying Covariates in Marginal Structural Models.

PubMed

Kyle, Ryan P; Moodie, Erica E M; Klein, Marina B; Abrahamowicz, Michał

2016-08-01

Unbiased estimation of causal parameters from marginal structural models (MSMs) requires a fundamental assumption of no unmeasured confounding. Unfortunately, the time-varying covariates used to obtain inverse probability weights are often error-prone. Although substantial measurement error in important confounders is known to undermine control of confounders in conventional unweighted regression models, this issue has received comparatively limited attention in the MSM literature. Here we propose a novel application of the simulation-extrapolation (SIMEX) procedure to address measurement error in time-varying covariates, and we compare 2 approaches. The direct approach to SIMEX-based correction targets outcome model parameters, while the indirect approach corrects the weights estimated using the exposure model. We assess the performance of the proposed methods in simulations under different clinically plausible assumptions. The simulations demonstrate that measurement errors in time-dependent covariates may induce substantial bias in MSM estimators of causal effects of time-varying exposures, and that both proposed SIMEX approaches yield practically unbiased estimates in scenarios featuring low-to-moderate degrees of error. We illustrate the proposed approach in a simple analysis of the relationship between sustained virological response and liver fibrosis progression among persons infected with hepatitis C virus, while accounting for measurement error in γ-glutamyltransferase, using data collected in the Canadian Co-infection Cohort Study from 2003 to 2014. © The Author 2016. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Computing the apparent centroid of radar targets

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lee, C.E.

1996-12-31

A high-frequency multibounce radar scattering code was used as a simulation platform for demonstrating an algorithm to compute the ARC of specific radar targets. To illustrate this simulation process, several targets models were used. Simulation results for a sphere model were used to determine the errors of approximation associated with the simulation; verifying the process. The severity of glint induced tracking errors was also illustrated using a model of an F-15 aircraft. It was shown, in a deterministic manner, that the ARC of a target can fall well outside its physical extent. Finally, the apparent radar centroid simulation based onmore » a ray casting procedure is well suited for use on most massively parallel computing platforms and could lead to the development of a near real-time radar tracking simulation for applications such as endgame fuzing, survivability, and vulnerability analyses using specific radar targets and fuze algorithms.« less

The impact of 14-nm photomask uncertainties on computational lithography solutions

NASA Astrophysics Data System (ADS)

Sturtevant, John; Tejnil, Edita; Lin, Tim; Schultze, Steffen; Buck, Peter; Kalk, Franklin; Nakagawa, Kent; Ning, Guoxiang; Ackmann, Paul; Gans, Fritz; Buergel, Christian

2013-04-01

Computational lithography solutions rely upon accurate process models to faithfully represent the imaging system output for a defined set of process and design inputs. These models, which must balance accuracy demands with simulation runtime boundary conditions, rely upon the accurate representation of multiple parameters associated with the scanner and the photomask. While certain system input variables, such as scanner numerical aperture, can be empirically tuned to wafer CD data over a small range around the presumed set point, it can be dangerous to do so since CD errors can alias across multiple input variables. Therefore, many input variables for simulation are based upon designed or recipe-requested values or independent measurements. It is known, however, that certain measurement methodologies, while precise, can have significant inaccuracies. Additionally, there are known errors associated with the representation of certain system parameters. With shrinking total CD control budgets, appropriate accounting for all sources of error becomes more important, and the cumulative consequence of input errors to the computational lithography model can become significant. In this work, we examine with a simulation sensitivity study, the impact of errors in the representation of photomask properties including CD bias, corner rounding, refractive index, thickness, and sidewall angle. The factors that are most critical to be accurately represented in the model are cataloged. CD Bias values are based on state of the art mask manufacturing data and other variables changes are speculated, highlighting the need for improved metrology and awareness.

Spatial-temporal-covariance-based modeling, analysis, and simulation of aero-optics wavefront aberrations.

PubMed

Vogel, Curtis R; Tyler, Glenn A; Wittich, Donald J

2014-07-01

We introduce a framework for modeling, analysis, and simulation of aero-optics wavefront aberrations that is based on spatial-temporal covariance matrices extracted from wavefront sensor measurements. Within this framework, we present a quasi-homogeneous structure function to analyze nonhomogeneous, mildly anisotropic spatial random processes, and we use this structure function to show that phase aberrations arising in aero-optics are, for an important range of operating parameters, locally Kolmogorov. This strongly suggests that the d5/3 power law for adaptive optics (AO) deformable mirror fitting error, where d denotes actuator separation, holds for certain important aero-optics scenarios. This framework also allows us to compute bounds on AO servo lag error and predictive control error. In addition, it provides us with the means to accurately simulate AO systems for the mitigation of aero-effects, and it may provide insight into underlying physical processes associated with turbulent flow. The techniques introduced here are demonstrated using data obtained from the Airborne Aero-Optics Laboratory.

Modeling and Development of INS-Aided PLLs in a GNSS/INS Deeply-Coupled Hardware Prototype for Dynamic Applications

PubMed Central

Zhang, Tisheng; Niu, Xiaoji; Ban, Yalong; Zhang, Hongping; Shi, Chuang; Liu, Jingnan

2015-01-01

A GNSS/INS deeply-coupled system can improve the satellite signals tracking performance by INS aiding tracking loops under dynamics. However, there was no literature available on the complete modeling of the INS branch in the INS-aided tracking loop, which caused the lack of a theoretical tool to guide the selections of inertial sensors, parameter optimization and quantitative analysis of INS-aided PLLs. This paper makes an effort on the INS branch in modeling and parameter optimization of phase-locked loops (PLLs) based on the scalar-based GNSS/INS deeply-coupled system. It establishes the transfer function between all known error sources and the PLL tracking error, which can be used to quantitatively evaluate the candidate inertial measurement unit (IMU) affecting the carrier phase tracking error. Based on that, a steady-state error model is proposed to design INS-aided PLLs and to analyze their tracking performance. Based on the modeling and error analysis, an integrated deeply-coupled hardware prototype is developed, with the optimization of the aiding information. Finally, the performance of the INS-aided PLLs designed based on the proposed steady-state error model is evaluated through the simulation and road tests of the hardware prototype. PMID:25569751

Estimation of Covariance Matrix on Bi-Response Longitudinal Data Analysis with Penalized Spline Regression

NASA Astrophysics Data System (ADS)

Islamiyati, A.; Fatmawati; Chamidah, N.

2018-03-01

The correlation assumption of the longitudinal data with bi-response occurs on the measurement between the subjects of observation and the response. It causes the auto-correlation of error, and this can be overcome by using a covariance matrix. In this article, we estimate the covariance matrix based on the penalized spline regression model. Penalized spline involves knot points and smoothing parameters simultaneously in controlling the smoothness of the curve. Based on our simulation study, the estimated regression model of the weighted penalized spline with covariance matrix gives a smaller error value compared to the error of the model without covariance matrix.

Tests for detecting overdispersion in models with measurement error in covariates.

PubMed

Yang, Yingsi; Wong, Man Yu

2015-11-30

Measurement error in covariates can affect the accuracy in count data modeling and analysis. In overdispersion identification, the true mean-variance relationship can be obscured under the influence of measurement error in covariates. In this paper, we propose three tests for detecting overdispersion when covariates are measured with error: a modified score test and two score tests based on the proposed approximate likelihood and quasi-likelihood, respectively. The proposed approximate likelihood is derived under the classical measurement error model, and the resulting approximate maximum likelihood estimator is shown to have superior efficiency. Simulation results also show that the score test based on approximate likelihood outperforms the test based on quasi-likelihood and other alternatives in terms of empirical power. By analyzing a real dataset containing the health-related quality-of-life measurements of a particular group of patients, we demonstrate the importance of the proposed methods by showing that the analyses with and without measurement error correction yield significantly different results. Copyright © 2015 John Wiley & Sons, Ltd.

HYDROLOGIC MODEL CALIBRATION AND UNCERTAINTY IN SCENARIO ANALYSIS

EPA Science Inventory

A systematic analysis of model performance during simulations based on

observed land-cover/use change is used to quantify error associated with water-yield

simulations for a series of known landscape conditions over a 24-year period with the

goal of evaluatin...

Model Performance Evaluation and Scenario Analysis ...

EPA Pesticide Factsheets

This tool consists of two parts: model performance evaluation and scenario analysis (MPESA). The model performance evaluation consists of two components: model performance evaluation metrics and model diagnostics. These metrics provides modelers with statistical goodness-of-fit measures that capture magnitude only, sequence only, and combined magnitude and sequence errors. The performance measures include error analysis, coefficient of determination, Nash-Sutcliffe efficiency, and a new weighted rank method. These performance metrics only provide useful information about the overall model performance. Note that MPESA is based on the separation of observed and simulated time series into magnitude and sequence components. The separation of time series into magnitude and sequence components and the reconstruction back to time series provides diagnostic insights to modelers. For example, traditional approaches lack the capability to identify if the source of uncertainty in the simulated data is due to the quality of the input data or the way the analyst adjusted the model parameters. This report presents a suite of model diagnostics that identify if mismatches between observed and simulated data result from magnitude or sequence related errors. MPESA offers graphical and statistical options that allow HSPF users to compare observed and simulated time series and identify the parameter values to adjust or the input data to modify. The scenario analysis part of the too

Modeling and simulation for fewer-axis grinding of complex surface

NASA Astrophysics Data System (ADS)

Li, Zhengjian; Peng, Xiaoqiang; Song, Ci

2017-10-01

As the basis of fewer-axis grinding of complex surface, the grinding mathematical model is of great importance. A mathematical model of the grinding wheel was established, and then coordinate and normal vector of the wheel profile could be calculated. Through normal vector matching at the cutter contact point and the coordinate system transformation, the grinding mathematical model was established to work out the coordinate of the cutter location point. Based on the model, interference analysis was simulated to find out the right position and posture of workpiece for grinding. Then positioning errors of the workpiece including the translation positioning error and the rotation positioning error were analyzed respectively, and the main locating datum was obtained. According to the analysis results, the grinding tool path was planned and generated to grind the complex surface, and good form accuracy was obtained. The grinding mathematical model is simple, feasible and can be widely applied.

Insights into the use of time-lapse GPR data as observations for inverse multiphase flow simulations of DNAPL migration

USGS Publications Warehouse

Johnson, R.H.; Poeter, E.P.

2007-01-01

Perchloroethylene (PCE) saturations determined from GPR surveys were used as observations for inversion of multiphase flow simulations of a PCE injection experiment (Borden 9??m cell), allowing for the estimation of optimal bulk intrinsic permeability values. The resulting fit statistics and analysis of residuals (observed minus simulated PCE saturations) were used to improve the conceptual model. These improvements included adjustment of the elevation of a permeability contrast, use of the van Genuchten versus Brooks-Corey capillary pressure-saturation curve, and a weighting scheme to account for greater measurement error with larger saturation values. A limitation in determining PCE saturations through one-dimensional GPR modeling is non-uniqueness when multiple GPR parameters are unknown (i.e., permittivity, depth, and gain function). Site knowledge, fixing the gain function, and multiphase flow simulations assisted in evaluating non-unique conceptual models of PCE saturation, where depth and layering were reinterpreted to provide alternate conceptual models. Remaining bias in the residuals is attributed to the violation of assumptions in the one-dimensional GPR interpretation (which assumes flat, infinite, horizontal layering) resulting from multidimensional influences that were not included in the conceptual model. While the limitations and errors in using GPR data as observations for inverse multiphase flow simulations are frustrating and difficult to quantify, simulation results indicate that the error and bias in the PCE saturation values are small enough to still provide reasonable optimal permeability values. The effort to improve model fit and reduce residual bias decreases simulation error even for an inversion based on biased observations and provides insight into alternate GPR data interpretations. Thus, this effort is warranted and provides information on bias in the observation data when this bias is otherwise difficult to assess. ?? 2006 Elsevier B.V. All rights reserved.

Quantifying Carbon Flux Estimation Errors

NASA Astrophysics Data System (ADS)

Wesloh, D.

2017-12-01

Atmospheric Bayesian inversions have been used to estimate surface carbon dioxide (CO2) fluxes from global to sub-continental scales using atmospheric mixing ratio measurements. These inversions use an atmospheric transport model, coupled to a set of fluxes, in order to simulate mixing ratios that can then be compared to the observations. The comparison is then used to update the fluxes to better match the observations in a manner consistent with the uncertainties prescribed for each. However, inversion studies disagree with each other at continental scales, prompting further investigations to examine the causes of these differences. Inter-comparison studies have shown that the errors resulting from atmospheric transport inaccuracies are comparable to those from the errors in the prior fluxes. However, not as much effort has gone into studying the origins of the errors induced by errors in the transport as by errors in the prior distribution. This study uses a mesoscale transport model to evaluate the effects of representation errors in the observations and of incorrect descriptions of the transport. To obtain realizations of these errors, we performed an Observing System Simulation Experiments (OSSEs), with the transport model used for the inversion operating at two resolutions, one typical of a global inversion and the other of a mesoscale, and with various prior flux distributions to. Transport error covariances are inferred from an ensemble of perturbed mesoscale simulations while flux error covariances are computed using prescribed distributions and magnitudes. We examine how these errors can be diagnosed in the inversion process using aircraft, ground-based, and satellite observations of meteorological variables and CO2.

A GPS Phase-Locked Loop Performance Metric Based on the Phase Discriminator Output

PubMed Central

Stevanovic, Stefan; Pervan, Boris

2018-01-01

We propose a novel GPS phase-lock loop (PLL) performance metric based on the standard deviation of tracking error (defined as the discriminator’s estimate of the true phase error), and explain its advantages over the popular phase jitter metric using theory, numerical simulation, and experimental results. We derive an augmented GPS phase-lock loop (PLL) linear model, which includes the effect of coherent averaging, to be used in conjunction with this proposed metric. The augmented linear model allows more accurate calculation of tracking error standard deviation in the presence of additive white Gaussian noise (AWGN) as compared to traditional linear models. The standard deviation of tracking error, with a threshold corresponding to half of the arctangent discriminator pull-in region, is shown to be a more reliable/robust measure of PLL performance under interference conditions than the phase jitter metric. In addition, the augmented linear model is shown to be valid up until this threshold, which facilitates efficient performance prediction, so that time-consuming direct simulations and costly experimental testing can be reserved for PLL designs that are much more likely to be successful. The effect of varying receiver reference oscillator quality on the tracking error metric is also considered. PMID:29351250

Predicting protein concentrations with ELISA microarray assays, monotonic splines and Monte Carlo simulation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Daly, Don S.; Anderson, Kevin K.; White, Amanda M.

Background: A microarray of enzyme-linked immunosorbent assays, or ELISA microarray, predicts simultaneously the concentrations of numerous proteins in a small sample. These predictions, however, are uncertain due to processing error and biological variability. Making sound biological inferences as well as improving the ELISA microarray process require require both concentration predictions and creditable estimates of their errors. Methods: We present a statistical method based on monotonic spline statistical models, penalized constrained least squares fitting (PCLS) and Monte Carlo simulation (MC) to predict concentrations and estimate prediction errors in ELISA microarray. PCLS restrains the flexible spline to a fit of assay intensitymore » that is a monotone function of protein concentration. With MC, both modeling and measurement errors are combined to estimate prediction error. The spline/PCLS/MC method is compared to a common method using simulated and real ELISA microarray data sets. Results: In contrast to the rigid logistic model, the flexible spline model gave credible fits in almost all test cases including troublesome cases with left and/or right censoring, or other asymmetries. For the real data sets, 61% of the spline predictions were more accurate than their comparable logistic predictions; especially the spline predictions at the extremes of the prediction curve. The relative errors of 50% of comparable spline and logistic predictions differed by less than 20%. Monte Carlo simulation rendered acceptable asymmetric prediction intervals for both spline and logistic models while propagation of error produced symmetric intervals that diverged unrealistically as the standard curves approached horizontal asymptotes. Conclusions: The spline/PCLS/MC method is a flexible, robust alternative to a logistic/NLS/propagation-of-error method to reliably predict protein concentrations and estimate their errors. The spline method simplifies model selection and fitting, and reliably estimates believable prediction errors. For the 50% of the real data sets fit well by both methods, spline and logistic predictions are practically indistinguishable, varying in accuracy by less than 15%. The spline method may be useful when automated prediction across simultaneous assays of numerous proteins must be applied routinely with minimal user intervention.« less

[Simulation of cropland soil moisture based on an ensemble Kalman filter].

PubMed

Liu, Zhao; Zhou, Yan-Lian; Ju, Wei-Min; Gao, Ping

2011-11-01

By using an ensemble Kalman filter (EnKF) to assimilate the observed soil moisture data, the modified boreal ecosystem productivity simulator (BEPS) model was adopted to simulate the dynamics of soil moisture in winter wheat root zones at Xuzhou Agro-meteorological Station, Jiangsu Province of China during the growth seasons in 2000-2004. After the assimilation of observed data, the determination coefficient, root mean square error, and average absolute error of simulated soil moisture were in the ranges of 0.626-0.943, 0.018-0.042, and 0.021-0.041, respectively, with the simulation precision improved significantly, as compared with that before assimilation, indicating the applicability of data assimilation in improving the simulation of soil moisture. The experimental results at single point showed that the errors in the forcing data and observations and the frequency and soil depth of the assimilation of observed data all had obvious effects on the simulated soil moisture.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Taylor, Paul A.; Cooper, Candice Frances; Burnett, Damon J.

Light body armor development for the warfighter is based on trial-and-error testing of prototype designs against ballistic projectiles. Torso armor testing against blast is virtually nonexistent but necessary to ensure adequate protection against injury to the heart and lungs. In this report, we discuss the development of a high-fidelity human torso model, it's merging with the existing Sandia Human Head-Neck Model, and development of the modeling & simulation (M&S) capabilities necessary to simulate wound injury scenarios. Using the new Sandia Human Torso Model, we demonstrate the advantage of virtual simulation in the investigation of wound injury as it relates tomore » the warfighter experience. We present the results of virtual simulations of blast loading and ballistic projectile impact to the tors o with and without notional protective armor. In this manner, we demonstrate the ad vantages of applying a modeling and simulation approach to the investigation of wound injury and relative merit assessments of protective body armor without the need for trial-and-error testing.« less

A simple analytical infiltration model for short-duration rainfall

NASA Astrophysics Data System (ADS)

Wang, Kaiwen; Yang, Xiaohua; Liu, Xiaomang; Liu, Changming

2017-12-01

Many infiltration models have been proposed to simulate infiltration process. Different initial soil conditions and non-uniform initial water content can lead to infiltration simulation errors, especially for short-duration rainfall (SHR). Few infiltration models are specifically derived to eliminate the errors caused by the complex initial soil conditions. We present a simple analytical infiltration model for SHR infiltration simulation, i.e., Short-duration Infiltration Process model (SHIP model). The infiltration simulated by 5 models (i.e., SHIP (high) model, SHIP (middle) model, SHIP (low) model, Philip model and Parlange model) were compared based on numerical experiments and soil column experiments. In numerical experiments, SHIP (middle) and Parlange models had robust solutions for SHR infiltration simulation of 12 typical soils under different initial soil conditions. The absolute values of percent bias were less than 12% and the values of Nash and Sutcliffe efficiency were greater than 0.83. Additionally, in soil column experiments, infiltration rate fluctuated in a range because of non-uniform initial water content. SHIP (high) and SHIP (low) models can simulate an infiltration range, which successfully covered the fluctuation range of the observed infiltration rate. According to the robustness of solutions and the coverage of fluctuation range of infiltration rate, SHIP model can be integrated into hydrologic models to simulate SHR infiltration process and benefit the flood forecast.

A Novel Error Model of Optical Systems and an On-Orbit Calibration Method for Star Sensors.

PubMed

Wang, Shuang; Geng, Yunhai; Jin, Rongyu

2015-12-12

In order to improve the on-orbit measurement accuracy of star sensors, the effects of image-plane rotary error, image-plane tilt error and distortions of optical systems resulting from the on-orbit thermal environment were studied in this paper. Since these issues will affect the precision of star image point positions, in this paper, a novel measurement error model based on the traditional error model is explored. Due to the orthonormal characteristics of image-plane rotary-tilt errors and the strong nonlinearity among these error parameters, it is difficult to calibrate all the parameters simultaneously. To solve this difficulty, for the new error model, a modified two-step calibration method based on the Extended Kalman Filter (EKF) and Least Square Methods (LSM) is presented. The former one is used to calibrate the main point drift, focal length error and distortions of optical systems while the latter estimates the image-plane rotary-tilt errors. With this calibration method, the precision of star image point position influenced by the above errors is greatly improved from 15.42% to 1.389%. Finally, the simulation results demonstrate that the presented measurement error model for star sensors has higher precision. Moreover, the proposed two-step method can effectively calibrate model error parameters, and the calibration precision of on-orbit star sensors is also improved obviously.

Survey and Method for Determination of Trajectory Predictor Requirements

NASA Technical Reports Server (NTRS)

Rentas, Tamika L.; Green, Steven M.; Cate, Karen Tung

2009-01-01

A survey of air-traffic-management researchers, representing a broad range of automation applications, was conducted to document trajectory-predictor requirements for future decision-support systems. Results indicated that the researchers were unable to articulate a basic set of trajectory-prediction requirements for their automation concepts. Survey responses showed the need to establish a process to help developers determine the trajectory-predictor-performance requirements for their concepts. Two methods for determining trajectory-predictor requirements are introduced. A fast-time simulation method is discussed that captures the sensitivity of a concept to the performance of its trajectory-prediction capability. A characterization method is proposed to provide quicker, yet less precise results, based on analysis and simulation to characterize the trajectory-prediction errors associated with key modeling options for a specific concept. Concept developers can then identify the relative sizes of errors associated with key modeling options, and qualitatively determine which options lead to significant errors. The characterization method is demonstrated for a case study involving future airport surface traffic management automation. Of the top four sources of error, results indicated that the error associated with accelerations to and from turn speeds was unacceptable, the error associated with the turn path model was acceptable, and the error associated with taxi-speed estimation was of concern and needed a higher fidelity concept simulation to obtain a more precise result

Measurement system and model for simultaneously measuring 6DOF geometric errors.

PubMed

Zhao, Yuqiong; Zhang, Bin; Feng, Qibo

2017-09-04

A measurement system to simultaneously measure six degree-of-freedom (6DOF) geometric errors is proposed. The measurement method is based on a combination of mono-frequency laser interferometry and laser fiber collimation. A simpler and more integrated optical configuration is designed. To compensate for the measurement errors introduced by error crosstalk, element fabrication error, laser beam drift, and nonparallelism of two measurement beam, a unified measurement model, which can improve the measurement accuracy, is deduced and established using the ray-tracing method. A numerical simulation using the optical design software Zemax is conducted, and the results verify the correctness of the model. Several experiments are performed to demonstrate the feasibility and effectiveness of the proposed system and measurement model.

Assessing sequential data assimilation techniques for integrating GRACE data into a hydrological model

NASA Astrophysics Data System (ADS)

Khaki, M.; Hoteit, I.; Kuhn, M.; Awange, J.; Forootan, E.; van Dijk, A. I. J. M.; Schumacher, M.; Pattiaratchi, C.

2017-09-01

The time-variable terrestrial water storage (TWS) products from the Gravity Recovery And Climate Experiment (GRACE) have been increasingly used in recent years to improve the simulation of hydrological models by applying data assimilation techniques. In this study, for the first time, we assess the performance of the most popular data assimilation sequential techniques for integrating GRACE TWS into the World-Wide Water Resources Assessment (W3RA) model. We implement and test stochastic and deterministic ensemble-based Kalman filters (EnKF), as well as Particle filters (PF) using two different resampling approaches of Multinomial Resampling and Systematic Resampling. These choices provide various opportunities for weighting observations and model simulations during the assimilation and also accounting for error distributions. Particularly, the deterministic EnKF is tested to avoid perturbing observations before assimilation (that is the case in an ordinary EnKF). Gaussian-based random updates in the EnKF approaches likely do not fully represent the statistical properties of the model simulations and TWS observations. Therefore, the fully non-Gaussian PF is also applied to estimate more realistic updates. Monthly GRACE TWS are assimilated into W3RA covering the entire Australia. To evaluate the filters performances and analyze their impact on model simulations, their estimates are validated by independent in-situ measurements. Our results indicate that all implemented filters improve the estimation of water storage simulations of W3RA. The best results are obtained using two versions of deterministic EnKF, i.e. the Square Root Analysis (SQRA) scheme and the Ensemble Square Root Filter (EnSRF), respectively, improving the model groundwater estimations errors by 34% and 31% compared to a model run without assimilation. Applying the PF along with Systematic Resampling successfully decreases the model estimation error by 23%.

Cocaine Dependence Treatment Data: Methods for Measurement Error Problems With Predictors Derived From Stationary Stochastic Processes

PubMed Central

Guan, Yongtao; Li, Yehua; Sinha, Rajita

2011-01-01

In a cocaine dependence treatment study, we use linear and nonlinear regression models to model posttreatment cocaine craving scores and first cocaine relapse time. A subset of the covariates are summary statistics derived from baseline daily cocaine use trajectories, such as baseline cocaine use frequency and average daily use amount. These summary statistics are subject to estimation error and can therefore cause biased estimators for the regression coefficients. Unlike classical measurement error problems, the error we encounter here is heteroscedastic with an unknown distribution, and there are no replicates for the error-prone variables or instrumental variables. We propose two robust methods to correct for the bias: a computationally efficient method-of-moments-based method for linear regression models and a subsampling extrapolation method that is generally applicable to both linear and nonlinear regression models. Simulations and an application to the cocaine dependence treatment data are used to illustrate the efficacy of the proposed methods. Asymptotic theory and variance estimation for the proposed subsampling extrapolation method and some additional simulation results are described in the online supplementary material. PMID:21984854

Accounting for uncertainty in pedotransfer functions in vulnerability assessments of pesticide leaching to groundwater.

PubMed

Stenemo, Fredrik; Jarvis, Nicholas

2007-09-01

A simulation tool for site-specific vulnerability assessments of pesticide leaching to groundwater was developed, based on the pesticide fate and transport model MACRO, parameterized using pedotransfer functions and reasonable worst-case parameter values. The effects of uncertainty in the pedotransfer functions on simulation results were examined for 48 combinations of soils, pesticides and application timings, by sampling pedotransfer function regression errors and propagating them through the simulation model in a Monte Carlo analysis. An uncertainty factor, f(u), was derived, defined as the ratio between the concentration simulated with no errors, c(sim), and the 80th percentile concentration for the scenario. The pedotransfer function errors caused a large variation in simulation results, with f(u) ranging from 1.14 to 1440, with a median of 2.8. A non-linear relationship was found between f(u) and c(sim), which can be used to account for parameter uncertainty by correcting the simulated concentration, c(sim), to an estimated 80th percentile value. For fine-textured soils, the predictions were most sensitive to errors in the pedotransfer functions for two parameters regulating macropore flow (the saturated matrix hydraulic conductivity, K(b), and the effective diffusion pathlength, d) and two water retention function parameters (van Genuchten's N and alpha parameters). For coarse-textured soils, the model was also sensitive to errors in the exponent in the degradation water response function and the dispersivity, in addition to K(b), but showed little sensitivity to d. To reduce uncertainty in model predictions, improved pedotransfer functions for K(b), d, N and alpha would therefore be most useful. 2007 Society of Chemical Industry

Optical laboratory solution and error model simulation of a linear time-varying finite element equation

NASA Technical Reports Server (NTRS)

Taylor, B. K.; Casasent, D. P.

1989-01-01

The use of simplified error models to accurately simulate and evaluate the performance of an optical linear-algebra processor is described. The optical architecture used to perform banded matrix-vector products is reviewed, along with a linear dynamic finite-element case study. The laboratory hardware and ac-modulation technique used are presented. The individual processor error-source models and their simulator implementation are detailed. Several significant simplifications are introduced to ease the computational requirements and complexity of the simulations. The error models are verified with a laboratory implementation of the processor, and are used to evaluate its potential performance.

Investigating different filter and rescaling methods on simulated GRACE-like TWS variations for hydrological applications

NASA Astrophysics Data System (ADS)

Zhang, Liangjing; Dahle, Christoph; Neumayer, Karl-Hans; Dobslaw, Henryk; Flechtner, Frank; Thomas, Maik

2016-04-01

Terrestrial water storage (TWS) variations obtained from GRACE play an increasingly important role in various hydrological and hydro-meteorological applications. Since monthly-mean gravity fields are contaminated by errors caused by a number of sources with distinct spatial correlation structures, filtering is needed to remove in particular high frequency noise. Subsequently, bias and leakage caused by the filtering need to be corrected before the final results are interpreted as GRACE-based observations of TWS. Knowledge about the reliability and performance of different post-processing methods is highly important for the GRACE users. In this contribution, we re-assess a number of commonly used post-processing methods using a simulated GRACE-like gravity field time-series based on realistic orbits and instrument error assumptions as well as background error assumptions out of the updated ESA Earth System Model. Two non-isotropic filter methods from Kusche (2007) and Swenson and Wahr (2006) are tested. Rescaling factors estimated from five different hydrological models and the ensemble median are applied to the post-processed simulated GRACE-like TWS estimates to correct the bias and leakage. Since TWS anomalies out of the post-processed simulation results can be readily compared to the time-variable Earth System Model initially used as "truth" during the forward simulation step, we are able to thoroughly check the plausibility of our error estimation assessment and will subsequently recommend a processing strategy that shall also be applied to planned GRACE and GRACE-FO Level-3 products for hydrological applications provided by GFZ. Kusche, J. (2007): Approximate decorrelation and non-isotropic smoothing of time-variable GRACE-type gravity field models. J. Geodesy, 81 (11), 733-749, doi:10.1007/s00190-007-0143-3. Swenson, S. and Wahr, J. (2006): Post-processing removal of correlated errors in GRACE data. Geophysical Research Letters, 33(8):L08402.

Assessing a local ensemble Kalman filter: perfect model experiments with the National Centers for Environmental Prediction global model

NASA Astrophysics Data System (ADS)

Szunyogh, Istvan; Kostelich, Eric J.; Gyarmati, G.; Patil, D. J.; Hunt, Brian R.; Kalnay, Eugenia; Ott, Edward; Yorke, James A.

2005-08-01

The accuracy and computational efficiency of the recently proposed local ensemble Kalman filter (LEKF) data assimilation scheme is investigated on a state-of-the-art operational numerical weather prediction model using simulated observations. The model selected for this purpose is the T62 horizontal- and 28-level vertical-resolution version of the Global Forecast System (GFS) of the National Center for Environmental Prediction. The performance of the data assimilation system is assessed for different configurations of the LEKF scheme. It is shown that a modest size (40-member) ensemble is sufficient to track the evolution of the atmospheric state with high accuracy. For this ensemble size, the computational time per analysis is less than 9 min on a cluster of PCs. The analyses are extremely accurate in the mid-latitude storm track regions. The largest analysis errors, which are typically much smaller than the observational errors, occur where parametrized physical processes play important roles. Because these are also the regions where model errors are expected to be the largest, limitations of a real-data implementation of the ensemble-based Kalman filter may be easily mistaken for model errors. In light of these results, the importance of testing the ensemble-based Kalman filter data assimilation systems on simulated observations is stressed.

On the accuracy and precision of numerical waveforms: effect of waveform extraction methodology

NASA Astrophysics Data System (ADS)

Chu, Tony; Fong, Heather; Kumar, Prayush; Pfeiffer, Harald P.; Boyle, Michael; Hemberger, Daniel A.; Kidder, Lawrence E.; Scheel, Mark A.; Szilagyi, Bela

2016-08-01

We present a new set of 95 numerical relativity simulations of non-precessing binary black holes (BBHs). The simulations sample comprehensively both black-hole spins up to spin magnitude of 0.9, and cover mass ratios 1-3. The simulations cover on average 24 inspiral orbits, plus merger and ringdown, with low initial orbital eccentricities e\\lt {10}-4. A subset of the simulations extends the coverage of non-spinning BBHs up to mass ratio q = 10. Gravitational waveforms at asymptotic infinity are computed with two independent techniques: extrapolation and Cauchy characteristic extraction. An error analysis based on noise-weighted inner products is performed. We find that numerical truncation error, error due to gravitational wave extraction, and errors due to the Fourier transformation of signals with finite length of the numerical waveforms are of similar magnitude, with gravitational wave extraction errors dominating at noise-weighted mismatches of ˜ 3× {10}-4. This set of waveforms will serve to validate and improve aligned-spin waveform models for gravitational wave science.

Special Issue on Uncertainty Quantification in Multiscale System Design and Simulation

DOE PAGES

Wang, Yan; Swiler, Laura

2017-09-07

The importance of uncertainty has been recognized in various modeling, simulation, and analysis applications, where inherent assumptions and simplifications affect the accuracy of model predictions for physical phenomena. As model predictions are now heavily relied upon for simulation-based system design, which includes new materials, vehicles, mechanical and civil structures, and even new drugs, wrong model predictions could potentially cause catastrophic consequences. Therefore, uncertainty and associated risks due to model errors should be quantified to support robust systems engineering.

Special Issue on Uncertainty Quantification in Multiscale System Design and Simulation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Yan; Swiler, Laura

The importance of uncertainty has been recognized in various modeling, simulation, and analysis applications, where inherent assumptions and simplifications affect the accuracy of model predictions for physical phenomena. As model predictions are now heavily relied upon for simulation-based system design, which includes new materials, vehicles, mechanical and civil structures, and even new drugs, wrong model predictions could potentially cause catastrophic consequences. Therefore, uncertainty and associated risks due to model errors should be quantified to support robust systems engineering.

Intercomparison of Streamflow Simulations between WRF-Hydro and Hydrology Laboratory-Research Distributed Hydrologic Model Frameworks

NASA Astrophysics Data System (ADS)

KIM, J.; Smith, M. B.; Koren, V.; Salas, F.; Cui, Z.; Johnson, D.

2017-12-01

The National Oceanic and Atmospheric Administration (NOAA)-National Weather Service (NWS) developed the Hydrology Laboratory-Research Distributed Hydrologic Model (HL-RDHM) framework as an initial step towards spatially distributed modeling at River Forecast Centers (RFCs). Recently, the NOAA/NWS worked with the National Center for Atmospheric Research (NCAR) to implement the National Water Model (NWM) for nationally-consistent water resources prediction. The NWM is based on the WRF-Hydro framework and is run at a 1km spatial resolution and 1-hour time step over the contiguous United States (CONUS) and contributing areas in Canada and Mexico. In this study, we compare streamflow simulations from HL-RDHM and WRF-Hydro to observations from 279 USGS stations. For streamflow simulations, HL-RDHM is run on 4km grids with the temporal resolution of 1 hour for a 5-year period (Water Years 2008-2012), using a priori parameters provided by NOAA-NWS. The WRF-Hydro streamflow simulations for the same time period are extracted from NCAR's 23 retrospective run of the NWM (version 1.0) over CONUS based on 1km grids. We choose 279 USGS stations which are relatively less affected by dams or reservoirs, in the domains of six different RFCs. We use the daily average values of simulations and observations for the convenience of comparison. The main purpose of this research is to evaluate how HL-RDHM and WRF-Hydro perform at USGS gauge stations. We compare daily time-series of observations and both simulations, and calculate the error values using a variety of error functions. Using these plots and error values, we evaluate the performances of HL-RDHM and WRF-Hydro models. Our results show a mix of model performance across geographic regions.

Enterprise Systems Analysis

DTIC Science & Technology

2016-03-14

flows , or continuous state changes, with feedback loops and lags modeled in the flow system. Agent based simulations operate using a discrete event...DeLand, S. M., Rutherford, B . M., Diegert, K. V., & Alvin, K. F. (2002). Error and uncertainty in modeling and simulation . Reliability Engineering...intrinsic complexity of the underlying social systems fundamentally limits the ability to make

Spatial interpolation schemes of daily precipitation for hydrologic modeling

USGS Publications Warehouse

Hwang, Y.; Clark, M.R.; Rajagopalan, B.; Leavesley, G.

2012-01-01

Distributed hydrologic models typically require spatial estimates of precipitation interpolated from sparsely located observational points to the specific grid points. We compare and contrast the performance of regression-based statistical methods for the spatial estimation of precipitation in two hydrologically different basins and confirmed that widely used regression-based estimation schemes fail to describe the realistic spatial variability of daily precipitation field. The methods assessed are: (1) inverse distance weighted average; (2) multiple linear regression (MLR); (3) climatological MLR; and (4) locally weighted polynomial regression (LWP). In order to improve the performance of the interpolations, the authors propose a two-step regression technique for effective daily precipitation estimation. In this simple two-step estimation process, precipitation occurrence is first generated via a logistic regression model before estimate the amount of precipitation separately on wet days. This process generated the precipitation occurrence, amount, and spatial correlation effectively. A distributed hydrologic model (PRMS) was used for the impact analysis in daily time step simulation. Multiple simulations suggested noticeable differences between the input alternatives generated by three different interpolation schemes. Differences are shown in overall simulation error against the observations, degree of explained variability, and seasonal volumes. Simulated streamflows also showed different characteristics in mean, maximum, minimum, and peak flows. Given the same parameter optimization technique, LWP input showed least streamflow error in Alapaha basin and CMLR input showed least error (still very close to LWP) in Animas basin. All of the two-step interpolation inputs resulted in lower streamflow error compared to the directly interpolated inputs. ?? 2011 Springer-Verlag.

Investigation and Development of Control Laws for the NASA Langley Research Center Cockpit Motion Facility

NASA Technical Reports Server (NTRS)

Coon, Craig R.; Cardullo, Frank M.; Zaychik, Kirill B.

2014-01-01

The ability to develop highly advanced simulators is a critical need that has the ability to significantly impact the aerospace industry. The aerospace industry is advancing at an ever increasing pace and flight simulators must match this development with ever increasing urgency. In order to address both current problems and potential advancements with flight simulator techniques, several aspects of current control law technology of the National Aeronautics and Space Administration (NASA) Langley Research Center's Cockpit Motion Facility (CMF) motion base simulator were examined. Preliminary investigation of linear models based upon hardware data were examined to ensure that the most accurate models are used. This research identified both system improvements in the bandwidth and more reliable linear models. Advancements in the compensator design were developed and verified through multiple techniques. The position error rate feedback, the acceleration feedback and the force feedback were all analyzed in the heave direction using the nonlinear model of the hardware. Improvements were made using the position error rate feedback technique. The acceleration feedback compensator also provided noteworthy improvement, while attempts at implementing a force feedback compensator proved unsuccessful.

Precipitation and Latent Heating Distributions from Satellite Passive Microwave Radiometry. Part 1; Method and Uncertainties

NASA Technical Reports Server (NTRS)

Olson, William S.; Kummerow, Christian D.; Yang, Song; Petty, Grant W.; Tao, Wei-Kuo; Bell, Thomas L.; Braun, Scott A.; Wang, Yansen; Lang, Stephen E.; Johnson, Daniel E.

2004-01-01

A revised Bayesian algorithm for estimating surface rain rate, convective rain proportion, and latent heating/drying 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 non-convective 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 instantaneous rain rate estimates at 0.5 deg resolution range from approximately 50% at 1 mm/h to 20% at 14 mm/h. These errors represent about 70-90% of the mean random deviation between collocated passive microwave and spaceborne radar rain rate estimates. The cumulative algorithm error in TMI estimates at monthly, 2.5 deg resolution is relatively small (less than 6% at 5 mm/day) compared to the random error due to infrequent satellite temporal sampling (8-35% at the same rain rate).

Effect of grid resolution on large eddy simulation of wall-bounded turbulence

NASA Astrophysics Data System (ADS)

Rezaeiravesh, S.; Liefvendahl, M.

2018-05-01

The effect of grid resolution on a large eddy simulation (LES) of a wall-bounded turbulent flow is investigated. A channel flow simulation campaign involving a systematic variation of the streamwise (Δx) and spanwise (Δz) grid resolution is used for this purpose. The main friction-velocity-based Reynolds number investigated is 300. Near the walls, the grid cell size is determined by the frictional scaling, Δx+ and Δz+, and strongly anisotropic cells, with first Δy+ ˜ 1, thus aiming for the wall-resolving LES. Results are compared to direct numerical simulations, and several quality measures are investigated, including the error in the predicted mean friction velocity and the error in cross-channel profiles of flow statistics. To reduce the total number of channel flow simulations, techniques from the framework of uncertainty quantification are employed. In particular, a generalized polynomial chaos expansion (gPCE) is used to create metamodels for the errors over the allowed parameter ranges. The differing behavior of the different quality measures is demonstrated and analyzed. It is shown that friction velocity and profiles of the velocity and Reynolds stress tensor are most sensitive to Δz+, while the error in the turbulent kinetic energy is mostly influenced by Δx+. Recommendations for grid resolution requirements are given, together with the quantification of the resulting predictive accuracy. The sensitivity of the results to the subgrid-scale (SGS) model and varying Reynolds number is also investigated. All simulations are carried out with second-order accurate finite-volume-based solver OpenFOAM. It is shown that the choice of numerical scheme for the convective term significantly influences the error portraits. It is emphasized that the proposed methodology, involving the gPCE, can be applied to other modeling approaches, i.e., other numerical methods and the choice of SGS model.

Error Analysis and Validation for Insar Height Measurement Induced by Slant Range

NASA Astrophysics Data System (ADS)

Zhang, X.; Li, T.; Fan, W.; Geng, X.

2018-04-01

InSAR technique is an important method for large area DEM extraction. Several factors have significant influence on the accuracy of height measurement. In this research, the effect of slant range measurement for InSAR height measurement was analysis and discussed. Based on the theory of InSAR height measurement, the error propagation model was derived assuming no coupling among different factors, which directly characterise the relationship between slant range error and height measurement error. Then the theoretical-based analysis in combination with TanDEM-X parameters was implemented to quantitatively evaluate the influence of slant range error to height measurement. In addition, the simulation validation of InSAR error model induced by slant range was performed on the basis of SRTM DEM and TanDEM-X parameters. The spatial distribution characteristics and error propagation rule of InSAR height measurement were further discussed and evaluated.

Satellite SAR geocoding with refined RPC model

NASA Astrophysics Data System (ADS)

Zhang, Lu; Balz, Timo; Liao, Mingsheng

2012-04-01

Recent studies have proved that the Rational Polynomial Camera (RPC) model is able to act as a reliable replacement of the rigorous Range-Doppler (RD) model for the geometric processing of satellite SAR datasets. But its capability in absolute geolocation of SAR images has not been evaluated quantitatively. Therefore, in this article the problems of error analysis and refinement of SAR RPC model are primarily investigated to improve the absolute accuracy of SAR geolocation. Range propagation delay and azimuth timing error are identified as two major error sources for SAR geolocation. An approach based on SAR image simulation and real-to-simulated image matching is developed to estimate and correct these two errors. Afterwards a refined RPC model can be built from the error-corrected RD model and then used in satellite SAR geocoding. Three experiments with different settings are designed and conducted to comprehensively evaluate the accuracies of SAR geolocation with both ordinary and refined RPC models. All the experimental results demonstrate that with RPC model refinement the absolute location accuracies of geocoded SAR images can be improved significantly, particularly in Easting direction. In another experiment the computation efficiencies of SAR geocoding with both RD and RPC models are compared quantitatively. The results show that by using the RPC model such efficiency can be remarkably improved by at least 16 times. In addition the problem of DEM data selection for SAR image simulation in RPC model refinement is studied by a comparative experiment. The results reveal that the best choice should be using the proper DEM datasets of spatial resolution comparable to that of the SAR images.

Numerically accurate computational techniques for optimal estimator analyses of multi-parameter models

NASA Astrophysics Data System (ADS)

Berger, Lukas; Kleinheinz, Konstantin; Attili, Antonio; Bisetti, Fabrizio; Pitsch, Heinz; Mueller, Michael E.

2018-05-01

Modelling unclosed terms in partial differential equations typically involves two steps: First, a set of known quantities needs to be specified as input parameters for a model, and second, a specific functional form needs to be defined to model the unclosed terms by the input parameters. Both steps involve a certain modelling error, with the former known as the irreducible error and the latter referred to as the functional error. Typically, only the total modelling error, which is the sum of functional and irreducible error, is assessed, but the concept of the optimal estimator enables the separate analysis of the total and the irreducible errors, yielding a systematic modelling error decomposition. In this work, attention is paid to the techniques themselves required for the practical computation of irreducible errors. Typically, histograms are used for optimal estimator analyses, but this technique is found to add a non-negligible spurious contribution to the irreducible error if models with multiple input parameters are assessed. Thus, the error decomposition of an optimal estimator analysis becomes inaccurate, and misleading conclusions concerning modelling errors may be drawn. In this work, numerically accurate techniques for optimal estimator analyses are identified and a suitable evaluation of irreducible errors is presented. Four different computational techniques are considered: a histogram technique, artificial neural networks, multivariate adaptive regression splines, and an additive model based on a kernel method. For multiple input parameter models, only artificial neural networks and multivariate adaptive regression splines are found to yield satisfactorily accurate results. Beyond a certain number of input parameters, the assessment of models in an optimal estimator analysis even becomes practically infeasible if histograms are used. The optimal estimator analysis in this paper is applied to modelling the filtered soot intermittency in large eddy simulations using a dataset of a direct numerical simulation of a non-premixed sooting turbulent flame.

Error Detection-Based Model to Assess Educational Outcomes in Crisis Resource Management Training: A Pilot Study.

PubMed

Bouhabel, Sarah; Kay-Rivest, Emily; Nhan, Carol; Bank, Ilana; Nugus, Peter; Fisher, Rachel; Nguyen, Lily Hp

2017-06-01

Otolaryngology-head and neck surgery (OTL-HNS) residents face a variety of difficult, high-stress situations, which may occur early in their training. Since these events occur infrequently, simulation-based learning has become an important part of residents' training and is already well established in fields such as anesthesia and emergency medicine. In the domain of OTL-HNS, it is gradually gaining in popularity. Crisis Resource Management (CRM), a program adapted from the aviation industry, aims to improve outcomes of crisis situations by attempting to mitigate human errors. Some examples of CRM principles include cultivating situational awareness; promoting proper use of available resources; and improving rapid decision making, particularly in high-acuity, low-frequency clinical situations. Our pilot project sought to integrate CRM principles into an airway simulation course for OTL-HNS residents, but most important, it evaluated whether learning objectives were met, through use of a novel error identification model.

Micro-scale finite element modeling of ultrasound propagation in aluminum trabecular bone-mimicking phantoms: A comparison between numerical simulation and experimental results.

PubMed

Vafaeian, B; Le, L H; Tran, T N H T; El-Rich, M; El-Bialy, T; Adeeb, S

2016-05-01

The present study investigated the accuracy of micro-scale finite element modeling for simulating broadband ultrasound propagation in water-saturated trabecular bone-mimicking phantoms. To this end, five commercially manufactured aluminum foam samples as trabecular bone-mimicking phantoms were utilized for ultrasonic immersion through-transmission experiments. Based on micro-computed tomography images of the same physical samples, three-dimensional high-resolution computational samples were generated to be implemented in the micro-scale finite element models. The finite element models employed the standard Galerkin finite element method (FEM) in time domain to simulate the ultrasonic experiments. The numerical simulations did not include energy dissipative mechanisms of ultrasonic attenuation; however, they expectedly simulated reflection, refraction, scattering, and wave mode conversion. The accuracy of the finite element simulations were evaluated by comparing the simulated ultrasonic attenuation and velocity with the experimental data. The maximum and the average relative errors between the experimental and simulated attenuation coefficients in the frequency range of 0.6-1.4 MHz were 17% and 6% respectively. Moreover, the simulations closely predicted the time-of-flight based velocities and the phase velocities of ultrasound with maximum relative errors of 20 m/s and 11 m/s respectively. The results of this study strongly suggest that micro-scale finite element modeling can effectively simulate broadband ultrasound propagation in water-saturated trabecular bone-mimicking structures. Copyright © 2016 Elsevier B.V. All rights reserved.

Accurate recapture identification for genetic mark–recapture studies with error-tolerant likelihood-based match calling and sample clustering

USGS Publications Warehouse

Sethi, Suresh; Linden, Daniel; Wenburg, John; Lewis, Cara; Lemons, Patrick R.; Fuller, Angela K.; Hare, Matthew P.

2016-01-01

Error-tolerant likelihood-based match calling presents a promising technique to accurately identify recapture events in genetic mark–recapture studies by combining probabilities of latent genotypes and probabilities of observed genotypes, which may contain genotyping errors. Combined with clustering algorithms to group samples into sets of recaptures based upon pairwise match calls, these tools can be used to reconstruct accurate capture histories for mark–recapture modelling. Here, we assess the performance of a recently introduced error-tolerant likelihood-based match-calling model and sample clustering algorithm for genetic mark–recapture studies. We assessed both biallelic (i.e. single nucleotide polymorphisms; SNP) and multiallelic (i.e. microsatellite; MSAT) markers using a combination of simulation analyses and case study data on Pacific walrus (Odobenus rosmarus divergens) and fishers (Pekania pennanti). A novel two-stage clustering approach is demonstrated for genetic mark–recapture applications. First, repeat captures within a sampling occasion are identified. Subsequently, recaptures across sampling occasions are identified. The likelihood-based matching protocol performed well in simulation trials, demonstrating utility for use in a wide range of genetic mark–recapture studies. Moderately sized SNP (64+) and MSAT (10–15) panels produced accurate match calls for recaptures and accurate non-match calls for samples from closely related individuals in the face of low to moderate genotyping error. Furthermore, matching performance remained stable or increased as the number of genetic markers increased, genotyping error notwithstanding.

Sensitivity of geographic information system outputs to errors in remotely sensed data

NASA Technical Reports Server (NTRS)

Ramapriyan, H. K.; Boyd, R. K.; Gunther, F. J.; Lu, Y. C.

1981-01-01

The sensitivity of the outputs of a geographic information system (GIS) to errors in inputs derived from remotely sensed data (RSD) is investigated using a suitability model with per-cell decisions and a gridded geographic data base whose cells are larger than the RSD pixels. The process of preparing RSD as input to a GIS is analyzed, and the errors associated with classification and registration are examined. In the case of the model considered, it is found that the errors caused during classification and registration are partially compensated by the aggregation of pixels. The compensation is quantified by means of an analytical model, a Monte Carlo simulation, and experiments with Landsat data. The results show that error reductions of the order of 50% occur because of aggregation when 25 pixels of RSD are used per cell in the geographic data base.

Using Modern Design Tools for Digital Avionics Development

NASA Technical Reports Server (NTRS)

Hyde, David W.; Lakin, David R., II; Asquith, Thomas E.

2000-01-01

Using Modem Design Tools for Digital Avionics Development Shrinking development time and increased complexity of new avionics forces the designer to use modem tools and methods during hardware development. Engineers at the Marshall Space Flight Center have successfully upgraded their design flow and used it to develop a Mongoose V based radiation tolerant processor board for the International Space Station's Water Recovery System. The design flow, based on hardware description languages, simulation, synthesis, hardware models, and full functional software model libraries, allowed designers to fully simulate the processor board from reset, through initialization before any boards were built. The fidelity of a digital simulation is limited to the accuracy of the models used and how realistically the designer drives the circuit's inputs during simulation. By using the actual silicon during simulation, device modeling errors are reduced. Numerous design flaws were discovered early in the design phase when they could be easily fixed. The use of hardware models and actual MIPS software loaded into full functional memory models also provided checkout of the software development environment. This paper will describe the design flow used to develop the processor board and give examples of errors that were found using the tools. An overview of the processor board firmware will also be covered.

Systematic ionospheric electron density tilts (SITs) at mid-latitudes and their associated HF bearing errors

NASA Astrophysics Data System (ADS)

Tedd, B. L.; Strangeways, H. J.; Jones, T. B.

1985-11-01

Systematic ionospheric tilts (SITs) at midlatitudes and the diurnal variation of bearing error for different transmission paths are examined. An explanation of diurnal variations of bearing error based on the dependence of ionospheric tilt on solar zenith angle and plasma transport processes is presented. The effect of vertical ion drift and the momentum transfer of neutral winds is investigated. During the daytime the transmissions are low and photochemical processes control SITs; however, at night transmissions are at higher heights and spatial and temporal variations of plasma transport processes influence SITs. A HF ray tracing technique which uses a three-dimensional ionospheric model based on predictions to simulate SIT-induced bearing errors is described; poor correlation with experimental data is observed and the causes for this are studied. A second model based on measured vertical-sounder data is proposed. Model two is applicable for predicting bearing error for a range of transmission paths and correlates well with experimental data.

Divergence compensation for hardware-in-the-loop simulation of stiffness-varying discrete contact in space

NASA Astrophysics Data System (ADS)

Qi, Chenkun; Zhao, Xianchao; Gao, Feng; Ren, Anye; Hu, Yan

2016-11-01

The hardware-in-the-loop (HIL) contact simulation for flying objects in space is challenging due to the divergence caused by the time delay. In this study, a divergence compensation approach is proposed for the stiffness-varying discrete contact. The dynamic response delay of the motion simulator and the force measurement delay are considered. For the force measurement delay, a phase lead based force compensation approach is used. For the dynamic response delay of the motion simulator, a response error based force compensation approach is used, where the compensation force is obtained from the real-time identified contact stiffness and real-time measured position response error. The dynamic response model of the motion simulator is not required. The simulations and experiments show that the simulation divergence can be compensated effectively and satisfactorily by using the proposed approach.

Neural network approximation of nonlinearity in laser nano-metrology system based on TLMI

NASA Astrophysics Data System (ADS)

Olyaee, Saeed; Hamedi, Samaneh

2011-02-01

In this paper, an approach based on neural network (NN) for nonlinearity modeling in a nano-metrology system using three-longitudinal-mode laser heterodyne interferometer (TLMI) for length and displacement measurements is presented. We model nonlinearity errors that arise from elliptically and non-orthogonally polarized laser beams, rotational error in the alignment of laser head with respect to the polarizing beam splitter, rotational error in the alignment of the mixing polarizer, and unequal transmission coefficients in the polarizing beam splitter. Here we use a neural network algorithm based on the multi-layer perceptron (MLP) network. The simulation results show that multi-layer feed forward perceptron network is successfully applicable to real noisy interferometer signals.

[Adaptability of APSIM model in Southwestern China: A case study of winter wheat in Chongqing City].

PubMed

Dai, Tong; Wang, Jing; He, Di; Zhang, Jian-ping; Wang, Na

2015-04-01

Field experimental data of winter wheat and parallel daily meteorological data at four typical stations in Chongqing City were used to calibrate and validate APSIM-wheat model and determine the genetic parameters for 12 varieties of winter wheat. The results showed that there was a good agreement between the simulated and observed growth periods from sowing to emergence, flowering and maturity of wheat. Root mean squared errors (RMSEs) between simulated and observed emergence, flowering and maturity were 0-3, 1-8, and 0-8 d, respectively. Normalized root mean squared errors (NRMSEs) between simulated and observed above-ground biomass for 12 study varieties were less than 30%. NRMSE between simulated and observed yields for 10 varieties out of 12 study varieties were less than 30%. APSIM-wheat model performed well in simulating phenology, aboveground biomass and yield of winter wheat in Chongqing City, which could provide a foundational support for assessing the impact of climate change on wheat production in the study area based on the model.

Combined PEST and Trial-Error approach to improve APEX calibration

USDA-ARS?s Scientific Manuscript database

The Agricultural Policy Environmental eXtender (APEX), a physically-based hydrologic model that simulates management impacts on the environment for small watersheds, requires improved understanding of the input parameters for improved simulations. However, most previously published studies used the ...

Coupled Stochastic Time-Inverted Lagrangian Transport/Weather Forecast and Research/Vegetation Photosynthesis and Respiration Model. Part II; Simulations of Tower-Based and Airborne CO2 Measurements

NASA Technical Reports Server (NTRS)

Eluszkiewicz, Janusz; Nehrkorn, Thomas; Wofsy, Steven C.; Matross, Daniel; Gerbig, Christoph; Lin, John C.; Freitas, Saulo; Longo, Marcos; Andrews, Arlyn E.; Peters, Wouter

2007-01-01

This paper evaluates simulations of atmospheric CO2 measured in 2004 at continental surface and airborne receptors, intended to test the capability to use data with high temporal and spatial resolution for analyses of carbon sources and sinks at regional and continental scales. The simulations were performed using the Stochastic Time-Inverted Lagrangian Transport (STILT) model driven by the Weather Forecast and Research (WRF) model, and linked to surface fluxes from the satellite-driven Vegetation Photosynthesis and Respiration Model (VPRM). The simulations provide detailed representations of hourly CO2 tower data and reproduce the shapes of airborne vertical profiles with high fidelity. WRF meteorology gives superior model performance compared with standard meteorological products, and the impact of including WRF convective mass fluxes in the STILT trajectory calculations is significant in individual cases. Important biases in the simulation are associated with the nighttime CO2 build-up and subsequent morning transition to convective conditions, and with errors in the advected lateral boundary condition. Comparison of STILT simulations driven by the WRF model against those driven by the Brazilian variant of the Regional Atmospheric Modeling System (BRAMS) shows that model-to-model differences are smaller than between an individual transport model and observations, pointing to systematic errors in the simulated transport. Future developments in the WRF model s data assimilation capabilities, basic research into the fundamental aspects of trajectory calculations, and intercomparison studies involving other transport models, are possible venues for reducing these errors. Overall, the STILT/WRF/VPRM offers a powerful tool for continental and regional scale carbon flux estimates.

Satellite-based emission constraint for nitrogen oxides: Capability and uncertainty

NASA Astrophysics Data System (ADS)

Lin, J.; McElroy, M. B.; Boersma, F.; Nielsen, C.; Zhao, Y.; Lei, Y.; Liu, Y.; Zhang, Q.; Liu, Z.; Liu, H.; Mao, J.; Zhuang, G.; Roozendael, M.; Martin, R.; Wang, P.; Spurr, R. J.; Sneep, M.; Stammes, P.; Clemer, K.; Irie, H.

2013-12-01

Vertical column densities (VCDs) of tropospheric nitrogen dioxide (NO2) retrieved from satellite remote sensing have been employed widely to constrain emissions of nitrogen oxides (NOx). A major strength of satellite-based emission constraint is analysis of emission trends and variability, while a crucial limitation is errors both in satellite NO2 data and in model simulations relating NOx emissions to NO2 columns. Through a series of studies, we have explored these aspects over China. We separate anthropogenic from natural sources of NOx by exploiting their different seasonality. We infer trends of NOx emissions in recent years and effects of a variety of socioeconomic events at different spatiotemporal scales including the general economic growth, global financial crisis, Chinese New Year, and Beijing Olympics. We further investigate the impact of growing NOx emissions on particulate matter (PM) pollution in China. As part of recent developments, we identify and correct errors in both satellite NO2 retrieval and model simulation that ultimately affect NOx emission constraint. We improve the treatments of aerosol optical effects, clouds and surface reflectance in the NO2 retrieval process, using as reference ground-based MAX-DOAS measurements to evaluate the improved retrieval results. We analyze the sensitivity of simulated NO2 to errors in the model representation of major meteorological and chemical processes with a subsequent correction of model bias. Future studies will implement these improvements to re-constrain NOx emissions.

A Sensor Dynamic Measurement Error Prediction Model Based on NAPSO-SVM.

PubMed

Jiang, Minlan; Jiang, Lan; Jiang, Dingde; Li, Fei; Song, Houbing

2018-01-15

Dynamic measurement error correction is an effective way to improve sensor precision. Dynamic measurement error prediction is an important part of error correction, and support vector machine (SVM) is often used for predicting the dynamic measurement errors of sensors. Traditionally, the SVM parameters were always set manually, which cannot ensure the model's performance. In this paper, a SVM method based on an improved particle swarm optimization (NAPSO) is proposed to predict the dynamic measurement errors of sensors. Natural selection and simulated annealing are added in the PSO to raise the ability to avoid local optima. To verify the performance of NAPSO-SVM, three types of algorithms are selected to optimize the SVM's parameters: the particle swarm optimization algorithm (PSO), the improved PSO optimization algorithm (NAPSO), and the glowworm swarm optimization (GSO). The dynamic measurement error data of two sensors are applied as the test data. The root mean squared error and mean absolute percentage error are employed to evaluate the prediction models' performances. The experimental results show that among the three tested algorithms the NAPSO-SVM method has a better prediction precision and a less prediction errors, and it is an effective method for predicting the dynamic measurement errors of sensors.

The development of an autonomous rendezvous and docking simulation using rapid integration and prototyping technology

NASA Technical Reports Server (NTRS)

Shackelford, John H.; Saugen, John D.; Wurst, Michael J.; Adler, James

1991-01-01

A generic planar 3 degree of freedom simulation was developed that supports hardware in the loop simulations, guidance and control analysis, and can directly generate flight software. This simulation was developed in a small amount of time utilizing rapid prototyping techniques. The approach taken to develop this simulation tool, the benefits seen using this approach to development, and on-going efforts to improve and extend this capability are described. The simulation is composed of 3 major elements: (1) Docker dynamics model, (2) Dockee dynamics model, and (3) Docker Control System. The docker and dockee models are based on simple planar orbital dynamics equations using a spherical earth gravity model. The docker control system is based on a phase plane approach to error correction.

Precipitation and Latent Heating Distributions from Satellite Passive Microwave Radiometry. Part 1; Improved Method and Uncertainties

NASA Technical Reports Server (NTRS)

Olson, William S.; Kummerow, Christian D.; Yang, Song; Petty, Grant W.; Tao, Wei-Kuo; Bell, Thomas L.; Braun, Scott A.; Wang, Yansen; Lang, Stephen E.; Johnson, Daniel E.;

Simulating Children's Retrieval Errors in Picture-Naming: A Test of Foygel and Dell's (2000) Semantic/Phonological Model of Speech Production

ERIC Educational Resources Information Center

Budd, Mary-Jane; Hanley, J. Richard; Griffiths, Yvonne

2011-01-01

This study investigated whether Foygel and Dell's (2000) interactive two-step model of speech production could simulate the number and type of errors made in picture-naming by 68 children of elementary-school age. Results showed that the model provided a satisfactory simulation of the mean error profile of children aged five, six, seven, eight and…

Use of remote-sensing reflectance to constrain a data assimilating marine biogeochemical model of the Great Barrier Reef

NASA Astrophysics Data System (ADS)

Jones, Emlyn M.; Baird, Mark E.; Mongin, Mathieu; Parslow, John; Skerratt, Jenny; Lovell, Jenny; Margvelashvili, Nugzar; Matear, Richard J.; Wild-Allen, Karen; Robson, Barbara; Rizwi, Farhan; Oke, Peter; King, Edward; Schroeder, Thomas; Steven, Andy; Taylor, John

2016-12-01

Skillful marine biogeochemical (BGC) models are required to understand a range of coastal and global phenomena such as changes in nitrogen and carbon cycles. The refinement of BGC models through the assimilation of variables calculated from observed in-water inherent optical properties (IOPs), such as phytoplankton absorption, is problematic. Empirically derived relationships between IOPs and variables such as chlorophyll-a concentration (Chl a), total suspended solids (TSS) and coloured dissolved organic matter (CDOM) have been shown to have errors that can exceed 100 % of the observed quantity. These errors are greatest in shallow coastal regions, such as the Great Barrier Reef (GBR), due to the additional signal from bottom reflectance. Rather than assimilate quantities calculated using IOP algorithms, this study demonstrates the advantages of assimilating quantities calculated directly from the less error-prone satellite remote-sensing reflectance (RSR). To assimilate the observed RSR, we use an in-water optical model to produce an equivalent simulated RSR and calculate the mismatch between the observed and simulated quantities to constrain the BGC model with a deterministic ensemble Kalman filter (DEnKF). The traditional assumption that simulated surface Chl a is equivalent to the remotely sensed OC3M estimate of Chl a resulted in a forecast error of approximately 75 %. We show this error can be halved by instead using simulated RSR to constrain the model via the assimilation system. When the analysis and forecast fields from the RSR-based assimilation system are compared with the non-assimilating model, a comparison against independent in situ observations of Chl a, TSS and dissolved inorganic nutrients (NO3, NH4 and DIP) showed that errors are reduced by up to 90 %. In all cases, the assimilation system improves the simulation compared to the non-assimilating model. Our approach allows for the incorporation of vast quantities of remote-sensing observations that have in the past been discarded due to shallow water and/or artefacts introduced by terrestrially derived TSS and CDOM or the lack of a calibrated regional IOP algorithm.

Evaluating the effects of modeling errors for isolated finite three-dimensional targets

NASA Astrophysics Data System (ADS)

Henn, Mark-Alexander; Barnes, Bryan M.; Zhou, Hui

2017-10-01

Optical three-dimensional (3-D) nanostructure metrology utilizes a model-based metrology approach to determine critical dimensions (CDs) that are well below the inspection wavelength. Our project at the National Institute of Standards and Technology is evaluating how to attain key CD and shape parameters from engineered in-die capable metrology targets. More specifically, the quantities of interest are determined by varying the input parameters for a physical model until the simulations agree with the actual measurements within acceptable error bounds. As in most applications, establishing a reasonable balance between model accuracy and time efficiency is a complicated task. A well-established simplification is to model the intrinsically finite 3-D nanostructures as either periodic or infinite in one direction, reducing the computationally expensive 3-D simulations to usually less complex two-dimensional (2-D) problems. Systematic errors caused by this simplified model can directly influence the fitting of the model to the measurement data and are expected to become more apparent with decreasing lengths of the structures. We identify these effects using selected simulation results and present experimental setups, e.g., illumination numerical apertures and focal ranges, that can increase the validity of the 2-D approach.

Development of an errorable car-following driver model

NASA Astrophysics Data System (ADS)

Yang, H.-H.; Peng, H.

2010-06-01

An errorable car-following driver model is presented in this paper. An errorable driver model is one that emulates human driver's functions and can generate both nominal (error-free), as well as devious (with error) behaviours. This model was developed for evaluation and design of active safety systems. The car-following data used for developing and validating the model were obtained from a large-scale naturalistic driving database. The stochastic car-following behaviour was first analysed and modelled as a random process. Three error-inducing behaviours were then introduced. First, human perceptual limitation was studied and implemented. Distraction due to non-driving tasks was then identified based on the statistical analysis of the driving data. Finally, time delay of human drivers was estimated through a recursive least-square identification process. By including these three error-inducing behaviours, rear-end collisions with the lead vehicle could occur. The simulated crash rate was found to be similar but somewhat higher than that reported in traffic statistics.

Error modeling of precision orientation sensors in a fixed base simulation. Ph.D. Thesis; [models of noise and dynamic characteristics of a gyro and autocollimator for very small signals

NASA Technical Reports Server (NTRS)

Klinger, D. L.

1974-01-01

Models of noise and dynamic characteristics of gyro and autocollimator for very small signal levels are presented. Measurements were evaluated using spectral techniques for identifying noise from base motion. The experiment was constructed to measure the precession, due to relativistic effects, of an extremely precise earth-orbiting gyroscope. The design goal for nonrelativistic gyro drift is 0.001 arcsec per year. An analogous fixed base simulator was used in developing methods of instrument error modeling and performance evaluation applicable to the relativity experiment sensors and other precision pointing instruments. Analysis of autocollimator spectra uncovered the presence of a platform gimbal resonance. The source of resonance was isolated to gimbal bearing elastic restraint properties most apparent at very small levels of motion. A model of these properties which include both elastic and coulomb friction characteristics is discussed, and a describing function developed.

Experiments in monthly mean simulation of the atmosphere with a coarse-mesh general circulation model

NASA Technical Reports Server (NTRS)

Lutz, R. J.; Spar, J.

1978-01-01

The Hansen atmospheric model was used to compute five monthly forecasts (October 1976 through February 1977). The comparison is based on an energetics analysis, meridional and vertical profiles, error statistics, and prognostic and observed mean maps. The monthly mean model simulations suffer from several defects. There is, in general, no skill in the simulation of the monthly mean sea-level pressure field, and only marginal skill is indicated for the 850 mb temperatures and 500 mb heights. The coarse-mesh model appears to generate a less satisfactory monthly mean simulation than the finer mesh GISS model.

A model-data comparison of gross primary productivity: Results from the North American Carbon Program site synthesis

Treesearch

Kevin Schaefer; Christopher R. Schwalm; Chris Williams; M. Altaf Arain; Alan Barr; Jing M. Chen; Kenneth J. Davis; Dimitre Dimitrov; Timothy W. Hilton; David Y. Hollinger; Elyn Humphreys; Benjamin Poulter; Brett M. Raczka; Andrew D. Richardson; Alok Sahoo; Peter Thornton; Rodrigo Vargas; Hans Verbeeck; Ryan Anderson; Ian Baker; T. Andrew Black; Paul Bolstad; Jiquan Chen; Peter S. Curtis; Ankur R. Desai; Michael Dietze; Danilo Dragoni; Christopher Gough; Robert F. Grant; Lianhong Gu; Atul Jain; Chris Kucharik; Beverly Law; Shuguang Liu; Erandathie Lokipitiya; Hank A. Margolis; Roser Matamala; J. Harry McCaughey; Russ Monson; J. William Munger; Walter Oechel; Changhui Peng; David T. Price; Dan Ricciuto; William J. Riley; Nigel Roulet; Hanqin Tian; Christina Tonitto; Margaret Torn; Ensheng Weng; Xiaolu Zhou

2012-01-01

Accurately simulating gross primary productivity (GPP) in terrestrial ecosystem models is critical because errors in simulated GPP propagate through the model to introduce additional errors in simulated biomass and other fluxes. We evaluated simulated, daily average GPP from 26 models against estimated GPP at 39 eddy covariance flux tower sites across the United States...

A multiphysical ensemble system of numerical snow modelling

NASA Astrophysics Data System (ADS)

Lafaysse, Matthieu; Cluzet, Bertrand; Dumont, Marie; Lejeune, Yves; Vionnet, Vincent; Morin, Samuel

2017-05-01

Physically based multilayer snowpack models suffer from various modelling errors. To represent these errors, we built the new multiphysical ensemble system ESCROC (Ensemble System Crocus) by implementing new representations of different physical processes in the deterministic coupled multilayer ground/snowpack model SURFEX/ISBA/Crocus. This ensemble was driven and evaluated at Col de Porte (1325 m a.s.l., French alps) over 18 years with a high-quality meteorological and snow data set. A total number of 7776 simulations were evaluated separately, accounting for the uncertainties of evaluation data. The ability of the ensemble to capture the uncertainty associated to modelling errors is assessed for snow depth, snow water equivalent, bulk density, albedo and surface temperature. Different sub-ensembles of the ESCROC system were studied with probabilistic tools to compare their performance. Results show that optimal members of the ESCROC system are able to explain more than half of the total simulation errors. Integrating members with biases exceeding the range corresponding to observational uncertainty is necessary to obtain an optimal dispersion, but this issue can also be a consequence of the fact that meteorological forcing uncertainties were not accounted for. The ESCROC system promises the integration of numerical snow-modelling errors in ensemble forecasting and ensemble assimilation systems in support of avalanche hazard forecasting and other snowpack-modelling applications.

Transfer Alignment Error Compensator Design Based on Robust State Estimation

NASA Astrophysics Data System (ADS)

Lyou, Joon; Lim, You-Chol

This paper examines the transfer alignment problem of the StrapDown Inertial Navigation System (SDINS), which is subject to the ship’s roll and pitch. Major error sources for velocity and attitude matching are lever arm effect, measurement time delay and ship-body flexure. To reduce these alignment errors, an error compensation method based on state augmentation and robust state estimation is devised. A linearized error model for the velocity and attitude matching transfer alignment system is derived first by linearizing the nonlinear measurement equation with respect to its time delay and dominant Y-axis flexure, and by augmenting the delay state and flexure state into conventional linear state equations. Then an H∞ filter is introduced to account for modeling uncertainties of time delay and the ship-body flexure. The simulation results show that this method considerably decreases azimuth alignment errors considerably.

NIR spectroscopic measurement of moisture content in Scots pine seeds.

PubMed

Lestander, Torbjörn A; Geladi, Paul

2003-04-01

When tree seeds are used for seedling production it is important that they are of high quality in order to be viable. One of the factors influencing viability is moisture content and an ideal quality control system should be able to measure this factor quickly for each seed. Seed moisture content within the range 3-34% was determined by near-infrared (NIR) spectroscopy on Scots pine (Pinus sylvestris L.) single seeds and on bulk seed samples consisting of 40-50 seeds. The models for predicting water content from the spectra were made by partial least squares (PLS) and ordinary least squares (OLS) regression. Different conditions were simulated involving both using less wavelengths and going from samples to single seeds. Reflectance and transmission measurements were used. Different spectral pretreatment methods were tested on the spectra. Including bias, the lowest prediction errors for PLS models based on reflectance within 780-2280 nm from bulk samples and single seeds were 0.8% and 1.9%, respectively. Reduction of the single seed reflectance spectrum to 850-1048 nm gave higher biases and prediction errors in the test set. In transmission (850-1048 nm) the prediction error was 2.7% for single seeds. OLS models based on simulated 4-sensor single seed system consisting of optical filters with Gaussian transmission indicated more than 3.4% error in prediction. A practical F-test based on test sets to differentiate models is introduced.

Neural network-based model reference adaptive control system.

PubMed

Patino, H D; Liu, D

2000-01-01

In this paper, an approach to model reference adaptive control based on neural networks is proposed and analyzed for a class of first-order continuous-time nonlinear dynamical systems. The controller structure can employ either a radial basis function network or a feedforward neural network to compensate adaptively the nonlinearities in the plant. A stable controller-parameter adjustment mechanism, which is determined using the Lyapunov theory, is constructed using a sigma-modification-type updating law. The evaluation of control error in terms of the neural network learning error is performed. That is, the control error converges asymptotically to a neighborhood of zero, whose size is evaluated and depends on the approximation error of the neural network. In the design and analysis of neural network-based control systems, it is important to take into account the neural network learning error and its influence on the control error of the plant. Simulation results showing the feasibility and performance of the proposed approach are given.

A Framework for Image-Based Modeling of Acute Myocardial Ischemia Using Intramurally Recorded Extracellular Potentials.

PubMed

Burton, Brett M; Aras, Kedar K; Good, Wilson W; Tate, Jess D; Zenger, Brian; MacLeod, Rob S

2018-05-21

The biophysical basis for electrocardiographic evaluation of myocardial ischemia stems from the notion that ischemic tissues develop, with relative uniformity, along the endocardial aspects of the heart. These injured regions of subendocardial tissue give rise to intramural currents that lead to ST segment deflections within electrocardiogram (ECG) recordings. The concept of subendocardial ischemic regions is often used in clinical practice, providing a simple and intuitive description of ischemic injury; however, such a model grossly oversimplifies the presentation of ischemic disease-inadvertently leading to errors in ECG-based diagnoses. Furthermore, recent experimental studies have brought into question the subendocardial ischemia paradigm suggesting instead a more distributed pattern of tissue injury. These findings come from experiments and so have both the impact and the limitations of measurements from living organisms. Computer models have often been employed to overcome the constraints of experimental approaches and have a robust history in cardiac simulation. To this end, we have developed a computational simulation framework aimed at elucidating the effects of ischemia on measurable cardiac potentials. To validate our framework, we simulated, visualized, and analyzed 226 experimentally derived acute myocardial ischemic events. Simulation outcomes agreed both qualitatively (feature comparison) and quantitatively (correlation, average error, and significance) with experimentally obtained epicardial measurements, particularly under conditions of elevated ischemic stress. Our simulation framework introduces a novel approach to incorporating subject-specific, geometric models and experimental results that are highly resolved in space and time into computational models. We propose this framework as a means to advance the understanding of the underlying mechanisms of ischemic disease while simultaneously putting in place the computational infrastructure necessary to study and improve ischemia models aimed at reducing diagnostic errors in the clinic.

Nonsequential modeling of laser diode stacks using Zemax: simulation, optimization, and experimental validation.

PubMed

Coluccelli, Nicola

2010-08-01

Modeling a real laser diode stack based on Zemax ray tracing software that operates in a nonsequential mode is reported. The implementation of the model is presented together with the geometric and optical parameters to be adjusted to calibrate the model and to match the simulated intensity irradiance profiles with the experimental profiles. The calibration of the model is based on a near-field and a far-field measurement. The validation of the model has been accomplished by comparing the simulated and experimental transverse irradiance profiles at different positions along the caustic formed by a lens. Spot sizes and waist location are predicted with a maximum error below 6%.

An investigation of error characteristics and coding performance

NASA Technical Reports Server (NTRS)

Ebel, William J.; Ingels, Frank M.

1992-01-01

The performance of forward error correcting coding schemes on errors anticipated for the Earth Observation System (EOS) Ku-band downlink are studied. The EOS transmits picture frame data to the ground via the Telemetry Data Relay Satellite System (TDRSS) to a ground-based receiver at White Sands. Due to unintentional RF interference from other systems operating in the Ku band, the noise at the receiver is non-Gaussian which may result in non-random errors output by the demodulator. That is, the downlink channel cannot be modeled by a simple memoryless Gaussian-noise channel. From previous experience, it is believed that those errors are bursty. The research proceeded by developing a computer based simulation, called Communication Link Error ANalysis (CLEAN), to model the downlink errors, forward error correcting schemes, and interleavers used with TDRSS. To date, the bulk of CLEAN was written, documented, debugged, and verified. The procedures for utilizing CLEAN to investigate code performance were established and are discussed.

Toward On-line Parameter Estimation of Concentric Tube Robots Using a Mechanics-based Kinematic Model

PubMed Central

Jang, Cheongjae; Ha, Junhyoung; Dupont, Pierre E.; Park, Frank Chongwoo

2017-01-01

Although existing mechanics-based models of concentric tube robots have been experimentally demonstrated to approximate the actual kinematics, determining accurate estimates of model parameters remains difficult due to the complex relationship between the parameters and available measurements. Further, because the mechanics-based models neglect some phenomena like friction, nonlinear elasticity, and cross section deformation, it is also not clear if model error is due to model simplification or to parameter estimation errors. The parameters of the superelastic materials used in these robots can be slowly time-varying, necessitating periodic re-estimation. This paper proposes a method for estimating the mechanics-based model parameters using an extended Kalman filter as a step toward on-line parameter estimation. Our methodology is validated through both simulation and experiments. PMID:28717554

Insar Unwrapping Error Correction Based on Quasi-Accurate Detection of Gross Errors (quad)

NASA Astrophysics Data System (ADS)

Kang, Y.; Zhao, C. Y.; Zhang, Q.; Yang, C. S.

2018-04-01

Unwrapping error is a common error in the InSAR processing, which will seriously degrade the accuracy of the monitoring results. Based on a gross error correction method, Quasi-accurate detection (QUAD), the method for unwrapping errors automatic correction is established in this paper. This method identifies and corrects the unwrapping errors by establishing a functional model between the true errors and interferograms. The basic principle and processing steps are presented. Then this method is compared with the L1-norm method with simulated data. Results show that both methods can effectively suppress the unwrapping error when the ratio of the unwrapping errors is low, and the two methods can complement each other when the ratio of the unwrapping errors is relatively high. At last the real SAR data is tested for the phase unwrapping error correction. Results show that this new method can correct the phase unwrapping errors successfully in the practical application.

Use of Numerical Groundwater Model and Analytical Empirical Orthogonal Function for Calibrating Spatiotemporal pattern of Pumpage, Recharge and Parameter

NASA Astrophysics Data System (ADS)

Huang, C. L.; Hsu, N. S.; Hsu, F. C.; Liu, H. J.

2016-12-01

This study develops a novel methodology for the spatiotemporal groundwater calibration of mega-quantitative recharge and parameters by coupling a specialized numerical model and analytical empirical orthogonal function (EOF). The actual spatiotemporal patterns of groundwater pumpage are estimated by an originally developed back propagation neural network-based response matrix with the electrical consumption analysis. The spatiotemporal patterns of the recharge from surface water and hydrogeological parameters (i.e. horizontal hydraulic conductivity and vertical leakance) are calibrated by EOF with the simulated error hydrograph of groundwater storage, in order to qualify the multiple error sources and quantify the revised volume. The objective function of the optimization model is minimizing the root mean square error of the simulated storage error percentage across multiple aquifers, meanwhile subject to mass balance of groundwater budget and the governing equation in transient state. The established method was applied on the groundwater system of Chou-Shui River Alluvial Fan. The simulated period is from January 2012 to December 2014. The total numbers of hydraulic conductivity, vertical leakance and recharge from surface water among four aquifers are 126, 96 and 1080, respectively. Results showed that the RMSE during the calibration process was decreased dramatically and can quickly converse within 6th iteration, because of efficient filtration of the transmission induced by the estimated error and recharge across the boundary. Moreover, the average simulated error percentage according to groundwater level corresponding to the calibrated budget variables and parameters of aquifer one is as small as 0.11%. It represent that the developed methodology not only can effectively detect the flow tendency and error source in all aquifers to achieve accurately spatiotemporal calibration, but also can capture the peak and fluctuation of groundwater level in shallow aquifer.

A simple model for studying rotation errors of gimbal mount axes in laser tracking system based on spherical mirror as a reflection unit

NASA Astrophysics Data System (ADS)

Song, Huixu; Shi, Zhaoyao; Chen, Hongfang; Sun, Yanqiang

2018-01-01

This paper presents a novel experimental approach and a simple model for verifying that spherical mirror of laser tracking system could lessen the effect of rotation errors of gimbal mount axes based on relative motion thinking. Enough material and evidence are provided to support that this simple model could replace complex optical system in laser tracking system. This experimental approach and model interchange the kinematic relationship between spherical mirror and gimbal mount axes in laser tracking system. Being fixed stably, gimbal mount axes' rotation error motions are replaced by spatial micro-displacements of spherical mirror. These motions are simulated by driving spherical mirror along the optical axis and vertical direction with the use of precision positioning platform. The effect on the laser ranging measurement accuracy of displacement caused by the rotation errors of gimbal mount axes could be recorded according to the outcome of laser interferometer. The experimental results show that laser ranging measurement error caused by the rotation errors is less than 0.1 μm if radial error motion and axial error motion are under 10 μm. The method based on relative motion thinking not only simplifies the experimental procedure but also achieves that spherical mirror owns the ability to reduce the effect of rotation errors of gimbal mount axes in laser tracking system.

Accuracy improvement of the H-drive air-levitating wafer inspection stage based on error analysis and compensation

NASA Astrophysics Data System (ADS)

Zhang, Fan; Liu, Pinkuan

2018-04-01

In order to improve the inspection precision of the H-drive air-bearing stage for wafer inspection, in this paper the geometric error of the stage is analyzed and compensated. The relationship between the positioning errors and error sources are initially modeled, and seven error components are identified that are closely related to the inspection accuracy. The most effective factor that affects the geometric error is identified by error sensitivity analysis. Then, the Spearman rank correlation method is applied to find the correlation between different error components, aiming at guiding the accuracy design and error compensation of the stage. Finally, different compensation methods, including the three-error curve interpolation method, the polynomial interpolation method, the Chebyshev polynomial interpolation method, and the B-spline interpolation method, are employed within the full range of the stage, and their results are compared. Simulation and experiment show that the B-spline interpolation method based on the error model has better compensation results. In addition, the research result is valuable for promoting wafer inspection accuracy and will greatly benefit the semiconductor industry.

Sampling errors for satellite-derived tropical rainfall - Monte Carlo study using a space-time stochastic model

NASA Technical Reports Server (NTRS)

Bell, Thomas L.; Abdullah, A.; Martin, Russell L.; North, Gerald R.

1990-01-01

Estimates of monthly average rainfall based on satellite observations from a low earth orbit will differ from the true monthly average because the satellite observes a given area only intermittently. This sampling error inherent in satellite monitoring of rainfall would occur even if the satellite instruments could measure rainfall perfectly. The size of this error is estimated for a satellite system being studied at NASA, the Tropical Rainfall Measuring Mission (TRMM). First, the statistical description of rainfall on scales from 1 to 1000 km is examined in detail, based on rainfall data from the Global Atmospheric Research Project Atlantic Tropical Experiment (GATE). A TRMM-like satellite is flown over a two-dimensional time-evolving simulation of rainfall using a stochastic model with statistics tuned to agree with GATE statistics. The distribution of sampling errors found from many months of simulated observations is found to be nearly normal, even though the distribution of area-averaged rainfall is far from normal. For a range of orbits likely to be employed in TRMM, sampling error is found to be less than 10 percent of the mean for rainfall averaged over a 500 x 500 sq km area.

Generalized Ordinary Differential Equation Models 1

PubMed Central

Miao, Hongyu; Wu, Hulin; Xue, Hongqi

2014-01-01

Existing estimation methods for ordinary differential equation (ODE) models are not applicable to discrete data. The generalized ODE (GODE) model is therefore proposed and investigated for the first time. We develop the likelihood-based parameter estimation and inference methods for GODE models. We propose robust computing algorithms and rigorously investigate the asymptotic properties of the proposed estimator by considering both measurement errors and numerical errors in solving ODEs. The simulation study and application of our methods to an influenza viral dynamics study suggest that the proposed methods have a superior performance in terms of accuracy over the existing ODE model estimation approach and the extended smoothing-based (ESB) method. PMID:25544787

Generalized Ordinary Differential Equation Models.

PubMed

Miao, Hongyu; Wu, Hulin; Xue, Hongqi

2014-10-01

Existing estimation methods for ordinary differential equation (ODE) models are not applicable to discrete data. The generalized ODE (GODE) model is therefore proposed and investigated for the first time. We develop the likelihood-based parameter estimation and inference methods for GODE models. We propose robust computing algorithms and rigorously investigate the asymptotic properties of the proposed estimator by considering both measurement errors and numerical errors in solving ODEs. The simulation study and application of our methods to an influenza viral dynamics study suggest that the proposed methods have a superior performance in terms of accuracy over the existing ODE model estimation approach and the extended smoothing-based (ESB) method.

Serial robot for the trajectory optimization and error compensation of TMT mask exchange system

NASA Astrophysics Data System (ADS)

Wang, Jianping; Zhang, Feifan; Zhou, Zengxiang; Zhai, Chao

2015-10-01

Mask exchange system is the main part of Multi-Object Broadband Imaging Echellette (MOBIE) on the Thirty Meter Telescope (TMT). According to the conception of the TMT mask exchange system, the pre-design was introduced in the paper which was based on IRB 140 robot. The stiffness model of IRB 140 in SolidWorks was analyzed under different gravity vectors for further error compensation. In order to find the right location and path planning, the robot and the mask cassette model was imported into MOBIE model to perform different schemes simulation. And obtained the initial installation position and routing. Based on these initial parameters, IRB 140 robot was operated to simulate the path and estimate the mask exchange time. Meanwhile, MATLAB and ADAMS software were used to perform simulation analysis and optimize the route to acquire the kinematics parameters and compare with the experiment results. After simulation and experimental research mentioned in the paper, the theoretical reference was acquired which could high efficient improve the structure of the mask exchange system parameters optimization of the path and precision of the robot position.

Advanced error diagnostics of the CMAQ and Chimere modelling systems within the AQMEII3 model evaluation framework

NASA Astrophysics Data System (ADS)

Solazzo, Efisio; Hogrefe, Christian; Colette, Augustin; Garcia-Vivanco, Marta; Galmarini, Stefano

2017-09-01

The work here complements the overview analysis of the modelling systems participating in the third phase of the Air Quality Model Evaluation International Initiative (AQMEII3) by focusing on the performance for hourly surface ozone by two modelling systems, Chimere for Europe and CMAQ for North America. The evaluation strategy outlined in the course of the three phases of the AQMEII activity, aimed to build up a diagnostic methodology for model evaluation, is pursued here and novel diagnostic methods are proposed. In addition to evaluating the base case simulation in which all model components are configured in their standard mode, the analysis also makes use of sensitivity simulations in which the models have been applied by altering and/or zeroing lateral boundary conditions, emissions of anthropogenic precursors, and ozone dry deposition. To help understand of the causes of model deficiencies, the error components (bias, variance, and covariance) of the base case and of the sensitivity runs are analysed in conjunction with timescale considerations and error modelling using the available error fields of temperature, wind speed, and NOx concentration. The results reveal the effectiveness and diagnostic power of the methods devised (which remains the main scope of this study), allowing the detection of the timescale and the fields that the two models are most sensitive to. The representation of planetary boundary layer (PBL) dynamics is pivotal to both models. In particular, (i) the fluctuations slower than ˜ 1.5 days account for 70-85 % of the mean square error of the full (undecomposed) ozone time series; (ii) a recursive, systematic error with daily periodicity is detected, responsible for 10-20 % of the quadratic total error; (iii) errors in representing the timing of the daily transition between stability regimes in the PBL are responsible for a covariance error as large as 9 ppb (as much as the standard deviation of the network-average ozone observations in summer in both Europe and North America); (iv) the CMAQ ozone error has a weak/negligible dependence on the errors in NO2, while the error in NO2 significantly impacts the ozone error produced by Chimere; (v) the response of the models to variations of anthropogenic emissions and boundary conditions show a pronounced spatial heterogeneity, while the seasonal variability of the response is found to be less marked. Only during the winter season does the zeroing of boundary values for North America produce a spatially uniform deterioration of the model accuracy across the majority of the continent.

Error Analysis of Magnetohydrodynamic Angular Rate Sensor Combing with Coriolis Effect at Low Frequency.

PubMed

Ji, Yue; Xu, Mengjie; Li, Xingfei; Wu, Tengfei; Tuo, Weixiao; Wu, Jun; Dong, Jiuzhi

2018-06-13

The magnetohydrodynamic (MHD) angular rate sensor (ARS) with low noise level in ultra-wide bandwidth is developed in lasing and imaging applications, especially the line-of-sight (LOS) system. A modified MHD ARS combined with the Coriolis effect was studied in this paper to expand the sensor’s bandwidth at low frequency (<1 Hz), which is essential for precision LOS pointing and wide-bandwidth LOS jitter suppression. The model and the simulation method were constructed and a comprehensive solving method based on the magnetic and electric interaction methods was proposed. The numerical results on the Coriolis effect and the frequency response of the modified MHD ARS were detailed. In addition, according to the experimental results of the designed sensor consistent with the simulation results, an error analysis of model errors was discussed. Our study provides an error analysis method of MHD ARS combined with the Coriolis effect and offers a framework for future studies to minimize the error.

Reducing Errors in Satellite Simulated Views of Clouds with an Improved Parameterization of Unresolved Scales

NASA Astrophysics Data System (ADS)

Hillman, B. R.; Marchand, R.; Ackerman, T. P.

2016-12-01

Satellite instrument simulators have emerged as a means to reduce errors in model evaluation by producing simulated or psuedo-retrievals from model fields, which account for limitations in the satellite retrieval process. Because of the mismatch in resolved scales between satellite retrievals and large-scale models, model cloud fields must first be downscaled to scales consistent with satellite retrievals. This downscaling is analogous to that required for model radiative transfer calculations. The assumption is often made in both model radiative transfer codes and satellite simulators that the unresolved clouds follow maximum-random overlap with horizontally homogeneous cloud condensate amounts. We examine errors in simulated MISR and CloudSat retrievals that arise due to these assumptions by applying the MISR and CloudSat simulators to cloud resolving model (CRM) output generated by the Super-parameterized Community Atmosphere Model (SP-CAM). Errors are quantified by comparing simulated retrievals performed directly on the CRM fields with those simulated by first averaging the CRM fields to approximately 2-degree resolution, applying a "subcolumn generator" to regenerate psuedo-resolved cloud and precipitation condensate fields, and then applying the MISR and CloudSat simulators on the regenerated condensate fields. We show that errors due to both assumptions of maximum-random overlap and homogeneous condensate are significant (relative to uncertainties in the observations and other simulator limitations). The treatment of precipitation is particularly problematic for CloudSat-simulated radar reflectivity. We introduce an improved subcolumn generator for use with the simulators, and show that these errors can be greatly reduced by replacing the maximum-random overlap assumption with the more realistic generalized overlap and incorporating a simple parameterization of subgrid-scale cloud and precipitation condensate heterogeneity. Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-AC04-94AL85000. SAND NO. SAND2016-7485 A

Cart3D Simulations for the First AIAA Sonic Boom Prediction Workshop

NASA Technical Reports Server (NTRS)

Aftosmis, Michael J.; Nemec, Marian

2014-01-01

Simulation results for the First AIAA Sonic Boom Prediction Workshop (LBW1) are presented using an inviscid, embedded-boundary Cartesian mesh method. The method employs adjoint-based error estimation and adaptive meshing to automatically determine resolution requirements of the computational domain. Results are presented for both mandatory and optional test cases. These include an axisymmetric body of revolution, a 69deg delta wing model and a complete model of the Lockheed N+2 supersonic tri-jet with V-tail and flow through nacelles. In addition to formal mesh refinement studies and examination of the adjoint-based error estimates, mesh convergence is assessed by presenting simulation results for meshes at several resolutions which are comparable in size to the unstructured grids distributed by the workshop organizers. Data provided includes both the pressure signals required by the workshop and information on code performance in both memory and processing time. Various enhanced techniques offering improved simulation efficiency will be demonstrated and discussed.

Comparison between artificial neural network and multilinear regression models in an evaluation of cognitive workload in a flight simulator.

PubMed

Hannula, Manne; Huttunen, Kerttu; Koskelo, Jukka; Laitinen, Tomi; Leino, Tuomo

2008-01-01

In this study, the performances of artificial neural network (ANN) analysis and multilinear regression (MLR) model-based estimation of heart rate were compared in an evaluation of individual cognitive workload. The data comprised electrocardiography (ECG) measurements and an evaluation of cognitive load that induces psychophysiological stress (PPS), collected from 14 interceptor fighter pilots during complex simulated F/A-18 Hornet air battles. In our data, the mean absolute error of the ANN estimate was 11.4 as a visual analog scale score, being 13-23% better than the mean absolute error of the MLR model in the estimation of cognitive workload.

Optimal Estimation with Two Process Models and No Measurements

DTIC Science & Technology

2015-08-01

models will be lost if either of the models includes deterministic modeling errors. 12 5. References and Notes 1. Brown RG, Hwang PYC. Introduction to...independent process models when no measurements are present. The observer follows a derivation similar to that of the discrete time Kalman filter. A simulation...discrete time Kalman filter. A simulation example is provided in which a process model based on the dynamics of a ballistic projectile is blended with an

A Bayesian approach to model structural error and input variability in groundwater modeling

NASA Astrophysics Data System (ADS)

Xu, T.; Valocchi, A. J.; Lin, Y. F. F.; Liang, F.

2015-12-01

Effective water resource management typically relies on numerical models to analyze groundwater flow and solute transport processes. Model structural error (due to simplification and/or misrepresentation of the "true" environmental system) and input forcing variability (which commonly arises since some inputs are uncontrolled or estimated with high uncertainty) are ubiquitous in groundwater models. Calibration that overlooks errors in model structure and input data can lead to biased parameter estimates and compromised predictions. We present a fully Bayesian approach for a complete assessment of uncertainty for spatially distributed groundwater models. The approach explicitly recognizes stochastic input and uses data-driven error models based on nonparametric kernel methods to account for model structural error. We employ exploratory data analysis to assist in specifying informative prior for error models to improve identifiability. The inference is facilitated by an efficient sampling algorithm based on DREAM-ZS and a parameter subspace multiple-try strategy to reduce the required number of forward simulations of the groundwater model. We demonstrate the Bayesian approach through a synthetic case study of surface-ground water interaction under changing pumping conditions. It is found that explicit treatment of errors in model structure and input data (groundwater pumping rate) has substantial impact on the posterior distribution of groundwater model parameters. Using error models reduces predictive bias caused by parameter compensation. In addition, input variability increases parametric and predictive uncertainty. The Bayesian approach allows for a comparison among the contributions from various error sources, which could inform future model improvement and data collection efforts on how to best direct resources towards reducing predictive uncertainty.

Scientific Impacts of Wind Direction Errors

NASA Technical Reports Server (NTRS)

Liu, W. Timothy; Kim, Seung-Bum; Lee, Tong; Song, Y. Tony; Tang, Wen-Qing; Atlas, Robert

2004-01-01

An assessment on the scientific impact of random errors in wind direction (less than 45 deg) retrieved from space-based observations under weak wind (less than 7 m/s ) conditions was made. averages, and these weak winds cover most of the tropical, sub-tropical, and coastal oceans. Introduction of these errors in the semi-daily winds causes, on average, 5% changes of the yearly mean Ekman and Sverdrup volume transports computed directly from the winds, respectively. These poleward movements of water are the main mechanisms to redistribute heat from the warmer tropical region to the colder high- latitude regions, and they are the major manifestations of the ocean's function in modifying Earth's climate. Simulation by an ocean general circulation model shows that the wind errors introduce a 5% error in the meridional heat transport at tropical latitudes. The simulation also shows that the erroneous winds cause a pile-up of warm surface water in the eastern tropical Pacific, similar to the conditions during El Nino episode. Similar wind directional errors cause significant change in sea-surface temperature and sea-level patterns in coastal oceans in a coastal model simulation. Previous studies have shown that assimilation of scatterometer winds improves 3-5 day weather forecasts in the Southern Hemisphere. When directional information below 7 m/s was withheld, approximately 40% of the improvement was lost

Sensitivities of simulated satellite views of clouds to subgrid-scale overlap and condensate heterogeneity

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hillman, Benjamin R.; Marchand, Roger T.; Ackerman, Thomas P.

Satellite simulators are often used to account for limitations in satellite retrievals of cloud properties in comparisons between models and satellite observations. The purpose of the simulator framework is to enable more robust evaluation of model cloud properties, so that di erences between models and observations can more con dently be attributed to model errors. However, these simulators are subject to uncertainties themselves. A fundamental uncertainty exists in connecting the spatial scales at which cloud properties are retrieved with those at which clouds are simulated in global models. In this study, we create a series of sensitivity tests using 4more » km global model output from the Multiscale Modeling Framework to evaluate the sensitivity of simulated satellite retrievals when applied to climate models whose grid spacing is many tens to hundreds of kilometers. In particular, we examine the impact of cloud and precipitation overlap and of condensate spatial variability. We find the simulated retrievals are sensitive to these assumptions. Specifically, using maximum-random overlap with homogeneous cloud and precipitation condensate, which is often used in global climate models, leads to large errors in MISR and ISCCP-simulated cloud cover and in CloudSat-simulated radar reflectivity. To correct for these errors, an improved treatment of unresolved clouds and precipitation is implemented for use with the simulator framework and is shown to substantially reduce the identified errors.« less

Synchronizing Two AGCMs via Ocean-Atmosphere Coupling (Invited)

NASA Astrophysics Data System (ADS)

Kirtman, B. P.

2009-12-01

A new approach for fusing or synchronizing to very different Atmospheric General Circulation Models (AGCMs) is described. The approach is also well suited for understand why two different coupled models have such large differences in their respective climate simulations. In the application presented here, the differences between the coupled models using the Center for Ocean-Land-Atmosphere Studies (COLA) and the National Center for Atmospheric Research (NCAR) atmospheric general circulation models (AGCMs) are examined. The intent is to isolate which component of the air-sea fluxes is most responsible for the differences between the coupled models and for the errors in their respective coupled simulations. The procedure is to simultaneously couple the two different atmospheric component models to a single ocean general circulation model (OGCM), in this case the Modular Ocean Model (MOM) developed at the Geophysical Fluid Dynamics Laboratory (GFDL). Each atmospheric component model experiences the same SST produced by the OGCM, but the OGCM is simultaneously coupled to both AGCMs using a cross coupling strategy. In the first experiment, the OGCM is coupled to the heat and fresh water flux from the NCAR AGCM (Community Atmospheric Model; CAM) and the momentum flux from the COLA AGCM. Both AGCMs feel the same SST. In the second experiment, the OGCM is coupled to the heat and fresh water flux from the COLA AGCM and the momentum flux from the CAM AGCM. Again, both atmospheric component models experience the same SST. By comparing these two experimental simulations with control simulations where only one AGCM is used, it is possible to argue which of the flux components are most responsible for the differences in the simulations and their respective errors. Based on these sensitivity experiments we conclude that the tropical ocean warm bias in the COLA coupled model is due to errors in the heat flux, and that the erroneous westward shift in the tropical Pacific cold tongue minimum in the NCAR model is due errors in the momentum flux. All the coupled simulations presented here have warm biases along the eastern boundary of the tropical oceans suggesting that the problem is common to both AGCMs. In terms of interannual variability in the tropical Pacific, the CAM momentum flux is responsible for the erroneous westward extension of the sea surface temperature anomalies (SSTA) and errors in the COLA momentum flux cause the erroneous eastward migration of the El Niño-Southern Oscillation (ENSO) events. These conclusions depend on assuming that the error due to the OGCM can be neglected.

A New Approach for Coupled GCM Sensitivity Studies

NASA Astrophysics Data System (ADS)

Kirtman, B. P.; Duane, G. S.

2011-12-01

A new multi-model approach for coupled GCM sensitivity studies is presented. The purpose of the sensitivity experiments is to understand why two different coupled models have such large differences in their respective climate simulations. In the application presented here, the differences between the coupled models using the Center for Ocean-Land-Atmosphere Studies (COLA) and the National Center for Atmospheric Research (NCAR) atmospheric general circulation models (AGCMs) are examined. The intent is to isolate which component of the air-sea fluxes is most responsible for the differences between the coupled models and for the errors in their respective coupled simulations. The procedure is to simultaneously couple the two different atmospheric component models to a single ocean general circulation model (OGCM), in this case the Modular Ocean Model (MOM) developed at the Geophysical Fluid Dynamics Laboratory (GFDL). Each atmospheric component model experiences the same SST produced by the OGCM, but the OGCM is simultaneously coupled to both AGCMs using a cross coupling strategy. In the first experiment, the OGCM is coupled to the heat and fresh water flux from the NCAR AGCM (Community Atmospheric Model; CAM) and the momentum flux from the COLA AGCM. Both AGCMs feel the same SST. In the second experiment, the OGCM is coupled to the heat and fresh water flux from the COLA AGCM and the momentum flux from the CAM AGCM. Again, both atmospheric component models experience the same SST. By comparing these two experimental simulations with control simulations where only one AGCM is used, it is possible to argue which of the flux components are most responsible for the differences in the simulations and their respective errors. Based on these sensitivity experiments we conclude that the tropical ocean warm bias in the COLA coupled model is due to errors in the heat flux, and that the erroneous westward shift in the tropical Pacific cold tongue minimum in the NCAR model is due errors in the momentum flux. All the coupled simulations presented here have warm biases along the eastern boundary of the tropical oceans suggesting that the problem is common to both AGCMs. In terms of interannual variability in the tropical Pacific, the CAM momentum flux is responsible for the erroneous westward extension of the sea surface temperature anomalies (SSTA) and errors in the COLA momentum flux cause the erroneous eastward migration of the El Niño-Southern Oscillation (ENSO) events. These conclusions depend on assuming that the error due to the OGCM can be neglected.

Instructional design affects the efficacy of simulation-based training in central venous catheterization.

PubMed

Craft, Christopher; Feldon, David F; Brown, Eric A

2014-05-01

Simulation-based learning is a common educational tool in health care training and frequently involves instructional designs based on Experiential Learning Theory (ELT). However, little research explores the effectiveness and efficiency of different instructional design methodologies appropriate for simulations. The aim of this study was to compare 2 instructional design models, ELT and Guided Experiential Learning (GEL), to determine which is more effective for training the central venous catheterization procedure. Using a quasi-experimental randomized block design, nurse anesthetists completed training under 1 of the 2 instructional design models. Performance was assessed using a checklist of central venous catheterization performance, pass rates, and critical action errors. Participants in the GEL condition performed significantly better than those in the ELT condition on the overall checklist score after controlling for individual practice time (F[1, 29] = 4.021, P = .027, Cohen's d = .71), had higher pass rates (P = .006, Cohen's d = 1.15), and had lower rates of failure due to critical action errors (P = .038, Cohen's d = .81). The GEL model of instructional design is significantly more effective than ELT for simulation-based learning of the central venous catheterization procedure, yielding large differences in effect size. Copyright © 2014 Elsevier Inc. All rights reserved.

CEDAR-GEM Challenge for Systematic Assessment of Ionosphere/Thermosphere Models in Predicting TEC During the 2006 December Storm Event

NASA Astrophysics Data System (ADS)

Shim, J. S.; Rastätter, L.; Kuznetsova, M.; Bilitza, D.; Codrescu, M.; Coster, A. J.; Emery, B. A.; Fedrizzi, M.; Förster, M.; Fuller-Rowell, T. J.; Gardner, L. C.; Goncharenko, L.; Huba, J.; McDonald, S. E.; Mannucci, A. J.; Namgaladze, A. A.; Pi, X.; Prokhorov, B. E.; Ridley, A. J.; Scherliess, L.; Schunk, R. W.; Sojka, J. J.; Zhu, L.

2017-10-01

In order to assess current modeling capability of reproducing storm impacts on total electron content (TEC), we considered quantities such as TEC, TEC changes compared to quiet time values, and the maximum value of the TEC and TEC changes during a storm. We compared the quantities obtained from ionospheric models against ground-based GPS TEC measurements during the 2006 AGU storm event (14-15 December 2006) in the selected eight longitude sectors. We used 15 simulations obtained from eight ionospheric models, including empirical, physics-based, coupled ionosphere-thermosphere, and data assimilation models. To quantitatively evaluate performance of the models in TEC prediction during the storm, we calculated skill scores such as RMS error, Normalized RMS error (NRMSE), ratio of the modeled to observed maximum increase (Yield), and the difference between the modeled peak time and observed peak time. Furthermore, to investigate latitudinal dependence of the performance of the models, the skill scores were calculated for five latitude regions. Our study shows that RMSE of TEC and TEC changes of the model simulations range from about 3 TECU (total electron content unit, 1 TECU = 1016 el m-2) (in high latitudes) to about 13 TECU (in low latitudes), which is larger than latitudinal average GPS TEC error of about 2 TECU. Most model simulations predict TEC better than TEC changes in terms of NRMSE and the difference in peak time, while the opposite holds true in terms of Yield. Model performance strongly depends on the quantities considered, the type of metrics used, and the latitude considered.

Prediction of pilot reserve attention capacity during air-to-air target tracking

NASA Technical Reports Server (NTRS)

Onstott, E. D.; Faulkner, W. H.

1977-01-01

Reserve attention capacity of a pilot was calculated using a pilot model that allocates exclusive model attention according to the ranking of task urgency functions whose variables are tracking error and error rate. The modeled task consisted of tracking a maneuvering target aircraft both vertically and horizontally, and when possible, performing a diverting side task which was simulated by the precise positioning of an electrical stylus and modeled as a task of constant urgency in the attention allocation algorithm. The urgency of the single loop vertical task is simply the magnitude of the vertical tracking error, while the multiloop horizontal task requires a nonlinear urgency measure of error and error rate terms. Comparison of model results with flight simulation data verified the computed model statistics of tracking error of both axes, lateral and longitudinal stick amplitude and rate, and side task episodes. Full data for the simulation tracking statistics as well as the explicit equations and structure of the urgency function multiaxis pilot model are presented.

Simulation of groundwater flow to assess future withdrawals associated with Base Realignment and Closure (BRAC) at Fort George G. Meade, Maryland

USGS Publications Warehouse

Raffensperger, Jeff P.; Fleming, Brandon J.; Banks, William S.L.; Horn, Marilee A.; Nardi, Mark R.; Andreasen, David C.

2010-01-01

Increased groundwater withdrawals from confined aquifers in the Maryland Coastal Plain to supply anticipated growth at Fort George G. Meade (Fort Meade) and surrounding areas resulting from the Department of Defense Base Realignment and Closure Program may have adverse effects in the outcrop or near-outcrop areas. Specifically, increased pumping from the Potomac Group aquifers (principally the Patuxent aquifer) could potentially reduce base flow in small streams below rates necessary for healthy biological functioning. Additionally, water levels may be lowered near, or possibly below, the top of the aquifer within the confined-unconfined transition zone near the outcrop area. A three-dimensional groundwater flow model was created to incorporate and analyze data on water withdrawals, streamflow, and hydraulic head in the region. The model is based on an earlier model developed to assess the effects of future withdrawals from well fields in Anne Arundel County, Maryland and surrounding areas, and includes some of the same features, including model extent, boundary conditions, and vertical discretization (layering). The resolution (horizontal grid discretization) of the earlier model limited its ability to simulate the effects of withdrawals on the outcrop and near-outcrop areas. The model developed for this study included a block-shaped higher-resolution local grid, referred to as the child model, centered on Fort Meade, which was coupled to the coarser-grid parent model using the shared node Local Grid Refinement capability of MODFLOW-LGR. A more detailed stream network was incorporated into the child model. In addition, for part of the transient simulation period, stress periods were reduced in length from 1 year to 3 months, to allow for simulation of the effects of seasonally varying withdrawals and recharge on the groundwater-flow system and simulated streamflow. This required revision of the database on withdrawals and estimation of seasonal variations in recharge represented in the earlier model. The calibrated model provides a tool for future forecasts of changes in the system under different management scenarios, and for simulating potential effects of withdrawals at Fort Meade and the surrounding area on water levels in the near-outcrop area and base flow in the outcrop area. Model error was assessed by comparing observed and simulated water levels from 62 wells (55 in the parent model and 7 in the child model). The root-mean-square error values for the parent and child model were 8.72 and 11.91 feet, respectively. Root-mean-square error values for the 55 parent model observation wells range from 0.95 to 30.31 feet; the range for the 7 child model observation wells is 5.00 to 24.17 feet. Many of the wells with higher root-mean-square error values occur at the perimeter of the child model and near large pumping centers, as well as updip in the confined aquifers. Root-mean-square error values decrease downdip and away from the large pumping centers. Both the parent and child models are sensitive to increasing withdrawal rates. The parent model is more sensitive than the child model to decreasing transmissivity of layers 3, 4, 5, and 6. The parent model is relatively insensitive to riverbed vertical conductance, however, the child model does exhibit some sensitivity to decreasing riverbed conductance. The overall water budget for the model included sources and sinks of water including recharge, surface-water bodies and rivers and streams, general-head boundaries, and withdrawals from permitted wells. Withdrawal from wells in 2005 was estimated to be equivalent to 8.5 percent of the total recharge rate.

Uncertainties in the Antarctic Ice Sheet Contribution to Sea Level Rise: Exploration of Model Response to Errors in Climate Forcing, Boundary Conditions, and Internal Parameters

NASA Astrophysics Data System (ADS)

Schlegel, N.; Seroussi, H. L.; Boening, C.; Larour, E. Y.; Limonadi, D.; Schodlok, M.; Watkins, M. M.

2017-12-01

The Jet Propulsion Laboratory-University of California at Irvine Ice Sheet System Model (ISSM) is a thermo-mechanical 2D/3D parallelized finite element software used to physically model the continental-scale flow of ice at high resolutions. Embedded into ISSM are uncertainty quantification (UQ) tools, based on the Design Analysis Kit for Optimization and Terascale Applications (DAKOTA) software. ISSM-DAKOTA offers various UQ methods for the investigation of how errors in model input impact uncertainty in simulation results. We utilize these tools to regionally sample model input and key parameters, based on specified bounds of uncertainty, and run a suite of continental-scale 100-year ISSM forward simulations of the Antarctic Ice Sheet. Resulting diagnostics (e.g., spread in local mass flux and regional mass balance) inform our conclusion about which parameters and/or forcing has the greatest impact on century-scale model simulations of ice sheet evolution. The results allow us to prioritize the key datasets and measurements that are critical for the minimization of ice sheet model uncertainty. Overall, we find that Antartica's total sea level contribution is strongly affected by grounding line retreat, which is driven by the magnitude of ice shelf basal melt rates and by errors in bedrock topography. In addition, results suggest that after 100 years of simulation, Thwaites glacier is the most significant source of model uncertainty, and its drainage basin has the largest potential for future sea level contribution. This work is performed at and supported by the California Institute of Technology's Jet Propulsion Laboratory. Supercomputing time is also supported through a contract with the National Aeronautics and Space Administration's Cryosphere program.

Monte Carlo simulation of expert judgments on human errors in chemical analysis--a case study of ICP-MS.

PubMed

Kuselman, Ilya; Pennecchi, Francesca; Epstein, Malka; Fajgelj, Ales; Ellison, Stephen L R

2014-12-01

Monte Carlo simulation of expert judgments on human errors in a chemical analysis was used for determination of distributions of the error quantification scores (scores of likelihood and severity, and scores of effectiveness of a laboratory quality system in prevention of the errors). The simulation was based on modeling of an expert behavior: confident, reasonably doubting and irresolute expert judgments were taken into account by means of different probability mass functions (pmfs). As a case study, 36 scenarios of human errors which may occur in elemental analysis of geological samples by ICP-MS were examined. Characteristics of the score distributions for three pmfs of an expert behavior were compared. Variability of the scores, as standard deviation of the simulated score values from the distribution mean, was used for assessment of the score robustness. A range of the score values, calculated directly from elicited data and simulated by a Monte Carlo method for different pmfs, was also discussed from the robustness point of view. It was shown that robustness of the scores, obtained in the case study, can be assessed as satisfactory for the quality risk management and improvement of a laboratory quality system against human errors. Copyright © 2014 Elsevier B.V. All rights reserved.

Study of Current Measurement Method Based on Circular Magnetic Field Sensing Array

PubMed Central

Li, Zhenhua; Zhang, Siqiu; Wu, Zhengtian; Tao, Yuan

2018-01-01

Classic core-based instrument transformers are more prone to magnetic saturation. This affects the measurement accuracy of such transformers and limits their applications in measuring large direct current (DC). Moreover, protection and control systems may exhibit malfunctions due to such measurement errors. This paper presents a more accurate method for current measurement based on a circular magnetic field sensing array. The proposed measurement approach utilizes multiple hall sensors that are evenly distributed on a circle. The average value of all hall sensors is regarded as the final measurement. The calculation model is established in the case of magnetic field interference of the parallel wire, and the simulation results show that the error decreases significantly when the number of hall sensors n is greater than 8. The measurement error is less than 0.06% when the wire spacing is greater than 2.5 times the radius of the sensor array. A simulation study on the off-center primary conductor is conducted, and a kind of hall sensor compensation method is adopted to improve the accuracy. The simulation and test results indicate that the measurement error of the system is less than 0.1%. PMID:29734742

Study of Current Measurement Method Based on Circular Magnetic Field Sensing Array.

PubMed

Li, Zhenhua; Zhang, Siqiu; Wu, Zhengtian; Abu-Siada, Ahmed; Tao, Yuan

2018-05-05

Classic core-based instrument transformers are more prone to magnetic saturation. This affects the measurement accuracy of such transformers and limits their applications in measuring large direct current (DC). Moreover, protection and control systems may exhibit malfunctions due to such measurement errors. This paper presents a more accurate method for current measurement based on a circular magnetic field sensing array. The proposed measurement approach utilizes multiple hall sensors that are evenly distributed on a circle. The average value of all hall sensors is regarded as the final measurement. The calculation model is established in the case of magnetic field interference of the parallel wire, and the simulation results show that the error decreases significantly when the number of hall sensors n is greater than 8. The measurement error is less than 0.06% when the wire spacing is greater than 2.5 times the radius of the sensor array. A simulation study on the off-center primary conductor is conducted, and a kind of hall sensor compensation method is adopted to improve the accuracy. The simulation and test results indicate that the measurement error of the system is less than 0.1%.

Analysis of Error Propagation Within Hierarchical Air Combat Models

DTIC Science & Technology

2016-06-01

Model Simulation MANA Map Aware Non-Uniform Automata MCET Mine Warfare Capabilities and Effectiveness Tool MOE measure of effectiveness MOP measure of...model for a two-versus-two air engagement between jet fighters in the stochastic, agent-based Map Aware Non- uniform Automata (MANA) simulation...Master’s thesis, Naval Postgraduate School, Monterey, CA. McIntosh, G. C. (2009). MANA-V (Map aware non-uniform automata – Vector) supplementary manual

Experiments in Error Propagation within Hierarchal Combat Models

DTIC Science & Technology

2015-09-01

Bayesian Information Criterion CNO Chief of Naval Operations DOE Design of Experiments DOD Department of Defense MANA Map Aware Non-uniform Automata ...ground up” approach. First, it develops a mission-level model for one on one submarine combat in Map Aware Non-uniform Automata (MANA) simulation, an... Automata (MANA), an agent based simulation that can model the different postures of submarines. It feeds the results from MANA into stochastic

A map overlay error model based on boundary geometry

USGS Publications Warehouse

Gaeuman, D.; Symanzik, J.; Schmidt, J.C.

2005-01-01

An error model for quantifying the magnitudes and variability of errors generated in the areas of polygons during spatial overlay of vector geographic information system layers is presented. Numerical simulation of polygon boundary displacements was used to propagate coordinate errors to spatial overlays. The model departs from most previous error models in that it incorporates spatial dependence of coordinate errors at the scale of the boundary segment. It can be readily adapted to match the scale of error-boundary interactions responsible for error generation on a given overlay. The area of error generated by overlay depends on the sinuosity of polygon boundaries, as well as the magnitude of the coordinate errors on the input layers. Asymmetry in boundary shape has relatively little effect on error generation. Overlay errors are affected by real differences in boundary positions on the input layers, as well as errors in the boundary positions. Real differences between input layers tend to compensate for much of the error generated by coordinate errors. Thus, the area of change measured on an overlay layer produced by the XOR overlay operation will be more accurate if the area of real change depicted on the overlay is large. The model presented here considers these interactions, making it especially useful for estimating errors studies of landscape change over time. ?? 2005 The Ohio State University.

Influence of hypo- and hyperthermia on death time estimation - A simulation study.

PubMed

Muggenthaler, H; Hubig, M; Schenkl, S; Mall, G

2017-09-01

Numerous physiological and pathological mechanisms can cause elevated or lowered body core temperatures. Deviations from the physiological level of about 37°C can influence temperature based death time estimations. However, it has not been investigated by means of thermodynamics, to which extent hypo- and hyperthermia bias death time estimates. Using numerical simulation, the present study investigates the errors inherent in temperature based death time estimation in case of elevated or lowered body core temperatures before death. The most considerable errors with regard to the normothermic model occur in the first few hours post-mortem. With decreasing body core temperature and increasing post-mortem time the error diminishes and stagnates at a nearly constant level. Copyright © 2017 Elsevier B.V. All rights reserved.

On Inertial Body Tracking in the Presence of Model Calibration Errors

PubMed Central

Miezal, Markus; Taetz, Bertram; Bleser, Gabriele

2016-01-01

In inertial body tracking, the human body is commonly represented as a biomechanical model consisting of rigid segments with known lengths and connecting joints. The model state is then estimated via sensor fusion methods based on data from attached inertial measurement units (IMUs). This requires the relative poses of the IMUs w.r.t. the segments—the IMU-to-segment calibrations, subsequently called I2S calibrations—to be known. Since calibration methods based on static poses, movements and manual measurements are still the most widely used, potentially large human-induced calibration errors have to be expected. This work compares three newly developed/adapted extended Kalman filter (EKF) and optimization-based sensor fusion methods with an existing EKF-based method w.r.t. their segment orientation estimation accuracy in the presence of model calibration errors with and without using magnetometer information. While the existing EKF-based method uses a segment-centered kinematic chain biomechanical model and a constant angular acceleration motion model, the newly developed/adapted methods are all based on a free segments model, where each segment is represented with six degrees of freedom in the global frame. Moreover, these methods differ in the assumed motion model (constant angular acceleration, constant angular velocity, inertial data as control input), the state representation (segment-centered, IMU-centered) and the estimation method (EKF, sliding window optimization). In addition to the free segments representation, the optimization-based method also represents each IMU with six degrees of freedom in the global frame. In the evaluation on simulated and real data from a three segment model (an arm), the optimization-based method showed the smallest mean errors, standard deviations and maximum errors throughout all tests. It also showed the lowest dependency on magnetometer information and motion agility. Moreover, it was insensitive w.r.t. I2S position and segment length errors in the tested ranges. Errors in the I2S orientations were, however, linearly propagated into the estimated segment orientations. In the absence of magnetic disturbances, severe model calibration errors and fast motion changes, the newly developed IMU centered EKF-based method yielded comparable results with lower computational complexity. PMID:27455266

Testing the Accuracy of Data-driven MHD Simulations of Active Region Evolution and Eruption

NASA Astrophysics Data System (ADS)

Leake, J. E.; Linton, M.; Schuck, P. W.

2017-12-01

Models for the evolution of the solar coronal magnetic field are vital for understanding solar activity, yet the best measurements of the magnetic field lie at the photosphere, necessitating the recent development of coronal models which are "data-driven" at the photosphere. Using magnetohydrodynamic simulations of active region formation and our recently created validation framework we investigate the source of errors in data-driven models that use surface measurements of the magnetic field, and derived MHD quantities, to model the coronal magnetic field. The primary sources of errors in these studies are the temporal and spatial resolution of the surface measurements. We will discuss the implications of theses studies for accurately modeling the build up and release of coronal magnetic energy based on photospheric magnetic field observations.

Impact of PBL and convection parameterization schemes for prediction of severe land-falling Bay of Bengal cyclones using WRF-ARW model

NASA Astrophysics Data System (ADS)

Singh, K. S.; Bhaskaran, Prasad K.

2017-12-01

This study evaluates the performance of the Advanced Research Weather Research and Forecasting (WRF-ARW) model for prediction of land-falling Bay of Bengal (BoB) tropical cyclones (TCs). Model integration was performed using two-way interactive double nested domains at 27 and 9 km resolutions. The present study comprises two major components. Firstly, the study explores the impact of five different planetary boundary layer (PBL) and six cumulus convection (CC) schemes on seven land-falling BoB TCs. A total of 85 numerical simulations were studied in detail, and the results signify that the model simulated better both the track and intensity by using a combination of Yonsei University (YSU) PBL and the old simplified Arakawa-Schubert CC scheme. Secondly, the study also investigated the model performance based on the best possible combinations of model physics on the real-time forecasts of four BoB cyclones (Phailin, Helen, Lehar, and Madi) that made landfall during 2013 based on another 15 numerical simulations. The predicted mean track error during 2013 was about 71 km, 114 km, 133 km, 148 km, and 130 km respectively from day-1 to day-5. The Root Mean Square Error (RMSE) for Minimum Central Pressure (MCP) was about 6 hPa and the same noticed for Maximum Surface Wind (MSW) was about 4.5 m s-1 noticed during the entire simulation period. In addition the study also reveals that the predicted track errors during 2013 cyclones improved respectively by 43%, 44%, and 52% from day-1 to day-3 as compared to cyclones simulated during the period 2006-2011. The improvements noticed can be attributed due to relatively better quality data that was specified for the initial mean position error (about 48 km) during 2013. Overall the study signifies that the track and intensity forecast for 2013 cyclones using the specified combinations listed in the first part of this study performed relatively better than the other NWP (Numerical Weather Prediction) models, and thereby finds application in real-time forecast.

A heteroskedastic error covariance matrix estimator using a first-order conditional autoregressive Markov simulation for deriving asympotical efficient estimates from ecological sampled Anopheles arabiensis aquatic habitat covariates

PubMed Central

Jacob, Benjamin G; Griffith, Daniel A; Muturi, Ephantus J; Caamano, Erick X; Githure, John I; Novak, Robert J

2009-01-01

Background Autoregressive regression coefficients for Anopheles arabiensis aquatic habitat models are usually assessed using global error techniques and are reported as error covariance matrices. A global statistic, however, will summarize error estimates from multiple habitat locations. This makes it difficult to identify where there are clusters of An. arabiensis aquatic habitats of acceptable prediction. It is therefore useful to conduct some form of spatial error analysis to detect clusters of An. arabiensis aquatic habitats based on uncertainty residuals from individual sampled habitats. In this research, a method of error estimation for spatial simulation models was demonstrated using autocorrelation indices and eigenfunction spatial filters to distinguish among the effects of parameter uncertainty on a stochastic simulation of ecological sampled Anopheles aquatic habitat covariates. A test for diagnostic checking error residuals in an An. arabiensis aquatic habitat model may enable intervention efforts targeting productive habitats clusters, based on larval/pupal productivity, by using the asymptotic distribution of parameter estimates from a residual autocovariance matrix. The models considered in this research extends a normal regression analysis previously considered in the literature. Methods Field and remote-sampled data were collected during July 2006 to December 2007 in Karima rice-village complex in Mwea, Kenya. SAS 9.1.4® was used to explore univariate statistics, correlations, distributions, and to generate global autocorrelation statistics from the ecological sampled datasets. A local autocorrelation index was also generated using spatial covariance parameters (i.e., Moran's Indices) in a SAS/GIS® database. The Moran's statistic was decomposed into orthogonal and uncorrelated synthetic map pattern components using a Poisson model with a gamma-distributed mean (i.e. negative binomial regression). The eigenfunction values from the spatial configuration matrices were then used to define expectations for prior distributions using a Markov chain Monte Carlo (MCMC) algorithm. A set of posterior means were defined in WinBUGS 1.4.3®. After the model had converged, samples from the conditional distributions were used to summarize the posterior distribution of the parameters. Thereafter, a spatial residual trend analyses was used to evaluate variance uncertainty propagation in the model using an autocovariance error matrix. Results By specifying coefficient estimates in a Bayesian framework, the covariate number of tillers was found to be a significant predictor, positively associated with An. arabiensis aquatic habitats. The spatial filter models accounted for approximately 19% redundant locational information in the ecological sampled An. arabiensis aquatic habitat data. In the residual error estimation model there was significant positive autocorrelation (i.e., clustering of habitats in geographic space) based on log-transformed larval/pupal data and the sampled covariate depth of habitat. Conclusion An autocorrelation error covariance matrix and a spatial filter analyses can prioritize mosquito control strategies by providing a computationally attractive and feasible description of variance uncertainty estimates for correctly identifying clusters of prolific An. arabiensis aquatic habitats based on larval/pupal productivity. PMID:19772590

Dynamic Simulation of Human Gait Model With Predictive Capability.

PubMed

Sun, Jinming; Wu, Shaoli; Voglewede, Philip A

2018-03-01

In this paper, it is proposed that the central nervous system (CNS) controls human gait using a predictive control approach in conjunction with classical feedback control instead of exclusive classical feedback control theory that controls based on past error. To validate this proposition, a dynamic model of human gait is developed using a novel predictive approach to investigate the principles of the CNS. The model developed includes two parts: a plant model that represents the dynamics of human gait and a controller that represents the CNS. The plant model is a seven-segment, six-joint model that has nine degrees-of-freedom (DOF). The plant model is validated using data collected from able-bodied human subjects. The proposed controller utilizes model predictive control (MPC). MPC uses an internal model to predict the output in advance, compare the predicted output to the reference, and optimize the control input so that the predicted error is minimal. To decrease the complexity of the model, two joints are controlled using a proportional-derivative (PD) controller. The developed predictive human gait model is validated by simulating able-bodied human gait. The simulation results show that the developed model is able to simulate the kinematic output close to experimental data.

Directional variance adjustment: bias reduction in covariance matrices based on factor analysis with an application to portfolio optimization.

PubMed

Bartz, Daniel; Hatrick, Kerr; Hesse, Christian W; Müller, Klaus-Robert; Lemm, Steven

2013-01-01

Robust and reliable covariance estimates play a decisive role in financial and many other applications. An important class of estimators is based on factor models. Here, we show by extensive Monte Carlo simulations that covariance matrices derived from the statistical Factor Analysis model exhibit a systematic error, which is similar to the well-known systematic error of the spectrum of the sample covariance matrix. Moreover, we introduce the Directional Variance Adjustment (DVA) algorithm, which diminishes the systematic error. In a thorough empirical study for the US, European, and Hong Kong stock market we show that our proposed method leads to improved portfolio allocation.

Directional Variance Adjustment: Bias Reduction in Covariance Matrices Based on Factor Analysis with an Application to Portfolio Optimization

PubMed Central

Bartz, Daniel; Hatrick, Kerr; Hesse, Christian W.; Müller, Klaus-Robert; Lemm, Steven

2013-01-01

Robust and reliable covariance estimates play a decisive role in financial and many other applications. An important class of estimators is based on factor models. Here, we show by extensive Monte Carlo simulations that covariance matrices derived from the statistical Factor Analysis model exhibit a systematic error, which is similar to the well-known systematic error of the spectrum of the sample covariance matrix. Moreover, we introduce the Directional Variance Adjustment (DVA) algorithm, which diminishes the systematic error. In a thorough empirical study for the US, European, and Hong Kong stock market we show that our proposed method leads to improved portfolio allocation. PMID:23844016

Simulation of the ground-water flow system at Naval Submarine Base Bangor and vicinity, Kitsap County, Washington

USGS Publications Warehouse

Heeswijk, Marijke van; Smith, Daniel T.

2002-01-01

An evaluation of the interaction between ground-water flow on Naval Submarine Base Bangor and the regional-flow system shows that for selected alternatives of future ground-water pumping on and near the base, the risk is low that significant concentrations of on-base ground-water contamination will reach off-base public-supply wells and hypothetical wells southwest of the base. The risk is low even if worst-case conditions are considered ? no containment and remediation of on-base contamination. The evaluation also shows that future saltwater encroachment of aquifers below sea level may be possible, but this determination has considerable uncertainty associated with it. The potential effects on the ground-water flow system resulting from four hypothetical ground-water pumping alternatives were considered, including no change in 1995 pumping rates, doubling the rates, and 2020 rates estimated from population projections with two different pumping distributions. All but a continuation of 1995 pumping rates demonstrate the possibility of future saltwater encroachment in the Sea-level aquifer on Naval Submarine Base Bangor. The amount of time it would take for encroachment to occur is unknown. For all pumping alternatives, future saltwater encroachment in the Sea-level aquifer also may be possible along Puget Sound east and southeast of the base. Future saltwater encroachment in the Deep aquifer also may be possible throughout large parts of the study area. Projections of saltwater encroachment are least certain outside the boundaries of Naval Submarine Base Bangor. The potential effects of the ground-water pumping alternatives were evaluated by simulating the ground-water flow system with a three-dimensional uniform-density ground-water flow model. The model was calibrated by trial-and-error by minimizing differences between simulated and measured or estimated variables. These included water levels from prior to January 17, 1977 (termed 'predevelopment'), water-level drawdowns since predevelopment until April 15, 1995, ground-water discharge to streams in water year 1995, and residence times of ground water in different parts of the flow system that were estimated in a separate but related study. Large amounts of ground water were pumped from 1977 through 1980 from the Sea-level aquifer on Naval Submarine Base Bangor to enable the construction of an off-shore drydock. Records of the flow-system responses to the applied stresses were used to help calibrate the model. Errors in the calibrated model were significant. The poor agreement between simulated and measured values could be improved by making many local changes to hydraulic parameters but these changes were not supported by other data. Model errors may have resulted in errors in the simulated effects of ground-water pumping alternatives.

Web-Based Model Visualization Tools to Aid in Model Optimization and Uncertainty Analysis

NASA Astrophysics Data System (ADS)

Alder, J.; van Griensven, A.; Meixner, T.

2003-12-01

Individuals applying hydrologic models have a need for a quick easy to use visualization tools to permit them to assess and understand model performance. We present here the Interactive Hydrologic Modeling (IHM) visualization toolbox. The IHM utilizes high-speed Internet access, the portability of the web and the increasing power of modern computers to provide an online toolbox for quick and easy model result visualization. This visualization interface allows for the interpretation and analysis of Monte-Carlo and batch model simulation results. Often times a given project will generate several thousands or even hundreds of thousands simulations. This large number of simulations creates a challenge for post-simulation analysis. IHM's goal is to try to solve this problem by loading all of the data into a database with a web interface that can dynamically generate graphs for the user according to their needs. IHM currently supports: a global samples statistics table (e.g. sum of squares error, sum of absolute differences etc.), top ten simulations table and graphs, graphs of an individual simulation using time step data, objective based dotty plots, threshold based parameter cumulative density function graphs (as used in the regional sensitivity analysis of Spear and Hornberger) and 2D error surface graphs of the parameter space. IHM is ideal for the simplest bucket model to the largest set of Monte-Carlo model simulations with a multi-dimensional parameter and model output space. By using a web interface, IHM offers the user complete flexibility in the sense that they can be anywhere in the world using any operating system. IHM can be a time saving and money saving alternative to spending time producing graphs or conducting analysis that may not be informative or being forced to purchase or use expensive and proprietary software. IHM is a simple, free, method of interpreting and analyzing batch model results, and is suitable for novice to expert hydrologic modelers.

Application of Biologically Based Lumping To Investigate the Toxicokinetic Interactions of a Complex Gasoline Mixture.

PubMed

Jasper, Micah N; Martin, Sheppard A; Oshiro, Wendy M; Ford, Jermaine; Bushnell, Philip J; El-Masri, Hisham

2016-03-15

People are often exposed to complex mixtures of environmental chemicals such as gasoline, tobacco smoke, water contaminants, or food additives. We developed an approach that applies chemical lumping methods to complex mixtures, in this case gasoline, based on biologically relevant parameters used in physiologically based pharmacokinetic (PBPK) modeling. Inhalation exposures were performed with rats to evaluate the performance of our PBPK model and chemical lumping method. There were 109 chemicals identified and quantified in the vapor in the chamber. The time-course toxicokinetic profiles of 10 target chemicals were also determined from blood samples collected during and following the in vivo experiments. A general PBPK model was used to compare the experimental data to the simulated values of blood concentration for 10 target chemicals with various numbers of lumps, iteratively increasing from 0 to 99. Large reductions in simulation error were gained by incorporating enzymatic chemical interactions, in comparison to simulating the individual chemicals separately. The error was further reduced by lumping the 99 nontarget chemicals. The same biologically based lumping approach can be used to simplify any complex mixture with tens, hundreds, or thousands of constituents.

INNOVATIVE INSTRUMENTATION AND ANALYSIS OF THE TEMPERATURE MEASUREMENT FOR HIGH TEMPERATURE GASIFICATION

DOE Office of Scientific and Technical Information (OSTI.GOV)

Seong W. Lee

During this reporting period, the literature survey including the gasifier temperature measurement literature, the ultrasonic application and its background study in cleaning application, and spray coating process are completed. The gasifier simulator (cold model) testing has been successfully conducted. Four factors (blower voltage, ultrasonic application, injection time intervals, particle weight) were considered as significant factors that affect the temperature measurement. The Analysis of Variance (ANOVA) was applied to analyze the test data. The analysis shows that all four factors are significant to the temperature measurements in the gasifier simulator (cold model). The regression analysis for the case with the normalizedmore » room temperature shows that linear model fits the temperature data with 82% accuracy (18% error). The regression analysis for the case without the normalized room temperature shows 72.5% accuracy (27.5% error). The nonlinear regression analysis indicates a better fit than that of the linear regression. The nonlinear regression model's accuracy is 88.7% (11.3% error) for normalized room temperature case, which is better than the linear regression analysis. The hot model thermocouple sleeve design and fabrication are completed. The gasifier simulator (hot model) design and the fabrication are completed. The system tests of the gasifier simulator (hot model) have been conducted and some modifications have been made. Based on the system tests and results analysis, the gasifier simulator (hot model) has met the proposed design requirement and the ready for system test. The ultrasonic cleaning method is under evaluation and will be further studied for the gasifier simulator (hot model) application. The progress of this project has been on schedule.« less

Application of Exactly Linearized Error Transport Equations to AIAA CFD Prediction Workshops

NASA Technical Reports Server (NTRS)

Derlaga, Joseph M.; Park, Michael A.; Rallabhandi, Sriram

2017-01-01

The computational fluid dynamics (CFD) prediction workshops sponsored by the AIAA have created invaluable opportunities in which to discuss the predictive capabilities of CFD in areas in which it has struggled, e.g., cruise drag, high-lift, and sonic boom pre diction. While there are many factors that contribute to disagreement between simulated and experimental results, such as modeling or discretization error, quantifying the errors contained in a simulation is important for those who make decisions based on the computational results. The linearized error transport equations (ETE) combined with a truncation error estimate is a method to quantify one source of errors. The ETE are implemented with a complex-step method to provide an exact linearization with minimal source code modifications to CFD and multidisciplinary analysis methods. The equivalency of adjoint and linearized ETE functional error correction is demonstrated. Uniformly refined grids from a series of AIAA prediction workshops demonstrate the utility of ETE for multidisciplinary analysis with a connection between estimated discretization error and (resolved or under-resolved) flow features.

Improving Lidar Turbulence Estimates for Wind Energy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Newman, Jennifer F.; Clifton, Andrew; Churchfield, Matthew J.

2016-10-06

Remote sensing devices (e.g., lidars) are quickly becoming a cost-effective and reliable alternative to meteorological towers for wind energy applications. Although lidars can measure mean wind speeds accurately, these devices measure different values of turbulence intensity (TI) than an instrument on a tower. In response to these issues, a lidar TI error reduction model was recently developed for commercially available lidars. The TI error model first applies physics-based corrections to the lidar measurements, then uses machine-learning techniques to further reduce errors in lidar TI estimates. The model was tested at two sites in the Southern Plains where vertically profiling lidarsmore » were collocated with meteorological towers. This presentation primarily focuses on the physics-based corrections, which include corrections for instrument noise, volume averaging, and variance contamination. As different factors affect TI under different stability conditions, the combination of physical corrections applied in L-TERRA changes depending on the atmospheric stability during each 10-minute time period. This stability-dependent version of L-TERRA performed well at both sites, reducing TI error and bringing lidar TI estimates closer to estimates from instruments on towers. However, there is still scatter evident in the lidar TI estimates, indicating that there are physics that are not being captured in the current version of L-TERRA. Two options are discussed for modeling the remainder of the TI error physics in L-TERRA: machine learning and lidar simulations. Lidar simulations appear to be a better approach, as they can help improve understanding of atmospheric effects on TI error and do not require a large training data set.« less

Systematic errors in Monsoon simulation: importance of the equatorial Indian Ocean processes

NASA Astrophysics Data System (ADS)

Annamalai, H.; Taguchi, B.; McCreary, J. P., Jr.; Nagura, M.; Miyama, T.

2015-12-01

H. Annamalai1, B. Taguchi2, J.P. McCreary1, J. Hafner1, M. Nagura2, and T. Miyama2 International Pacific Research Center, University of Hawaii, USA Application Laboratory, JAMSTEC, Japan In climate models, simulating the monsoon precipitation climatology remains a grand challenge. Compared to CMIP3, the multi-model-mean (MMM) errors for Asian-Australian monsoon (AAM) precipitation climatology in CMIP5, relative to GPCP observations, have shown little improvement. One of the implications is that uncertainties in the future projections of time-mean changes to AAM rainfall may not have reduced from CMIP3 to CMIP5. Despite dedicated efforts by the modeling community, the progress in monsoon modeling is rather slow. This leads us to wonder: Has the scientific community reached a "plateau" in modeling mean monsoon precipitation? Our focus here is to better understanding of the coupled air-sea interactions, and moist processes that govern the precipitation characteristics over the tropical Indian Ocean where large-scale errors persist. A series idealized coupled model experiments are performed to test the hypothesis that errors in the coupled processes along the equatorial Indian Ocean during inter-monsoon seasons could potentially influence systematic errors during the monsoon season. Moist static energy budget diagnostics has been performed to identify the leading moist and radiative processes that account for the large-scale errors in the simulated precipitation. As a way forward, we propose three coordinated efforts, and they are: (i) idealized coupled model experiments; (ii) process-based diagnostics and (iii) direct observations to constrain model physics. We will argue that a systematic and coordinated approach in the identification of the various interactive processes that shape the precipitation basic state needs to be carried out, and high-quality observations over the data sparse monsoon region are needed to validate models and further improve model physics.

Systems modeling and simulation applications for critical care medicine

PubMed Central

2012-01-01

Critical care delivery is a complex, expensive, error prone, medical specialty and remains the focal point of major improvement efforts in healthcare delivery. Various modeling and simulation techniques offer unique opportunities to better understand the interactions between clinical physiology and care delivery. The novel insights gained from the systems perspective can then be used to develop and test new treatment strategies and make critical care delivery more efficient and effective. However, modeling and simulation applications in critical care remain underutilized. This article provides an overview of major computer-based simulation techniques as applied to critical care medicine. We provide three application examples of different simulation techniques, including a) pathophysiological model of acute lung injury, b) process modeling of critical care delivery, and c) an agent-based model to study interaction between pathophysiology and healthcare delivery. Finally, we identify certain challenges to, and opportunities for, future research in the area. PMID:22703718

Energy Performance Assessment of Radiant Cooling System through Modeling and Calibration at Component Level

DOE Office of Scientific and Technical Information (OSTI.GOV)

Khan, Yasin; Mathur, Jyotirmay; Bhandari, Mahabir S

2016-01-01

The paper describes a case study of an information technology office building with a radiant cooling system and a conventional variable air volume (VAV) system installed side by side so that performancecan be compared. First, a 3D model of the building involving architecture, occupancy, and HVAC operation was developed in EnergyPlus, a simulation tool. Second, a different calibration methodology was applied to develop the base case for assessing the energy saving potential. This paper details the calibration of the whole building energy model to the component level, including lighting, equipment, and HVAC components such as chillers, pumps, cooling towers, fans,more » etc. Also a new methodology for the systematic selection of influence parameter has been developed for the calibration of a simulated model which requires large time for the execution. The error at the whole building level [measured in mean bias error (MBE)] is 0.2%, and the coefficient of variation of root mean square error (CvRMSE) is 3.2%. The total errors in HVAC at the hourly are MBE = 8.7% and CvRMSE = 23.9%, which meet the criteria of ASHRAE 14 (2002) for hourly calibration. Different suggestions have been pointed out to generalize the energy saving of radiant cooling system through the existing building system. So a base case model was developed by using the calibrated model for quantifying the energy saving potential of the radiant cooling system. It was found that a base case radiant cooling system integrated with DOAS can save 28% energy compared with the conventional VAV system.« less

Determination of Barometric Altimeter Errors for the Orion Exploration Flight Test-1 Entry

NASA Technical Reports Server (NTRS)

Brown, Denise L.; Munoz, Jean-Philippe; Gay, Robert

2011-01-01

The EFT-1 mission is the unmanned flight test for the upcoming Multi-Purpose Crew Vehicle (MPCV). During entry, the EFT-1 vehicle will trigger several Landing and Recovery System (LRS) events, such as parachute deployment, based on onboard altitude information. The primary altitude source is the filtered navigation solution updated with GPS measurement data. The vehicle also has three barometric altimeters that will be used to measure atmospheric pressure during entry. In the event that GPS data is not available during entry, the altitude derived from the barometric altimeter pressure will be used to trigger chute deployment for the drogues and main parachutes. Therefore it is important to understand the impact of error sources on the pressure measured by the barometric altimeters and on the altitude derived from that pressure. There are four primary error sources impacting the sensed pressure: sensor errors, Analog to Digital conversion errors, aerodynamic errors, and atmosphere modeling errors. This last error source is induced by the conversion from pressure to altitude in the vehicle flight software, which requires an atmosphere model such as the US Standard 1976 Atmosphere model. There are several secondary error sources as well, such as waves, tides, and latencies in data transmission. Typically, for error budget calculations it is assumed that all error sources are independent, normally distributed variables. Thus, the initial approach to developing the EFT-1 barometric altimeter altitude error budget was to create an itemized error budget under these assumptions. This budget was to be verified by simulation using high fidelity models of the vehicle hardware and software. The simulation barometric altimeter model includes hardware error sources and a data-driven model of the aerodynamic errors expected to impact the pressure in the midbay compartment in which the sensors are located. The aerodynamic model includes the pressure difference between the midbay compartment and the free stream pressure as a function of altitude, oscillations in sensed pressure due to wake effects, and an acoustics model capturing fluctuations in pressure due to motion of the passive vents separating the barometric altimeters from the outside of the vehicle.

A contrastive study on the influences of radial and three-dimensional satellite gravity gradiometry on the accuracy of the Earth's gravitational field recovery

NASA Astrophysics Data System (ADS)

Zheng, Wei; Hsu, Hou-Tse; Zhong, Min; Yun, Mei-Juan

2012-10-01

The accuracy of the Earth's gravitational field measured from the gravity field and steady-state ocean circulation explorer (GOCE), up to 250 degrees, influenced by the radial gravity gradient Vzz and three-dimensional gravity gradient Vij from the satellite gravity gradiometry (SGG) are contrastively demonstrated based on the analytical error model and numerical simulation, respectively. Firstly, the new analytical error model of the cumulative geoid height, influenced by the radial gravity gradient Vzz and three-dimensional gravity gradient Vij are established, respectively. In 250 degrees, the GOCE cumulative geoid height error measured by the radial gravity gradient Vzz is about 2½ times higher than that measured by the three-dimensional gravity gradient Vij. Secondly, the Earth's gravitational field from GOCE completely up to 250 degrees is recovered using the radial gravity gradient Vzz and three-dimensional gravity gradient Vij by numerical simulation, respectively. The study results show that when the measurement error of the gravity gradient is 3 × 10-12/s2, the cumulative geoid height errors using the radial gravity gradient Vzz and three-dimensional gravity gradient Vij are 12.319 cm and 9.295 cm at 250 degrees, respectively. The accuracy of the cumulative geoid height using the three-dimensional gravity gradient Vij is improved by 30%-40% on average compared with that using the radial gravity gradient Vzz in 250 degrees. Finally, by mutual verification of the analytical error model and numerical simulation, the orders of magnitude from the accuracies of the Earth's gravitational field recovery make no substantial differences based on the radial and three-dimensional gravity gradients, respectively. Therefore, it is feasible to develop in advance a radial cold-atom interferometric gradiometer with a measurement accuracy of 10-13/s2-10-15/s2 for precisely producing the next-generation GOCE Follow-On Earth gravity field model with a high spatial resolution.

A simulation of the San Andreas fault experiment

NASA Technical Reports Server (NTRS)

Agreen, R. W.; Smith, D. E.

1973-01-01

The San Andreas Fault Experiment, which employs two laser tracking systems for measuring the relative motion of two points on opposite sides of the fault, was simulated for an eight year observation period. The two tracking stations are located near San Diego on the western side of the fault and near Quincy on the eastern side; they are roughly 900 kilometers apart. Both will simultaneously track laser reflector equipped satellites as they pass near the stations. Tracking of the Beacon Explorer C Spacecraft was simulated for these two stations during August and September for eight consecutive years. An error analysis of the recovery of the relative location of Quincy from the data was made, allowing for model errors in the mass of the earth, the gravity field, solar radiation pressure, atmospheric drag, errors in the position of the San Diego site, and laser systems range biases and noise. The results of this simulation indicate that the distance of Quincy from San Diego will be determined each year with a precision of about 10 centimeters. This figure is based on the accuracy of earth models and other parameters available in 1972.

Simulation of ground-water flow in glaciofluvial aquifers in the Grand Rapids area, Minnesota

USGS Publications Warehouse

Jones, Perry M.

2004-01-01

A calibrated steady-state, finite-difference, ground-waterflow model was constructed to simulate ground-water flow in three glaciofluvial aquifers, defined in this report as the upper, middle, and lower aquifers, in an area of about 114 mi2 surrounding the city of Grand Rapids in north-central Minnesota. The calibrated model will be used by Minnesota Department of Health and communities in the Grand Rapids area in the development of wellhead protection plans for their water supplies. The model was calibrated through comparison of simulated ground-water levels to measured static water levels in 351 wells, and comparison of simulated base-flow rates to estimated base-flow rates for reaches of the Mississippi and Prairie Rivers. Model statistics indicate that the model tends to overestimate ground-water levels. The root mean square errors ranged from +12.83 ft in wells completed in the upper aquifer to +19.10 ft in wells completed in the middle aquifer. Mean absolute differences between simulated and measured water levels ranged from +4.43 ft for wells completed in the upper aquifer to +9.25 ft for wells completed in the middle aquifer. Mean algebraic differences ranged from +9.35 ft for wells completed in the upper aquifer to +14.44 ft for wells completed in the middle aquifer, with the positive differences indicating that the simulated water levels were higher than the measured water levels. Percentage errors between simulated and estimated base-flow rates for the three monitored reaches all were less than 10 percent, indicating good agreement. Simulated ground-water levels were most sensitive to changes in general-head boundary conductance, indicating that this characteristic is the predominant model input variable controlling steady-state water-level conditions. Simulated groundwater flow to stream reaches was most sensitive to changes in horizontal hydraulic conductivity, indicating that this characteristic is the predominant model input variable controlling steady-state flow conditions.

An ensemble-ANFIS based uncertainty assessment model for forecasting multi-scalar standardized precipitation index

NASA Astrophysics Data System (ADS)

Ali, Mumtaz; Deo, Ravinesh C.; Downs, Nathan J.; Maraseni, Tek

2018-07-01

Forecasting drought by means of the World Meteorological Organization-approved Standardized Precipitation Index (SPI) is considered to be a fundamental task to support socio-economic initiatives and effectively mitigating the climate-risk. This study aims to develop a robust drought modelling strategy to forecast multi-scalar SPI in drought-rich regions of Pakistan where statistically significant lagged combinations of antecedent SPI are used to forecast future SPI. With ensemble-Adaptive Neuro Fuzzy Inference System ('ensemble-ANFIS') executed via a 10-fold cross-validation procedure, a model is constructed by randomly partitioned input-target data. Resulting in 10-member ensemble-ANFIS outputs, judged by mean square error and correlation coefficient in the training period, the optimal forecasts are attained by the averaged simulations, and the model is benchmarked with M5 Model Tree and Minimax Probability Machine Regression (MPMR). The results show the proposed ensemble-ANFIS model's preciseness was notably better (in terms of the root mean square and mean absolute error including the Willmott's, Nash-Sutcliffe and Legates McCabe's index) for the 6- and 12- month compared to the 3-month forecasts as verified by the largest error proportions that registered in smallest error band. Applying 10-member simulations, ensemble-ANFIS model was validated for its ability to forecast severity (S), duration (D) and intensity (I) of drought (including the error bound). This enabled uncertainty between multi-models to be rationalized more efficiently, leading to a reduction in forecast error caused by stochasticity in drought behaviours. Through cross-validations at diverse sites, a geographic signature in modelled uncertainties was also calculated. Considering the superiority of ensemble-ANFIS approach and its ability to generate uncertainty-based information, the study advocates the versatility of a multi-model approach for drought-risk forecasting and its prime importance for estimating drought properties over confidence intervals to generate better information for strategic decision-making.

Degradation data analysis based on a generalized Wiener process subject to measurement error

NASA Astrophysics Data System (ADS)

Li, Junxing; Wang, Zhihua; Zhang, Yongbo; Fu, Huimin; Liu, Chengrui; Krishnaswamy, Sridhar

2017-09-01

Wiener processes have received considerable attention in degradation modeling over the last two decades. In this paper, we propose a generalized Wiener process degradation model that takes unit-to-unit variation, time-correlated structure and measurement error into considerations simultaneously. The constructed methodology subsumes a series of models studied in the literature as limiting cases. A simple method is given to determine the transformed time scale forms of the Wiener process degradation model. Then model parameters can be estimated based on a maximum likelihood estimation (MLE) method. The cumulative distribution function (CDF) and the probability distribution function (PDF) of the Wiener process with measurement errors are given based on the concept of the first hitting time (FHT). The percentiles of performance degradation (PD) and failure time distribution (FTD) are also obtained. Finally, a comprehensive simulation study is accomplished to demonstrate the necessity of incorporating measurement errors in the degradation model and the efficiency of the proposed model. Two illustrative real applications involving the degradation of carbon-film resistors and the wear of sliding metal are given. The comparative results show that the constructed approach can derive a reasonable result and an enhanced inference precision.

Improved Statistics for Genome-Wide Interaction Analysis

PubMed Central

Ueki, Masao; Cordell, Heather J.

2012-01-01

Recently, Wu and colleagues [1] proposed two novel statistics for genome-wide interaction analysis using case/control or case-only data. In computer simulations, their proposed case/control statistic outperformed competing approaches, including the fast-epistasis option in PLINK and logistic regression analysis under the correct model; however, reasons for its superior performance were not fully explored. Here we investigate the theoretical properties and performance of Wu et al.'s proposed statistics and explain why, in some circumstances, they outperform competing approaches. Unfortunately, we find minor errors in the formulae for their statistics, resulting in tests that have higher than nominal type 1 error. We also find minor errors in PLINK's fast-epistasis and case-only statistics, although theory and simulations suggest that these errors have only negligible effect on type 1 error. We propose adjusted versions of all four statistics that, both theoretically and in computer simulations, maintain correct type 1 error rates under the null hypothesis. We also investigate statistics based on correlation coefficients that maintain similar control of type 1 error. Although designed to test specifically for interaction, we show that some of these previously-proposed statistics can, in fact, be sensitive to main effects at one or both loci, particularly in the presence of linkage disequilibrium. We propose two new “joint effects” statistics that, provided the disease is rare, are sensitive only to genuine interaction effects. In computer simulations we find, in most situations considered, that highest power is achieved by analysis under the correct genetic model. Such an analysis is unachievable in practice, as we do not know this model. However, generally high power over a wide range of scenarios is exhibited by our joint effects and adjusted Wu statistics. We recommend use of these alternative or adjusted statistics and urge caution when using Wu et al.'s originally-proposed statistics, on account of the inflated error rate that can result. PMID:22496670

Surrogate Model Application to the Identification of Optimal Groundwater Exploitation Scheme Based on Regression Kriging Method—A Case Study of Western Jilin Province

PubMed Central

An, Yongkai; Lu, Wenxi; Cheng, Weiguo

2015-01-01

This paper introduces a surrogate model to identify an optimal exploitation scheme, while the western Jilin province was selected as the study area. A numerical simulation model of groundwater flow was established first, and four exploitation wells were set in the Tongyu county and Qian Gorlos county respectively so as to supply water to Daan county. Second, the Latin Hypercube Sampling (LHS) method was used to collect data in the feasible region for input variables. A surrogate model of the numerical simulation model of groundwater flow was developed using the regression kriging method. An optimization model was established to search an optimal groundwater exploitation scheme using the minimum average drawdown of groundwater table and the minimum cost of groundwater exploitation as multi-objective functions. Finally, the surrogate model was invoked by the optimization model in the process of solving the optimization problem. Results show that the relative error and root mean square error of the groundwater table drawdown between the simulation model and the surrogate model for 10 validation samples are both lower than 5%, which is a high approximation accuracy. The contrast between the surrogate-based simulation optimization model and the conventional simulation optimization model for solving the same optimization problem, shows the former only needs 5.5 hours, and the latter needs 25 days. The above results indicate that the surrogate model developed in this study could not only considerably reduce the computational burden of the simulation optimization process, but also maintain high computational accuracy. This can thus provide an effective method for identifying an optimal groundwater exploitation scheme quickly and accurately. PMID:26264008

Low resolution brain electromagnetic tomography in a realistic geometry head model: a simulation study

NASA Astrophysics Data System (ADS)

Ding, Lei; Lai, Yuan; He, Bin

2005-01-01

It is of importance to localize neural sources from scalp recorded EEG. Low resolution brain electromagnetic tomography (LORETA) has received considerable attention for localizing brain electrical sources. However, most such efforts have used spherical head models in representing the head volume conductor. Investigation of the performance of LORETA in a realistic geometry head model, as compared with the spherical model, will provide useful information guiding interpretation of data obtained by using the spherical head model. The performance of LORETA was evaluated by means of computer simulations. The boundary element method was used to solve the forward problem. A three-shell realistic geometry (RG) head model was constructed from MRI scans of a human subject. Dipole source configurations of a single dipole located at different regions of the brain with varying depth were used to assess the performance of LORETA in different regions of the brain. A three-sphere head model was also used to approximate the RG head model, and similar simulations performed, and results compared with the RG-LORETA with reference to the locations of the simulated sources. Multi-source localizations were discussed and examples given in the RG head model. Localization errors employing the spherical LORETA, with reference to the source locations within the realistic geometry head, were about 20-30 mm, for four brain regions evaluated: frontal, parietal, temporal and occipital regions. Localization errors employing the RG head model were about 10 mm over the same four brain regions. The present simulation results suggest that the use of the RG head model reduces the localization error of LORETA, and that the RG head model based LORETA is desirable if high localization accuracy is needed.

Bayesian inversions of a dynamic vegetation model in four European grassland sites

NASA Astrophysics Data System (ADS)

Minet, J.; Laloy, E.; Tychon, B.; François, L.

2015-01-01

Eddy covariance data from four European grassland sites are used to probabilistically invert the CARAIB dynamic vegetation model (DVM) with ten unknown parameters, using the DREAM(ZS) Markov chain Monte Carlo (MCMC) sampler. We compare model inversions considering both homoscedastic and heteroscedastic eddy covariance residual errors, with variances either fixed a~priori or jointly inferred with the model parameters. Agreements between measured and simulated data during calibration are comparable with previous studies, with root-mean-square error (RMSE) of simulated daily gross primary productivity (GPP), ecosystem respiration (RECO) and evapotranspiration (ET) ranging from 1.73 to 2.19 g C m-2 day-1, 1.04 to 1.56 g C m-2 day-1, and 0.50 to 1.28 mm day-1, respectively. In validation, mismatches between measured and simulated data are larger, but still with Nash-Sutcliffe efficiency scores above 0.5 for three out of the four sites. Although measurement errors associated with eddy covariance data are known to be heteroscedastic, we showed that assuming a classical linear heteroscedastic model of the residual errors in the inversion do not fully remove heteroscedasticity. Since the employed heteroscedastic error model allows for larger deviations between simulated and measured data as the magnitude of the measured data increases, this error model expectedly lead to poorer data fitting compared to inversions considering a constant variance of the residual errors. Furthermore, sampling the residual error variances along with model parameters results in overall similar model parameter posterior distributions as those obtained by fixing these variances beforehand, while slightly improving model performance. Despite the fact that the calibrated model is generally capable of fitting the data within measurement errors, systematic bias in the model simulations are observed. These are likely due to model inadequacies such as shortcomings in the photosynthesis modelling. Besides model behaviour, difference between model parameter posterior distributions among the four grassland sites are also investigated. It is shown that the marginal distributions of the specific leaf area and characteristic mortality time parameters can be explained by site-specific ecophysiological characteristics. Lastly, the possibility of finding a common set of parameters among the four experimental sites is discussed.

Error simulation of paired-comparison-based scaling methods

NASA Astrophysics Data System (ADS)

Cui, Chengwu

2000-12-01

Subjective image quality measurement usually resorts to psycho physical scaling. However, it is difficult to evaluate the inherent precision of these scaling methods. Without knowing the potential errors of the measurement, subsequent use of the data can be misleading. In this paper, the errors on scaled values derived form paired comparison based scaling methods are simulated with randomly introduced proportion of choice errors that follow the binomial distribution. Simulation results are given for various combinations of the number of stimuli and the sampling size. The errors are presented in the form of average standard deviation of the scaled values and can be fitted reasonably well with an empirical equation that can be sued for scaling error estimation and measurement design. The simulation proves paired comparison based scaling methods can have large errors on the derived scaled values when the sampling size and the number of stimuli are small. Examples are also given to show the potential errors on actually scaled values of color image prints as measured by the method of paired comparison.

Impact of geophysical model error for recovering temporal gravity field model

NASA Astrophysics Data System (ADS)

Zhou, Hao; Luo, Zhicai; Wu, Yihao; Li, Qiong; Xu, Chuang

2016-07-01

The impact of geophysical model error on recovered temporal gravity field models with both real and simulated GRACE observations is assessed in this paper. With real GRACE observations, we build four temporal gravity field models, i.e., HUST08a, HUST11a, HUST04 and HUST05. HUST08a and HUST11a are derived from different ocean tide models (EOT08a and EOT11a), while HUST04 and HUST05 are derived from different non-tidal models (AOD RL04 and AOD RL05). The statistical result shows that the discrepancies of the annual mass variability amplitudes in six river basins between HUST08a and HUST11a models, HUST04 and HUST05 models are all smaller than 1 cm, which demonstrates that geophysical model error slightly affects the current GRACE solutions. The impact of geophysical model error for future missions with more accurate satellite ranging is also assessed by simulation. The simulation results indicate that for current mission with range rate accuracy of 2.5 × 10- 7 m/s, observation error is the main reason for stripe error. However, when the range rate accuracy improves to 5.0 × 10- 8 m/s in the future mission, geophysical model error will be the main source for stripe error, which will limit the accuracy and spatial resolution of temporal gravity model. Therefore, observation error should be the primary error source taken into account at current range rate accuracy level, while more attention should be paid to improving the accuracy of background geophysical models for the future mission.

Modeling the effects of high-G stress on pilots in a tracking task

NASA Technical Reports Server (NTRS)

Korn, J.; Kleinman, D. L.

1978-01-01

Air-to-air tracking experiments were conducted at the Aerospace Medical Research Laboratories using both fixed and moving base dynamic environment simulators. The obtained data, which includes longitudinal error of a simulated air-to-air tracking task as well as other auxiliary variables, was analyzed using an ensemble averaging method. In conjunction with these experiments, the optimal control model is applied to model a human operator under high-G stress.

Hydraulic correction method (HCM) to enhance the efficiency of SRTM DEM in flood modeling

NASA Astrophysics Data System (ADS)

Chen, Huili; Liang, Qiuhua; Liu, Yong; Xie, Shuguang

2018-04-01

Digital Elevation Model (DEM) is one of the most important controlling factors determining the simulation accuracy of hydraulic models. However, the currently available global topographic data is confronted with limitations for application in 2-D hydraulic modeling, mainly due to the existence of vegetation bias, random errors and insufficient spatial resolution. A hydraulic correction method (HCM) for the SRTM DEM is proposed in this study to improve modeling accuracy. Firstly, we employ the global vegetation corrected DEM (i.e. Bare-Earth DEM), developed from the SRTM DEM to include both vegetation height and SRTM vegetation signal. Then, a newly released DEM, removing both vegetation bias and random errors (i.e. Multi-Error Removed DEM), is employed to overcome the limitation of height errors. Last, an approach to correct the Multi-Error Removed DEM is presented to account for the insufficiency of spatial resolution, ensuring flow connectivity of the river networks. The approach involves: (a) extracting river networks from the Multi-Error Removed DEM using an automated algorithm in ArcGIS; (b) correcting the location and layout of extracted streams with the aid of Google Earth platform and Remote Sensing imagery; and (c) removing the positive biases of the raised segment in the river networks based on bed slope to generate the hydraulically corrected DEM. The proposed HCM utilizes easily available data and tools to improve the flow connectivity of river networks without manual adjustment. To demonstrate the advantages of HCM, an extreme flood event in Huifa River Basin (China) is simulated on the original DEM, Bare-Earth DEM, Multi-Error removed DEM, and hydraulically corrected DEM using an integrated hydrologic-hydraulic model. A comparative analysis is subsequently performed to assess the simulation accuracy and performance of four different DEMs and favorable results have been obtained on the corrected DEM.

A simulation study to quantify the impacts of exposure measurement error on air pollution health risk estimates in copollutant time-series models.

EPA Science Inventory

BackgroundExposure measurement error in copollutant epidemiologic models has the potential to introduce bias in relative risk (RR) estimates. A simulation study was conducted using empirical data to quantify the impact of correlated measurement errors in time-series analyses of a...

Three-dimensional ray-tracing model for the study of advanced refractive errors in keratoconus.

PubMed

Schedin, Staffan; Hallberg, Per; Behndig, Anders

2016-01-20

We propose a numerical three-dimensional (3D) ray-tracing model for the analysis of advanced corneal refractive errors. The 3D modeling was based on measured corneal elevation data by means of Scheimpflug photography. A mathematical description of the measured corneal surfaces from a keratoconus (KC) patient was used for the 3D ray tracing, based on Snell's law of refraction. A model of a commercial intraocular lens (IOL) was included in the analysis. By modifying the posterior IOL surface, it was shown that the imaging quality could be significantly improved. The RMS values were reduced by approximately 50% close to the retina, both for on- and off-axis geometries. The 3D ray-tracing model can constitute a basis for simulation of customized IOLs that are able to correct the advanced, irregular refractive errors in KC.

The Diurnal Cycle of Clouds and Precipitation at the ARM SGP Site: An Atmospheric State-Based Analysis and Error Decomposition of a Multiscale Modeling Framework Simulation

NASA Astrophysics Data System (ADS)

Zhao, Wei; Marchand, Roger; Fu, Qiang

2017-12-01

Long-term reflectivity data collected by a millimeter cloud radar at the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site are used to examine the diurnal cycle of clouds and precipitation and are compared with the diurnal cycle simulated by a Multiscale Modeling Framework (MMF) climate model. The study uses a set of atmospheric states that were created specifically for the SGP and for the purpose of investigating under what synoptic conditions models compare well with observations on a statistical basis (rather than using case studies or seasonal or longer time scale averaging). Differences in the annual mean diurnal cycle between observations and the MMF are decomposed into differences due to the relative frequency of states, the daily mean vertical profile of hydrometeor occurrence, and the (normalized) diurnal variation of hydrometeors in each state. Here the hydrometeors are classified as cloud or precipitation based solely on the reflectivity observed by a millimeter radar or generated by a radar simulator. The results show that the MMF does not capture the diurnal variation of low clouds well in any of the states but does a reasonable job capturing the diurnal variations of high clouds and precipitation in some states. In particular, the diurnal variations in states that occur during summer are reasonably captured by the MMF, while the diurnal variations in states that occur during the transition seasons (spring and fall) are not well captured. Overall, the errors in the annual composite are due primarily to errors in the daily mean of hydrometeor occurrence (rather than diurnal variations), but errors in the state frequency (that is, the distribution of weather states in the model) also play a significant role.

Error-compensation model for simultaneous measurement of five degrees of freedom motion errors of a rotary axis

NASA Astrophysics Data System (ADS)

Bao, Chuanchen; Li, Jiakun; Feng, Qibo; Zhang, Bin

2018-07-01

This paper introduces an error-compensation model for our measurement method to measure five motion errors of a rotary axis based on fibre laser collimation. The error-compensation model is established in a matrix form using the homogeneous coordinate transformation theory. The influences of the installation errors, error crosstalk, and manufacturing errors are analysed. The model is verified by both ZEMAX simulation and measurement experiments. The repeatability values of the radial and axial motion errors are significantly suppressed by more than 50% after compensation. The repeatability experiments of five degrees of freedom motion errors and the comparison experiments of two degrees of freedom motion errors of an indexing table were performed by our measuring device and a standard instrument. The results show that the repeatability values of the angular positioning error ε z and tilt motion error around the Y axis ε y are 1.2″ and 4.4″, and the comparison deviations of the two motion errors are 4.0″ and 4.4″, respectively. The repeatability values of the radial and axial motion errors, δ y and δ z , are 1.3 and 0.6 µm, respectively. The repeatability value of the tilt motion error around the X axis ε x is 3.8″.

Adjustment of Measurements with Multiplicative Errors: Error Analysis, Estimates of the Variance of Unit Weight, and Effect on Volume Estimation from LiDAR-Type Digital Elevation Models

PubMed Central

Shi, Yun; Xu, Peiliang; Peng, Junhuan; Shi, Chuang; Liu, Jingnan

2014-01-01

Modern observation technology has verified that measurement errors can be proportional to the true values of measurements such as GPS, VLBI baselines and LiDAR. Observational models of this type are called multiplicative error models. This paper is to extend the work of Xu and Shimada published in 2000 on multiplicative error models to analytical error analysis of quantities of practical interest and estimates of the variance of unit weight. We analytically derive the variance-covariance matrices of the three least squares (LS) adjustments, the adjusted measurements and the corrections of measurements in multiplicative error models. For quality evaluation, we construct five estimators for the variance of unit weight in association of the three LS adjustment methods. Although LiDAR measurements are contaminated with multiplicative random errors, LiDAR-based digital elevation models (DEM) have been constructed as if they were of additive random errors. We will simulate a model landslide, which is assumed to be surveyed with LiDAR, and investigate the effect of LiDAR-type multiplicative error measurements on DEM construction and its effect on the estimate of landslide mass volume from the constructed DEM. PMID:24434880

A Novel Grid SINS/DVL Integrated Navigation Algorithm for Marine Application

PubMed Central

Kang, Yingyao; Zhao, Lin; Cheng, Jianhua; Fan, Xiaoliang

2018-01-01

Integrated navigation algorithms under the grid frame have been proposed based on the Kalman filter (KF) to solve the problem of navigation in some special regions. However, in the existing study of grid strapdown inertial navigation system (SINS)/Doppler velocity log (DVL) integrated navigation algorithms, the Earth models of the filter dynamic model and the SINS mechanization are not unified. Besides, traditional integrated systems with the KF based correction scheme are susceptible to measurement errors, which would decrease the accuracy and robustness of the system. In this paper, an adaptive robust Kalman filter (ARKF) based hybrid-correction grid SINS/DVL integrated navigation algorithm is designed with the unified reference ellipsoid Earth model to improve the navigation accuracy in middle-high latitude regions for marine application. Firstly, to unify the Earth models, the mechanization of grid SINS is introduced and the error equations are derived based on the same reference ellipsoid Earth model. Then, a more accurate grid SINS/DVL filter model is designed according to the new error equations. Finally, a hybrid-correction scheme based on the ARKF is proposed to resist the effect of measurement errors. Simulation and experiment results show that, compared with the traditional algorithms, the proposed navigation algorithm can effectively improve the navigation performance in middle-high latitude regions by the unified Earth models and the ARKF based hybrid-correction scheme. PMID:29373549

An automated construction of error models for uncertainty quantification and model calibration

NASA Astrophysics Data System (ADS)

Josset, L.; Lunati, I.

2015-12-01

To reduce the computational cost of stochastic predictions, it is common practice to rely on approximate flow solvers (or «proxy»), which provide an inexact, but computationally inexpensive response [1,2]. Error models can be constructed to correct the proxy response: based on a learning set of realizations for which both exact and proxy simulations are performed, a transformation is sought to map proxy into exact responses. Once the error model is constructed a prediction of the exact response is obtained at the cost of a proxy simulation for any new realization. Despite its effectiveness [2,3], the methodology relies on several user-defined parameters, which impact the accuracy of the predictions. To achieve a fully automated construction, we propose a novel methodology based on an iterative scheme: we first initialize the error model with a small training set of realizations; then, at each iteration, we add a new realization both to improve the model and to evaluate its performance. More specifically, at each iteration we use the responses predicted by the updated model to identify the realizations that need to be considered to compute the quantity of interest. Another user-defined parameter is the number of dimensions of the response spaces between which the mapping is sought. To identify the space dimensions that optimally balance mapping accuracy and risk of overfitting, we follow a Leave-One-Out Cross Validation. Also, the definition of a stopping criterion is central to an automated construction. We use a stability measure based on bootstrap techniques to stop the iterative procedure when the iterative model has converged. The methodology is illustrated with two test cases in which an inverse problem has to be solved and assess the performance of the method. We show that an iterative scheme is crucial to increase the applicability of the approach. [1] Josset, L., and I. Lunati, Local and global error models for improving uncertainty quantification, Math.ematical Geosciences, 2013 [2] Josset, L., D. Ginsbourger, and I. Lunati, Functional Error Modeling for uncertainty quantification in hydrogeology, Water Resources Research, 2015 [3] Josset, L., V. Demyanov, A.H. Elsheikhb, and I. Lunati, Accelerating Monte Carlo Markov chains with proxy and error models, Computer & Geosciences, 2015 (In press)

Probabilistic parameter estimation in a 2-step chemical kinetics model for n-dodecane jet autoignition

NASA Astrophysics Data System (ADS)

Hakim, Layal; Lacaze, Guilhem; Khalil, Mohammad; Sargsyan, Khachik; Najm, Habib; Oefelein, Joseph

2018-05-01

This paper demonstrates the development of a simple chemical kinetics model designed for autoignition of n-dodecane in air using Bayesian inference with a model-error representation. The model error, i.e. intrinsic discrepancy from a high-fidelity benchmark model, is represented by allowing additional variability in selected parameters. Subsequently, we quantify predictive uncertainties in the results of autoignition simulations of homogeneous reactors at realistic diesel engine conditions. We demonstrate that these predictive error bars capture model error as well. The uncertainty propagation is performed using non-intrusive spectral projection that can also be used in principle with larger scale computations, such as large eddy simulation. While the present calibration is performed to match a skeletal mechanism, it can be done with equal success using experimental data only (e.g. shock-tube measurements). Since our method captures the error associated with structural model simplifications, we believe that the optimised model could then lead to better qualified predictions of autoignition delay time in high-fidelity large eddy simulations than the existing detailed mechanisms. This methodology provides a way to reduce the cost of reaction kinetics in simulations systematically, while quantifying the accuracy of predictions of important target quantities.

Adjusting Satellite Rainfall Error in Mountainous Areas for Flood Modeling Applications

NASA Astrophysics Data System (ADS)

Zhang, X.; Anagnostou, E. N.; Astitha, M.; Vergara, H. J.; Gourley, J. J.; Hong, Y.

2014-12-01

This study aims to investigate the use of high-resolution Numerical Weather Prediction (NWP) for evaluating biases of satellite rainfall estimates of flood-inducing storms in mountainous areas and associated improvements in flood modeling. Satellite-retrieved precipitation has been considered as a feasible data source for global-scale flood modeling, given that satellite has the spatial coverage advantage over in situ (rain gauges and radar) observations particularly over mountainous areas. However, orographically induced heavy precipitation events tend to be underestimated and spatially smoothed by satellite products, which error propagates non-linearly in flood simulations.We apply a recently developed retrieval error and resolution effect correction method (Zhang et al. 2013*) on the NOAA Climate Prediction Center morphing technique (CMORPH) product based on NWP analysis (or forecasting in the case of real-time satellite products). The NWP rainfall is derived from the Weather Research and Forecasting Model (WRF) set up with high spatial resolution (1-2 km) and explicit treatment of precipitation microphysics.In this study we will show results on NWP-adjusted CMORPH rain rates based on tropical cyclones and a convective precipitation event measured during NASA's IPHEX experiment in the South Appalachian region. We will use hydrologic simulations over different basins in the region to evaluate propagation of bias correction in flood simulations. We show that the adjustment reduced the underestimation of high rain rates thus moderating the strong rainfall magnitude dependence of CMORPH rainfall bias, which results in significant improvement in flood peak simulations. Further study over Blue Nile Basin (western Ethiopia) will be investigated and included in the presentation. *Zhang, X. et al. 2013: Using NWP Simulations in Satellite Rainfall Estimation of Heavy Precipitation Events over Mountainous Areas. J. Hydrometeor, 14, 1844-1858.

High-accuracy process based on the corrective calibration of removal function in the magnetorheological finishing

NASA Astrophysics Data System (ADS)

Zhong, Xianyun; Fan, Bin; Wu, Fan

2017-08-01

The corrective calibration of the removal function plays an important role in the magnetorheological finishing (MRF) high-accuracy process. This paper mainly investigates the asymmetrical characteristic of the MRF removal function shape and further analyzes its influence on the surface residual error by means of an iteration algorithm and simulations. By comparing the ripple errors and convergence ratios based on the ideal MRF tool function and the deflected tool function, the mathematical models for calibrating the deviation of horizontal and flowing directions are presented. Meanwhile, revised mathematical models for the coordinate transformation of an MRF machine is also established. Furthermore, a Ø140-mm fused silica plane and a Ø196 mm, f/1∶1, fused silica concave sphere samples are taken as the experiments. After two runs, the plane mirror final surface error reaches PV 17.7 nm, RMS 1.75 nm, and the polishing time is 16 min in total; after three runs, the sphere mirror final surfer error reaches RMS 2.7 nm and the polishing time is 70 min in total. The convergence ratios are 96.2% and 93.5%, respectively. The spherical simulation error and the polishing result are almost consistent, which fully validate the efficiency and feasibility of the calibration method of MRF removal function error using for the high-accuracy subaperture optical manufacturing.

Triple collocation-based estimation of spatially correlated observation error covariance in remote sensing soil moisture data assimilation

NASA Astrophysics Data System (ADS)

Wu, Kai; Shu, Hong; Nie, Lei; Jiao, Zhenhang

2018-01-01

Spatially correlated errors are typically ignored in data assimilation, thus degenerating the observation error covariance R to a diagonal matrix. We argue that a nondiagonal R carries more observation information making assimilation results more accurate. A method, denoted TC_Cov, was proposed for soil moisture data assimilation to estimate spatially correlated observation error covariance based on triple collocation (TC). Assimilation experiments were carried out to test the performance of TC_Cov. AMSR-E soil moisture was assimilated with a diagonal R matrix computed using the TC and assimilated using a nondiagonal R matrix, as estimated by proposed TC_Cov. The ensemble Kalman filter was considered as the assimilation method. Our assimilation results were validated against climate change initiative data and ground-based soil moisture measurements using the Pearson correlation coefficient and unbiased root mean square difference metrics. These experiments confirmed that deterioration of diagonal R assimilation results occurred when model simulation is more accurate than observation data. Furthermore, nondiagonal R achieved higher correlation coefficient and lower ubRMSD values over diagonal R in experiments and demonstrated the effectiveness of TC_Cov to estimate richly structuralized R in data assimilation. In sum, compared with diagonal R, nondiagonal R may relieve the detrimental effects of assimilation when simulated model results outperform observation data.

Application of round grating angle measurement composite error amendment in the online measurement accuracy improvement of large diameter

NASA Astrophysics Data System (ADS)

Wang, Biao; Yu, Xiaofen; Li, Qinzhao; Zheng, Yu

2008-10-01

The paper aiming at the influence factor of round grating dividing error, rolling-wheel produce eccentricity and surface shape errors provides an amendment method based on rolling-wheel to get the composite error model which includes all influence factors above, and then corrects the non-circle measurement angle error of the rolling-wheel. We make soft simulation verification and have experiment; the result indicates that the composite error amendment method can improve the diameter measurement accuracy with rolling-wheel theory. It has wide application prospect for the measurement accuracy higher than 5 μm/m.

Improvement of GPS radio occultation retrieval error of E region electron density: COSMIC measurement and IRI model simulation

NASA Astrophysics Data System (ADS)

Wu, Kang-Hung; Su, Ching-Lun; Chu, Yen-Hsyang

2015-03-01

In this article, we use the International Reference Ionosphere (IRI) model to simulate temporal and spatial distributions of global E region electron densities retrieved by the FORMOSAT-3/COSMIC satellites by means of GPS radio occultation (RO) technique. Despite regional discrepancies in the magnitudes of the E region electron density, the IRI model simulations can, on the whole, describe the COSMIC measurements in quality and quantity. On the basis of global ionosonde network and the IRI model, the retrieval errors of the global COSMIC-measured E region peak electron density (NmE) from July 2006 to July 2011 are examined and simulated. The COSMIC measurement and the IRI model simulation both reveal that the magnitudes of the percentage error (PE) and root mean-square-error (RMSE) of the relative RO retrieval errors of the NmE values are dependent on local time (LT) and geomagnetic latitude, with minimum in the early morning and at high latitudes and maximum in the afternoon and at middle latitudes. In addition, the seasonal variation of PE and RMSE values seems to be latitude dependent. After removing the IRI model-simulated GPS RO retrieval errors from the original COSMIC measurements, the average values of the annual and monthly mean percentage errors of the RO retrieval errors of the COSMIC-measured E region electron density are, respectively, substantially reduced by a factor of about 2.95 and 3.35, and the corresponding root-mean-square errors show averaged decreases of 15.6% and 15.4%, respectively. It is found that, with this process, the largest reduction in the PE and RMSE of the COSMIC-measured NmE occurs at the equatorial anomaly latitudes 10°N-30°N in the afternoon from 14 to 18 LT, with a factor of 25 and 2, respectively. Statistics show that the residual errors that remained in the corrected COSMIC-measured NmE vary in a range of -20% to 38%, which are comparable to or larger than the percentage errors of the IRI-predicted NmE fluctuating in a range of -6.5% to 20%.

Enabling Predictive Simulation and UQ of Complex Multiphysics PDE Systems by the Development of Goal-Oriented Variational Sensitivity Analysis and a-Posteriori Error Estimation Methods

DOE Office of Scientific and Technical Information (OSTI.GOV)

Estep, Donald

2015-11-30

This project addressed the challenge of predictive computational analysis of strongly coupled, highly nonlinear multiphysics systems characterized by multiple physical phenomena that span a large range of length- and time-scales. Specifically, the project was focused on computational estimation of numerical error and sensitivity analysis of computational solutions with respect to variations in parameters and data. In addition, the project investigated the use of accurate computational estimates to guide efficient adaptive discretization. The project developed, analyzed and evaluated new variational adjoint-based techniques for integration, model, and data error estimation/control and sensitivity analysis, in evolutionary multiphysics multiscale simulations.

Error management training and simulation education.

PubMed

Gardner, Aimee; Rich, Michelle

2014-12-01

The integration of simulation into the training of health care professionals provides context for decision making and procedural skills in a high-fidelity environment, without risk to actual patients. It was hypothesised that a novel approach to simulation-based education - error management training - would produce higher performance ratings compared with traditional step-by-step instruction. Radiology technology students were randomly assigned to participate in traditional procedural-based instruction (n = 11) or vicarious error management training (n = 11). All watched an instructional video and discussed how well each incident was handled (traditional instruction group) or identified where the errors were made (vicarious error management training). Students then participated in a 30-minute case-based simulation. Simulations were videotaped for performance analysis. Blinded experts evaluated performance using a predefined evaluation tool created specifically for the scenario. Blinded experts evaluated performance using a predefined evaluation tool created specifically for the scenario The vicarious error management group scored higher on observer-rated performance (Mean = 9.49) than students in the traditional instruction group (Mean = 9.02; p < 0.01). These findings suggest that incorporating the discussion of errors and how to handle errors during the learning session will better equip students when performing hands-on procedures and skills. This pilot study provides preliminary evidence for integrating error management skills into medical curricula and for the design of learning goals in simulation-based education. © 2014 John Wiley & Sons Ltd.

Robust radio interferometric calibration using the t-distribution

NASA Astrophysics Data System (ADS)

Kazemi, S.; Yatawatta, S.

2013-10-01

A major stage of radio interferometric data processing is calibration or the estimation of systematic errors in the data and the correction for such errors. A stochastic error (noise) model is assumed, and in most cases, this underlying model is assumed to be Gaussian. However, outliers in the data due to interference or due to errors in the sky model would have adverse effects on processing based on a Gaussian noise model. Most of the shortcomings of calibration such as the loss in flux or coherence, and the appearance of spurious sources, could be attributed to the deviations of the underlying noise model. In this paper, we propose to improve the robustness of calibration by using a noise model based on Student's t-distribution. Student's t-noise is a special case of Gaussian noise when the variance is unknown. Unlike Gaussian-noise-model-based calibration, traditional least-squares minimization would not directly extend to a case when we have a Student's t-noise model. Therefore, we use a variant of the expectation-maximization algorithm, called the expectation-conditional maximization either algorithm, when we have a Student's t-noise model and use the Levenberg-Marquardt algorithm in the maximization step. We give simulation results to show the robustness of the proposed calibration method as opposed to traditional Gaussian-noise-model-based calibration, especially in preserving the flux of weaker sources that are not included in the calibration model.

Simulator for beam-based LHC collimator alignment

NASA Astrophysics Data System (ADS)

Valentino, Gianluca; Aßmann, Ralph; Redaelli, Stefano; Sammut, Nicholas

2014-02-01

In the CERN Large Hadron Collider, collimators need to be set up to form a multistage hierarchy to ensure efficient multiturn cleaning of halo particles. Automatic algorithms were introduced during the first run to reduce the beam time required for beam-based setup, improve the alignment accuracy, and reduce the risk of human errors. Simulating the alignment procedure would allow for off-line tests of alignment policies and algorithms. A simulator was developed based on a diffusion beam model to generate the characteristic beam loss signal spike and decay produced when a collimator jaw touches the beam, which is observed in a beam loss monitor (BLM). Empirical models derived from the available measurement data are used to simulate the steady-state beam loss and crosstalk between multiple BLMs. The simulator design is presented, together with simulation results and comparison to measurement data.

Error-Based Simulation for Error-Awareness in Learning Mechanics: An Evaluation

ERIC Educational Resources Information Center

Horiguchi, Tomoya; Imai, Isao; Toumoto, Takahito; Hirashima, Tsukasa

2014-01-01

Error-based simulation (EBS) has been developed to generate phenomena by using students' erroneous ideas and also offers promise for promoting students' awareness of errors. In this paper, we report the evaluation of EBS used in learning "normal reaction" in a junior high school. An EBS class, where students learned the concept…

Simplification of the Kalman filter for meteorological data assimilation

NASA Technical Reports Server (NTRS)

Dee, Dick P.

1991-01-01

The paper proposes a new statistical method of data assimilation that is based on a simplification of the Kalman filter equations. The forecast error covariance evolution is approximated simply by advecting the mass-error covariance field, deriving the remaining covariances geostrophically, and accounting for external model-error forcing only at the end of each forecast cycle. This greatly reduces the cost of computation of the forecast error covariance. In simulations with a linear, one-dimensional shallow-water model and data generated artificially, the performance of the simplified filter is compared with that of the Kalman filter and the optimal interpolation (OI) method. The simplified filter produces analyses that are nearly optimal, and represents a significant improvement over OI.

Modulation of the error-related negativity by response conflict.

PubMed

Danielmeier, Claudia; Wessel, Jan R; Steinhauser, Marco; Ullsperger, Markus

2009-11-01

An arrow version of the Eriksen flanker task was employed to investigate the influence of conflict on the error-related negativity (ERN). The degree of conflict was modulated by varying the distance between flankers and the target arrow (CLOSE and FAR conditions). Error rates and reaction time data from a behavioral experiment were used to adapt a connectionist model of this task. This model was based on the conflict monitoring theory and simulated behavioral and event-related potential data. The computational model predicted an increased ERN amplitude in FAR incompatible (the low-conflict condition) compared to CLOSE incompatible errors (the high-conflict condition). A subsequent ERP experiment confirmed the model predictions. The computational model explains this finding with larger post-response conflict in far trials. In addition, data and model predictions of the N2 and the LRP support the conflict interpretation of the ERN.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nagayama, T.; Bailey, J. E.; Loisel, G. P.

Iron opacity calculations presently disagree with measurements at an electron temperature of ~180–195 eV and an electron density of (2–4)×10 22cm –3, conditions similar to those at the base of the solar convection zone. The measurements use x rays to volumetrically heat a thin iron sample that is tamped with low-Z materials. The opacity is inferred from spectrally resolved x-ray transmission measurements. Plasma self-emission, tamper attenuation, and temporal and spatial gradients can all potentially cause systematic errors in the measured opacity spectra. In this article we quantitatively evaluate these potential errors with numerical investigations. The analysis exploits computer simulations thatmore » were previously found to reproduce the experimentally measured plasma conditions. The simulations, combined with a spectral synthesis model, enable evaluations of individual and combined potential errors in order to estimate their potential effects on the opacity measurement. Lastly, the results show that the errors considered here do not account for the previously observed model-data discrepancies.« less

Addressing the impact of environmental uncertainty in plankton model calibration with a dedicated software system: the Marine Model Optimization Testbed (MarMOT 1.1 alpha)

NASA Astrophysics Data System (ADS)

Hemmings, J. C. P.; Challenor, P. G.

2012-04-01

A wide variety of different plankton system models have been coupled with ocean circulation models, with the aim of understanding and predicting aspects of environmental change. However, an ability to make reliable inferences about real-world processes from the model behaviour demands a quantitative understanding of model error that remains elusive. Assessment of coupled model output is inhibited by relatively limited observing system coverage of biogeochemical components. Any direct assessment of the plankton model is further inhibited by uncertainty in the physical state. Furthermore, comparative evaluation of plankton models on the basis of their design is inhibited by the sensitivity of their dynamics to many adjustable parameters. Parameter uncertainty has been widely addressed by calibrating models at data-rich ocean sites. However, relatively little attention has been given to quantifying uncertainty in the physical fields required by the plankton models at these sites, and tendencies in the biogeochemical properties due to the effects of horizontal processes are often neglected. Here we use model twin experiments, in which synthetic data are assimilated to estimate a system's known "true" parameters, to investigate the impact of error in a plankton model's environmental input data. The experiments are supported by a new software tool, the Marine Model Optimization Testbed, designed for rigorous analysis of plankton models in a multi-site 1-D framework. Simulated errors are derived from statistical characterizations of the mixed layer depth, the horizontal flux divergence tendencies of the biogeochemical tracers and the initial state. Plausible patterns of uncertainty in these data are shown to produce strong temporal and spatial variability in the expected simulation error variance over an annual cycle, indicating variation in the significance attributable to individual model-data differences. An inverse scheme using ensemble-based estimates of the simulation error variance to allow for this environment error performs well compared with weighting schemes used in previous calibration studies, giving improved estimates of the known parameters. The efficacy of the new scheme in real-world applications will depend on the quality of statistical characterizations of the input data. Practical approaches towards developing reliable characterizations are discussed.

Simulation of DNAPL migration in heterogeneous translucent porous media based on estimation of representative elementary volume

NASA Astrophysics Data System (ADS)

Wu, Ming; Wu, Jianfeng; Wu, Jichun

2017-10-01

When the dense nonaqueous phase liquid (DNAPL) comes into the subsurface environment, its migration behavior is crucially affected by the permeability and entry pressure of subsurface porous media. A prerequisite for accurately simulating DNAPL migration in aquifers is then the determination of the permeability, entry pressure and corresponding representative elementary volumes (REV) of porous media. However, the permeability, entry pressure and corresponding representative elementary volumes (REV) are hard to determine clearly. This study utilizes the light transmission micro-tomography (LTM) method to determine the permeability and entry pressure of two dimensional (2D) translucent porous media and integrates the LTM with a criterion of relative gradient error to quantify the corresponding REV of porous media. As a result, the DNAPL migration in porous media might be accurately simulated by discretizing the model at the REV dimension. To validate the quantification methods, an experiment of perchloroethylene (PCE) migration is conducted in a two-dimensional heterogeneous bench-scale aquifer cell. Based on the quantifications of permeability, entry pressure and REV scales of 2D porous media determined by the LTM and relative gradient error, different models with different sizes of discretization grid are used to simulate the PCE migration. It is shown that the model based on REV size agrees well with the experimental results over the entire migration period including calibration, verification and validation processes. This helps to better understand the microstructures of porous media and achieve accurately simulating DNAPL migration in aquifers based on the REV estimation.

Forecasting the Water Demand in Chongqing, China Using a Grey Prediction Model and Recommendations for the Sustainable Development of Urban Water Consumption.

PubMed

Wu, Hua'an; Zeng, Bo; Zhou, Meng

2017-11-15

High accuracy in water demand predictions is an important basis for the rational allocation of city water resources and forms the basis for sustainable urban development. The shortage of water resources in Chongqing, the youngest central municipality in Southwest China, has significantly increased with the population growth and rapid economic development. In this paper, a new grey water-forecasting model (GWFM) was built based on the data characteristics of water consumption. The parameter estimation and error checking methods of the GWFM model were investigated. Then, the GWFM model was employed to simulate the water demands of Chongqing from 2009 to 2015 and forecast it in 2016. The simulation and prediction errors of the GWFM model was checked, and the results show the GWFM model exhibits better simulation and prediction precisions than those of the classical Grey Model with one variable and single order equation GM(1,1) for short and the frequently-used Discrete Grey Model with one variable and single order equation, DGM(1,1) for short. Finally, the water demand in Chongqing from 2017 to 2022 was forecasted, and some corresponding control measures and recommendations were provided based on the prediction results to ensure a viable water supply and promote the sustainable development of the Chongqing economy.

Error analysis of mechanical system and wavelength calibration of monochromator

NASA Astrophysics Data System (ADS)

Zhang, Fudong; Chen, Chen; Liu, Jie; Wang, Zhihong

2018-02-01

This study focuses on improving the accuracy of a grating monochromator on the basis of the grating diffraction equation in combination with an analysis of the mechanical transmission relationship between the grating, the sine bar, and the screw of the scanning mechanism. First, the relationship between the mechanical error in the monochromator with the sine drive and the wavelength error is analyzed. Second, a mathematical model of the wavelength error and mechanical error is developed, and an accurate wavelength calibration method based on the sine bar's length adjustment and error compensation is proposed. Based on the mathematical model and calibration method, experiments using a standard light source with known spectral lines and a pre-adjusted sine bar length are conducted. The model parameter equations are solved, and subsequent parameter optimization simulations are performed to determine the optimal length ratio. Lastly, the length of the sine bar is adjusted. The experimental results indicate that the wavelength accuracy is ±0.3 nm, which is better than the original accuracy of ±2.6 nm. The results confirm the validity of the error analysis of the mechanical system of the monochromator as well as the validity of the calibration method.

Optimization of processing parameters of UAV integral structural components based on yield response

NASA Astrophysics Data System (ADS)

Chen, Yunsheng

2018-05-01

In order to improve the overall strength of unmanned aerial vehicle (UAV), it is necessary to optimize the processing parameters of UAV structural components, which is affected by initial residual stress in the process of UAV structural components processing. Because machining errors are easy to occur, an optimization model for machining parameters of UAV integral structural components based on yield response is proposed. The finite element method is used to simulate the machining parameters of UAV integral structural components. The prediction model of workpiece surface machining error is established, and the influence of the path of walking knife on residual stress of UAV integral structure is studied, according to the stress of UAV integral component. The yield response of the time-varying stiffness is analyzed, and the yield response and the stress evolution mechanism of the UAV integral structure are analyzed. The simulation results show that this method is used to optimize the machining parameters of UAV integral structural components and improve the precision of UAV milling processing. The machining error is reduced, and the deformation prediction and error compensation of UAV integral structural parts are realized, thus improving the quality of machining.

Analysis on the dynamic error for optoelectronic scanning coordinate measurement network

NASA Astrophysics Data System (ADS)

Shi, Shendong; Yang, Linghui; Lin, Jiarui; Guo, Siyang; Ren, Yongjie

2018-01-01

Large-scale dynamic three-dimension coordinate measurement technique is eagerly demanded in equipment manufacturing. Noted for advantages of high accuracy, scale expandability and multitask parallel measurement, optoelectronic scanning measurement network has got close attention. It is widely used in large components jointing, spacecraft rendezvous and docking simulation, digital shipbuilding and automated guided vehicle navigation. At present, most research about optoelectronic scanning measurement network is focused on static measurement capacity and research about dynamic accuracy is insufficient. Limited by the measurement principle, the dynamic error is non-negligible and restricts the application. The workshop measurement and positioning system is a representative which can realize dynamic measurement function in theory. In this paper we conduct deep research on dynamic error resources and divide them two parts: phase error and synchronization error. Dynamic error model is constructed. Based on the theory above, simulation about dynamic error is carried out. Dynamic error is quantized and the rule of volatility and periodicity has been found. Dynamic error characteristics are shown in detail. The research result lays foundation for further accuracy improvement.

Minimizing systematic errors from atmospheric multiple scattering and satellite viewing geometry in coastal zone color scanner level IIA imagery

NASA Technical Reports Server (NTRS)

Martin, D. L.; Perry, M. J.

1994-01-01

Water-leaving radiances and phytoplankton pigment concentrations are calculated from coastal zone color scanner (CZCS) radiance measurements by removing atmospheric Rayleigh and aerosol radiances from the total radiance signal measured at the satellite. The single greatest source of error in CZCS atmospheric correction algorithms in the assumption that these Rayleigh and aerosol radiances are separable. Multiple-scattering interactions between Rayleigh and aerosol components cause systematic errors in calculated aerosol radiances, and the magnitude of these errors is dependent on aerosol type and optical depth and on satellite viewing geometry. A technique was developed which extends the results of previous radiative transfer modeling by Gordon and Castano to predict the magnitude of these systematic errors for simulated CZCS orbital passes in which the ocean is viewed through a modeled, physically realistic atmosphere. The simulated image mathematically duplicates the exact satellite, Sun, and pixel locations of an actual CZCS image. Errors in the aerosol radiance at 443 nm are calculated for a range of aerosol optical depths. When pixels in the simulated image exceed an error threshhold, the corresponding pixels in the actual CZCS image are flagged and excluded from further analysis or from use in image compositing or compilation of pigment concentration databases. Studies based on time series analyses or compositing of CZCS imagery which do not address Rayleigh-aerosol multiple scattering should be interpreted cautiously, since the fundamental assumption used in their atmospheric correction algorithm is flawed.

A simulation of GPS and differential GPS sensors

NASA Technical Reports Server (NTRS)

Rankin, James M.

1993-01-01

The Global Positioning System (GPS) is a revolutionary advance in navigation. Users can determine latitude, longitude, and altitude by receiving range information from at least four satellites. The statistical accuracy of the user's position is directly proportional to the statistical accuracy of the range measurement. Range errors are caused by clock errors, ephemeris errors, atmospheric delays, multipath errors, and receiver noise. Selective Availability, which the military uses to intentionally degrade accuracy for non-authorized users, is a major error source. The proportionality constant relating position errors to range errors is the Dilution of Precision (DOP) which is a function of the satellite geometry. Receivers separated by relatively short distances have the same satellite and atmospheric errors. Differential GPS (DGPS) removes these errors by transmitting pseudorange corrections from a fixed receiver to a mobile receiver. The corrected pseudorange at the moving receiver is now corrupted only by errors from the receiver clock, multipath, and measurement noise. This paper describes a software package that models position errors for various GPS and DGPS systems. The error model is used in the Real-Time Simulator and Cockpit Technology workstation simulations at NASA-LaRC. The GPS/DGPS sensor can simulate enroute navigation, instrument approaches, or on-airport navigation.

Bias correction by use of errors-in-variables regression models in studies with K-X-ray fluorescence bone lead measurements.

PubMed

Lamadrid-Figueroa, Héctor; Téllez-Rojo, Martha M; Angeles, Gustavo; Hernández-Ávila, Mauricio; Hu, Howard

2011-01-01

In-vivo measurement of bone lead by means of K-X-ray fluorescence (KXRF) is the preferred biological marker of chronic exposure to lead. Unfortunately, considerable measurement error associated with KXRF estimations can introduce bias in estimates of the effect of bone lead when this variable is included as the exposure in a regression model. Estimates of uncertainty reported by the KXRF instrument reflect the variance of the measurement error and, although they can be used to correct the measurement error bias, they are seldom used in epidemiological statistical analyzes. Errors-in-variables regression (EIV) allows for correction of bias caused by measurement error in predictor variables, based on the knowledge of the reliability of such variables. The authors propose a way to obtain reliability coefficients for bone lead measurements from uncertainty data reported by the KXRF instrument and compare, by the use of Monte Carlo simulations, results obtained using EIV regression models vs. those obtained by the standard procedures. Results of the simulations show that Ordinary Least Square (OLS) regression models provide severely biased estimates of effect, and that EIV provides nearly unbiased estimates. Although EIV effect estimates are more imprecise, their mean squared error is much smaller than that of OLS estimates. In conclusion, EIV is a better alternative than OLS to estimate the effect of bone lead when measured by KXRF. Copyright © 2010 Elsevier Inc. All rights reserved.

A-posteriori error estimation for second order mechanical systems

NASA Astrophysics Data System (ADS)

Ruiner, Thomas; Fehr, Jörg; Haasdonk, Bernard; Eberhard, Peter

2012-06-01

One important issue for the simulation of flexible multibody systems is the reduction of the flexible bodies degrees of freedom. As far as safety questions are concerned knowledge about the error introduced by the reduction of the flexible degrees of freedom is helpful and very important. In this work, an a-posteriori error estimator for linear first order systems is extended for error estimation of mechanical second order systems. Due to the special second order structure of mechanical systems, an improvement of the a-posteriori error estimator is achieved. A major advantage of the a-posteriori error estimator is that the estimator is independent of the used reduction technique. Therefore, it can be used for moment-matching based, Gramian matrices based or modal based model reduction techniques. The capability of the proposed technique is demonstrated by the a-posteriori error estimation of a mechanical system, and a sensitivity analysis of the parameters involved in the error estimation process is conducted.

A stochastic dynamic model for human error analysis in nuclear power plants

NASA Astrophysics Data System (ADS)

Delgado-Loperena, Dharma

Nuclear disasters like Three Mile Island and Chernobyl indicate that human performance is a critical safety issue, sending a clear message about the need to include environmental press and competence aspects in research. This investigation was undertaken to serve as a roadmap for studying human behavior through the formulation of a general solution equation. The theoretical model integrates models from two heretofore-disassociated disciplines (behavior specialists and technical specialists), that historically have independently studied the nature of error and human behavior; including concepts derived from fractal and chaos theory; and suggests re-evaluation of base theory regarding human error. The results of this research were based on comprehensive analysis of patterns of error, with the omnipresent underlying structure of chaotic systems. The study of patterns lead to a dynamic formulation, serving for any other formula used to study human error consequences. The search for literature regarding error yielded insight for the need to include concepts rooted in chaos theory and strange attractors---heretofore unconsidered by mainstream researchers who investigated human error in nuclear power plants or those who employed the ecological model in their work. The study of patterns obtained from the rupture of a steam generator tube (SGTR) event simulation, provided a direct application to aspects of control room operations in nuclear power plant operations. In doing so, the conceptual foundation based in the understanding of the patterns of human error analysis can be gleaned, resulting in reduced and prevent undesirable events.

Direct model reference adaptive control with application to flexible robots

NASA Technical Reports Server (NTRS)

Steinvorth, Rodrigo; Kaufman, Howard; Neat, Gregory W.

1992-01-01

A modification to a direct command generator tracker-based model reference adaptive control (MRAC) system is suggested in this paper. This modification incorporates a feedforward into the reference model's output as well as the plant's output. Its purpose is to eliminate the bounded model following error present in steady state when previous MRAC systems were used. The algorithm was evaluated using the dynamics for a single-link flexible-joint arm. The results of these simulations show a response with zero steady state model following error. These results encourage further use of MRAC for various types of nonlinear plants.

Joint Seasonal ARMA Approach for Modeling of Load Forecast Errors in Planning Studies

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hafen, Ryan P.; Samaan, Nader A.; Makarov, Yuri V.

2014-04-14

To make informed and robust decisions in the probabilistic power system operation and planning process, it is critical to conduct multiple simulations of the generated combinations of wind and load parameters and their forecast errors to handle the variability and uncertainty of these time series. In order for the simulation results to be trustworthy, the simulated series must preserve the salient statistical characteristics of the real series. In this paper, we analyze day-ahead load forecast error data from multiple balancing authority locations and characterize statistical properties such as mean, standard deviation, autocorrelation, correlation between series, time-of-day bias, and time-of-day autocorrelation.more » We then construct and validate a seasonal autoregressive moving average (ARMA) model to model these characteristics, and use the model to jointly simulate day-ahead load forecast error series for all BAs.« less

Measurement Model and Precision Analysis of Accelerometers for Maglev Vibration Isolation Platforms.

PubMed

Wu, Qianqian; Yue, Honghao; Liu, Rongqiang; Zhang, Xiaoyou; Ding, Liang; Liang, Tian; Deng, Zongquan

2015-08-14

High precision measurement of acceleration levels is required to allow active control for vibration isolation platforms. It is necessary to propose an accelerometer configuration measurement model that yields such a high measuring precision. In this paper, an accelerometer configuration to improve measurement accuracy is proposed. The corresponding calculation formulas of the angular acceleration were derived through theoretical analysis. A method is presented to minimize angular acceleration noise based on analysis of the root mean square noise of the angular acceleration. Moreover, the influence of installation position errors and accelerometer orientation errors on the calculation precision of the angular acceleration is studied. Comparisons of the output differences between the proposed configuration and the previous planar triangle configuration under the same installation errors are conducted by simulation. The simulation results show that installation errors have a relatively small impact on the calculation accuracy of the proposed configuration. To further verify the high calculation precision of the proposed configuration, experiments are carried out for both the proposed configuration and the planar triangle configuration. On the basis of the results of simulations and experiments, it can be concluded that the proposed configuration has higher angular acceleration calculation precision and can be applied to different platforms.

Measurement Model and Precision Analysis of Accelerometers for Maglev Vibration Isolation Platforms

PubMed Central

Wu, Qianqian; Yue, Honghao; Liu, Rongqiang; Zhang, Xiaoyou; Ding, Liang; Liang, Tian; Deng, Zongquan

2015-01-01

High precision measurement of acceleration levels is required to allow active control for vibration isolation platforms. It is necessary to propose an accelerometer configuration measurement model that yields such a high measuring precision. In this paper, an accelerometer configuration to improve measurement accuracy is proposed. The corresponding calculation formulas of the angular acceleration were derived through theoretical analysis. A method is presented to minimize angular acceleration noise based on analysis of the root mean square noise of the angular acceleration. Moreover, the influence of installation position errors and accelerometer orientation errors on the calculation precision of the angular acceleration is studied. Comparisons of the output differences between the proposed configuration and the previous planar triangle configuration under the same installation errors are conducted by simulation. The simulation results show that installation errors have a relatively small impact on the calculation accuracy of the proposed configuration. To further verify the high calculation precision of the proposed configuration, experiments are carried out for both the proposed configuration and the planar triangle configuration. On the basis of the results of simulations and experiments, it can be concluded that the proposed configuration has higher angular acceleration calculation precision and can be applied to different platforms. PMID:26287203

Error and Uncertainty Quantification in the Numerical Simulation of Complex Fluid Flows

NASA Technical Reports Server (NTRS)

Barth, Timothy J.

2010-01-01

The failure of numerical simulation to predict physical reality is often a direct consequence of the compounding effects of numerical error arising from finite-dimensional approximation and physical model uncertainty resulting from inexact knowledge and/or statistical representation. In this topical lecture, we briefly review systematic theories for quantifying numerical errors and restricted forms of model uncertainty occurring in simulations of fluid flow. A goal of this lecture is to elucidate both positive and negative aspects of applying these theories to practical fluid flow problems. Finite-element and finite-volume calculations of subsonic and hypersonic fluid flow are presented to contrast the differing roles of numerical error and model uncertainty. for these problems.

A continuous optimization approach for inferring parameters in mathematical models of regulatory networks.

PubMed

Deng, Zhimin; Tian, Tianhai

2014-07-29

The advances of systems biology have raised a large number of sophisticated mathematical models for describing the dynamic property of complex biological systems. One of the major steps in developing mathematical models is to estimate unknown parameters of the model based on experimentally measured quantities. However, experimental conditions limit the amount of data that is available for mathematical modelling. The number of unknown parameters in mathematical models may be larger than the number of observation data. The imbalance between the number of experimental data and number of unknown parameters makes reverse-engineering problems particularly challenging. To address the issue of inadequate experimental data, we propose a continuous optimization approach for making reliable inference of model parameters. This approach first uses a spline interpolation to generate continuous functions of system dynamics as well as the first and second order derivatives of continuous functions. The expanded dataset is the basis to infer unknown model parameters using various continuous optimization criteria, including the error of simulation only, error of both simulation and the first derivative, or error of simulation as well as the first and second derivatives. We use three case studies to demonstrate the accuracy and reliability of the proposed new approach. Compared with the corresponding discrete criteria using experimental data at the measurement time points only, numerical results of the ERK kinase activation module show that the continuous absolute-error criteria using both function and high order derivatives generate estimates with better accuracy. This result is also supported by the second and third case studies for the G1/S transition network and the MAP kinase pathway, respectively. This suggests that the continuous absolute-error criteria lead to more accurate estimates than the corresponding discrete criteria. We also study the robustness property of these three models to examine the reliability of estimates. Simulation results show that the models with estimated parameters using continuous fitness functions have better robustness properties than those using the corresponding discrete fitness functions. The inference studies and robustness analysis suggest that the proposed continuous optimization criteria are effective and robust for estimating unknown parameters in mathematical models.

Measurement uncertainty and feasibility study of a flush airdata system for a hypersonic flight experiment

NASA Technical Reports Server (NTRS)

Whitmore, Stephen A.; Moes, Timothy R.

1994-01-01

Presented is a feasibility and error analysis for a hypersonic flush airdata system on a hypersonic flight experiment (HYFLITE). HYFLITE heating loads make intrusive airdata measurement impractical. Although this analysis is specifically for the HYFLITE vehicle and trajectory, the problems analyzed are generally applicable to hypersonic vehicles. A layout of the flush-port matrix is shown. Surface pressures are related airdata parameters using a simple aerodynamic model. The model is linearized using small perturbations and inverted using nonlinear least-squares. Effects of various error sources on the overall uncertainty are evaluated using an error simulation. Error sources modeled include boundarylayer/viscous interactions, pneumatic lag, thermal transpiration in the sensor pressure tubing, misalignment in the matrix layout, thermal warping of the vehicle nose, sampling resolution, and transducer error. Using simulated pressure data for input to the estimation algorithm, effects caused by various error sources are analyzed by comparing estimator outputs with the original trajectory. To obtain ensemble averages the simulation is run repeatedly and output statistics are compiled. Output errors resulting from the various error sources are presented as a function of Mach number. Final uncertainties with all modeled error sources included are presented as a function of Mach number.

Clinical validation of a new control-oriented model of insulin and glucose dynamics in subjects with type 1 diabetes.

PubMed

Fabietti, Pier Giorgio; Canonico, Valentina; Orsini-Federici, Marco; Sarti, Eugenio; Massi-Benedetti, Massimo

2007-08-01

The development of an artificial pancreas requires an accurate representation of diabetes pathophysiology to create effective and safe control systems for automatic insulin infusion regulation. The aim of the present study is the assessment of a previously developed mathematical model of insulin and glucose metabolism in type 1 diabetes and the evaluation of its effectiveness for the development and testing of control algorithms. Based on the already existing "minimal model" a new mathematical model was developed composed of glucose and insulin submodels. The glucose model includes the representation of peripheral uptake, hepatic uptake and release, and renal clearance. The insulin model describes the kinetics of exogenous insulin injected either subcutaneously or intravenously. The estimation of insulin sensitivity allows the model to personalize parameters to each subject. Data sets from two different clinical trials were used here for model validation through simulation studies. The first set had subcutaneous insulin injection, while the second set had intravenous insulin injection. The root mean square error between simulated and real blood glucose profiles (G(rms)) and the Clarke error grid analysis were used to evaluate the system efficacy. Results from our study demonstrated the model's capability in identifying individual characteristics even under different experimental conditions. This was reflected by an effective simulation as indicated by G(rms), and clinical acceptability by the Clarke error grid analysis, in both clinical data series. Simulation results confirmed the capacity of the model to faithfully represent the glucose-insulin relationship in type 1 diabetes in different circumstances.

A Global Carbon Assimilation System using a modified EnKF assimilation method

NASA Astrophysics Data System (ADS)

Zhang, S.; Zheng, X.; Chen, Z.; Dan, B.; Chen, J. M.; Yi, X.; Wang, L.; Wu, G.

2014-10-01

A Global Carbon Assimilation System based on Ensemble Kalman filter (GCAS-EK) is developed for assimilating atmospheric CO2 abundance data into an ecosystem model to simultaneously estimate the surface carbon fluxes and atmospheric CO2 distribution. This assimilation approach is based on the ensemble Kalman filter (EnKF), but with several new developments, including using analysis states to iteratively estimate ensemble forecast errors, and a maximum likelihood estimation of the inflation factors of the forecast and observation errors. The proposed assimilation approach is tested in observing system simulation experiments and then used to estimate the terrestrial ecosystem carbon fluxes and atmospheric CO2 distributions from 2002 to 2008. The results showed that this assimilation approach can effectively reduce the biases and uncertainties of the carbon fluxes simulated by the ecosystem model.

Mission Simulation of Space Lidar Measurements for Seasonal and Regional CO2 Variations

NASA Technical Reports Server (NTRS)

Kawa, Stephan; Collatz, G. J.; Mao, J.; Abshire, J. B.; Sun, X.; Weaver, C. J.

2010-01-01

Results of mission simulation studies are presented for a laser-based atmospheric [82 sounder. The simulations are based on real-time carbon cycle process modeling and data analysis. The mission concept corresponds to the Active Sensing of [82 over Nights, Days, and Seasons (ASCENDS) recommended by the US National Academy of Sciences Decadal Survey of Earth Science and Applications from Space. One prerequisite for meaningful quantitative sensor evaluation is realistic CO2 process modeling across a wide range of scales, i.e., does the model have representative spatial and temporal gradients? Examples of model comparison with data will be shown. Another requirement is a relatively complete description of the atmospheric and surface state, which we have obtained from meteorological data assimilation and satellite measurements from MODIS and [ALIPS0. We use radiative transfer model calculations, an instrument model with representative errors ' and a simple retrieval approach to complete the cycle from "nature" run to "pseudo-data" CO2, Several mission and instrument configuration options are examined/ and the sensitivity to key design variables is shown. We use the simulation framework to demonstrate that within reasonable technological assumptions for the system performance, relatively high measurement precision can be obtained, but errors depend strongly on environmental conditions as well as instrument specifications. Examples are also shown of how the resulting pseudo - measurements might be used to address key carbon cycle science questions.

Investigation of Climate Change Impact on Water Resources for an Alpine Basin in Northern Italy: Implications for Evapotranspiration Modeling Complexity

PubMed Central

Ravazzani, Giovanni; Ghilardi, Matteo; Mendlik, Thomas; Gobiet, Andreas; Corbari, Chiara; Mancini, Marco

2014-01-01

Assessing the future effects of climate change on water availability requires an understanding of how precipitation and evapotranspiration rates will respond to changes in atmospheric forcing. Use of simplified hydrological models is required beacause of lack of meteorological forcings with the high space and time resolutions required to model hydrological processes in mountains river basins, and the necessity of reducing the computational costs. The main objective of this study was to quantify the differences between a simplified hydrological model, which uses only precipitation and temperature to compute the hydrological balance when simulating the impact of climate change, and an enhanced version of the model, which solves the energy balance to compute the actual evapotranspiration. For the meteorological forcing of future scenario, at-site bias-corrected time series based on two regional climate models were used. A quantile-based error-correction approach was used to downscale the regional climate model simulations to a point scale and to reduce its error characteristics. The study shows that a simple temperature-based approach for computing the evapotranspiration is sufficiently accurate for performing hydrological impact investigations of climate change for the Alpine river basin which was studied. PMID:25285917

Investigation of climate change impact on water resources for an Alpine basin in northern Italy: implications for evapotranspiration modeling complexity.

PubMed

Ravazzani, Giovanni; Ghilardi, Matteo; Mendlik, Thomas; Gobiet, Andreas; Corbari, Chiara; Mancini, Marco

2014-01-01

Assessing the future effects of climate change on water availability requires an understanding of how precipitation and evapotranspiration rates will respond to changes in atmospheric forcing. Use of simplified hydrological models is required because of lack of meteorological forcings with the high space and time resolutions required to model hydrological processes in mountains river basins, and the necessity of reducing the computational costs. The main objective of this study was to quantify the differences between a simplified hydrological model, which uses only precipitation and temperature to compute the hydrological balance when simulating the impact of climate change, and an enhanced version of the model, which solves the energy balance to compute the actual evapotranspiration. For the meteorological forcing of future scenario, at-site bias-corrected time series based on two regional climate models were used. A quantile-based error-correction approach was used to downscale the regional climate model simulations to a point scale and to reduce its error characteristics. The study shows that a simple temperature-based approach for computing the evapotranspiration is sufficiently accurate for performing hydrological impact investigations of climate change for the Alpine river basin which was studied.

Accurate lithography simulation model based on convolutional neural networks

NASA Astrophysics Data System (ADS)

Watanabe, Yuki; Kimura, Taiki; Matsunawa, Tetsuaki; Nojima, Shigeki

2017-07-01

Lithography simulation is an essential technique for today's semiconductor manufacturing process. In order to calculate an entire chip in realistic time, compact resist model is commonly used. The model is established for faster calculation. To have accurate compact resist model, it is necessary to fix a complicated non-linear model function. However, it is difficult to decide an appropriate function manually because there are many options. This paper proposes a new compact resist model using CNN (Convolutional Neural Networks) which is one of deep learning techniques. CNN model makes it possible to determine an appropriate model function and achieve accurate simulation. Experimental results show CNN model can reduce CD prediction errors by 70% compared with the conventional model.

Unscented predictive variable structure filter for satellite attitude estimation with model errors when using low precision sensors

NASA Astrophysics Data System (ADS)

Cao, Lu; Li, Hengnian

2016-10-01

For the satellite attitude estimation problem, the serious model errors always exist and hider the estimation performance of the Attitude Determination and Control System (ACDS), especially for a small satellite with low precision sensors. To deal with this problem, a new algorithm for the attitude estimation, referred to as the unscented predictive variable structure filter (UPVSF) is presented. This strategy is proposed based on the variable structure control concept and unscented transform (UT) sampling method. It can be implemented in real time with an ability to estimate the model errors on-line, in order to improve the state estimation precision. In addition, the model errors in this filter are not restricted only to the Gaussian noises; therefore, it has the advantages to deal with the various kinds of model errors or noises. It is anticipated that the UT sampling strategy can further enhance the robustness and accuracy of the novel UPVSF. Numerical simulations show that the proposed UPVSF is more effective and robustness in dealing with the model errors and low precision sensors compared with the traditional unscented Kalman filter (UKF).

Recommendations for choosing an analysis method that controls Type I error for unbalanced cluster sample designs with Gaussian outcomes.

PubMed

Johnson, Jacqueline L; Kreidler, Sarah M; Catellier, Diane J; Murray, David M; Muller, Keith E; Glueck, Deborah H

2015-11-30

We used theoretical and simulation-based approaches to study Type I error rates for one-stage and two-stage analytic methods for cluster-randomized designs. The one-stage approach uses the observed data as outcomes and accounts for within-cluster correlation using a general linear mixed model. The two-stage model uses the cluster specific means as the outcomes in a general linear univariate model. We demonstrate analytically that both one-stage and two-stage models achieve exact Type I error rates when cluster sizes are equal. With unbalanced data, an exact size α test does not exist, and Type I error inflation may occur. Via simulation, we compare the Type I error rates for four one-stage and six two-stage hypothesis testing approaches for unbalanced data. With unbalanced data, the two-stage model, weighted by the inverse of the estimated theoretical variance of the cluster means, and with variance constrained to be positive, provided the best Type I error control for studies having at least six clusters per arm. The one-stage model with Kenward-Roger degrees of freedom and unconstrained variance performed well for studies having at least 14 clusters per arm. The popular analytic method of using a one-stage model with denominator degrees of freedom appropriate for balanced data performed poorly for small sample sizes and low intracluster correlation. Because small sample sizes and low intracluster correlation are common features of cluster-randomized trials, the Kenward-Roger method is the preferred one-stage approach. Copyright © 2015 John Wiley & Sons, Ltd.

Basic Diagnosis and Prediction of Persistent Contrail Occurrence using High-resolution Numerical Weather Analyses/Forecasts and Logistic Regression. Part I: Effects of Random Error

NASA Technical Reports Server (NTRS)

Duda, David P.; Minnis, Patrick

2009-01-01

Straightforward application of the Schmidt-Appleman contrail formation criteria to diagnose persistent contrail occurrence from numerical weather prediction data is hindered by significant bias errors in the upper tropospheric humidity. Logistic models of contrail occurrence have been proposed to overcome this problem, but basic questions remain about how random measurement error may affect their accuracy. A set of 5000 synthetic contrail observations is created to study the effects of random error in these probabilistic models. The simulated observations are based on distributions of temperature, humidity, and vertical velocity derived from Advanced Regional Prediction System (ARPS) weather analyses. The logistic models created from the simulated observations were evaluated using two common statistical measures of model accuracy, the percent correct (PC) and the Hanssen-Kuipers discriminant (HKD). To convert the probabilistic results of the logistic models into a dichotomous yes/no choice suitable for the statistical measures, two critical probability thresholds are considered. The HKD scores are higher when the climatological frequency of contrail occurrence is used as the critical threshold, while the PC scores are higher when the critical probability threshold is 0.5. For both thresholds, typical random errors in temperature, relative humidity, and vertical velocity are found to be small enough to allow for accurate logistic models of contrail occurrence. The accuracy of the models developed from synthetic data is over 85 percent for both the prediction of contrail occurrence and non-occurrence, although in practice, larger errors would be anticipated.

Feedback loops and temporal misalignment in component-based hydrologic modeling

NASA Astrophysics Data System (ADS)

Elag, Mostafa M.; Goodall, Jonathan L.; Castronova, Anthony M.

2011-12-01

In component-based modeling, a complex system is represented as a series of loosely integrated components with defined interfaces and data exchanges that allow the components to be coupled together through shared boundary conditions. Although the component-based paradigm is commonly used in software engineering, it has only recently been applied for modeling hydrologic and earth systems. As a result, research is needed to test and verify the applicability of the approach for modeling hydrologic systems. The objective of this work was therefore to investigate two aspects of using component-based software architecture for hydrologic modeling: (1) simulation of feedback loops between components that share a boundary condition and (2) data transfers between temporally misaligned model components. We investigated these topics using a simple case study where diffusion of mass is modeled across a water-sediment interface. We simulated the multimedia system using two model components, one for the water and one for the sediment, coupled using the Open Modeling Interface (OpenMI) standard. The results were compared with a more conventional numerical approach for solving the system where the domain is represented by a single multidimensional array. Results showed that the component-based approach was able to produce the same results obtained with the more conventional numerical approach. When the two components were temporally misaligned, we explored the use of different interpolation schemes to minimize mass balance error within the coupled system. The outcome of this work provides evidence that component-based modeling can be used to simulate complicated feedback loops between systems and guidance as to how different interpolation schemes minimize mass balance error introduced when components are temporally misaligned.

Error analysis and new dual-cosine window for estimating the sensor frequency response function from the step response data

NASA Astrophysics Data System (ADS)

Yang, Shuang-Long; Liang, Li-Ping; Liu, Hou-De; Xu, Ke-Jun

2018-03-01

Aiming at reducing the estimation error of the sensor frequency response function (FRF) estimated by the commonly used window-based spectral estimation method, the error models of interpolation and transient errors are derived in the form of non-parameter models. Accordingly, window effects on the errors are analyzed and reveal that the commonly used hanning window leads to smaller interpolation error which can also be significantly eliminated by the cubic spline interpolation method when estimating the FRF from the step response data, and window with smaller front-end value can restrain more transient error. Thus, a new dual-cosine window with its non-zero discrete Fourier transform bins at -3, -1, 0, 1, and 3 is constructed for FRF estimation. Compared with the hanning window, the new dual-cosine window has the equivalent interpolation error suppression capability and better transient error suppression capability when estimating the FRF from the step response; specifically, it reduces the asymptotic property of the transient error from O(N-2) of the hanning window method to O(N-4) while only increases the uncertainty slightly (about 0.4 dB). Then, one direction of a wind tunnel strain gauge balance which is a high order, small damping, and non-minimum phase system is employed as the example for verifying the new dual-cosine window-based spectral estimation method. The model simulation result shows that the new dual-cosine window method is better than the hanning window method for FRF estimation, and compared with the Gans method and LPM method, it has the advantages of simple computation, less time consumption, and short data requirement; the actual data calculation result of the balance FRF is consistent to the simulation result. Thus, the new dual-cosine window is effective and practical for FRF estimation.

Comparison of SWAT Hydrological Model Results from TRMM 3B42, NEXRAD Stage III, and Oklahoma Mesonet Data

NASA Astrophysics Data System (ADS)

Tobin, K. J.; Bennett, M. E.

2008-05-01

The Cimarron River Basin (3110 sq km) between Dodge and Guthrie, Oklahoma is located in northern Oklahoma and was used as a test bed to compare the hydrological model performance associated with different methods of precipitation quantification. The Soil and Water Assessment Tool (SWAT) was selected for this project, which is a comprehensive model that, besides quantifying watershed hydrology, can simulate water quality as well as nutrient and sediment loading within stream reaches. An advantage of this location is the extensive monitoring of MET parameters (precipitation, temperature, relative humidity, wind speed, solar radiation) afforded by the Oklahoma Mesonet, which has been documented to improve the performance of SWAT. The utility of TRMM 3B42 and NEXRAD Stage III data in supporting the hydrologic modeling of Cimarron River Basin is demonstrated. Minor adjustments to selected model parameters were made to make parameter values more realistic based on results from previous studies and information and to more realistically simulate base flow. Significantly, no ad hoc adjustments to major parameters such as Curve Number or Available Soil Water were made and robust simulations were obtained. TRMM and NEXRAD data are aggregated into an average daily estimate of precipitation for each TRMM grid cell (0.25 degree X 0.25 degree). Preliminary simulation of stream flow (year 2004 to 2006) in the Cimarron River Basin yields acceptable monthly results with very little adjustment of model parameters using TRMM 3B42 precipitation data (mass balance error = 3 percent; Monthly Nash-Sutcliffe efficiency coefficients (NS) = 0.77). However, both Oklahoma Mesonet rain gauge (mass balance error = 13 percent; Monthly NS = 0.91; Daily NS = 0.64) and NEXRAD Stage III data (mass balance error = -5 percent; Monthly NS = 0.95; Daily NS = 0.69) produces superior simulations even at a sub-monthly time scale; daily results are time averaged over a three day period. Note that all types of precipitation data perform better than a synthetic precipitation dataset generated using a weather simulator (mass balance error = 12 percent; Monthly NS = 0.40). Our study again documents that merged precipitation satellite products, such as TRMM 3B42, can support semi-distributed hydrologic modeling at the watershed scale. However, apparently additional work is required to improve TRMM precipitation retrievals over land to generate a product that yields more robust hydrological simulations especially at finer time scales. Additionally, ongoing work in this basin will compare TRMM results with stream flow model results generated using CMORPH precipitation estimates. Finally, in the future we plan to use simulated, semi-distributed soil moisture values determined by SWAT for comparison with gridded soil moisture estimates from TRMM-TMI that should provide further validation of our modeling efforts.

Bi-Objective Optimal Control Modification Adaptive Control for Systems with Input Uncertainty

NASA Technical Reports Server (NTRS)

Nguyen, Nhan T.

2012-01-01

This paper presents a new model-reference adaptive control method based on a bi-objective optimal control formulation for systems with input uncertainty. A parallel predictor model is constructed to relate the predictor error to the estimation error of the control effectiveness matrix. In this work, we develop an optimal control modification adaptive control approach that seeks to minimize a bi-objective linear quadratic cost function of both the tracking error norm and predictor error norm simultaneously. The resulting adaptive laws for the parametric uncertainty and control effectiveness uncertainty are dependent on both the tracking error and predictor error, while the adaptive laws for the feedback gain and command feedforward gain are only dependent on the tracking error. The optimal control modification term provides robustness to the adaptive laws naturally from the optimal control framework. Simulations demonstrate the effectiveness of the proposed adaptive control approach.

BEM-based simulation of lung respiratory deformation for CT-guided biopsy.

PubMed

Chen, Dong; Chen, Weisheng; Huang, Lipeng; Feng, Xuegang; Peters, Terry; Gu, Lixu

2017-09-01

Accurate and real-time prediction of the lung and lung tumor deformation during respiration are important considerations when performing a peripheral biopsy procedure. However, most existing work focused on offline whole lung simulation using 4D image data, which is not applicable in real-time image-guided biopsy with limited image resources. In this paper, we propose a patient-specific biomechanical model based on the boundary element method (BEM) computed from CT images to estimate the respiration motion of local target lesion region, vessel tree and lung surface for the real-time biopsy guidance. This approach applies pre-computation of various BEM parameters to facilitate the requirement for real-time lung motion simulation. The resulting boundary condition at end inspiratory phase is obtained using a nonparametric discrete registration with convex optimization, and the simulation of the internal tissue is achieved by applying a tetrahedron-based interpolation method depend on expert-determined feature points on the vessel tree model. A reference needle is tracked to update the simulated lung motion during biopsy guidance. We evaluate the model by applying it for respiratory motion estimations of ten patients. The average symmetric surface distance (ASSD) and the mean target registration error (TRE) are employed to evaluate the proposed model. Results reveal that it is possible to predict the lung motion with ASSD of [Formula: see text] mm and a mean TRE of [Formula: see text] mm at largest over the entire respiratory cycle. In the CT-/electromagnetic-guided biopsy experiment, the whole process was assisted by our BEM model and final puncture errors in two studies were 3.1 and 2.0 mm, respectively. The experiment results reveal that both the accuracy of simulation and real-time performance meet the demands of clinical biopsy guidance.

Multimodel ensembles of wheat growth: many models are better than one.

PubMed

Martre, Pierre; Wallach, Daniel; Asseng, Senthold; Ewert, Frank; Jones, James W; Rötter, Reimund P; Boote, Kenneth J; Ruane, Alex C; Thorburn, Peter J; Cammarano, Davide; Hatfield, Jerry L; Rosenzweig, Cynthia; Aggarwal, Pramod K; Angulo, Carlos; Basso, Bruno; Bertuzzi, Patrick; Biernath, Christian; Brisson, Nadine; Challinor, Andrew J; Doltra, Jordi; Gayler, Sebastian; Goldberg, Richie; Grant, Robert F; Heng, Lee; Hooker, Josh; Hunt, Leslie A; Ingwersen, Joachim; Izaurralde, Roberto C; Kersebaum, Kurt Christian; Müller, Christoph; Kumar, Soora Naresh; Nendel, Claas; O'leary, Garry; Olesen, Jørgen E; Osborne, Tom M; Palosuo, Taru; Priesack, Eckart; Ripoche, Dominique; Semenov, Mikhail A; Shcherbak, Iurii; Steduto, Pasquale; Stöckle, Claudio O; Stratonovitch, Pierre; Streck, Thilo; Supit, Iwan; Tao, Fulu; Travasso, Maria; Waha, Katharina; White, Jeffrey W; Wolf, Joost

2015-02-01

Crop models of crop growth are increasingly used to quantify the impact of global changes due to climate or crop management. Therefore, accuracy of simulation results is a major concern. Studies with ensembles of crop models can give valuable information about model accuracy and uncertainty, but such studies are difficult to organize and have only recently begun. We report on the largest ensemble study to date, of 27 wheat models tested in four contrasting locations for their accuracy in simulating multiple crop growth and yield variables. The relative error averaged over models was 24-38% for the different end-of-season variables including grain yield (GY) and grain protein concentration (GPC). There was little relation between error of a model for GY or GPC and error for in-season variables. Thus, most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamics. Ensemble simulations, taking either the mean (e-mean) or median (e-median) of simulated values, gave better estimates than any individual model when all variables were considered. Compared to individual models, e-median ranked first in simulating measured GY and third in GPC. The error of e-mean and e-median declined with an increasing number of ensemble members, with little decrease beyond 10 models. We conclude that multimodel ensembles can be used to create new estimators with improved accuracy and consistency in simulating growth dynamics. We argue that these results are applicable to other crop species, and hypothesize that they apply more generally to ecological system models. © 2014 John Wiley & Sons Ltd.

Multimodel Ensembles of Wheat Growth: More Models are Better than One

NASA Technical Reports Server (NTRS)

Martre, Pierre; Wallach, Daniel; Asseng, Senthold; Ewert, Frank; Jones, James W.; Rotter, Reimund P.; Boote, Kenneth J.; Ruane, Alex C.; Thorburn, Peter J.; Cammarano, Davide;

Multimodel Ensembles of Wheat Growth: Many Models are Better than One

NASA Technical Reports Server (NTRS)

Martre, Pierre; Wallach, Daniel; Asseng, Senthold; Ewert, Frank; Jones, James W.; Rotter, Reimund P.; Boote, Kenneth J.; Ruane, Alexander C.; Thorburn, Peter J.; Cammarano, Davide;

Updating finite element dynamic models using an element-by-element sensitivity methodology

NASA Technical Reports Server (NTRS)

Farhat, Charbel; Hemez, Francois M.

1993-01-01

A sensitivity-based methodology for improving the finite element model of a given structure using test modal data and a few sensors is presented. The proposed method searches for both the location and sources of the mass and stiffness errors and does not interfere with the theory behind the finite element model while correcting these errors. The updating algorithm is derived from the unconstrained minimization of the squared L sub 2 norms of the modal dynamic residuals via an iterative two-step staggered procedure. At each iteration, the measured mode shapes are first expanded assuming that the model is error free, then the model parameters are corrected assuming that the expanded mode shapes are exact. The numerical algorithm is implemented in an element-by-element fashion and is capable of 'zooming' on the detected error locations. Several simulation examples which demonstate the potential of the proposed methodology are discussed.

Random Weighting, Strong Tracking, and Unscented Kalman Filter for Soft Tissue Characterization.

PubMed

Shin, Jaehyun; Zhong, Yongmin; Oetomo, Denny; Gu, Chengfan

2018-05-21

This paper presents a new nonlinear filtering method based on the Hunt-Crossley model for online nonlinear soft tissue characterization. This method overcomes the problem of performance degradation in the unscented Kalman filter due to contact model error. It adopts the concept of Mahalanobis distance to identify contact model error, and further incorporates a scaling factor in predicted state covariance to compensate identified model error. This scaling factor is determined according to the principle of innovation orthogonality to avoid the cumbersome computation of Jacobian matrix, where the random weighting concept is adopted to improve the estimation accuracy of innovation covariance. A master-slave robotic indentation system is developed to validate the performance of the proposed method. Simulation and experimental results as well as comparison analyses demonstrate that the efficacy of the proposed method for online characterization of soft tissue parameters in the presence of contact model error.

Performance modeling of the effects of aperture phase error, turbulence, and thermal blooming on tiled subaperture systems

NASA Astrophysics Data System (ADS)

Leakeas, Charles L.; Capehart, Shay R.; Bartell, Richard J.; Cusumano, Salvatore J.; Whiteley, Matthew R.

2011-06-01

Laser weapon systems comprised of tiled subapertures are rapidly emerging in importance in the directed energy community. Performance models of these laser weapon systems have been developed from numerical simulations of a high fidelity wave-optics code called WaveTrain which is developed by MZA Associates. System characteristics such as mutual coherence, differential jitter, and beam quality rms wavefront error are defined for a focused beam on the target. Engagement scenarios are defined for various platform and target altitudes, speeds, headings, and slant ranges along with the natural wind speed and heading. Inputs to the performance model include platform and target height and velocities, Fried coherence length, Rytov number, isoplanatic angle, thermal blooming distortion number, Greenwood and Tyler frequencies, and atmospheric transmission. The performance model fit is based on power-in-the-bucket (PIB) values against the PIB from the simulation results for the vacuum diffraction-limited spot size as the bucket. The goal is to develop robust performance models for aperture phase error, turbulence, and thermal blooming effects in tiled subaperture systems.

Wavelet-based multiscale performance analysis: An approach to assess and improve hydrological models

NASA Astrophysics Data System (ADS)

Rathinasamy, Maheswaran; Khosa, Rakesh; Adamowski, Jan; ch, Sudheer; Partheepan, G.; Anand, Jatin; Narsimlu, Boini

2014-12-01

The temporal dynamics of hydrological processes are spread across different time scales and, as such, the performance of hydrological models cannot be estimated reliably from global performance measures that assign a single number to the fit of a simulated time series to an observed reference series. Accordingly, it is important to analyze model performance at different time scales. Wavelets have been used extensively in the area of hydrological modeling for multiscale analysis, and have been shown to be very reliable and useful in understanding dynamics across time scales and as these evolve in time. In this paper, a wavelet-based multiscale performance measure for hydrological models is proposed and tested (i.e., Multiscale Nash-Sutcliffe Criteria and Multiscale Normalized Root Mean Square Error). The main advantage of this method is that it provides a quantitative measure of model performance across different time scales. In the proposed approach, model and observed time series are decomposed using the Discrete Wavelet Transform (known as the à trous wavelet transform), and performance measures of the model are obtained at each time scale. The applicability of the proposed method was explored using various case studies-both real as well as synthetic. The synthetic case studies included various kinds of errors (e.g., timing error, under and over prediction of high and low flows) in outputs from a hydrologic model. The real time case studies investigated in this study included simulation results of both the process-based Soil Water Assessment Tool (SWAT) model, as well as statistical models, namely the Coupled Wavelet-Volterra (WVC), Artificial Neural Network (ANN), and Auto Regressive Moving Average (ARMA) methods. For the SWAT model, data from Wainganga and Sind Basin (India) were used, while for the Wavelet Volterra, ANN and ARMA models, data from the Cauvery River Basin (India) and Fraser River (Canada) were used. The study also explored the effect of the choice of the wavelets in multiscale model evaluation. It was found that the proposed wavelet-based performance measures, namely the MNSC (Multiscale Nash-Sutcliffe Criteria) and MNRMSE (Multiscale Normalized Root Mean Square Error), are a more reliable measure than traditional performance measures such as the Nash-Sutcliffe Criteria (NSC), Root Mean Square Error (RMSE), and Normalized Root Mean Square Error (NRMSE). Further, the proposed methodology can be used to: i) compare different hydrological models (both physical and statistical models), and ii) help in model calibration.

Exact Hybrid Particle/Population Simulation of Rule-Based Models of Biochemical Systems

PubMed Central

Stover, Lori J.; Nair, Niketh S.; Faeder, James R.

2014-01-01

Detailed modeling and simulation of biochemical systems is complicated by the problem of combinatorial complexity, an explosion in the number of species and reactions due to myriad protein-protein interactions and post-translational modifications. Rule-based modeling overcomes this problem by representing molecules as structured objects and encoding their interactions as pattern-based rules. This greatly simplifies the process of model specification, avoiding the tedious and error prone task of manually enumerating all species and reactions that can potentially exist in a system. From a simulation perspective, rule-based models can be expanded algorithmically into fully-enumerated reaction networks and simulated using a variety of network-based simulation methods, such as ordinary differential equations or Gillespie's algorithm, provided that the network is not exceedingly large. Alternatively, rule-based models can be simulated directly using particle-based kinetic Monte Carlo methods. This “network-free” approach produces exact stochastic trajectories with a computational cost that is independent of network size. However, memory and run time costs increase with the number of particles, limiting the size of system that can be feasibly simulated. Here, we present a hybrid particle/population simulation method that combines the best attributes of both the network-based and network-free approaches. The method takes as input a rule-based model and a user-specified subset of species to treat as population variables rather than as particles. The model is then transformed by a process of “partial network expansion” into a dynamically equivalent form that can be simulated using a population-adapted network-free simulator. The transformation method has been implemented within the open-source rule-based modeling platform BioNetGen, and resulting hybrid models can be simulated using the particle-based simulator NFsim. Performance tests show that significant memory savings can be achieved using the new approach and a monetary cost analysis provides a practical measure of its utility. PMID:24699269

Exact hybrid particle/population simulation of rule-based models of biochemical systems.

PubMed

Hogg, Justin S; Harris, Leonard A; Stover, Lori J; Nair, Niketh S; Faeder, James R

2014-04-01

Detailed modeling and simulation of biochemical systems is complicated by the problem of combinatorial complexity, an explosion in the number of species and reactions due to myriad protein-protein interactions and post-translational modifications. Rule-based modeling overcomes this problem by representing molecules as structured objects and encoding their interactions as pattern-based rules. This greatly simplifies the process of model specification, avoiding the tedious and error prone task of manually enumerating all species and reactions that can potentially exist in a system. From a simulation perspective, rule-based models can be expanded algorithmically into fully-enumerated reaction networks and simulated using a variety of network-based simulation methods, such as ordinary differential equations or Gillespie's algorithm, provided that the network is not exceedingly large. Alternatively, rule-based models can be simulated directly using particle-based kinetic Monte Carlo methods. This "network-free" approach produces exact stochastic trajectories with a computational cost that is independent of network size. However, memory and run time costs increase with the number of particles, limiting the size of system that can be feasibly simulated. Here, we present a hybrid particle/population simulation method that combines the best attributes of both the network-based and network-free approaches. The method takes as input a rule-based model and a user-specified subset of species to treat as population variables rather than as particles. The model is then transformed by a process of "partial network expansion" into a dynamically equivalent form that can be simulated using a population-adapted network-free simulator. The transformation method has been implemented within the open-source rule-based modeling platform BioNetGen, and resulting hybrid models can be simulated using the particle-based simulator NFsim. Performance tests show that significant memory savings can be achieved using the new approach and a monetary cost analysis provides a practical measure of its utility.

Automatic training and reliability estimation for 3D ASM applied to cardiac MRI segmentation

NASA Astrophysics Data System (ADS)

Tobon-Gomez, Catalina; Sukno, Federico M.; Butakoff, Constantine; Huguet, Marina; Frangi, Alejandro F.

2012-07-01

Training active shape models requires collecting manual ground-truth meshes in a large image database. While shape information can be reused across multiple imaging modalities, intensity information needs to be imaging modality and protocol specific. In this context, this study has two main purposes: (1) to test the potential of using intensity models learned from MRI simulated datasets and (2) to test the potential of including a measure of reliability during the matching process to increase robustness. We used a population of 400 virtual subjects (XCAT phantom), and two clinical populations of 40 and 45 subjects. Virtual subjects were used to generate simulated datasets (MRISIM simulator). Intensity models were trained both on simulated and real datasets. The trained models were used to segment the left ventricle (LV) and right ventricle (RV) from real datasets. Segmentations were also obtained with and without reliability information. Performance was evaluated with point-to-surface and volume errors. Simulated intensity models obtained average accuracy comparable to inter-observer variability for LV segmentation. The inclusion of reliability information reduced volume errors in hypertrophic patients (EF errors from 17 ± 57% to 10 ± 18% LV MASS errors from -27 ± 22 g to -14 ± 25 g), and in heart failure patients (EF errors from -8 ± 42% to -5 ± 14%). The RV model of the simulated images needs further improvement to better resemble image intensities around the myocardial edges. Both for real and simulated models, reliability information increased segmentation robustness without penalizing accuracy.

Automatic training and reliability estimation for 3D ASM applied to cardiac MRI segmentation.

PubMed

Tobon-Gomez, Catalina; Sukno, Federico M; Butakoff, Constantine; Huguet, Marina; Frangi, Alejandro F

2012-07-07

Training active shape models requires collecting manual ground-truth meshes in a large image database. While shape information can be reused across multiple imaging modalities, intensity information needs to be imaging modality and protocol specific. In this context, this study has two main purposes: (1) to test the potential of using intensity models learned from MRI simulated datasets and (2) to test the potential of including a measure of reliability during the matching process to increase robustness. We used a population of 400 virtual subjects (XCAT phantom), and two clinical populations of 40 and 45 subjects. Virtual subjects were used to generate simulated datasets (MRISIM simulator). Intensity models were trained both on simulated and real datasets. The trained models were used to segment the left ventricle (LV) and right ventricle (RV) from real datasets. Segmentations were also obtained with and without reliability information. Performance was evaluated with point-to-surface and volume errors. Simulated intensity models obtained average accuracy comparable to inter-observer variability for LV segmentation. The inclusion of reliability information reduced volume errors in hypertrophic patients (EF errors from 17 ± 57% to 10 ± 18%; LV MASS errors from -27 ± 22 g to -14 ± 25 g), and in heart failure patients (EF errors from -8 ± 42% to -5 ± 14%). The RV model of the simulated images needs further improvement to better resemble image intensities around the myocardial edges. Both for real and simulated models, reliability information increased segmentation robustness without penalizing accuracy.

Fine-resolution imaging of solar features using Phase-Diverse Speckle

NASA Technical Reports Server (NTRS)

Paxman, Richard G.

1995-01-01

Phase-diverse speckle (PDS) is a novel imaging technique intended to overcome the degrading effects of atmospheric turbulence on fine-resolution imaging. As its name suggests, PDS is a blend of phase-diversity and speckle-imaging concepts. PDS reconstructions on solar data were validated by simulation, by demonstrating internal consistency of PDS estimates, and by comparing PDS reconstructions with those produced from well accepted speckle-imaging processing. Several sources of error in data collected with the Swedish Vacuum Solar Telescope (SVST) were simulated: CCD noise, quantization error, image misalignment, and defocus error, as well as atmospheric turbulence model error. The simulations demonstrate that fine-resolution information can be reliably recovered out to at least 70% of the diffraction limit without significant introduction of image artifacts. Additional confidence in the SVST restoration is obtained by comparing its spatial power spectrum with previously-published power spectra derived from both space-based images and earth-based images corrected with traditional speckle-imaging techniques; the shape of the spectrum is found to match well the previous measurements. In addition, the imagery is found to be consistent with, but slightly sharper than, imagery reconstructed with accepted speckle-imaging techniques.

Simulation of streamflow and water quality in the White Clay Creek subbasin of the Christina River Basin, Pennsylvania and Delaware, 1994-98

USGS Publications Warehouse

Senior, Lisa A.; Koerkle, Edward H.

2003-01-01

The Christina River Basin drains 565 square miles (mi2) in Pennsylvania, Maryland, and Delaware. Water from the basin is used for recreation, drinking water supply, and to support aquatic life. The Christina River Basin includes the major subbasins of Brandywine Creek, White Clay Creek, and Red Clay Creek. The White Clay Creek is the second largest of the subbasins and drains an area of 108 mi2. Water quality in some parts of the Christina River Basin is impaired and does not support designated uses of the streams. A multi-agency water-quality management strategy included a modeling component to evaluate the effects of point and nonpoint-source contributions of nutrients and suspended sediment on stream water quality. To assist in non point-source evaluation, four independent models, one for each of the three major subbasins and for the Christina River, were developed and calibrated using the model code Hydrological Simulation Program—Fortran (HSPF). Water-quality data for model calibration were collected in each of the four main subbasins and in smaller subbasins predominantly covered by one land use following a nonpoint-source monitoring plan. Under this plan, stormflow and base- flow samples were collected during 1998 at two sites in the White Clay Creek subbasin and at nine sites in the other subbasins.The HSPF model for the White Clay Creek Basin simulates streamflow, suspended sediment, and the nutrients, nitrogen and phosphorus. In addition, the model simulates water temperature, dissolved oxygen, biochemical oxygen demand, and plankton as secondary objectives needed to support the sediment and nutrient simulations. For the model, the basin was subdivided into 17 reaches draining areas that ranged from 1.37 to 13 mi2. Ten different pervious land uses and two impervious land uses were selected for simulation. Land-use areas were determined from 1995 land-use data. The predominant land uses in the White Clay Creek Basin are agricultural, forested, residential, and urban.The hydrologic component of the model was run at an hourly time step and primarily calibrated using streamflow data from two U.S. Geological Survey (USGS) streamflow-measurement stations for the period of October 1, 1994, through October 29, 1998. Additional calibration was done using data from two other USGS streamflow-measurement stations with periods of record shorter than the calibration period. Daily precipitation data from two National Oceanic and Atmospheric Administration (NOAA) gages and hourly precipitation and other meteorological data for one NOAA gage were used for model input. The difference between simulated and observed streamflow volume ranged from -0.9 to 1.8 percent for the 4-year period at the two calibration sites with 4-year records. Annual differences between observed and simulated streamflow generally were greater than the overall error. For example, at a site near the bottom of the basin (drainage area of 89.1 mi2), annual differences between observed and simulated streamflow ranged from -5.8 to 14.4 percent and the overall error for the 4-year period was -0.9 percent. Calibration errors for 36 storm periods at the two calibration sites for total volume, low-flowrecession rate, 50-percent lowest flows, 10-percent highest flows, and storm peaks were within the recommended criteria of 20 percent or less. Much of the error in simulating storm events on an hourly time step can be attributed to uncertainty in the hourly rainfall data.The water-quality component of the model was calibrated using data collected by the USGS and state agencies at three USGS streamflow-measurement stations with variable water-quality monitoring periods ending October 1998. Because of availability, monitoring data for suspended-solids concentrations were used as surrogates for suspended-sediment concentrations, although suspended solids may underestimate suspended sediment and affect apparent accuracy of the suspended-sediment simulation. Comparison of observed to simulated loads for up to five storms in 1998 at each of the two nonpoint-source monitoring sites in the White Clay Creek Basin indicate that simulation error is commonly as large as an order of magnitude for suspended sediment and nutrients. The simulation error tends to be smaller for dissolved nutrients than for particulate nutrients. Errors of 40 percent or less for monthly or annual values indicate a fair to good water-quality calibration according to recommended criteria, with much larger errors possible for individual events. The accuracy of the water-quality calibration under stormflow conditions is limited by the relatively small amount of water-quality data available for the White Clay Creek Basin.Users of the White Clay Creek HSPF model should be aware of model limitations and consider the following if the model is used for predictive purposes: streamflow and water quality for individual storm events may not be well simulated, but the model performance is reasonable when evaluated over longer periods of time; the observed flow-duration curve for the simulation period is similar to the long-term flow-duration curve at White Clay Creek near Newark, Del., indicating that the calibration period is representative of all but highest 0.1 percent and lowest 0.1 percent of flows at that site; relative errors in streamflow and water-quality simulations are greater for smaller drainage areas than for larger areas; and calibration for water-quality was based on sparse data.

A system dynamics approach to analyze laboratory test errors.

PubMed

Guo, Shijing; Roudsari, Abdul; Garcez, Artur d'Avila

2015-01-01

Although many researches have been carried out to analyze laboratory test errors during the last decade, it still lacks a systemic view of study, especially to trace errors during test process and evaluate potential interventions. This study implements system dynamics modeling into laboratory errors to trace the laboratory error flows and to simulate the system behaviors while changing internal variable values. The change of the variables may reflect a change in demand or a proposed intervention. A review of literature on laboratory test errors was given and provided as the main data source for the system dynamics model. Three "what if" scenarios were selected for testing the model. System behaviors were observed and compared under different scenarios over a period of time. The results suggest system dynamics modeling has potential effectiveness of helping to understand laboratory errors, observe model behaviours, and provide a risk-free simulation experiments for possible strategies.

Synchronizing movements with the metronome: nonlinear error correction and unstable periodic orbits.

PubMed

Engbert, Ralf; Krampe, Ralf Th; Kurths, Jürgen; Kliegl, Reinhold

2002-02-01

The control of human hand movements is investigated in a simple synchronization task. We propose and analyze a stochastic model based on nonlinear error correction; a mechanism which implies the existence of unstable periodic orbits. This prediction is tested in an experiment with human subjects. We find that our experimental data are in good agreement with numerical simulations of our theoretical model. These results suggest that feedback control of the human motor systems shows nonlinear behavior. Copyright 2001 Elsevier Science (USA).

SU-E-T-503: IMRT Optimization Using Monte Carlo Dose Engine: The Effect of Statistical Uncertainty.

PubMed

Tian, Z; Jia, X; Graves, Y; Uribe-Sanchez, A; Jiang, S

2012-06-01

With the development of ultra-fast GPU-based Monte Carlo (MC) dose engine, it becomes clinically realistic to compute the dose-deposition coefficients (DDC) for IMRT optimization using MC simulation. However, it is still time-consuming if we want to compute DDC with small statistical uncertainty. This work studies the effects of the statistical error in DDC matrix on IMRT optimization. The MC-computed DDC matrices are simulated here by adding statistical uncertainties at a desired level to the ones generated with a finite-size pencil beam algorithm. A statistical uncertainty model for MC dose calculation is employed. We adopt a penalty-based quadratic optimization model and gradient descent method to optimize fluence map and then recalculate the corresponding actual dose distribution using the noise-free DDC matrix. The impacts of DDC noise are assessed in terms of the deviation of the resulted dose distributions. We have also used a stochastic perturbation theory to theoretically estimate the statistical errors of dose distributions on a simplified optimization model. A head-and-neck case is used to investigate the perturbation to IMRT plan due to MC's statistical uncertainty. The relative errors of the final dose distributions of the optimized IMRT are found to be much smaller than those in the DDC matrix, which is consistent with our theoretical estimation. When history number is decreased from 108 to 106, the dose-volume-histograms are still very similar to the error-free DVHs while the error in DDC is about 3.8%. The results illustrate that the statistical errors in the DDC matrix have a relatively small effect on IMRT optimization in dose domain. This indicates we can use relatively small number of histories to obtain the DDC matrix with MC simulation within a reasonable amount of time, without considerably compromising the accuracy of the optimized treatment plan. This work is supported by Varian Medical Systems through a Master Research Agreement. © 2012 American Association of Physicists in Medicine.

Comparison of different tree sap flow up-scaling procedures using Monte-Carlo simulations

NASA Astrophysics Data System (ADS)

Tatarinov, Fyodor; Preisler, Yakir; Roahtyn, Shani; Yakir, Dan

2015-04-01

An important task in determining forest ecosystem water balance is the estimation of stand transpiration, allowing separating evapotranspiration into transpiration and soil evaporation. This can be based on up-scaling measurements of sap flow in representative trees (SF), which can be done by different mathematical algorithms. The aim of the present study was to evaluate the error associated with different up-scaling algorithms under different conditions. Other types of errors (such as, measurement error, within tree SF variability, choice of sample plot etc.) were not considered here. A set of simulation experiments using Monte-Carlo technique was carried out and three up-scaling procedures were tested. (1) Multiplying mean stand sap flux density based on unit sapwood cross-section area (SFD) by total sapwood area (Klein et al, 2014); (2) deriving of linear dependence of tree sap flow on tree DBH and calculating SFstand using predicted SF by DBH classes and stand DBH distribution (Cermak et al., 2004); (3) same as method 2 but using non-linear dependency. Simulations were performed under different SFD(DBH) slope (bs, positive, negative, zero); different DBH and SFD standard deviations (Δd and Δs, respectively) and DBH class size. It was assumed that all trees in a unit area are measured and the total SF of all trees in the experimental plot was taken as the reference SFstand value. Under negative bs all models tend to overestimate SFstand and the error increases exponentially with decreasing bs. Under bs >0 all models tend to underestimate SFstand, but the error is much smaller than for bs

Imperfect practice makes perfect: error management training improves transfer of learning.

PubMed

Dyre, Liv; Tabor, Ann; Ringsted, Charlotte; Tolsgaard, Martin G

2017-02-01

Traditionally, trainees are instructed to practise with as few errors as possible during simulation-based training. However, transfer of learning may improve if trainees are encouraged to commit errors. The aim of this study was to assess the effects of error management instructions compared with error avoidance instructions during simulation-based ultrasound training. Medical students (n = 60) with no prior ultrasound experience were randomised to error management training (EMT) (n = 32) or error avoidance training (EAT) (n = 28). The EMT group was instructed to deliberately make errors during training. The EAT group was instructed to follow the simulator instructions and to commit as few errors as possible. Training consisted of 3 hours of simulation-based ultrasound training focusing on fetal weight estimation. Simulation-based tests were administered before and after training. Transfer tests were performed on real patients 7-10 days after the completion of training. Primary outcomes were transfer test performance scores and diagnostic accuracy. Secondary outcomes included performance scores and diagnostic accuracy during the simulation-based pre- and post-tests. A total of 56 participants completed the study. On the transfer test, EMT group participants attained higher performance scores (mean score: 67.7%, 95% confidence interval [CI]: 62.4-72.9%) than EAT group members (mean score: 51.7%, 95% CI: 45.8-57.6%) (p < 0.001; Cohen's d = 1.1, 95% CI: 0.5-1.7). There was a moderate improvement in diagnostic accuracy in the EMT group compared with the EAT group (16.7%, 95% CI: 10.2-23.3% weight deviation versus 26.6%, 95% CI: 16.5-36.7% weight deviation [p = 0.082; Cohen's d = 0.46, 95% CI: -0.06 to 1.0]). No significant interaction effects between group and performance improvements between the pre- and post-tests were found in either performance scores (p = 0.25) or diagnostic accuracy (p = 0.09). The provision of error management instructions during simulation-based training improves the transfer of learning to the clinical setting compared with error avoidance instructions. Rather than teaching to avoid errors, the use of errors for learning should be explored further in medical education theory and practice. © 2016 John Wiley & Sons Ltd and The Association for the Study of Medical Education.

Quantitative evaluation of statistical errors in small-angle X-ray scattering measurements

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sedlak, Steffen M.; Bruetzel, Linda K.; Lipfert, Jan

A new model is proposed for the measurement errors incurred in typical small-angle X-ray scattering (SAXS) experiments, which takes into account the setup geometry and physics of the measurement process. The model accurately captures the experimentally determined errors from a large range of synchrotron and in-house anode-based measurements. Its most general formulation gives for the variance of the buffer-subtracted SAXS intensity σ 2(q) = [I(q) + const.]/(kq), whereI(q) is the scattering intensity as a function of the momentum transferq;kand const. are fitting parameters that are characteristic of the experimental setup. The model gives a concrete procedure for calculating realistic measurementmore » errors for simulated SAXS profiles. In addition, the results provide guidelines for optimizing SAXS measurements, which are in line with established procedures for SAXS experiments, and enable a quantitative evaluation of measurement errors.« less

A Quantitative Dynamic Simulation of Bremia lactucae Airborne Conidia Concentration above a Lettuce Canopy.

PubMed

Fall, Mamadou Lamine; Van der Heyden, Hervé; Carisse, Odile

2016-01-01

Lettuce downy mildew, caused by the oomycete Bremia lactucae Regel, is a major threat to lettuce production worldwide. Lettuce downy mildew is a polycyclic disease driven by airborne spores. A weather-based dynamic simulation model for B. lactucae airborne spores was developed to simulate the aerobiological characteristics of the pathogen. The model was built using the STELLA platform by following the system dynamics methodology. The model was developed using published equations describing disease subprocesses (e.g., sporulation) and assembled knowledge of the interactions among pathogen, host, and weather. The model was evaluated with four years of independent data by comparing model simulations with observations of hourly and daily airborne spore concentrations. The results show an accurate simulation of the trend and shape of B. lactucae temporal dynamics of airborne spore concentration. The model simulated hourly and daily peaks in airborne spore concentrations. More than 95% of the simulation runs, the daily-simulated airborne conidia concentration was 0 when airborne conidia were not observed. Also, the relationship between the simulated and the observed airborne spores was linear. In more than 94% of the simulation runs, the proportion of the linear variation in the hourly-observed values explained by the variation in the hourly-simulated values was greater than 0.7 in all years except one. Most of the errors came from the deviation from the 1:1 line, and the proportion of errors due to the model bias was low. This model is the only dynamic model developed to mimic the dynamics of airborne inoculum and represents an initial step towards improved lettuce downy mildew understanding, forecasting and management.

A Quantitative Dynamic Simulation of Bremia lactucae Airborne Conidia Concentration above a Lettuce Canopy

PubMed Central

Fall, Mamadou Lamine; Van der Heyden, Hervé; Carisse, Odile

2016-01-01

Lettuce downy mildew, caused by the oomycete Bremia lactucae Regel, is a major threat to lettuce production worldwide. Lettuce downy mildew is a polycyclic disease driven by airborne spores. A weather-based dynamic simulation model for B. lactucae airborne spores was developed to simulate the aerobiological characteristics of the pathogen. The model was built using the STELLA platform by following the system dynamics methodology. The model was developed using published equations describing disease subprocesses (e.g., sporulation) and assembled knowledge of the interactions among pathogen, host, and weather. The model was evaluated with four years of independent data by comparing model simulations with observations of hourly and daily airborne spore concentrations. The results show an accurate simulation of the trend and shape of B. lactucae temporal dynamics of airborne spore concentration. The model simulated hourly and daily peaks in airborne spore concentrations. More than 95% of the simulation runs, the daily-simulated airborne conidia concentration was 0 when airborne conidia were not observed. Also, the relationship between the simulated and the observed airborne spores was linear. In more than 94% of the simulation runs, the proportion of the linear variation in the hourly-observed values explained by the variation in the hourly-simulated values was greater than 0.7 in all years except one. Most of the errors came from the deviation from the 1:1 line, and the proportion of errors due to the model bias was low. This model is the only dynamic model developed to mimic the dynamics of airborne inoculum and represents an initial step towards improved lettuce downy mildew understanding, forecasting and management. PMID:26953691

Precision of a CAD/CAM-engineered surgical template based on a facebow for orthognathic surgery: an experiment with a rapid prototyping maxillary model.

PubMed

Lee, Jae-Won; Lim, Se-Ho; Kim, Moon-Key; Kang, Sang-Hoon

2015-12-01

We examined the precision of a computer-aided design/computer-aided manufacturing-engineered, manufactured, facebow-based surgical guide template (facebow wafer) by comparing it with a bite splint-type orthognathic computer-aided design/computer-aided manufacturing-engineered surgical guide template (bite wafer). We used 24 rapid prototyping (RP) models of the craniofacial skeleton with maxillary deformities. Twelve RP models each were used for the facebow wafer group and the bite wafer group (experimental group). Experimental maxillary orthognathic surgery was performed on the RP models of both groups. Errors were evaluated through comparisons with surgical simulations. We measured the minimum distances from 3 planes of reference to determine the vertical, lateral, and anteroposterior errors at specific measurement points. The measured errors were compared between experimental groups using a t test. There were significant intergroup differences in the lateral error when we compared the absolute values of the 3-D linear distance, as well as vertical, lateral, and anteroposterior errors between experimental groups. The bite wafer method exhibited little lateral error overall and little error in the anterior tooth region. The facebow wafer method exhibited very little vertical error in the posterior molar region. The clinical precision of the facebow wafer method did not significantly exceed that of the bite wafer method. Copyright © 2015 Elsevier Inc. All rights reserved.

Effects of uncertainty and variability on population declines and IUCN Red List classifications.

PubMed

Rueda-Cediel, Pamela; Anderson, Kurt E; Regan, Tracey J; Regan, Helen M

2018-01-22

The International Union for Conservation of Nature (IUCN) Red List Categories and Criteria is a quantitative framework for classifying species according to extinction risk. Population models may be used to estimate extinction risk or population declines. Uncertainty and variability arise in threat classifications through measurement and process error in empirical data and uncertainty in the models used to estimate extinction risk and population declines. Furthermore, species traits are known to affect extinction risk. We investigated the effects of measurement and process error, model type, population growth rate, and age at first reproduction on the reliability of risk classifications based on projected population declines on IUCN Red List classifications. We used an age-structured population model to simulate true population trajectories with different growth rates, reproductive ages and levels of variation, and subjected them to measurement error. We evaluated the ability of scalar and matrix models parameterized with these simulated time series to accurately capture the IUCN Red List classification generated with true population declines. Under all levels of measurement error tested and low process error, classifications were reasonably accurate; scalar and matrix models yielded roughly the same rate of misclassifications, but the distribution of errors differed; matrix models led to greater overestimation of extinction risk than underestimations; process error tended to contribute to misclassifications to a greater extent than measurement error; and more misclassifications occurred for fast, rather than slow, life histories. These results indicate that classifications of highly threatened taxa (i.e., taxa with low growth rates) under criterion A are more likely to be reliable than for less threatened taxa when assessed with population models. Greater scrutiny needs to be placed on data used to parameterize population models for species with high growth rates, particularly when available evidence indicates a potential transition to higher risk categories. © 2018 Society for Conservation Biology.

The Role of Model and Initial Condition Error in Numerical Weather Forecasting Investigated with an Observing System Simulation Experiment

NASA Technical Reports Server (NTRS)

Prive, Nikki C.; Errico, Ronald M.

2013-01-01

A series of experiments that explore the roles of model and initial condition error in numerical weather prediction are performed using an observing system simulation experiment (OSSE) framework developed at the National Aeronautics and Space Administration Global Modeling and Assimilation Office (NASA/GMAO). The use of an OSSE allows the analysis and forecast errors to be explicitly calculated, and different hypothetical observing networks can be tested with ease. In these experiments, both a full global OSSE framework and an 'identical twin' OSSE setup are utilized to compare the behavior of the data assimilation system and evolution of forecast skill with and without model error. The initial condition error is manipulated by varying the distribution and quality of the observing network and the magnitude of observation errors. The results show that model error has a strong impact on both the quality of the analysis field and the evolution of forecast skill, including both systematic and unsystematic model error components. With a realistic observing network, the analysis state retains a significant quantity of error due to systematic model error. If errors of the analysis state are minimized, model error acts to rapidly degrade forecast skill during the first 24-48 hours of forward integration. In the presence of model error, the impact of observation errors on forecast skill is small, but in the absence of model error, observation errors cause a substantial degradation of the skill of medium range forecasts.

Adaptive reduction of constitutive model-form error using a posteriori error estimation techniques

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bishop, Joseph E.; Brown, Judith Alice

In engineering practice, models are typically kept as simple as possible for ease of setup and use, computational efficiency, maintenance, and overall reduced complexity to achieve robustness. In solid mechanics, a simple and efficient constitutive model may be favored over one that is more predictive, but is difficult to parameterize, is computationally expensive, or is simply not available within a simulation tool. In order to quantify the modeling error due to the choice of a relatively simple and less predictive constitutive model, we adopt the use of a posteriori model-form error-estimation techniques. Based on local error indicators in the energymore » norm, an algorithm is developed for reducing the modeling error by spatially adapting the material parameters in the simpler constitutive model. The resulting material parameters are not material properties per se, but depend on the given boundary-value problem. As a first step to the more general nonlinear case, we focus here on linear elasticity in which the “complex” constitutive model is general anisotropic elasticity and the chosen simpler model is isotropic elasticity. As a result, the algorithm for adaptive error reduction is demonstrated using two examples: (1) A transversely-isotropic plate with hole subjected to tension, and (2) a transversely-isotropic tube with two side holes subjected to torsion.« less

Adaptive reduction of constitutive model-form error using a posteriori error estimation techniques

DOE PAGES

Bishop, Joseph E.; Brown, Judith Alice

2018-06-15

In engineering practice, models are typically kept as simple as possible for ease of setup and use, computational efficiency, maintenance, and overall reduced complexity to achieve robustness. In solid mechanics, a simple and efficient constitutive model may be favored over one that is more predictive, but is difficult to parameterize, is computationally expensive, or is simply not available within a simulation tool. In order to quantify the modeling error due to the choice of a relatively simple and less predictive constitutive model, we adopt the use of a posteriori model-form error-estimation techniques. Based on local error indicators in the energymore » norm, an algorithm is developed for reducing the modeling error by spatially adapting the material parameters in the simpler constitutive model. The resulting material parameters are not material properties per se, but depend on the given boundary-value problem. As a first step to the more general nonlinear case, we focus here on linear elasticity in which the “complex” constitutive model is general anisotropic elasticity and the chosen simpler model is isotropic elasticity. As a result, the algorithm for adaptive error reduction is demonstrated using two examples: (1) A transversely-isotropic plate with hole subjected to tension, and (2) a transversely-isotropic tube with two side holes subjected to torsion.« less

Information and complexity measures for hydrologic model evaluation

USDA-ARS?s Scientific Manuscript database

Hydrological models are commonly evaluated through the residual-based performance measures such as the root-mean square error or efficiency criteria. Such measures, however, do not evaluate the degree of similarity of patterns in simulated and measured time series. The objective of this study was to...

Software-In-the-Loop based Modeling and Simulation of Unmanned Semi-submersible Vehicle for Performance Verification of Autonomous Navigation

NASA Astrophysics Data System (ADS)

Lee, Kwangkook; Jeong, Mijin; Kim, Dong Hun

2017-12-01

Since an unmanned semi-submersible is mainly used for the purpose of carrying out dangerous missions in the sea, it is possible to work in a region where it is difficult to access due to safety reasons. In this study, an USV hull design was determined using Myring hull profile, and reinforcement work was performed by designing and implementing inner stiffener member for 3D printing. In order to simulate a sea state 5.0 or more at sea, which is difficult to implement in practice, a regular and irregular wave equation was implemented in Matlab / Simulink. We performed modeling and simulation of semi - submersible simulation based on DMWorks considering the rolling motion in wave. To verify and improve unpredicted errors, we implemented a numeric and physical simulation model of the USV based on software-in-the-loop (SIL) method. This simulation allows shipbuilders to participate in new value-added markets such as engineering, procurement, construction, installation, commissioning, operation, and maintenance for the USV.

The interaction of the flux errors and transport errors in modeled atmospheric carbon dioxide concentrations

NASA Astrophysics Data System (ADS)

Feng, S.; Lauvaux, T.; Butler, M. P.; Keller, K.; Davis, K. J.; Jacobson, A. R.; Schuh, A. E.; Basu, S.; Liu, J.; Baker, D.; Crowell, S.; Zhou, Y.; Williams, C. A.

2017-12-01

Regional estimates of biogenic carbon fluxes over North America from top-down atmospheric inversions and terrestrial biogeochemical (or bottom-up) models remain inconsistent at annual and sub-annual time scales. While top-down estimates are impacted by limited atmospheric data, uncertain prior flux estimates and errors in the atmospheric transport models, bottom-up fluxes are affected by uncertain driver data, uncertain model parameters and missing mechanisms across ecosystems. This study quantifies both flux errors and transport errors, and their interaction in the CO2 atmospheric simulation. These errors are assessed by an ensemble approach. The WRF-Chem model is set up with 17 biospheric fluxes from the Multiscale Synthesis and Terrestrial Model Intercomparison Project, CarbonTracker-Near Real Time, and the Simple Biosphere model. The spread of the flux ensemble members represents the flux uncertainty in the modeled CO2 concentrations. For the transport errors, WRF-Chem is run using three physical model configurations with three stochastic perturbations to sample the errors from both the physical parameterizations of the model and the initial conditions. Additionally, the uncertainties from boundary conditions are assessed using four CO2 global inversion models which have assimilated tower and satellite CO2 observations. The error structures are assessed in time and space. The flux ensemble members overall overestimate CO2 concentrations. They also show larger temporal variability than the observations. These results suggest that the flux ensemble is overdispersive. In contrast, the transport ensemble is underdispersive. The averaged spatial distribution of modeled CO2 shows strong positive biogenic signal in the southern US and strong negative signals along the eastern coast of Canada. We hypothesize that the former is caused by the 3-hourly downscaling algorithm from which the nighttime respiration dominates the daytime modeled CO2 signals and that the latter is mainly caused by the large-scale transport associated with the jet stream that carries the negative biogenic CO2 signals to the northeastern coast. We apply comprehensive statistics to eliminate outliers. We generate a set of flux perturbations based on pre-calibrated flux ensemble members and apply them to the simulations.

Using APEX to Model Anticipated Human Error: Analysis of a GPS Navigational Aid

NASA Technical Reports Server (NTRS)

VanSelst, Mark; Freed, Michael; Shefto, Michael (Technical Monitor)

1997-01-01

The interface development process can be dramatically improved by predicting design facilitated human error at an early stage in the design process. The approach we advocate is to SIMULATE the behavior of a human agent carrying out tasks with a well-specified user interface, ANALYZE the simulation for instances of human error, and then REFINE the interface or protocol to minimize predicted error. This approach, incorporated into the APEX modeling architecture, differs from past approaches to human simulation in Its emphasis on error rather than e.g. learning rate or speed of response. The APEX model consists of two major components: (1) a powerful action selection component capable of simulating behavior in complex, multiple-task environments; and (2) a resource architecture which constrains cognitive, perceptual, and motor capabilities to within empirically demonstrated limits. The model mimics human errors arising from interactions between limited human resources and elements of the computer interface whose design falls to anticipate those limits. We analyze the design of a hand-held Global Positioning System (GPS) device used for radical and navigational decisions in small yacht recalls. The analysis demonstrates how human system modeling can be an effective design aid, helping to accelerate the process of refining a product (or procedure).

AQMEII3: the EU and NA regional scale program of the ...

EPA Pesticide Factsheets

The presentation builds on the work presented last year at the 14th CMAS meeting and it is applied to the work performed in the context of the AQMEII-HTAP collaboration. The analysis is conducted within the framework of the third phase of AQMEII (Air Quality Model Evaluation International Initiative) and encompasses the gauging of model performance through measurement-to-model comparison, error decomposition and time series analysis of the models biases. Through the comparison of several regional-scale chemistry transport modelling systems applied to simulate meteorology and air quality over two continental areas, this study aims at i) apportioning the error to the responsible processes through time-scale analysis, and ii) help detecting causes of models error, and iii) identify the processes and scales most urgently requiring dedicated investigations. The operational metrics (magnitude of the error, sign of the bias, associativity) provide an overall sense of model strengths and deficiencies, while the apportioning of the error into its constituent parts (bias, variance and covariance) can help assess the nature and quality of the error. Each of the error components is analysed independently and apportioned to specific processes based on the corresponding timescale (long scale, synoptic, diurnal, and intra-day) using the error apportionment technique devised in the previous phases of AQMEII. The National Exposure Research Laboratory (NERL) Computational Exposur

Constraining Stochastic Parametrisation Schemes Using High-Resolution Model Simulations

NASA Astrophysics Data System (ADS)

Christensen, H. M.; Dawson, A.; Palmer, T.

2017-12-01

Stochastic parametrisations are used in weather and climate models as a physically motivated way to represent model error due to unresolved processes. Designing new stochastic schemes has been the target of much innovative research over the last decade. While a focus has been on developing physically motivated approaches, many successful stochastic parametrisation schemes are very simple, such as the European Centre for Medium-Range Weather Forecasts (ECMWF) multiplicative scheme `Stochastically Perturbed Parametrisation Tendencies' (SPPT). The SPPT scheme improves the skill of probabilistic weather and seasonal forecasts, and so is widely used. However, little work has focused on assessing the physical basis of the SPPT scheme. We address this matter by using high-resolution model simulations to explicitly measure the `error' in the parametrised tendency that SPPT seeks to represent. The high resolution simulations are first coarse-grained to the desired forecast model resolution before they are used to produce initial conditions and forcing data needed to drive the ECMWF Single Column Model (SCM). By comparing SCM forecast tendencies with the evolution of the high resolution model, we can measure the `error' in the forecast tendencies. In this way, we provide justification for the multiplicative nature of SPPT, and for the temporal and spatial scales of the stochastic perturbations. However, we also identify issues with the SPPT scheme. It is therefore hoped these measurements will improve both holistic and process based approaches to stochastic parametrisation. Figure caption: Instantaneous snapshot of the optimal SPPT stochastic perturbation, derived by comparing high-resolution simulations with a low resolution forecast model.

Flood Simulation based on ArcGIS in the Ungauged Area from Fugu to Wubao of the middle Yellow River

NASA Astrophysics Data System (ADS)

Jin, Shuangyan; Yan, Yiqi; Jiang, Xinhui

2017-12-01

The Qingliangsigou and Jialuhe in the middle Yellow River are selected as the typical tributaries, history flood data in 1980-2013 and Horton infiltration capacity curve are used to calculate the stable infiltration rate and establish the model of runoff yield and concentration, the parameters are calibrated and applied in the ungauged area from Fugu to Wubao. The study area is divided into 20 units based on ArcGIS, Muskingum method parameters in each unit are calibrated, and typical floods of ungauged area from Fugu to Wubao are simulated. The results show that the simulation effects are good: the average error of peak time is about -0.4h, the error of peak discharge is in the forecasting allowable range, and the deterministic coefficient is 0.66.

Validation of a fibula graft cutting guide for mandibular reconstruction: experiment with rapid prototyping mandible model.

PubMed

Lim, Se-Ho; Kim, Yeon-Ho; Kim, Moon-Key; Nam, Woong; Kang, Sang-Hoon

2016-12-01

We examined whether cutting a fibula graft with a surgical guide template, prepared with computer-aided design/computer-aided manufacturing (CAD/CAM), would improve the precision and accuracy of mandibular reconstruction. Thirty mandibular rapid prototype (RP) models were allocated to experimental (N = 15) and control (N = 15) groups. Thirty identical fibular RP models were assigned randomly, 15 to each group. For reference, we prepared a reconstructed mandibular RP model with a three-dimensional printer, based on surgical simulation. In the experimental group, a stereolithography (STL) surgical guide template, based on simulation, was used for cutting the fibula graft. In the control group, the fibula graft was cut manually, with reference to the reconstructed RP mandible model. The mandibular reconstructions were compared to the surgical simulation, and errors were calculated for both the STL surgical guide and the manual methods. The average differences in three-dimensional, minimum distances between the reconstruction and simulation were 9.87 ± 6.32 mm (mean ± SD) for the STL surgical guide method and 14.76 ± 10.34 mm (mean ± SD) for the manual method. The STL surgical guide method incurred less error than the manual method in mandibular reconstruction. A fibula cutting guide improved the precision of reconstructing the mandible with a fibula graft.

Development of a VOR/DME model for an advanced concepts simulator

NASA Technical Reports Server (NTRS)

Steinmetz, G. G.; Bowles, R. L.

1984-01-01

The report presents a definition of a VOR/DME, airborne and ground systems simulation model. This description was drafted in response to a need in the creation of an advanced concepts simulation in which flight station design for the 1980 era can be postulated and examined. The simulation model described herein provides a reasonable representation of VOR/DME station in the continental United States including area coverage by type and noise errors. The detail in which the model has been cast provides the interested researcher with a moderate fidelity level simulator tool for conducting research and evaluation of navigator algorithms. Assumptions made within the development are listed and place certain responsibilities (data bases, communication with other simulation modules, uniform round earth, etc.) upon the researcher.

Error correcting circuit design with carbon nanotube field effect transistors

NASA Astrophysics Data System (ADS)

Liu, Xiaoqiang; Cai, Li; Yang, Xiaokuo; Liu, Baojun; Liu, Zhongyong

2018-03-01

In this work, a parallel error correcting circuit based on (7, 4) Hamming code is designed and implemented with carbon nanotube field effect transistors, and its function is validated by simulation in HSpice with the Stanford model. A grouping method which is able to correct multiple bit errors in 16-bit and 32-bit application is proposed, and its error correction capability is analyzed. Performance of circuits implemented with CNTFETs and traditional MOSFETs respectively is also compared, and the former shows a 34.4% decrement of layout area and a 56.9% decrement of power consumption.

Sensorimotor synchronization with tempo-changing auditory sequences: Modeling temporal adaptation and anticipation.

PubMed

van der Steen, M C Marieke; Jacoby, Nori; Fairhurst, Merle T; Keller, Peter E

2015-11-11

The current study investigated the human ability to synchronize movements with event sequences containing continuous tempo changes. This capacity is evident, for example, in ensemble musicians who maintain precise interpersonal coordination while modulating the performance tempo for expressive purposes. Here we tested an ADaptation and Anticipation Model (ADAM) that was developed to account for such behavior by combining error correction processes (adaptation) with a predictive temporal extrapolation process (anticipation). While previous computational models of synchronization incorporate error correction, they do not account for prediction during tempo-changing behavior. The fit between behavioral data and computer simulations based on four versions of ADAM was assessed. These versions included a model with adaptation only, one in which adaptation and anticipation act in combination (error correction is applied on the basis of predicted tempo changes), and two models in which adaptation and anticipation were linked in a joint module that corrects for predicted discrepancies between the outcomes of adaptive and anticipatory processes. The behavioral experiment required participants to tap their finger in time with three auditory pacing sequences containing tempo changes that differed in the rate of change and the number of turning points. Behavioral results indicated that sensorimotor synchronization accuracy and precision, while generally high, decreased with increases in the rate of tempo change and number of turning points. Simulations and model-based parameter estimates showed that adaptation mechanisms alone could not fully explain the observed precision of sensorimotor synchronization. Including anticipation in the model increased the precision of simulated sensorimotor synchronization and improved the fit of model to behavioral data, especially when adaptation and anticipation mechanisms were linked via a joint module based on the notion of joint internal models. Overall results suggest that adaptation and anticipation mechanisms both play an important role during sensorimotor synchronization with tempo-changing sequences. This article is part of a Special Issue entitled SI: Prediction and Attention. Copyright © 2015 Elsevier B.V. All rights reserved.

Addressing global uncertainty and sensitivity in first-principles based microkinetic models by an adaptive sparse grid approach

NASA Astrophysics Data System (ADS)

Döpking, Sandra; Plaisance, Craig P.; Strobusch, Daniel; Reuter, Karsten; Scheurer, Christoph; Matera, Sebastian

2018-01-01

In the last decade, first-principles-based microkinetic modeling has been developed into an important tool for a mechanistic understanding of heterogeneous catalysis. A commonly known, but hitherto barely analyzed issue in this kind of modeling is the presence of sizable errors from the use of approximate Density Functional Theory (DFT). We here address the propagation of these errors to the catalytic turnover frequency (TOF) by global sensitivity and uncertainty analysis. Both analyses require the numerical quadrature of high-dimensional integrals. To achieve this efficiently, we utilize and extend an adaptive sparse grid approach and exploit the confinement of the strongly non-linear behavior of the TOF to local regions of the parameter space. We demonstrate the methodology on a model of the oxygen evolution reaction at the Co3O4 (110)-A surface, using a maximum entropy error model that imposes nothing but reasonable bounds on the errors. For this setting, the DFT errors lead to an absolute uncertainty of several orders of magnitude in the TOF. We nevertheless find that it is still possible to draw conclusions from such uncertain models about the atomistic aspects controlling the reactivity. A comparison with derivative-based local sensitivity analysis instead reveals that this more established approach provides incomplete information. Since the adaptive sparse grids allow for the evaluation of the integrals with only a modest number of function evaluations, this approach opens the way for a global sensitivity analysis of more complex models, for instance, models based on kinetic Monte Carlo simulations.

Design considerations for case series models with exposure onset measurement error.

PubMed

Mohammed, Sandra M; Dalrymple, Lorien S; Sentürk, Damla; Nguyen, Danh V

2013-02-28

The case series model allows for estimation of the relative incidence of events, such as cardiovascular events, within a pre-specified time window after an exposure, such as an infection. The method requires only cases (individuals with events) and controls for all fixed/time-invariant confounders. The measurement error case series model extends the original case series model to handle imperfect data, where the timing of an infection (exposure) is not known precisely. In this work, we propose a method for power/sample size determination for the measurement error case series model. Extensive simulation studies are used to assess the accuracy of the proposed sample size formulas. We also examine the magnitude of the relative loss of power due to exposure onset measurement error, compared with the ideal situation where the time of exposure is measured precisely. To facilitate the design of case series studies, we provide publicly available web-based tools for determining power/sample size for both the measurement error case series model as well as the standard case series model. Copyright © 2012 John Wiley & Sons, Ltd.

End-to-end Coronagraphic Modeling Including a Low-order Wavefront Sensor

NASA Technical Reports Server (NTRS)

Krist, John E.; Trauger, John T.; Unwin, Stephen C.; Traub, Wesley A.

2012-01-01

To evaluate space-based coronagraphic techniques, end-to-end modeling is necessary to simulate realistic fields containing speckles caused by wavefront errors. Real systems will suffer from pointing errors and thermal and motioninduced mechanical stresses that introduce time-variable wavefront aberrations that can reduce the field contrast. A loworder wavefront sensor (LOWFS) is needed to measure these changes at a sufficiently high rate to maintain the contrast level during observations. We implement here a LOWFS and corresponding low-order wavefront control subsystem (LOWFCS) in end-to-end models of a space-based coronagraph. Our goal is to be able to accurately duplicate the effect of the LOWFS+LOWFCS without explicitly evaluating the end-to-end model at numerous time steps.

Partitioning the Uncertainty in Estimates of Mean Basal Area Obtained from 10-year Diameter Growth Model Predictions

Treesearch

Ronald E. McRoberts

2005-01-01

Uncertainty in model-based predictions of individual tree diameter growth is attributed to three sources: measurement error for predictor variables, residual variability around model predictions, and uncertainty in model parameter estimates. Monte Carlo simulations are used to propagate the uncertainty from the three sources through a set of diameter growth models to...

Poisson regression models outperform the geometrical model in estimating the peak-to-trough ratio of seasonal variation: a simulation study.

PubMed

Christensen, A L; Lundbye-Christensen, S; Dethlefsen, C

2011-12-01

Several statistical methods of assessing seasonal variation are available. Brookhart and Rothman [3] proposed a second-order moment-based estimator based on the geometrical model derived by Edwards [1], and reported that this estimator is superior in estimating the peak-to-trough ratio of seasonal variation compared with Edwards' estimator with respect to bias and mean squared error. Alternatively, seasonal variation may be modelled using a Poisson regression model, which provides flexibility in modelling the pattern of seasonal variation and adjustments for covariates. Based on a Monte Carlo simulation study three estimators, one based on the geometrical model, and two based on log-linear Poisson regression models, were evaluated in regards to bias and standard deviation (SD). We evaluated the estimators on data simulated according to schemes varying in seasonal variation and presence of a secular trend. All methods and analyses in this paper are available in the R package Peak2Trough[13]. Applying a Poisson regression model resulted in lower absolute bias and SD for data simulated according to the corresponding model assumptions. Poisson regression models had lower bias and SD for data simulated to deviate from the corresponding model assumptions than the geometrical model. This simulation study encourages the use of Poisson regression models in estimating the peak-to-trough ratio of seasonal variation as opposed to the geometrical model. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Stress testing hydrologic models using bottom-up climate change assessment

NASA Astrophysics Data System (ADS)

Stephens, C.; Johnson, F.; Marshall, L. A.

2017-12-01

Bottom-up climate change assessment is a promising approach for understanding the vulnerability of a system to potential future changes. The technique has been utilised successfully in risk-based assessments of future flood severity and infrastructure vulnerability. We find that it is also an ideal tool for assessing hydrologic model performance in a changing climate. In this study, we applied bottom-up climate change to compare the performance of two different hydrologic models (an event-based and a continuous model) under increasingly severe climate change scenarios. This allowed us to diagnose likely sources of future prediction error in the two models. The climate change scenarios were based on projections for southern Australia, which indicate drier average conditions with increased extreme rainfall intensities. We found that the key weakness in using the event-based model to simulate drier future scenarios was the model's inability to dynamically account for changing antecedent conditions. This led to increased variability in model performance relative to the continuous model, which automatically accounts for the wetness of a catchment through dynamic simulation of water storages. When considering more intense future rainfall events, representation of antecedent conditions became less important than assumptions around (non)linearity in catchment response. The linear continuous model we applied may underestimate flood risk in a future climate with greater extreme rainfall intensity. In contrast with the recommendations of previous studies, this indicates that continuous simulation is not necessarily the key to robust flood modelling under climate change. By applying bottom-up climate change assessment, we were able to understand systematic changes in relative model performance under changing conditions and deduce likely sources of prediction error in the two models.

Methodology for Computational Fluid Dynamic Validation for Medical Use: Application to Intracranial Aneurysm.

PubMed

Paliwal, Nikhil; Damiano, Robert J; Varble, Nicole A; Tutino, Vincent M; Dou, Zhongwang; Siddiqui, Adnan H; Meng, Hui

2017-12-01

Computational fluid dynamics (CFD) is a promising tool to aid in clinical diagnoses of cardiovascular diseases. However, it uses assumptions that simplify the complexities of the real cardiovascular flow. Due to high-stakes in the clinical setting, it is critical to calculate the effect of these assumptions in the CFD simulation results. However, existing CFD validation approaches do not quantify error in the simulation results due to the CFD solver's modeling assumptions. Instead, they directly compare CFD simulation results against validation data. Thus, to quantify the accuracy of a CFD solver, we developed a validation methodology that calculates the CFD model error (arising from modeling assumptions). Our methodology identifies independent error sources in CFD and validation experiments, and calculates the model error by parsing out other sources of error inherent in simulation and experiments. To demonstrate the method, we simulated the flow field of a patient-specific intracranial aneurysm (IA) in the commercial CFD software star-ccm+. Particle image velocimetry (PIV) provided validation datasets for the flow field on two orthogonal planes. The average model error in the star-ccm+ solver was 5.63 ± 5.49% along the intersecting validation line of the orthogonal planes. Furthermore, we demonstrated that our validation method is superior to existing validation approaches by applying three representative existing validation techniques to our CFD and experimental dataset, and comparing the validation results. Our validation methodology offers a streamlined workflow to extract the "true" accuracy of a CFD solver.

Modeling error in experimental assays using the bootstrap principle: Understanding discrepancies between assays using different dispensing technologies

PubMed Central

Hanson, Sonya M.; Ekins, Sean; Chodera, John D.

2015-01-01

All experimental assay data contains error, but the magnitude, type, and primary origin of this error is often not obvious. Here, we describe a simple set of assay modeling techniques based on the bootstrap principle that allow sources of error and bias to be simulated and propagated into assay results. We demonstrate how deceptively simple operations—such as the creation of a dilution series with a robotic liquid handler—can significantly amplify imprecision and even contribute substantially to bias. To illustrate these techniques, we review an example of how the choice of dispensing technology can impact assay measurements, and show how large contributions to discrepancies between assays can be easily understood and potentially corrected for. These simple modeling techniques—illustrated with an accompanying IPython notebook—can allow modelers to understand the expected error and bias in experimental datasets, and even help experimentalists design assays to more effectively reach accuracy and imprecision goals. PMID:26678597

Investigation on coupling error characteristics in angular rate matching based ship deformation measurement approach

NASA Astrophysics Data System (ADS)

Yang, Shuai; Wu, Wei; Wang, Xingshu; Xu, Zhiguang

2018-01-01

The coupling error in the measurement of ship hull deformation can significantly influence the attitude accuracy of the shipborne weapons and equipments. It is therefore important to study the characteristics of the coupling error. In this paper, an comprehensive investigation on the coupling error is reported, which has a potential of deducting the coupling error in the future. Firstly, the causes and characteristics of the coupling error are analyzed theoretically based on the basic theory of measuring ship deformation. Then, simulations are conducted for verifying the correctness of the theoretical analysis. Simulation results show that the cross-correlation between dynamic flexure and ship angular motion leads to the coupling error in measuring ship deformation, and coupling error increases with the correlation value between them. All the simulation results coincide with the theoretical analysis.

[Simulation of water and carbon fluxes in harvard forest area based on data assimilation method].

PubMed

Zhang, Ting-Long; Sun, Rui; Zhang, Rong-Hua; Zhang, Lei

2013-10-01

Model simulation and in situ observation are the two most important means in studying the water and carbon cycles of terrestrial ecosystems, but have their own advantages and shortcomings. To combine these two means would help to reflect the dynamic changes of ecosystem water and carbon fluxes more accurately. Data assimilation provides an effective way to integrate the model simulation and in situ observation. Based on the observation data from the Harvard Forest Environmental Monitoring Site (EMS), and by using ensemble Kalman Filter algorithm, this paper assimilated the field measured LAI and remote sensing LAI into the Biome-BGC model to simulate the water and carbon fluxes in Harvard forest area. As compared with the original model simulated without data assimilation, the improved Biome-BGC model with the assimilation of the field measured LAI in 1998, 1999, and 2006 increased the coefficient of determination R2 between model simulation and flux observation for the net ecosystem exchange (NEE) and evapotranspiration by 8.4% and 10.6%, decreased the sum of absolute error (SAE) and root mean square error (RMSE) of NEE by 17.7% and 21.2%, and decreased the SAE and RMSE of the evapotranspiration by 26. 8% and 28.3%, respectively. After assimilated the MODIS LAI products of 2000-2004 into the improved Biome-BGC model, the R2 between simulated and observed results of NEE and evapotranspiration was increased by 7.8% and 4.7%, the SAE and RMSE of NEE were decreased by 21.9% and 26.3%, and the SAE and RMSE of evapotranspiration were decreased by 24.5% and 25.5%, respectively. It was suggested that the simulation accuracy of ecosystem water and carbon fluxes could be effectively improved if the field measured LAI or remote sensing LAI was integrated into the model.

A comparative analysis of errors in long-term econometric forecasts

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tepel, R.

1986-04-01

The growing body of literature that documents forecast accuracy falls generally into two parts. The first is prescriptive and is carried out by modelers who use simulation analysis as a tool for model improvement. These studies are ex post, that is, they make use of known values for exogenous variables and generate an error measure wholly attributable to the model. The second type of analysis is descriptive and seeks to measure errors, identify patterns among errors and variables and compare forecasts from different sources. Most descriptive studies use an ex ante approach, that is, they evaluate model outputs based onmore » estimated (or forecasted) exogenous variables. In this case, it is the forecasting process, rather than the model, that is under scrutiny. This paper uses an ex ante approach to measure errors in forecast series prepared by Data Resources Incorporated (DRI), Wharton Econometric Forecasting Associates (Wharton), and Chase Econometrics (Chase) and to determine if systematic patterns of errors can be discerned between services, types of variables (by degree of aggregation), length of forecast and time at which the forecast is made. Errors are measured as the percent difference between actual and forecasted values for the historical period of 1971 to 1983.« less

Error identification and recovery by student nurses using human patient simulation: opportunity to improve patient safety.

PubMed

Henneman, Elizabeth A; Roche, Joan P; Fisher, Donald L; Cunningham, Helene; Reilly, Cheryl A; Nathanson, Brian H; Henneman, Philip L

2010-02-01

This study examined types of errors that occurred or were recovered in a simulated environment by student nurses. Errors occurred in all four rule-based error categories, and all students committed at least one error. The most frequent errors occurred in the verification category. Another common error was related to physician interactions. The least common errors were related to coordinating information with the patient and family. Our finding that 100% of student subjects committed rule-based errors is cause for concern. To decrease errors and improve safe clinical practice, nurse educators must identify effective strategies that students can use to improve patient surveillance. Copyright 2010 Elsevier Inc. All rights reserved.

Data and results of a laboratory investigation of microprocessor upset caused by simulated lightning-induced analog transients

NASA Technical Reports Server (NTRS)

Belcastro, C. M.

1984-01-01

Advanced composite aircraft designs include fault-tolerant computer-based digital control systems with thigh reliability requirements for adverse as well as optimum operating environments. Since aircraft penetrate intense electromagnetic fields during thunderstorms, onboard computer systems maya be subjected to field-induced transient voltages and currents resulting in functional error modes which are collectively referred to as digital system upset. A methodology was developed for assessing the upset susceptibility of a computer system onboard an aircraft flying through a lightning environment. Upset error modes in a general-purpose microprocessor were studied via tests which involved the random input of analog transients which model lightning-induced signals onto interface lines of an 8080-based microcomputer from which upset error data were recorded. The application of Markov modeling to upset susceptibility estimation is discussed and a stochastic model development.

Error-growth dynamics and predictability of surface thermally induced atmospheric flow

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zeng, X.; Pielke, R.A.

1993-09-01

Using the CSU Regional Atmospheric Modeling System (RAMS) in its nonhydrostatic and compressible configuration, over 200 two-dimensional simulations with [Delta]x = 2 km and [Delta]x = 100 m are performed to study in detail the initial adjustment process and the error-growth dynamics of surface thermally induced circulation including the sensitivity to initial conditions, boundary conditions, and model parameters, and to study the predictability as a function of the size of surface heat patches under a calm mean wind. It is found that the error growth is not sensitive to the characterisitics of the initial perturbations. The numerical smoothing has amore » strong impact on the initial adjustment process and on the error-growth dynamics. The predictability and flow structures, it is found that the vertical velocity field is strongly affected by the mean wind, and the flow structures are quite sensitive to the initial soil water content. The transition from organized flow to the situation in which fluxes are dominated by noncoherent turbulent eddies under a calm mean wind is quantitatively evaluated and this transition is different for different variables. The relationship between the predictability of a realization and of an ensemble average is discussed. The predictability and the coherent circulations modulated by the surface inhomogeneities are also studied by computing the autocorrelations and the power spectra. The three-dimensional mesoscale and large-eddy simulations are performed to verify the above results. It is found that the two-dimensional mesoscale (or fine resolution) simulation yields very close or similar results regarding the predictability as those from the three-dimensional mesoscale (or large eddy) simulation. The horizontally averaged quantities based on two-dimensional fine-resolution simulations are insensitive to initial perturbations and agree with those based on three-dimensional large-eddy simulations. 87 refs., 25 figs.« less

Unifying distance-based goodness-of-fit indicators for hydrologic model assessment

NASA Astrophysics Data System (ADS)

Cheng, Qinbo; Reinhardt-Imjela, Christian; Chen, Xi; Schulte, Achim

2014-05-01

The goodness-of-fit indicator, i.e. efficiency criterion, is very important for model calibration. However, recently the knowledge about the goodness-of-fit indicators is all empirical and lacks a theoretical support. Based on the likelihood theory, a unified distance-based goodness-of-fit indicator termed BC-GED model is proposed, which uses the Box-Cox (BC) transformation to remove the heteroscedasticity of model errors and the generalized error distribution (GED) with zero-mean to fit the distribution of model errors after BC. The BC-GED model can unify all recent distance-based goodness-of-fit indicators, and reveals the mean square error (MSE) and the mean absolute error (MAE) that are widely used goodness-of-fit indicators imply statistic assumptions that the model errors follow the Gaussian distribution and the Laplace distribution with zero-mean, respectively. The empirical knowledge about goodness-of-fit indicators can be also easily interpreted by BC-GED model, e.g. the sensitivity to high flow of the goodness-of-fit indicators with large power of model errors results from the low probability of large model error in the assumed distribution of these indicators. In order to assess the effect of the parameters (i.e. the BC transformation parameter λ and the GED kurtosis coefficient β also termed the power of model errors) of BC-GED model on hydrologic model calibration, six cases of BC-GED model were applied in Baocun watershed (East China) with SWAT-WB-VSA model. Comparison of the inferred model parameters and model simulation results among the six indicators demonstrates these indicators can be clearly separated two classes by the GED kurtosis β: β >1 and β ≤ 1. SWAT-WB-VSA calibrated by the class β >1 of distance-based goodness-of-fit indicators captures high flow very well and mimics the baseflow very badly, but it calibrated by the class β ≤ 1 mimics the baseflow very well, because first the larger value of β, the greater emphasis is put on high flow and second the derivative of GED probability density function at zero is zero as β >1, but discontinuous as β ≤ 1, and even infinity as β < 1 with which the maximum likelihood estimation can guarantee the model errors approach zero as well as possible. The BC-GED that estimates the parameters (i.e. λ and β) of BC-GED model as well as hydrologic model parameters is the best distance-based goodness-of-fit indicator because not only the model validation using groundwater levels is very good, but also the model errors fulfill the statistic assumption best. However, in some cases of model calibration with a few observations e.g. calibration of single-event model, for avoiding estimation of the parameters of BC-GED model the MAE i.e. the boundary indicator (β = 1) of the two classes, can replace the BC-GED, because the model validation of MAE is best.

Adaptive and predictive control of a simulated robot arm.

PubMed

Tolu, Silvia; Vanegas, Mauricio; Garrido, Jesús A; Luque, Niceto R; Ros, Eduardo

2013-06-01

In this work, a basic cerebellar neural layer and a machine learning engine are embedded in a recurrent loop which avoids dealing with the motor error or distal error problem. The presented approach learns the motor control based on available sensor error estimates (position, velocity, and acceleration) without explicitly knowing the motor errors. The paper focuses on how to decompose the input into different components in order to facilitate the learning process using an automatic incremental learning model (locally weighted projection regression (LWPR) algorithm). LWPR incrementally learns the forward model of the robot arm and provides the cerebellar module with optimal pre-processed signals. We present a recurrent adaptive control architecture in which an adaptive feedback (AF) controller guarantees a precise, compliant, and stable control during the manipulation of objects. Therefore, this approach efficiently integrates a bio-inspired module (cerebellar circuitry) with a machine learning component (LWPR). The cerebellar-LWPR synergy makes the robot adaptable to changing conditions. We evaluate how this scheme scales for robot-arms of a high number of degrees of freedom (DOFs) using a simulated model of a robot arm of the new generation of light weight robots (LWRs).

Forecasting the Water Demand in Chongqing, China Using a Grey Prediction Model and Recommendations for the Sustainable Development of Urban Water Consumption

PubMed Central

Wu, Hua’an; Zhou, Meng

2017-01-01

High accuracy in water demand predictions is an important basis for the rational allocation of city water resources and forms the basis for sustainable urban development. The shortage of water resources in Chongqing, the youngest central municipality in Southwest China, has significantly increased with the population growth and rapid economic development. In this paper, a new grey water-forecasting model (GWFM) was built based on the data characteristics of water consumption. The parameter estimation and error checking methods of the GWFM model were investigated. Then, the GWFM model was employed to simulate the water demands of Chongqing from 2009 to 2015 and forecast it in 2016. The simulation and prediction errors of the GWFM model was checked, and the results show the GWFM model exhibits better simulation and prediction precisions than those of the classical Grey Model with one variable and single order equation GM(1,1) for short and the frequently-used Discrete Grey Model with one variable and single order equation, DGM(1,1) for short. Finally, the water demand in Chongqing from 2017 to 2022 was forecasted, and some corresponding control measures and recommendations were provided based on the prediction results to ensure a viable water supply and promote the sustainable development of the Chongqing economy. PMID:29140266

Regression-based model of skin diffuse reflectance for skin color analysis

NASA Astrophysics Data System (ADS)

Tsumura, Norimichi; Kawazoe, Daisuke; Nakaguchi, Toshiya; Ojima, Nobutoshi; Miyake, Yoichi

2008-11-01

A simple regression-based model of skin diffuse reflectance is developed based on reflectance samples calculated by Monte Carlo simulation of light transport in a two-layered skin model. This reflectance model includes the values of spectral reflectance in the visible spectra for Japanese women. The modified Lambert Beer law holds in the proposed model with a modified mean free path length in non-linear density space. The averaged RMS and maximum errors of the proposed model were 1.1 and 3.1%, respectively, in the above range.

Combining inferences from models of capture efficiency, detectability, and suitable habitat to classify landscapes for conservation of threatened bull trout

USGS Publications Warehouse

Peterson, J.; Dunham, J.B.

2003-01-01

Effective conservation efforts for at-risk species require knowledge of the locations of existing populations. Species presence can be estimated directly by conducting field-sampling surveys or alternatively by developing predictive models. Direct surveys can be expensive and inefficient, particularly for rare and difficult-to-sample species, and models of species presence may produce biased predictions. We present a Bayesian approach that combines sampling and model-based inferences for estimating species presence. The accuracy and cost-effectiveness of this approach were compared to those of sampling surveys and predictive models for estimating the presence of the threatened bull trout ( Salvelinus confluentus ) via simulation with existing models and empirical sampling data. Simulations indicated that a sampling-only approach would be the most effective and would result in the lowest presence and absence misclassification error rates for three thresholds of detection probability. When sampling effort was considered, however, the combined approach resulted in the lowest error rates per unit of sampling effort. Hence, lower probability-of-detection thresholds can be specified with the combined approach, resulting in lower misclassification error rates and improved cost-effectiveness.

Control of a simulated arm using a novel combination of Cerebellar learning mechanisms

NASA Technical Reports Server (NTRS)

Assad, C.; Hartmann, M.; Paulin, M. G.

2001-01-01

We present a model of cerebellar cortex that combines two types of learning: feedforward predicitve association based on local Hebbian-type learning between granule cell ascending branch and parallel fiber inputs, and reinforcement learning with feedback error correction based on climbing fiber activity.

A study of different modeling choices for simulating platelets within the immersed boundary method

PubMed Central

Shankar, Varun; Wright, Grady B.; Fogelson, Aaron L.; Kirby, Robert M.

2012-01-01

The Immersed Boundary (IB) method is a widely-used numerical methodology for the simulation of fluid–structure interaction problems. The IB method utilizes an Eulerian discretization for the fluid equations of motion while maintaining a Lagrangian representation of structural objects. Operators are defined for transmitting information (forces and velocities) between these two representations. Most IB simulations represent their structures with piecewise linear approximations and utilize Hookean spring models to approximate structural forces. Our specific motivation is the modeling of platelets in hemodynamic flows. In this paper, we study two alternative representations – radial basis functions (RBFs) and Fourier-based (trigonometric polynomials and spherical harmonics) representations – for the modeling of platelets in two and three dimensions within the IB framework, and compare our results with the traditional piecewise linear approximation methodology. For different representative shapes, we examine the geometric modeling errors (position and normal vectors), force computation errors, and computational cost and provide an engineering trade-off strategy for when and why one might select to employ these different representations. PMID:23585704

A material sensitivity study on the accuracy of deformable organ registration using linear biomechanical models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chi, Y.; Liang, J.; Yan, D.

2006-02-15

Model-based deformable organ registration techniques using the finite element method (FEM) have recently been investigated intensively and applied to image-guided adaptive radiotherapy (IGART). These techniques assume that human organs are linearly elastic material, and their mechanical properties are predetermined. Unfortunately, the accurate measurement of the tissue material properties is challenging and the properties usually vary between patients. A common issue is therefore the achievable accuracy of the calculation due to the limited access to tissue elastic material constants. In this study, we performed a systematic investigation on this subject based on tissue biomechanics and computer simulations to establish the relationshipsmore » between achievable registration accuracy and tissue mechanical and organ geometrical properties. Primarily we focused on image registration for three organs: rectal wall, bladder wall, and prostate. The tissue anisotropy due to orientation preference in tissue fiber alignment is captured by using an orthotropic or a transversely isotropic elastic model. First we developed biomechanical models for the rectal wall, bladder wall, and prostate using simplified geometries and investigated the effect of varying material parameters on the resulting organ deformation. Then computer models based on patient image data were constructed, and image registrations were performed. The sensitivity of registration errors was studied by perturbating the tissue material properties from their mean values while fixing the boundary conditions. The simulation results demonstrated that registration error for a subvolume increases as its distance from the boundary increases. Also, a variable associated with material stability was found to be a dominant factor in registration accuracy in the context of material uncertainty. For hollow thin organs such as rectal walls and bladder walls, the registration errors are limited. Given 30% in material uncertainty, the registration error is limited to within 1.3 mm. For a solid organ such as the prostate, the registration errors are much larger. Given 30% in material uncertainty, the registration error can reach 4.5 mm. However, the registration error distribution for prostates shows that most of the subvolumes have a much smaller registration error. A deformable organ registration technique that uses FEM is a good candidate in IGART if the mean material parameters are available.« less

The CFS-PML in numerical simulation of ATEM

NASA Astrophysics Data System (ADS)

Zhao, Xuejiao; Ji, Yanju; Qiu, Shuo; Guan, Shanshan; Wu, Yanqi

2017-01-01

In the simulation of airborne transient electromagnetic method (ATEM) in time-domain, the truncated boundary reflection can bring a big error to the results. The complex frequency shifted perfectly matched layer (CFS-PML) absorbing boundary condition has been proved to have a better absorption of low frequency incident wave and can reduce the late reflection greatly. In this paper, we apply the CFS-PML to three-dimensional numerical simulation of ATEM in time-domain to achieve a high precision .The expression of divergence equation in CFS-PML is confirmed and its explicit iteration format based on the finite difference method and the recursive convolution technique is deduced. Finally, we use the uniformity half space model and the anomalous model to test the validity of this method. Results show that the CFS-PML can reduce the average relative error to 2.87% and increase the accuracy of the anomaly recognition.

Upscaling NZ-DNDC using a regression based meta-model to estimate direct N2O emissions from New Zealand grazed pastures.

PubMed

Giltrap, Donna L; Ausseil, Anne-Gaëlle E

2016-01-01

The availability of detailed input data frequently limits the application of process-based models at large scale. In this study, we produced simplified meta-models of the simulated nitrous oxide (N2O) emission factors (EF) using NZ-DNDC. Monte Carlo simulations were performed and the results investigated using multiple regression analysis to produce simplified meta-models of EF. These meta-models were then used to estimate direct N2O emissions from grazed pastures in New Zealand. New Zealand EF maps were generated using the meta-models with data from national scale soil maps. Direct emissions of N2O from grazed pasture were calculated by multiplying the EF map with a nitrogen (N) input map. Three meta-models were considered. Model 1 included only the soil organic carbon in the top 30cm (SOC30), Model 2 also included a clay content factor, and Model 3 added the interaction between SOC30 and clay. The median annual national direct N2O emissions from grazed pastures estimated using each model (assuming model errors were purely random) were: 9.6GgN (Model 1), 13.6GgN (Model 2), and 11.9GgN (Model 3). These values corresponded to an average EF of 0.53%, 0.75% and 0.63% respectively, while the corresponding average EF using New Zealand national inventory values was 0.67%. If the model error can be assumed to be independent for each pixel then the 95% confidence interval for the N2O emissions was of the order of ±0.4-0.7%, which is much lower than existing methods. However, spatial correlations in the model errors could invalidate this assumption. Under the extreme assumption that the model error for each pixel was identical the 95% confidence interval was approximately ±100-200%. Therefore further work is needed to assess the degree of spatial correlation in the model errors. Copyright © 2015 Elsevier B.V. All rights reserved.

A Single-column Model Ensemble Approach Applied to the TWP-ICE Experiment

NASA Technical Reports Server (NTRS)

Davies, L.; Jakob, C.; Cheung, K.; DelGenio, A.; Hill, A.; Hume, T.; Keane, R. J.; Komori, T.; Larson, V. E.; Lin, Y.;

ITOUGH2(UNIX). Inverse Modeling for TOUGH2 Family of Multiphase Flow Simulators

DOE Office of Scientific and Technical Information (OSTI.GOV)

Finsterle, S.

1999-03-01

ITOUGH2 provides inverse modeling capabilities for the TOUGH2 family of numerical simulators for non-isothermal multiphase flows in fractured-porous media. The ITOUGH2 can be used for estimating parameters by automatic modeling calibration, for sensitivity analyses, and for uncertainity propagation analyses (linear and Monte Carlo simulations). Any input parameter to the TOUGH2 simulator can be estimated based on any type of observation for which a corresponding TOUGH2 output is calculated. ITOUGH2 solves a non-linear least-squares problem using direct or gradient-based minimization algorithms. A detailed residual and error analysis is performed, which includes the evaluation of model identification criteria. ITOUGH2 can also bemore » run in forward mode, solving subsurface flow problems related to nuclear waste isolation, oil, gas, and geothermal resevoir engineering, and vadose zone hydrology.« less

Impact of nonzero boresight pointing errors on the performance of a relay-assisted free-space optical communication system over exponentiated Weibull fading channels.

PubMed

Wang, Ping; Liu, Xiaoxia; Cao, Tian; Fu, Huihua; Wang, Ranran; Guo, Lixin

2016-09-20

The impact of nonzero boresight pointing errors on the system performance of decode-and-forward protocol-based multihop parallel optical wireless communication systems is studied. For the aggregated fading channel, the atmospheric turbulence is simulated by an exponentiated Weibull model, and pointing errors are described by one recently proposed statistical model including both boresight and jitter. The binary phase-shift keying subcarrier intensity modulation-based analytical average bit error rate (ABER) and outage probability expressions are achieved for a nonidentically and independently distributed system. The ABER and outage probability are then analyzed with different turbulence strengths, receiving aperture sizes, structure parameters (P and Q), jitter variances, and boresight displacements. The results show that aperture averaging offers almost the same system performance improvement with boresight included or not, despite the values of P and Q. The performance enhancement owing to the increase of cooperative path (P) is more evident with nonzero boresight than that with zero boresight (jitter only), whereas the performance deterioration because of the increasing hops (Q) with nonzero boresight is almost the same as that with zero boresight. Monte Carlo simulation is offered to verify the validity of ABER and outage probability expressions.

New Approaches to Quantifying Transport Model Error in Atmospheric CO2 Simulations

NASA Technical Reports Server (NTRS)

Ott, L.; Pawson, S.; Zhu, Z.; Nielsen, J. E.; Collatz, G. J.; Gregg, W. W.

2012-01-01

In recent years, much progress has been made in observing CO2 distributions from space. However, the use of these observations to infer source/sink distributions in inversion studies continues to be complicated by difficulty in quantifying atmospheric transport model errors. We will present results from several different experiments designed to quantify different aspects of transport error using the Goddard Earth Observing System, Version 5 (GEOS-5) Atmospheric General Circulation Model (AGCM). In the first set of experiments, an ensemble of simulations is constructed using perturbations to parameters in the model s moist physics and turbulence parameterizations that control sub-grid scale transport of trace gases. Analysis of the ensemble spread and scales of temporal and spatial variability among the simulations allows insight into how parameterized, small-scale transport processes influence simulated CO2 distributions. In the second set of experiments, atmospheric tracers representing model error are constructed using observation minus analysis statistics from NASA's Modern-Era Retrospective Analysis for Research and Applications (MERRA). The goal of these simulations is to understand how errors in large scale dynamics are distributed, and how they propagate in space and time, affecting trace gas distributions. These simulations will also be compared to results from NASA's Carbon Monitoring System Flux Pilot Project that quantified the impact of uncertainty in satellite constrained CO2 flux estimates on atmospheric mixing ratios to assess the major factors governing uncertainty in global and regional trace gas distributions.

Model selection for marginal regression analysis of longitudinal data with missing observations and covariate measurement error.

PubMed

Shen, Chung-Wei; Chen, Yi-Hau

2015-10-01

Missing observations and covariate measurement error commonly arise in longitudinal data. However, existing methods for model selection in marginal regression analysis of longitudinal data fail to address the potential bias resulting from these issues. To tackle this problem, we propose a new model selection criterion, the Generalized Longitudinal Information Criterion, which is based on an approximately unbiased estimator for the expected quadratic error of a considered marginal model accounting for both data missingness and covariate measurement error. The simulation results reveal that the proposed method performs quite well in the presence of missing data and covariate measurement error. On the contrary, the naive procedures without taking care of such complexity in data may perform quite poorly. The proposed method is applied to data from the Taiwan Longitudinal Study on Aging to assess the relationship of depression with health and social status in the elderly, accommodating measurement error in the covariate as well as missing observations. © The Author 2015. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Precipitation and Latent Heating Distributions from Satellite Passive Microwave Radiometry. Part II: Evaluation of Estimates Using Independent Data

NASA Technical Reports Server (NTRS)

Yang, Song; Olson, William S.; Wang, Jian-Jian; Bell, Thomas L.; Smith, Eric A.; Kummerow, Christian D.

2006-01-01

Rainfall rate estimates from spaceborne microwave radiometers are generally accepted as reliable by a majority of the atmospheric science community. One of the Tropical Rainfall Measuring Mission (TRMM) facility rain-rate algorithms is based upon passive microwave observations from the TRMM Microwave Imager (TMI). In Part I of this series, improvements of the TMI algorithm that are required to introduce latent heating as an additional algorithm product are described. Here, estimates of surface rain rate, convective proportion, and latent heating are evaluated using independent ground-based estimates and satellite products. Instantaneous, 0.5 deg. -resolution estimates of surface rain rate over ocean from the improved TMI algorithm are well correlated with independent radar estimates (r approx. 0.88 over the Tropics), but bias reduction is the most significant improvement over earlier algorithms. The bias reduction is attributed to the greater breadth of cloud-resolving model simulations that support the improved algorithm and the more consistent and specific convective/stratiform rain separation method utilized. The bias of monthly 2.5 -resolution estimates is similarly reduced, with comparable correlations to radar estimates. Although the amount of independent latent heating data is limited, TMI-estimated latent heating profiles compare favorably with instantaneous estimates based upon dual-Doppler radar observations, and time series of surface rain-rate and heating profiles are generally consistent with those derived from rawinsonde analyses. Still, some biases in profile shape are evident, and these may be resolved with (a) additional contextual information brought to the estimation problem and/or (b) physically consistent and representative databases supporting the algorithm. A model of the random error in instantaneous 0.5 deg. -resolution rain-rate estimates appears to be consistent with the levels of error determined from TMI comparisons with collocated radar. Error model modifications for nonraining situations will be required, however. Sampling error represents only a portion of the total error in monthly 2.5 -resolution TMI estimates; the remaining error is attributed to random and systematic algorithm errors arising from the physical inconsistency and/or nonrepresentativeness of cloud-resolving-model-simulated profiles that support the algorithm.

Assessing the Effects of Data Compression in Simulations Using Physically Motivated Metrics

DOE PAGES

Laney, Daniel; Langer, Steven; Weber, Christopher; ...

2014-01-01

This paper examines whether lossy compression can be used effectively in physics simulations as a possible strategy to combat the expected data-movement bottleneck in future high performance computing architectures. We show that, for the codes and simulations we tested, compression levels of 3–5X can be applied without causing significant changes to important physical quantities. Rather than applying signal processing error metrics, we utilize physics-based metrics appropriate for each code to assess the impact of compression. We evaluate three different simulation codes: a Lagrangian shock-hydrodynamics code, an Eulerian higher-order hydrodynamics turbulence modeling code, and an Eulerian coupled laser-plasma interaction code. Wemore » compress relevant quantities after each time-step to approximate the effects of tightly coupled compression and study the compression rates to estimate memory and disk-bandwidth reduction. We find that the error characteristics of compression algorithms must be carefully considered in the context of the underlying physics being modeled.« less

Multi-Site λ-dynamics for simulated Structure-Activity Relationship studies

PubMed Central

Knight, Jennifer L.; Brooks, Charles L.

2011-01-01

Multi-Site λ-dynamics (MSλD) is a new free energy simulation method that is based on λ-dynamics. It has been developed to enable multiple substituents at multiple sites on a common ligand core to be modeled simultaneously and their free energies assessed. The efficacy of MSλD for estimating relative hydration free energies and relative binding affinties is demonstrated using three test systems. Model compounds representing multiple identical benzene, dihydroxybenzene and dimethoxybenzene molecules show total combined MSλD trajectory lengths of ~1.5 ns are sufficient to reliably achieve relative hydration free energy estimates within 0.2 kcal/mol and are less sensitive to the number of trajectories that are used to generate these estimates for hybrid ligands that contain up to ten substituents modeled at a single site or five substituents modeled at each of two sites. Relative hydration free energies among six benzene derivatives calculated from MSλD simulations are in very good agreement with those from alchemical free energy simulations (with average unsigned differences of 0.23 kcal/mol and R2=0.991) and experiment (with average unsigned errors of 1.8 kcal/mol and R2=0.959). Estimates of the relative binding affinities among 14 inhibitors of HIV-1 reverse transcriptase obtained from MSλD simulations are in reasonable agreement with those from traditional free energy simulations and experiment (average unsigned errors of 0.9 kcal/mol and R2=0.402). For the same level of accuracy and precision MSλD simulations are achieved ~20–50 times faster than traditional free energy simulations and thus with reliable force field parameters can be used effectively to screen tens to hundreds of compounds in structure-based drug design applications. PMID:22125476

Numerical investigations of potential systematic uncertainties in iron opacity measurements at solar interior temperatures

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nagayama, T.; Bailey, J. E.; Loisel, G. P.

Iron opacity calculations presently disagree with measurements at an electron temperature of ~180–195 eV and an electron density of (2–4)×10 22cm –3, conditions similar to those at the base of the solar convection zone. The measurements use x rays to volumetrically heat a thin iron sample that is tamped with low-Z materials. The opacity is inferred from spectrally resolved x-ray transmission measurements. Plasma self-emission, tamper attenuation, and temporal and spatial gradients can all potentially cause systematic errors in the measured opacity spectra. In this article we quantitatively evaluate these potential errors with numerical investigations. The analysis exploits computer simulations thatmore » were previously found to reproduce the experimentally measured plasma conditions. The simulations, combined with a spectral synthesis model, enable evaluations of individual and combined potential errors in order to estimate their potential effects on the opacity measurement. Lastly, the results show that the errors considered here do not account for the previously observed model-data discrepancies.« less

Numerical investigations of potential systematic uncertainties in iron opacity measurements at solar interior temperatures

DOE PAGES

Nagayama, T.; Bailey, J. E.; Loisel, G. P.; ...

2017-06-26

Iron opacity calculations presently disagree with measurements at an electron temperature of ~180–195 eV and an electron density of (2–4)×10 22cm –3, conditions similar to those at the base of the solar convection zone. The measurements use x rays to volumetrically heat a thin iron sample that is tamped with low-Z materials. The opacity is inferred from spectrally resolved x-ray transmission measurements. Plasma self-emission, tamper attenuation, and temporal and spatial gradients can all potentially cause systematic errors in the measured opacity spectra. In this article we quantitatively evaluate these potential errors with numerical investigations. The analysis exploits computer simulations thatmore » were previously found to reproduce the experimentally measured plasma conditions. The simulations, combined with a spectral synthesis model, enable evaluations of individual and combined potential errors in order to estimate their potential effects on the opacity measurement. Lastly, the results show that the errors considered here do not account for the previously observed model-data discrepancies.« less

Videopanorama Frame Rate Requirements Derived from Visual Discrimination of Deceleration During Simulated Aircraft Landing

NASA Technical Reports Server (NTRS)

Furnstenau, Norbert; Ellis, Stephen R.

2015-01-01

In order to determine the required visual frame rate (FR) for minimizing prediction errors with out-the-window video displays at remote/virtual airport towers, thirteen active air traffic controllers viewed high dynamic fidelity simulations of landing aircraft and decided whether aircraft would stop as if to be able to make a turnoff or whether a runway excursion would be expected. The viewing conditions and simulation dynamics replicated visual rates and environments of transport aircraft landing at small commercial airports. The required frame rate was estimated using Bayes inference on prediction errors by linear FRextrapolation of event probabilities conditional on predictions (stop, no-stop). Furthermore estimates were obtained from exponential model fits to the parametric and non-parametric perceptual discriminabilities d' and A (average area under ROC-curves) as dependent on FR. Decision errors are biased towards preference of overshoot and appear due to illusionary increase in speed at low frames rates. Both Bayes and A - extrapolations yield a framerate requirement of 35 < FRmin < 40 Hz. When comparing with published results [12] on shooter game scores the model based d'(FR)-extrapolation exhibits the best agreement and indicates even higher FRmin > 40 Hz for minimizing decision errors. Definitive recommendations require further experiments with FR > 30 Hz.

Verifiable fault tolerance in measurement-based quantum computation

NASA Astrophysics Data System (ADS)

Fujii, Keisuke; Hayashi, Masahito

2017-09-01

Quantum systems, in general, cannot be simulated efficiently by a classical computer, and hence are useful for solving certain mathematical problems and simulating quantum many-body systems. This also implies, unfortunately, that verification of the output of the quantum systems is not so trivial, since predicting the output is exponentially hard. As another problem, the quantum system is very delicate for noise and thus needs an error correction. Here, we propose a framework for verification of the output of fault-tolerant quantum computation in a measurement-based model. In contrast to existing analyses on fault tolerance, we do not assume any noise model on the resource state, but an arbitrary resource state is tested by using only single-qubit measurements to verify whether or not the output of measurement-based quantum computation on it is correct. Verifiability is equipped by a constant time repetition of the original measurement-based quantum computation in appropriate measurement bases. Since full characterization of quantum noise is exponentially hard for large-scale quantum computing systems, our framework provides an efficient way to practically verify the experimental quantum error correction.

Application of Biologically-Based Lumping To Investigate the ...

EPA Pesticide Factsheets

People are often exposed to complex mixtures of environmental chemicals such as gasoline, tobacco smoke, water contaminants, or food additives. However, investigators have often considered complex mixtures as one lumped entity. Valuable information can be obtained from these experiments, though this simplification provides little insight into the impact of a mixture's chemical composition on toxicologically-relevant metabolic interactions that may occur among its constituents. We developed an approach that applies chemical lumping methods to complex mixtures, in this case gasoline, based on biologically relevant parameters used in physiologically-based pharmacokinetic (PBPK) modeling. Inhalation exposures were performed with rats to evaluate performance of our PBPK model. There were 109 chemicals identified and quantified in the vapor in the chamber. The time-course kinetic profiles of 10 target chemicals were also determined from blood samples collected during and following the in vivo experiments. A general PBPK model was used to compare the experimental data to the simulated values of blood concentration for the 10 target chemicals with various numbers of lumps, iteratively increasing from 0 to 99. Large reductions in simulation error were gained by incorporating enzymatic chemical interactions, in comparison to simulating the individual chemicals separately. The error was further reduced by lumping the 99 non-target chemicals. Application of this biologic

An improved estimator for the hydration of fat-free mass from in vivo measurements subject to additive technical errors.

PubMed

Kinnamon, Daniel D; Lipsitz, Stuart R; Ludwig, David A; Lipshultz, Steven E; Miller, Tracie L

2010-04-01

The hydration of fat-free mass, or hydration fraction (HF), is often defined as a constant body composition parameter in a two-compartment model and then estimated from in vivo measurements. We showed that the widely used estimator for the HF parameter in this model, the mean of the ratios of measured total body water (TBW) to fat-free mass (FFM) in individual subjects, can be inaccurate in the presence of additive technical errors. We then proposed a new instrumental variables estimator that accurately estimates the HF parameter in the presence of such errors. In Monte Carlo simulations, the mean of the ratios of TBW to FFM was an inaccurate estimator of the HF parameter, and inferences based on it had actual type I error rates more than 13 times the nominal 0.05 level under certain conditions. The instrumental variables estimator was accurate and maintained an actual type I error rate close to the nominal level in all simulations. When estimating and performing inference on the HF parameter, the proposed instrumental variables estimator should yield accurate estimates and correct inferences in the presence of additive technical errors, but the mean of the ratios of TBW to FFM in individual subjects may not.

Integrating models that depend on variable data

NASA Astrophysics Data System (ADS)

Banks, A. T.; Hill, M. C.

2016-12-01

Models of human-Earth systems are often developed with the goal of predicting the behavior of one or more dependent variables from multiple independent variables, processes, and parameters. Often dependent variable values range over many orders of magnitude, which complicates evaluation of the fit of the dependent variable values to observations. Many metrics and optimization methods have been proposed to address dependent variable variability, with little consensus being achieved. In this work, we evaluate two such methods: log transformation (based on the dependent variable being log-normally distributed with a constant variance) and error-based weighting (based on a multi-normal distribution with variances that tend to increase as the dependent variable value increases). Error-based weighting has the advantage of encouraging model users to carefully consider data errors, such as measurement and epistemic errors, while log-transformations can be a black box for typical users. Placing the log-transformation into the statistical perspective of error-based weighting has not formerly been considered, to the best of our knowledge. To make the evaluation as clear and reproducible as possible, we use multiple linear regression (MLR). Simulations are conducted with MatLab. The example represents stream transport of nitrogen with up to eight independent variables. The single dependent variable in our example has values that range over 4 orders of magnitude. Results are applicable to any problem for which individual or multiple data types produce a large range of dependent variable values. For this problem, the log transformation produced good model fit, while some formulations of error-based weighting worked poorly. Results support previous suggestions fthat error-based weighting derived from a constant coefficient of variation overemphasizes low values and degrades model fit to high values. Applying larger weights to the high values is inconsistent with the log-transformation. Greater consistency is obtained by imposing smaller (by up to a factor of 1/35) weights on the smaller dependent-variable values. From an error-based perspective, the small weights are consistent with large standard deviations. This work considers the consequences of these two common ways of addressing variable data.

Numerical dissipation vs. subgrid-scale modelling for large eddy simulation

NASA Astrophysics Data System (ADS)

Dairay, Thibault; Lamballais, Eric; Laizet, Sylvain; Vassilicos, John Christos

2017-05-01

This study presents an alternative way to perform large eddy simulation based on a targeted numerical dissipation introduced by the discretization of the viscous term. It is shown that this regularisation technique is equivalent to the use of spectral vanishing viscosity. The flexibility of the method ensures high-order accuracy while controlling the level and spectral features of this purely numerical viscosity. A Pao-like spectral closure based on physical arguments is used to scale this numerical viscosity a priori. It is shown that this way of approaching large eddy simulation is more efficient and accurate than the use of the very popular Smagorinsky model in standard as well as in dynamic version. The main strength of being able to correctly calibrate numerical dissipation is the possibility to regularise the solution at the mesh scale. Thanks to this property, it is shown that the solution can be seen as numerically converged. Conversely, the two versions of the Smagorinsky model are found unable to ensure regularisation while showing a strong sensitivity to numerical errors. The originality of the present approach is that it can be viewed as implicit large eddy simulation, in the sense that the numerical error is the source of artificial dissipation, but also as explicit subgrid-scale modelling, because of the equivalence with spectral viscosity prescribed on a physical basis.

Limited-memory adaptive snapshot selection for proper orthogonal decomposition

DOE Office of Scientific and Technical Information (OSTI.GOV)

Oxberry, Geoffrey M.; Kostova-Vassilevska, Tanya; Arrighi, Bill

2015-04-02

Reduced order models are useful for accelerating simulations in many-query contexts, such as optimization, uncertainty quantification, and sensitivity analysis. However, offline training of reduced order models can have prohibitively expensive memory and floating-point operation costs in high-performance computing applications, where memory per core is limited. To overcome this limitation for proper orthogonal decomposition, we propose a novel adaptive selection method for snapshots in time that limits offline training costs by selecting snapshots according an error control mechanism similar to that found in adaptive time-stepping ordinary differential equation solvers. The error estimator used in this work is related to theory boundingmore » the approximation error in time of proper orthogonal decomposition-based reduced order models, and memory usage is minimized by computing the singular value decomposition using a single-pass incremental algorithm. Results for a viscous Burgers’ test problem demonstrate convergence in the limit as the algorithm error tolerances go to zero; in this limit, the full order model is recovered to within discretization error. The resulting method can be used on supercomputers to generate proper orthogonal decomposition-based reduced order models, or as a subroutine within hyperreduction algorithms that require taking snapshots in time, or within greedy algorithms for sampling parameter space.« less

CAUSES: On the Role of Surface Energy Budget Errors to the Warm Surface Air Temperature Error Over the Central United States

DOE PAGES

Ma, H. -Y.; Klein, S. A.; Xie, S.; ...

2018-02-27

Many weather forecast and climate models simulate warm surface air temperature (T 2m) biases over midlatitude continents during the summertime, especially over the Great Plains. We present here one of a series of papers from a multimodel intercomparison project (CAUSES: Cloud Above the United States and Errors at the Surface), which aims to evaluate the role of cloud, radiation, and precipitation biases in contributing to the T 2m bias using a short-term hindcast approach during the spring and summer of 2011. Observations are mainly from the Atmospheric Radiation Measurement Southern Great Plains sites. The present study examines the contributions ofmore » surface energy budget errors. All participating models simulate too much net shortwave and longwave fluxes at the surface but with no consistent mean bias sign in turbulent fluxes over the Central United States and Southern Great Plains. Nevertheless, biases in the net shortwave and downward longwave fluxes as well as surface evaporative fraction (EF) are contributors to T 2m bias. Radiation biases are largely affected by cloud simulations, while EF bias is largely affected by soil moisture modulated by seasonal accumulated precipitation and evaporation. An approximate equation based upon the surface energy budget is derived to further quantify the magnitudes of radiation and EF contributions to T 2m bias. Our analysis ascribes that a large EF underestimate is the dominant source of error in all models with a large positive temperature bias, whereas an EF overestimate compensates for an excess of absorbed shortwave radiation in nearly all the models with the smallest temperature bias.« less

CAUSES: On the Role of Surface Energy Budget Errors to the Warm Surface Air Temperature Error Over the Central United States

NASA Astrophysics Data System (ADS)

Ma, H.-Y.; Klein, S. A.; Xie, S.; Zhang, C.; Tang, S.; Tang, Q.; Morcrette, C. J.; Van Weverberg, K.; Petch, J.; Ahlgrimm, M.; Berg, L. K.; Cheruy, F.; Cole, J.; Forbes, R.; Gustafson, W. I.; Huang, M.; Liu, Y.; Merryfield, W.; Qian, Y.; Roehrig, R.; Wang, Y.-C.

2018-03-01

Many weather forecast and climate models simulate warm surface air temperature (T2m) biases over midlatitude continents during the summertime, especially over the Great Plains. We present here one of a series of papers from a multimodel intercomparison project (CAUSES: Cloud Above the United States and Errors at the Surface), which aims to evaluate the role of cloud, radiation, and precipitation biases in contributing to the T2m bias using a short-term hindcast approach during the spring and summer of 2011. Observations are mainly from the Atmospheric Radiation Measurement Southern Great Plains sites. The present study examines the contributions of surface energy budget errors. All participating models simulate too much net shortwave and longwave fluxes at the surface but with no consistent mean bias sign in turbulent fluxes over the Central United States and Southern Great Plains. Nevertheless, biases in the net shortwave and downward longwave fluxes as well as surface evaporative fraction (EF) are contributors to T2m bias. Radiation biases are largely affected by cloud simulations, while EF bias is largely affected by soil moisture modulated by seasonal accumulated precipitation and evaporation. An approximate equation based upon the surface energy budget is derived to further quantify the magnitudes of radiation and EF contributions to T2m bias. Our analysis ascribes that a large EF underestimate is the dominant source of error in all models with a large positive temperature bias, whereas an EF overestimate compensates for an excess of absorbed shortwave radiation in nearly all the models with the smallest temperature bias.

CAUSES: On the Role of Surface Energy Budget Errors to the Warm Surface Air Temperature Error Over the Central United States

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ma, H. -Y.; Klein, S. A.; Xie, S.

Many weather forecast and climate models simulate warm surface air temperature (T 2m) biases over midlatitude continents during the summertime, especially over the Great Plains. We present here one of a series of papers from a multimodel intercomparison project (CAUSES: Cloud Above the United States and Errors at the Surface), which aims to evaluate the role of cloud, radiation, and precipitation biases in contributing to the T 2m bias using a short-term hindcast approach during the spring and summer of 2011. Observations are mainly from the Atmospheric Radiation Measurement Southern Great Plains sites. The present study examines the contributions ofmore » surface energy budget errors. All participating models simulate too much net shortwave and longwave fluxes at the surface but with no consistent mean bias sign in turbulent fluxes over the Central United States and Southern Great Plains. Nevertheless, biases in the net shortwave and downward longwave fluxes as well as surface evaporative fraction (EF) are contributors to T 2m bias. Radiation biases are largely affected by cloud simulations, while EF bias is largely affected by soil moisture modulated by seasonal accumulated precipitation and evaporation. An approximate equation based upon the surface energy budget is derived to further quantify the magnitudes of radiation and EF contributions to T 2m bias. Our analysis ascribes that a large EF underestimate is the dominant source of error in all models with a large positive temperature bias, whereas an EF overestimate compensates for an excess of absorbed shortwave radiation in nearly all the models with the smallest temperature bias.« less

Simulation of streamflow and water quality in the Red Clay Creek subbasin of the Christina River Basin, Pennsylvania and Delaware, 1994-98

USGS Publications Warehouse

Senior, Lisa A.; Koerkle, Edward H.

2003-01-01

The Christina River Basin drains 565 square miles (mi2) in Pennsylvania and Delaware and includes the major subbasins of Red Clay Creek, White Clay Creek, Brandywine Creek, and Christina River. The Red Clay Creek is the smallest of the subbasins and drains an area of 54 mi2. Streams in the Christina River Basin are used for recreation, drinking-water supply, and to support aquatic life. Water quality in some parts of the Christina River Basin is impaired and does not support designated uses of the stream. A multi-agency, waterquality management strategy included a modeling component to evaluate the effects of point and nonpointsource contributions of nutrients and suspended sediment on stream water quality. To assist in nonpointsource evaluation, four independent models, one for each of the four main subbasins of the Christina River Basin, were developed and calibrated using the model code Hydrological Simulation Program?Fortran (HSPF). Water-quality data for model calibration were collected in each of the four main subbasins and in smaller subbasins predominantly covered by one land use following a nonpoint-source monitoring plan. Under this plan, stormflow and base-flow samples were collected during 1998 at 1 site in the Red Clay Creek subbasin and at 10 sites elsewhere in the Christina River Basin.The HSPF model for the Red Clay Creek subbasin simulates streamflow, suspended sediment, and the nutrients, nitrogen and phosphorus. In addition, the model simulates water temperature, dissolved oxygen, biochemical oxygen demand, and plankton as secondary objectives needed to support the sediment and nutrient simulations. For the model, the basin was subdivided into nine reaches draining areas that ranged from 1.7 to 10 mi2. One of the reaches contains a regulated reservoir. Ten different pervious land uses and two impervious land uses were selected for simulation. Land-use areas were determined from 1995 land-use data. The predominant land uses in the Red Clay Creek subbasin are agricultural, forested, residential, and urban.The hydrologic component of the model was run at an hourly time step and calibrated using streamflow data from three U.S. Geological Survey (USGS) streamflow-measurement stations for the period of October 1, 1994, through October 29, 1998. Daily precipitation data from one National Oceanic and Atmospheric Administration (NOAA) gage and hourly data from one NOAA gage were used for model input. The difference between observed and simulated stream- flow volume ranged from -0.8 to 2.1 percent for the 4-year period at the three calibration sites. Annual differences between observed and simulated streamflow generally were greater than the overall error for the 4-year period. For example, at a site near Stanton, Del., near the bottom of the basin (drainage area of 50.2 mi2), annual differences between observed and simulated streamflow ranged from -5.8 to 6.0 percent and the overall error for the 4-year period was -0.8 percent. Calibration errors for 36 storm periods at the three calibration sites for total volume, low-flow-recession rate, 50-percent lowest flows, 10-percent highest flows, and storm peaks were 20 percent or less. Much of the error in simulating storm events on an hourly time step can be attributed to uncertainty in the rainfall data.The water-quality component of the model was calibrated using nonpoint-source monitoring data collected in 1998 at one USGS streamflowmeasurement station and other water-quality monitoring data collected at three USGS streamflowmeasurement stations. The period of record for waterquality monitoring was variable at the stations, with an end date of October 1998 but the start date ranging from October 1994 to January 1998. Because of availability, monitoring data for suspended-solids concentrations were used as surrogates for suspendedsediment concentrations, although suspended solids may underestimate suspended sediment and affect apparent accuracy of the suspended-sediment simulation. Comparison of observed to simulated loads for five storms in 1998 at the one nonpoint-source monitoring site at Wooddale, Del., indicates that simulation error commonly is as large as an order of magnitude for suspended sediment and nutrients. The simulation error tends to be smaller for dissolved utrients than particulate nutrients. Errors of 40 percent or less for monthly or annual values indicate a fair to good water-quality calibration according to recommended criteria, with much larger errors possible for individual storm events. Assessment of the accuracy of the water-quality calibration under stormflow conditions is limited by the sparsity of available water-quality data in the basin.Users of the Red Clay Creek HSPF model should be aware of model limitations and consider the following when predictive scenarios are desired: streamflow-duration curves indicate the model simulates stream-flow reasonably well when evaluated over a broad range of conditions and time, although streamflow and the corresponding water quality for individual storm events may not be well simulated; streamflow-duration curves for the simulation period compare well with duration curves for the 57.5-year period ending in 2001 at Wooddale, Del., and include all but the extreme high-flow and low-flow events; calibration for water quality was based on sparse data, with the result of increasing uncertainty in the water-quality simulation.

Air-Sea Interaction Processes in Low and High-Resolution Coupled Climate Model Simulations for the Southeast Pacific

NASA Astrophysics Data System (ADS)

Porto da Silveira, I.; Zuidema, P.; Kirtman, B. P.

2017-12-01

The rugged topography of the Andes Cordillera along with strong coastal upwelling, strong sea surface temperatures (SST) gradients and extensive but geometrically-thin stratocumulus decks turns the Southeast Pacific (SEP) into a challenge for numerical modeling. In this study, hindcast simulations using the Community Climate System Model (CCSM4) at two resolutions were analyzed to examine the importance of resolution alone, with the parameterizations otherwise left unchanged. The hindcasts were initialized on January 1 with the real-time oceanic and atmospheric reanalysis (CFSR) from 1982 to 2003, forming a 10-member ensemble. The two resolutions are (0.1o oceanic and 0.5o atmospheric) and (1.125o oceanic and 0.9o atmospheric). The SST error growth in the first six days of integration (fast errors) and those resulted from model drift (saturated errors) are assessed and compared towards evaluating the model processes responsible for the SST error growth. For the high-resolution simulation, SST fast errors are positive (+0.3oC) near the continental borders and negative offshore (-0.1oC). Both are associated with a decrease in cloud cover, a weakening of the prevailing southwesterly winds and a reduction of latent heat flux. The saturated errors possess a similar spatial pattern, but are larger and are more spatially concentrated. This suggests that the processes driving the errors already become established within the first week, in contrast to the low-resolution simulations. These, instead, manifest too-warm SSTs related to too-weak upwelling, driven by too-strong winds and Ekman pumping. Nevertheless, the ocean surface tends to be cooler in the low-resolution simulation than the high-resolution due to a higher cloud cover. Throughout the integration, saturated SST errors become positive and could reach values up to +4oC. These are accompanied by upwelling dumping and a decrease in cloud cover. High and low resolution models presented notable differences in how SST errors variability drove atmospheric changes, especially because the high resolution is sensitive to resurgence regions. This allows the model to resolve cloud heights and establish different radiative feedbacks.

Accurate evaluation of sensitivity for calibration between a LiDAR and a panoramic camera used for remote sensing

NASA Astrophysics Data System (ADS)

García-Moreno, Angel-Iván; González-Barbosa, José-Joel; Ramírez-Pedraza, Alfonso; Hurtado-Ramos, Juan B.; Ornelas-Rodriguez, Francisco-Javier

2016-04-01

Computer-based reconstruction models can be used to approximate urban environments. These models are usually based on several mathematical approximations and the usage of different sensors, which implies dependency on many variables. The sensitivity analysis presented in this paper is used to weigh the relative importance of each uncertainty contributor into the calibration of a panoramic camera-LiDAR system. Both sensors are used for three-dimensional urban reconstruction. Simulated and experimental tests were conducted. For the simulated tests we analyze and compare the calibration parameters using the Monte Carlo and Latin hypercube sampling techniques. Sensitivity analysis for each variable involved into the calibration was computed by the Sobol method, which is based on the analysis of the variance breakdown, and the Fourier amplitude sensitivity test method, which is based on Fourier's analysis. Sensitivity analysis is an essential tool in simulation modeling and for performing error propagation assessments.

Simulation-based medical education: time for a pedagogical shift.

PubMed

Kalaniti, Kaarthigeyan; Campbell, Douglas M

2015-01-01

The purpose of medical education at all levels is to prepare physicians with the knowledge and comprehensive skills, required to deliver safe and effective patient care. The traditional 'apprentice' learning model in medical education is undergoing a pedagogical shift to a 'simulation-based' learning model. Experiential learning, deliberate practice and the ability to provide immediate feedback are the primary advantages of simulation-based medical education. It is an effective way to develop new skills, identify knowledge gaps, reduce medical errors, and maintain infrequently used clinical skills even among experienced clinical teams, with the overall goal of improving patient care. Although simulation cannot replace clinical exposure as a form of experiential learning, it promotes learning without compromising patient safety. This new paradigm shift is revolutionizing medical education in the Western world. It is time that the developing countries embrace this new pedagogical shift.

Assessment of errors in static electrical impedance tomography with adjacent and trigonometric current patterns.

PubMed

Kolehmainen, V; Vauhkonen, M; Karjalainen, P A; Kaipio, J P

1997-11-01

In electrical impedance tomography (EIT), difference imaging is often preferred over static imaging. This is because of the many unknowns in the forward modelling which make it difficult to obtain reliable absolute resistivity estimates. However, static imaging and absolute resistivity values are needed in some potential applications of EIT. In this paper we demonstrate by simulation the effects of different error components that are included in the reconstruction of static EIT images. All simulations are carried out in two dimensions with the so-called complete electrode model. Errors that are considered are the modelling error in the boundary shape of an object, errors in the electrode sizes and localizations and errors in the contact impedances under the electrodes. Results using both adjacent and trigonometric current patterns are given.

Grinding Method and Error Analysis of Eccentric Shaft Parts

NASA Astrophysics Data System (ADS)

Wang, Zhiming; Han, Qiushi; Li, Qiguang; Peng, Baoying; Li, Weihua

2017-12-01

RV reducer and various mechanical transmission parts are widely used in eccentric shaft parts, The demand of precision grinding technology for eccentric shaft parts now, In this paper, the model of X-C linkage relation of eccentric shaft grinding is studied; By inversion method, the contour curve of the wheel envelope is deduced, and the distance from the center of eccentric circle is constant. The simulation software of eccentric shaft grinding is developed, the correctness of the model is proved, the influence of the X-axis feed error, the C-axis feed error and the wheel radius error on the grinding process is analyzed, and the corresponding error calculation model is proposed. The simulation analysis is carried out to provide the basis for the contour error compensation.

Generation of Simulated Tracking Data for LADEE Operational Readiness Testing

NASA Technical Reports Server (NTRS)

Woodburn, James; Policastri, Lisa; Owens, Brandon

2015-01-01

Operational Readiness Tests were an important part of the pre-launch preparation for the LADEE mission. The generation of simulated tracking data to stress the Flight Dynamics System and the Flight Dynamics Team was important for satisfying the testing goal of demonstrating that the software and the team were ready to fly the operational mission. The simulated tracking was generated in a manner to incorporate the effects of errors in the baseline dynamical model, errors in maneuver execution and phenomenology associated with various tracking system based components. The ability of the mission team to overcome these challenges in a realistic flight dynamics scenario indicated that the team and flight dynamics system were ready to fly the LADEE mission. Lunar Atmosphere and Dust Environment.

Modeling SMAP Spacecraft Attitude Control Estimation Error Using Signal Generation Model

NASA Technical Reports Server (NTRS)

Rizvi, Farheen

2016-01-01

Two ground simulation software are used to model the SMAP spacecraft dynamics. The CAST software uses a higher fidelity model than the ADAMS software. The ADAMS software models the spacecraft plant, controller and actuator models, and assumes a perfect sensor and estimator model. In this simulation study, the spacecraft dynamics results from the ADAMS software are used as CAST software is unavailable. The main source of spacecraft dynamics error in the higher fidelity CAST software is due to the estimation error. A signal generation model is developed to capture the effect of this estimation error in the overall spacecraft dynamics. Then, this signal generation model is included in the ADAMS software spacecraft dynamics estimate such that the results are similar to CAST. This signal generation model has similar characteristics mean, variance and power spectral density as the true CAST estimation error. In this way, ADAMS software can still be used while capturing the higher fidelity spacecraft dynamics modeling from CAST software.

A supply model for nurse workforce projection in Malaysia.

PubMed

Abas, Zuraida Abal; Ramli, Mohamad Raziff; Desa, Mohamad Ishak; Saleh, Nordin; Hanafiah, Ainul Nadziha; Aziz, Nuraini; Abidin, Zaheera Zainal; Shibghatullah, Abdul Samad; Rahman, Ahmad Fadzli Nizam Abdul; Musa, Haslinda

2017-08-18

The paper aims to provide an insight into the significance of having a simulation model to forecast the supply of registered nurses for health workforce planning policy using System Dynamics. A model is highly in demand to predict the workforce demand for nurses in the future, which it supports for complete development of a needs-based nurse workforce projection using Malaysia as a case study. The supply model consists of three sub-models to forecast the number of registered nurses for the next 15 years: training model, population model and Full Time Equivalent (FTE) model. In fact, the training model is for predicting the number of newly registered nurses after training is completed. Furthermore, the population model is for indicating the number of registered nurses in the nation and the FTE model is useful for counting the number of registered nurses with direct patient care. Each model is described in detail with the logical connection and mathematical governing equation for accurate forecasting. The supply model is validated using error analysis approach in terms of the root mean square percent error and the Theil inequality statistics, which is mportant for evaluating the simulation results. Moreover, the output of simulation results provides a useful insight for policy makers as a what-if analysis is conducted. Some recommendations are proposed in order to deal with the nursing deficit. It must be noted that the results from the simulation model will be used for the next stage of the Needs-Based Nurse Workforce projection project. The impact of this study is that it provides the ability for greater planning and policy making with better predictions.

Generating classes of 3D virtual mandibles for AR-based medical simulation.

PubMed

Hippalgaonkar, Neha R; Sider, Alexa D; Hamza-Lup, Felix G; Santhanam, Anand P; Jaganathan, Bala; Imielinska, Celina; Rolland, Jannick P

2008-01-01

Simulation and modeling represent promising tools for several application domains from engineering to forensic science and medicine. Advances in 3D imaging technology convey paradigms such as augmented reality (AR) and mixed reality inside promising simulation tools for the training industry. Motivated by the requirement for superimposing anatomically correct 3D models on a human patient simulator (HPS) and visualizing them in an AR environment, the purpose of this research effort was to develop and validate a method for scaling a source human mandible to a target human mandible within a 2 mm root mean square (RMS) error. Results show that, given a distance between 2 same landmarks on 2 different mandibles, a relative scaling factor may be computed. Using this scaling factor, results show that a 3D virtual mandible model can be made morphometrically equivalent to a real target-specific mandible within a 1.30 mm RMS error. The virtual mandible may be further used as a reference target for registering other anatomic models, such as the lungs, on the HPS. Such registration will be made possible by physical constraints among the mandible and the spinal column in the horizontal normal rest position.

Optimal Tuner Selection for Kalman Filter-Based Aircraft Engine Performance Estimation

NASA Technical Reports Server (NTRS)

Simon, Donald L.; Garg, Sanjay

2010-01-01

A linear point design methodology for minimizing the error in on-line Kalman filter-based aircraft engine performance estimation applications is presented. This technique specifically addresses the underdetermined estimation problem, where there are more unknown parameters than available sensor measurements. A systematic approach is applied to produce a model tuning parameter vector of appropriate dimension to enable estimation by a Kalman filter, while minimizing the estimation error in the parameters of interest. Tuning parameter selection is performed using a multi-variable iterative search routine which seeks to minimize the theoretical mean-squared estimation error. This paper derives theoretical Kalman filter estimation error bias and variance values at steady-state operating conditions, and presents the tuner selection routine applied to minimize these values. Results from the application of the technique to an aircraft engine simulation are presented and compared to the conventional approach of tuner selection. Experimental simulation results are found to be in agreement with theoretical predictions. The new methodology is shown to yield a significant improvement in on-line engine performance estimation accuracy

A Hybrid Multiscale Framework for Subsurface Flow and Transport Simulations

DOE PAGES

Scheibe, Timothy D.; Yang, Xiaofan; Chen, Xingyuan; ...

2015-06-01

Extensive research efforts have been invested in reducing model errors to improve the predictive ability of biogeochemical earth and environmental system simulators, with applications ranging from contaminant transport and remediation to impacts of biogeochemical elemental cycling (e.g., carbon and nitrogen) on local ecosystems and regional to global climate. While the bulk of this research has focused on improving model parameterizations in the face of observational limitations, the more challenging type of model error/uncertainty to identify and quantify is model structural error which arises from incorrect mathematical representations of (or failure to consider) important physical, chemical, or biological processes, properties, ormore » system states in model formulations. While improved process understanding can be achieved through scientific study, such understanding is usually developed at small scales. Process-based numerical models are typically designed for a particular characteristic length and time scale. For application-relevant scales, it is generally necessary to introduce approximations and empirical parameterizations to describe complex systems or processes. This single-scale approach has been the best available to date because of limited understanding of process coupling combined with practical limitations on system characterization and computation. While computational power is increasing significantly and our understanding of biological and environmental processes at fundamental scales is accelerating, using this information to advance our knowledge of the larger system behavior requires the development of multiscale simulators. Accordingly there has been much recent interest in novel multiscale methods in which microscale and macroscale models are explicitly coupled in a single hybrid multiscale simulation. A limited number of hybrid multiscale simulations have been developed for biogeochemical earth systems, but they mostly utilize application-specific and sometimes ad-hoc approaches for model coupling. We are developing a generalized approach to hierarchical model coupling designed for high-performance computational systems, based on the Swift computing workflow framework. In this presentation we will describe the generalized approach and provide two use cases: 1) simulation of a mixing-controlled biogeochemical reaction coupling pore- and continuum-scale models, and 2) simulation of biogeochemical impacts of groundwater – river water interactions coupling fine- and coarse-grid model representations. This generalized framework can be customized for use with any pair of linked models (microscale and macroscale) with minimal intrusiveness to the at-scale simulators. It combines a set of python scripts with the Swift workflow environment to execute a complex multiscale simulation utilizing an approach similar to the well-known Heterogeneous Multiscale Method. User customization is facilitated through user-provided input and output file templates and processing function scripts, and execution within a high-performance computing environment is handled by Swift, such that minimal to no user modification of at-scale codes is required.« less

Improving material removal determinacy based on the compensation of tool influence function

NASA Astrophysics Data System (ADS)

Zhong, Bo; Chen, Xian-hua; Deng, Wen-hui; Zhao, Shi-jie; Zheng, Nan

2018-03-01

In the process of computer-controlled optical surfacing (CCOS), the key of correcting the surface error of optical components is to ensure the consistency between the simulated tool influence function and the actual tool influence function (TIF). The existing removal model usually adopts the fixed-point TIF to remove the material with the planning path and velocity, and it considers that the polishing process is linear and time invariant. However, in the actual polishing process, the TIF is a function related to the feed speed. In this paper, the relationship between the actual TIF and the feed speed (i.e. the compensation relationship between static removal and dynamic removal) is determined by experimental method. Then, the existing removal model is modified based on the compensation relationship, to improve the conformity between simulated and actual processing. Finally, the surface error modification correction test are carried out. The results show that the fitting degree of the simulated surface and the experimental surface is better than 88%, and the surface correction accuracy can be better than 1/10 λ (Λ=632.8nm).

A simulation study to quantify the impacts of exposure ...

EPA Pesticide Factsheets

A simulation study to quantify the impacts of exposure measurement error on air pollution health risk estimates in copollutant time-series models The National Exposure Research Laboratory (NERL) Computational Exposure Division (CED) develops and evaluates data, decision-support tools, and models to be applied to media-specific or receptor-specific problem areas. CED uses modeling-based approaches to characterize exposures, evaluate fate and transport, and support environmental diagnostics/forensics with input from multiple data sources. It also develops media- and receptor-specific models, process models, and decision support tools for use both within and outside of EPA.

Aerial robot intelligent control method based on back-stepping

NASA Astrophysics Data System (ADS)

Zhou, Jian; Xue, Qian

2018-05-01

The aerial robot is characterized as strong nonlinearity, high coupling and parameter uncertainty, a self-adaptive back-stepping control method based on neural network is proposed in this paper. The uncertain part of the aerial robot model is compensated online by the neural network of Cerebellum Model Articulation Controller and robust control items are designed to overcome the uncertainty error of the system during online learning. At the same time, particle swarm algorithm is used to optimize and fix parameters so as to improve the dynamic performance, and control law is obtained by the recursion of back-stepping regression. Simulation results show that the designed control law has desired attitude tracking performance and good robustness in case of uncertainties and large errors in the model parameters.

Backus Effect on a Perpendicular Errors in Harmonic Models of Real vs. Synthetic Data

NASA Technical Reports Server (NTRS)

Voorhies, C. V.; Santana, J.; Sabaka, T.

1999-01-01

Measurements of geomagnetic scalar intensity on a thin spherical shell alone are not enough to separate internal from external source fields; moreover, such scalar data are not enough for accurate modeling of the vector field from internal sources because of unmodeled fields and small data errors. Spherical harmonic models of the geomagnetic potential fitted to scalar data alone therefore suffer from well-understood Backus effect and perpendicular errors. Curiously, errors in some models of simulated 'data' are very much less than those in models of real data. We analyze select Magsat vector and scalar measurements separately to illustrate Backus effect and perpendicular errors in models of real scalar data. By using a model to synthesize 'data' at the observation points, and by adding various types of 'noise', we illustrate such errors in models of synthetic 'data'. Perpendicular errors prove quite sensitive to the maximum degree in the spherical harmonic expansion of the potential field model fitted to the scalar data. Small errors in models of synthetic 'data' are found to be an artifact of matched truncation levels. For example, consider scalar synthetic 'data' computed from a degree 14 model. A degree 14 model fitted to such synthetic 'data' yields negligible error, but amplifies 4 nT (rmss) added noise into a 60 nT error (rmss); however, a degree 12 model fitted to the noisy 'data' suffers a 492 nT error (rmms through degree 12). Geomagnetic measurements remain unaware of model truncation, so the small errors indicated by some simulations cannot be realized in practice. Errors in models fitted to scalar data alone approach 1000 nT (rmss) and several thousand nT (maximum).

DSMC Simulation and Experimental Validation of Shock Interaction in Hypersonic Low Density Flow

PubMed Central

2014-01-01

Direct simulation Monte Carlo (DSMC) of shock interaction in hypersonic low density flow is developed. Three collision molecular models, including hard sphere (HS), variable hard sphere (VHS), and variable soft sphere (VSS), are employed in the DSMC study. The simulations of double-cone and Edney's type IV hypersonic shock interactions in low density flow are performed. Comparisons between DSMC and experimental data are conducted. Investigation of the double-cone hypersonic flow shows that three collision molecular models can predict the trend of pressure coefficient and the Stanton number. HS model shows the best agreement between DSMC simulation and experiment among three collision molecular models. Also, it shows that the agreement between DSMC and experiment is generally good for HS and VHS models in Edney's type IV shock interaction. However, it fails in the VSS model. Both double-cone and Edney's type IV shock interaction simulations show that the DSMC errors depend on the Knudsen number and the models employed for intermolecular interaction. With the increase in the Knudsen number, the DSMC error is decreased. The error is the smallest in HS compared with those in the VHS and VSS models. When the Knudsen number is in the level of 10−4, the DSMC errors, for pressure coefficient, the Stanton number, and the scale of interaction region, are controlled within 10%. PMID:24672360

Skill of Ensemble Seasonal Probability Forecasts

NASA Astrophysics Data System (ADS)

Smith, Leonard A.; Binter, Roman; Du, Hailiang; Niehoerster, Falk

2010-05-01

In operational forecasting, the computational complexity of large simulation models is, ideally, justified by enhanced performance over simpler models. We will consider probability forecasts and contrast the skill of ENSEMBLES-based seasonal probability forecasts of interest to the finance sector (specifically temperature forecasts for Nino 3.4 and the Atlantic Main Development Region (MDR)). The ENSEMBLES model simulations will be contrasted against forecasts from statistical models based on the observations (climatological distributions) and empirical dynamics based on the observations but conditioned on the current state (dynamical climatology). For some start dates, individual ENSEMBLES models yield significant skill even at a lead-time of 14 months. The nature of this skill is discussed, and chances of application are noted. Questions surrounding the interpretation of probability forecasts based on these multi-model ensemble simulations are then considered; the distributions considered are formed by kernel dressing the ensemble and blending with the climatology. The sources of apparent (RMS) skill in distributions based on multi-model simulations is discussed, and it is demonstrated that the inclusion of "zero-skill" models in the long range can improve Root-Mean-Square-Error scores, casting some doubt on the common justification for the claim that all models should be included in forming an operational probability forecast. It is argued that the rational response varies with lead time.

ECHO: A reference-free short-read error correction algorithm

PubMed Central

Kao, Wei-Chun; Chan, Andrew H.; Song, Yun S.

2011-01-01

Developing accurate, scalable algorithms to improve data quality is an important computational challenge associated with recent advances in high-throughput sequencing technology. In this study, a novel error-correction algorithm, called ECHO, is introduced for correcting base-call errors in short-reads, without the need of a reference genome. Unlike most previous methods, ECHO does not require the user to specify parameters of which optimal values are typically unknown a priori. ECHO automatically sets the parameters in the assumed model and estimates error characteristics specific to each sequencing run, while maintaining a running time that is within the range of practical use. ECHO is based on a probabilistic model and is able to assign a quality score to each corrected base. Furthermore, it explicitly models heterozygosity in diploid genomes and provides a reference-free method for detecting bases that originated from heterozygous sites. On both real and simulated data, ECHO is able to improve the accuracy of previous error-correction methods by several folds to an order of magnitude, depending on the sequence coverage depth and the position in the read. The improvement is most pronounced toward the end of the read, where previous methods become noticeably less effective. Using a whole-genome yeast data set, it is demonstrated here that ECHO is capable of coping with nonuniform coverage. Also, it is shown that using ECHO to perform error correction as a preprocessing step considerably facilitates de novo assembly, particularly in the case of low-to-moderate sequence coverage depth. PMID:21482625

High dimensional linear regression models under long memory dependence and measurement error

NASA Astrophysics Data System (ADS)

Kaul, Abhishek

This dissertation consists of three chapters. The first chapter introduces the models under consideration and motivates problems of interest. A brief literature review is also provided in this chapter. The second chapter investigates the properties of Lasso under long range dependent model errors. Lasso is a computationally efficient approach to model selection and estimation, and its properties are well studied when the regression errors are independent and identically distributed. We study the case, where the regression errors form a long memory moving average process. We establish a finite sample oracle inequality for the Lasso solution. We then show the asymptotic sign consistency in this setup. These results are established in the high dimensional setup (p> n) where p can be increasing exponentially with n. Finally, we show the consistency, n½ --d-consistency of Lasso, along with the oracle property of adaptive Lasso, in the case where p is fixed. Here d is the memory parameter of the stationary error sequence. The performance of Lasso is also analysed in the present setup with a simulation study. The third chapter proposes and investigates the properties of a penalized quantile based estimator for measurement error models. Standard formulations of prediction problems in high dimension regression models assume the availability of fully observed covariates and sub-Gaussian and homogeneous model errors. This makes these methods inapplicable to measurement errors models where covariates are unobservable and observations are possibly non sub-Gaussian and heterogeneous. We propose weighted penalized corrected quantile estimators for the regression parameter vector in linear regression models with additive measurement errors, where unobservable covariates are nonrandom. The proposed estimators forgo the need for the above mentioned model assumptions. We study these estimators in both the fixed dimension and high dimensional sparse setups, in the latter setup, the dimensionality can grow exponentially with the sample size. In the fixed dimensional setting we provide the oracle properties associated with the proposed estimators. In the high dimensional setting, we provide bounds for the statistical error associated with the estimation, that hold with asymptotic probability 1, thereby providing the ℓ1-consistency of the proposed estimator. We also establish the model selection consistency in terms of the correctly estimated zero components of the parameter vector. A simulation study that investigates the finite sample accuracy of the proposed estimator is also included in this chapter.

A Physiologically Based Pharmacokinetic Model to Predict the Pharmacokinetics of Highly Protein-Bound Drugs and Impact of Errors in Plasma Protein Binding

PubMed Central

Ye, Min; Nagar, Swati; Korzekwa, Ken

2015-01-01

Predicting the pharmacokinetics of highly protein-bound drugs is difficult. Also, since historical plasma protein binding data was often collected using unbuffered plasma, the resulting inaccurate binding data could contribute to incorrect predictions. This study uses a generic physiologically based pharmacokinetic (PBPK) model to predict human plasma concentration-time profiles for 22 highly protein-bound drugs. Tissue distribution was estimated from in vitro drug lipophilicity data, plasma protein binding, and blood: plasma ratio. Clearance was predicted with a well-stirred liver model. Underestimated hepatic clearance for acidic and neutral compounds was corrected by an empirical scaling factor. Predicted values (pharmacokinetic parameters, plasma concentration-time profile) were compared with observed data to evaluate model accuracy. Of the 22 drugs, less than a 2-fold error was obtained for terminal elimination half-life (t1/2, 100% of drugs), peak plasma concentration (Cmax, 100%), area under the plasma concentration-time curve (AUC0–t, 95.4%), clearance (CLh, 95.4%), mean retention time (MRT, 95.4%), and steady state volume (Vss, 90.9%). The impact of fup errors on CLh and Vss prediction was evaluated. Errors in fup resulted in proportional errors in clearance prediction for low-clearance compounds, and in Vss prediction for high-volume neutral drugs. For high-volume basic drugs, errors in fup did not propagate to errors in Vss prediction. This is due to the cancellation of errors in the calculations for tissue partitioning of basic drugs. Overall, plasma profiles were well simulated with the present PBPK model. PMID:26531057

A Fast Visible-Infrared Imaging Radiometer Suite Simulator for Cloudy Atmopheres

NASA Technical Reports Server (NTRS)

Liu, Chao; Yang, Ping; Nasiri, Shaima L.; Platnick, Steven; Meyer, Kerry G.; Wang, Chen Xi; Ding, Shouguo

2015-01-01

A fast instrument simulator is developed to simulate the observations made in cloudy atmospheres by the Visible Infrared Imaging Radiometer Suite (VIIRS). The correlated k-distribution (CKD) technique is used to compute the transmissivity of absorbing atmospheric gases. The bulk scattering properties of ice clouds used in this study are based on the ice model used for the MODIS Collection 6 ice cloud products. Two fast radiative transfer models based on pre-computed ice cloud look-up-tables are used for the VIIRS solar and infrared channels. The accuracy and efficiency of the fast simulator are quantify in comparison with a combination of the rigorous line-by-line (LBLRTM) and discrete ordinate radiative transfer (DISORT) models. Relative errors are less than 2 for simulated TOA reflectances for the solar channels and the brightness temperature differences for the infrared channels are less than 0.2 K. The simulator is over three orders of magnitude faster than the benchmark LBLRTM+DISORT model. Furthermore, the cloudy atmosphere reflectances and brightness temperatures from the fast VIIRS simulator compare favorably with those from VIIRS observations.

The Effect of Photon Statistics and Pulse Shaping on the Performance of the Wiener Filter Crystal Identification Algorithm Applied to LabPET Phoswich Detectors

NASA Astrophysics Data System (ADS)

Yousefzadeh, Hoorvash Camilia; Lecomte, Roger; Fontaine, Réjean

2012-06-01

A fast Wiener filter-based crystal identification (WFCI) algorithm was recently developed to discriminate crystals with close scintillation decay times in phoswich detectors. Despite the promising performance of WFCI, the influence of various physical factors and electrical noise sources of the data acquisition chain (DAQ) on the crystal identification process was not fully investigated. This paper examines the effect of different noise sources, such as photon statistics, avalanche photodiode (APD) excess multiplication noise, and front-end electronic noise, as well as the influence of different shaping filters on the performance of the WFCI algorithm. To this end, a PET-like signal simulator based on a model of the LabPET DAQ, a small animal APD-based digital PET scanner, was developed. Simulated signals were generated under various noise conditions with CR-RC shapers of order 1, 3, and 5 having different time constants (τ). Applying the WFCI algorithm to these simulated signals showed that the non-stationary Poisson photon statistics is the main contributor to the identification error of WFCI algorithm. A shaping filter of order 1 with τ = 50 ns yielded the best WFCI performance (error 1%), while a longer shaping time of τ = 100 ns slightly degraded the WFCI performance (error 3%). Filters of higher orders with fast shaping time constants (10-33 ns) also produced good WFCI results (error 1.4% to 1.6%). This study shows the advantage of the pulse simulator in evaluating various DAQ conditions and confirms the influence of the detection chain on the WFCI performance.

High-order dynamic modeling and parameter identification of structural discontinuities in Timoshenko beams by using reflection coefficients

NASA Astrophysics Data System (ADS)

Fan, Qiang; Huang, Zhenyu; Zhang, Bing; Chen, Dayue

2013-02-01

Properties of discontinuities, such as bolt joints and cracks in the waveguide structures, are difficult to evaluate by either analytical or numerical methods due to the complexity and uncertainty of the discontinuities. In this paper, the discontinuity in a Timoshenko beam is modeled with high-order parameters and then these parameters are identified by using reflection coefficients at the discontinuity. The high-order model is composed of several one-order sub-models in series and each sub-model consists of inertia, stiffness and damping components in parallel. The order of the discontinuity model is determined based on the characteristics of the reflection coefficient curve and the accuracy requirement of the dynamic modeling. The model parameters are identified through the least-square fitting iteration method, of which the undetermined model parameters are updated in iteration to fit the dynamic reflection coefficient curve with the wave-based one. By using the spectral super-element method (SSEM), simulation cases, including one-order discontinuities on infinite- and finite-beams and a two-order discontinuity on an infinite beam, were employed to evaluate both the accuracy of the discontinuity model and the effectiveness of the identification method. For practical considerations, effects of measurement noise on the discontinuity parameter identification are investigated by adding different levels of noise to the simulated data. The simulation results were then validated by the corresponding experiments. Both the simulation and experimental results show that (1) the one-order discontinuities can be identified accurately with the maximum errors of 6.8% and 8.7%, respectively; (2) and the high-order discontinuities can be identified with the maximum errors of 15.8% and 16.2%, respectively; and (3) the high-order model can predict the complex discontinuity much more accurately than the one-order discontinuity model.

Background Error Correlation Modeling with Diffusion Operators

DTIC Science & Technology

2013-01-01

RESPONSIBLE PERSON 19b. TELEPHONE NUMBER (Include area code) 07-10-2013 Book Chapter Background Error Correlation Modeling with Diffusion Operators...normalization Unclassified Unclassified Unclassified UU 27 Max Yaremchuk (228) 688-5259 Reset Chapter 8 Background error correlation modeling with diffusion ...field, then a structure like this simulates enhanced diffusive transport of model errors in the regions of strong cur- rents on the background of

Parameter recovery, bias and standard errors in the linear ballistic accumulator model.

PubMed

Visser, Ingmar; Poessé, Rens

2017-05-01

The linear ballistic accumulator (LBA) model (Brown & Heathcote, , Cogn. Psychol., 57, 153) is increasingly popular in modelling response times from experimental data. An R package, glba, has been developed to fit the LBA model using maximum likelihood estimation which is validated by means of a parameter recovery study. At sufficient sample sizes parameter recovery is good, whereas at smaller sample sizes there can be large bias in parameters. In a second simulation study, two methods for computing parameter standard errors are compared. The Hessian-based method is found to be adequate and is (much) faster than the alternative bootstrap method. The use of parameter standard errors in model selection and inference is illustrated in an example using data from an implicit learning experiment (Visser et al., , Mem. Cogn., 35, 1502). It is shown that typical implicit learning effects are captured by different parameters of the LBA model. © 2017 The British Psychological Society.

Numerical Analysis of Modeling Based on Improved Elman Neural Network

PubMed Central

Jie, Shao

2014-01-01

A modeling based on the improved Elman neural network (IENN) is proposed to analyze the nonlinear circuits with the memory effect. The hidden layer neurons are activated by a group of Chebyshev orthogonal basis functions instead of sigmoid functions in this model. The error curves of the sum of squared error (SSE) varying with the number of hidden neurons and the iteration step are studied to determine the number of the hidden layer neurons. Simulation results of the half-bridge class-D power amplifier (CDPA) with two-tone signal and broadband signals as input have shown that the proposed behavioral modeling can reconstruct the system of CDPAs accurately and depict the memory effect of CDPAs well. Compared with Volterra-Laguerre (VL) model, Chebyshev neural network (CNN) model, and basic Elman neural network (BENN) model, the proposed model has better performance. PMID:25054172

SiC-VJFETs power switching devices: an improved model and parameter optimization technique

NASA Astrophysics Data System (ADS)

Ben Salah, T.; Lahbib, Y.; Morel, H.

2009-12-01

Silicon carbide junction field effect transistor (SiC-JFETs) is a mature power switch newly applied in several industrial applications. SiC-JFETs are often simulated by Spice model in order to predict their electrical behaviour. Although such a model provides sufficient accuracy for some applications, this paper shows that it presents serious shortcomings in terms of the neglect of the body diode model, among many others in circuit model topology. Simulation correction is then mandatory and a new model should be proposed. Moreover, this paper gives an enhanced model based on experimental dc and ac data. New devices are added to the conventional circuit model giving accurate static and dynamic behaviour, an effect not accounted in the Spice model. The improved model is implemented into VHDL-AMS language and steady-state dynamic and transient responses are simulated for many SiC-VJFETs samples. Very simple and reliable optimization algorithm based on the optimization of a cost function is proposed to extract the JFET model parameters. The obtained parameters are verified by comparing errors between simulations results and experimental data.

A biologically inspired neural model for visual and proprioceptive integration including sensory training.

PubMed

Saidi, Maryam; Towhidkhah, Farzad; Gharibzadeh, Shahriar; Lari, Abdolaziz Azizi

2013-12-01

Humans perceive the surrounding world by integration of information through different sensory modalities. Earlier models of multisensory integration rely mainly on traditional Bayesian and causal Bayesian inferences for single causal (source) and two causal (for two senses such as visual and auditory systems), respectively. In this paper a new recurrent neural model is presented for integration of visual and proprioceptive information. This model is based on population coding which is able to mimic multisensory integration of neural centers in the human brain. The simulation results agree with those achieved by casual Bayesian inference. The model can also simulate the sensory training process of visual and proprioceptive information in human. Training process in multisensory integration is a point with less attention in the literature before. The effect of proprioceptive training on multisensory perception was investigated through a set of experiments in our previous study. The current study, evaluates the effect of both modalities, i.e., visual and proprioceptive training and compares them with each other through a set of new experiments. In these experiments, the subject was asked to move his/her hand in a circle and estimate its position. The experiments were performed on eight subjects with proprioception training and eight subjects with visual training. Results of the experiments show three important points: (1) visual learning rate is significantly more than that of proprioception; (2) means of visual and proprioceptive errors are decreased by training but statistical analysis shows that this decrement is significant for proprioceptive error and non-significant for visual error, and (3) visual errors in training phase even in the beginning of it, is much less than errors of the main test stage because in the main test, the subject has to focus on two senses. The results of the experiments in this paper is in agreement with the results of the neural model simulation.

Simulating a transmon implementation of the surface code, Part I

NASA Astrophysics Data System (ADS)

Tarasinski, Brian; O'Brien, Thomas; Rol, Adriaan; Bultink, Niels; Dicarlo, Leo

Current experimental efforts aim to realize Surface-17, a distance-3 surface-code logical qubit, using transmon qubits in a circuit QED architecture. Following experimental proposals for this device, and currently achieved fidelities on physical qubits, we define a detailed error model that takes experimentally relevant error sources into account, such as amplitude and phase damping, imperfect gate pulses, and coherent errors due to low-frequency flux noise. Using the GPU-accelerated software package 'quantumsim', we simulate the density matrix evolution of the logical qubit under this error model. Combining the simulation results with a minimum-weight matching decoder, we obtain predictions for the error rate of the resulting logical qubit when used as a quantum memory, and estimate the contribution of different error sources to the logical error budget. Research funded by the Foundation for Fundamental Research on Matter (FOM), the Netherlands Organization for Scientific Research (NWO/OCW), IARPA, an ERC Synergy Grant, the China Scholarship Council, and Intel Corporation.

Generalized Structured Component Analysis with Uniqueness Terms for Accommodating Measurement Error

PubMed Central

Hwang, Heungsun; Takane, Yoshio; Jung, Kwanghee

2017-01-01

Generalized structured component analysis (GSCA) is a component-based approach to structural equation modeling (SEM), where latent variables are approximated by weighted composites of indicators. It has no formal mechanism to incorporate errors in indicators, which in turn renders components prone to the errors as well. We propose to extend GSCA to account for errors in indicators explicitly. This extension, called GSCAM, considers both common and unique parts of indicators, as postulated in common factor analysis, and estimates a weighted composite of indicators with their unique parts removed. Adding such unique parts or uniqueness terms serves to account for measurement errors in indicators in a manner similar to common factor analysis. Simulation studies are conducted to compare parameter recovery of GSCAM and existing methods. These methods are also applied to fit a substantively well-established model to real data. PMID:29270146

Modeling and Control of a Tailsitter with a Ducted Fan

NASA Astrophysics Data System (ADS)

Argyle, Matthew Elliott

There are two traditional aircraft categories: fixed-wing which have a long endurance and a high cruise airspeed and rotorcraft which can take-off and land vertically. The tailsitter is a type of aircraft that has the strengths of both platforms, with no additional mechanical complexity, because it takes off and lands vertically on its tail and can transition the entire aircraft horizontally into high-speed flight. In this dissertation, we develop the entire control system for a tailsitter with a ducted fan. The standard method to compute the quaternion-based attitude error does not generate ideal trajectories for a hovering tailsitter for some situations. In addition, the only approach in the literature to mitigate this breaks down for large attitude errors. We develop an alternative quaternion-based error method which generates better trajectories than the standard approach and can handle large errors. We also derive a hybrid backstepping controller with almost global asymptotic stability based on this error method. Many common altitude and airspeed control schemes for a fixed-wing airplane assume that the altitude and airspeed dynamics are decoupled which leads to errors. The Total Energy Control System (TECS) is an approach that controls the altitude and airspeed by manipulating the total energy rate and energy distribution rate, of the aircraft, in a manner which accounts for the dynamic coupling. In this dissertation, a nonlinear controller, which can handle inaccurate thrust and drag models, based on the TECS principles is derived. Simulation results show that the nonlinear controller has better performance than the standard PI TECS control schemes. Most constant altitude transitions are accomplished by generating an optimal trajectory, and potentially actuator inputs, based on a high fidelity model of the aircraft. While there are several approaches to mitigate the effects of modeling errors, these do not fully remove the accurate model requirement. In this dissertation, we develop two different approaches that can achieve near constant altitude transitions for some types of aircraft. The first method, based on multiple LQR controllers, requires a high fidelity model of the aircraft. However, the second method, based on the energy along the body axes, requires almost no aerodynamic information.

Simulating polarized light scattering in terrestrial snow based on bicontinuous random medium and Monte Carlo ray tracing

NASA Astrophysics Data System (ADS)

Xiong, Chuan; Shi, Jiancheng

2014-01-01

To date, the light scattering models of snow consider very little about the real snow microstructures. The ideal spherical or other single shaped particle assumptions in previous snow light scattering models can cause error in light scattering modeling of snow and further cause errors in remote sensing inversion algorithms. This paper tries to build up a snow polarized reflectance model based on bicontinuous medium, with which the real snow microstructure is considered. The accurate specific surface area of bicontinuous medium can be analytically derived. The polarized Monte Carlo ray tracing technique is applied to the computer generated bicontinuous medium. With proper algorithms, the snow surface albedo, bidirectional reflectance distribution function (BRDF) and polarized BRDF can be simulated. The validation of model predicted spectral albedo and bidirectional reflectance factor (BRF) using experiment data shows good results. The relationship between snow surface albedo and snow specific surface area (SSA) were predicted, and this relationship can be used for future improvement of snow specific surface area (SSA) inversion algorithms. The model predicted polarized reflectance is validated and proved accurate, which can be further applied in polarized remote sensing.

Global optimization method based on ray tracing to achieve optimum figure error compensation

NASA Astrophysics Data System (ADS)

Liu, Xiaolin; Guo, Xuejia; Tang, Tianjin

2017-02-01

Figure error would degrade the performance of optical system. When predicting the performance and performing system assembly, compensation by clocking of optical components around the optical axis is a conventional but user-dependent method. Commercial optical software cannot optimize this clocking. Meanwhile existing automatic figure-error balancing methods can introduce approximate calculation error and the build process of optimization model is complex and time-consuming. To overcome these limitations, an accurate and automatic global optimization method of figure error balancing is proposed. This method is based on precise ray tracing to calculate the wavefront error, not approximate calculation, under a given elements' rotation angles combination. The composite wavefront error root-mean-square (RMS) acts as the cost function. Simulated annealing algorithm is used to seek the optimal combination of rotation angles of each optical element. This method can be applied to all rotational symmetric optics. Optimization results show that this method is 49% better than previous approximate analytical method.

Investigating different filter and rescaling methods on simulated GRACE-like TWS variations for hydrological applications

NASA Astrophysics Data System (ADS)

Zhang, Liangjing; Dobslaw, Henryk; Dahle, Christoph; Thomas, Maik; Neumayer, Karl-Hans; Flechtner, Frank

2017-04-01

By operating for more than one decade now, the GRACE satellite provides valuable information on the total water storage (TWS) for hydrological and hydro-meteorological applications. The increasing interest in use of the GRACE-based TWS requires an in-depth assessment of the reliability of the outputs and also its uncertainties. Through years of development, different post-processing methods have been suggested for TWS estimation. However, since GRACE offers an unique way to provide high spatial and temporal scale TWS, there is no global ground truth data available to fully validate the results. In this contribution, we re-assess a number of commonly used post-processing methods using a simulated GRACE-type gravity field time-series based on realistic orbits and instrument error assumptions as well as background error assumptions out of the updated ESA Earth System Model. Three non-isotropic filter methods from Kusche (2007) and a combined filter from DDK1 and DDK3 based on the ground tracks are tested. Rescaling factors estimated from five different hydrological models and the ensemble median are applied to the post-processed simulated GRACE-type TWS estimates to correct the bias and leakage. Time variant rescaling factors as monthly scaling factors and scaling factors for seasonal and long-term variations separately are investigated as well. Since TWS anomalies out of the post-processed simulation results can be readily compared to the time-variable Earth System Model initially used as "truth" during the forward simulation step, we are able to thoroughly check the plausibility of our error estimation assessment (Zhang et al., 2016) and will subsequently recommend a processing strategy that shall also be applied for planned GRACE and GRACE-FO Level-3 products for terrestrial applications provided by GFZ. Kusche, J., 2007:Approximate decorrelation and non-isotropic smoothing of time-variable GRACE-type gravity field models. J. Geodesy, 81 (11), 733-749, doi:10.1007/s00190-007-0143-3. Zhang L, Dobslaw H, Thomas M (2016) Globally gridded terrestrial water storage variations from GRACE satellite gravimetry for hydrometeorological applications. Geophysical Journal International 206(1):368-378, DOI 10.1093/gji/ggw153.

Simulation of streamflow and water quality in the Brandywine Creek subbasin of the Christina River basin, Pennsylvania and Delaware, 1994-98

USGS Publications Warehouse

Senior, Lisa A.; Koerkle, Edward H.

2003-01-01

The Christina River Basin drains 565 mi2 (square miles) in Pennsylvania and Delaware. Water from the basin is used for recreation, drinking-water supply, and to support aquatic life. The Christina River Basin includes the major subbasins of Brandywine Creek, Red Clay Creek, White Clay Creek, and Christina River. The Brandywine Creek is the largest of the subbasins and drains an area of 327 mi2. Water quality in some parts of the Christina River Basin is impaired and does not support designated uses of the streams. A multi-agency water-quality management strategy included a modeling component to evaluate the effects of point and nonpoint-source contributions of nutrients and suspended sediment on streamwater quality. To assist in nonpoint-source evaluation, four independent models, one for each of the four main subbasins of the Christina River Basin, were developed and calibrated using the model code Hydrological Simulation Program—Fortran (HSPF). Water-quality data for model calibration were collected in each of the four main subbasins and in small subbasins predominantly covered by one land use following a nonpoint-source monitoring plan. Under this plan, stormflow and base-flow samples were collected during 1998 at six sites in the Brandywine Creek subbasin and five sites in the other subbasins.The HSPF model for the Brandywine Creek Basin simulates streamflow, suspended sediment, and the nutrients, nitrogen and phosphorus. In addition, the model simulates water temperature, dissolved oxygen, biochemical oxygen demand, and plankton as secondary objectives needed to support the sediment and nutrient simulations. For the model, the basin was subdivided into 35 reaches draining areas that ranged from 0.6 to 18 mi2. Three of the reaches contain regulated reservoir. Eleven different pervious land uses and two impervious land uses were selected for simulation. Land-use areas were determined from 1995 land-use data. The predominant land uses in the basin are forested, agricultural, residential, and urban. The hydrologic component of the model was run at an hourly time step and calibrated using streamflow data for eight U.S. Geological Survey (USGS) stream-flow-measurement stations for the period of January 1, 1994, through October 29, 1998. Daily precipitation data for three National Oceanic and Atmospheric Administration (NOAA) gages and hourly data for one NOAA gage were used for model input. The difference between observed and simulated streamflow volume ranged from -2.7 to 3.9 percent for the nearly 5-year period at the eight calibration sites. Annual differences between observed and simulated streamflow generally were greater than the overall error. For example, at a site near the bottom of the basin (drainage area of 237 mi2), annual differences between observed and simulated streamflow ranged from -14.0 to 18.8 percent and the overall error for the 5-year period was 1.0 percent. Calibration errors for 36 storm periods at the eight calibration sites for total volume, low-flow-recession rate, 50-percent lowest flows, 10-percent highest flows, and storm peaks were within the recommended criteria of 20 percent or less. Much of the error in simulating storm events on an hourly time step can be attributed to uncertainty in the rainfall data.The water-quality component of the model was calibrated using monitoring data collected at six USGS streamflow-measurement stations with variable water quality monitoring periods ending October 1998. Because of availability, monitoring data for suspended solids concentrations were used as surrogates for suspended-sediment concentrations, although suspended-solids data may underestimate suspended sediment and affect apparent accuracy of the suspended-sediment simulation. Comparison of observed to simulated loads for two to six individual storms in 1998 at each of the six monitoring sites indicate that simulation error is commonly as large as an order of magnitude for suspended sediment and nutrients. The simulation error tends to be smaller for dissolved nutrients than for particulate nutrients. Errors of 40 percent or less for monthly or annual values indicate a fair to good water-quality calibration according to recommended criteria, with much larger errors possible for individual events. Assessment of the water-quality calibration under stormflow conditions is limited by the relatively small amount of available water-quality data in the basin. Duration curves for simulated and reported sediment concentration at Brandywine Creek at Wilmington, Del., are similar, indicating model performance is better when evaluated over longer periods than when evaluated on individual storm events.

Enabling full-field physics-based optical proximity correction via dynamic model generation

NASA Astrophysics Data System (ADS)

Lam, Michael; Clifford, Chris; Raghunathan, Ananthan; Fenger, Germain; Adam, Kostas

2017-07-01

As extreme ultraviolet lithography becomes closer to reality for high volume production, its peculiar modeling challenges related to both inter and intrafield effects have necessitated building an optical proximity correction (OPC) infrastructure that operates with field position dependency. Previous state-of-the-art approaches to modeling field dependency used piecewise constant models where static input models are assigned to specific x/y-positions within the field. OPC and simulation could assign the proper static model based on simulation-level placement. However, in the realm of 7 and 5 nm feature sizes, small discontinuities in OPC from piecewise constant model changes can cause unacceptable levels of edge placement errors. The introduction of dynamic model generation (DMG) can be shown to effectively avoid these dislocations by providing unique mask and optical models per simulation region, allowing a near continuum of models through the field. DMG allows unique models for electromagnetic field, apodization, aberrations, etc. to vary through the entire field and provides a capability to precisely and accurately model systematic field signatures.

Sensitivity of tumor motion simulation accuracy to lung biomechanical modeling approaches and parameters.

PubMed

Tehrani, Joubin Nasehi; Yang, Yin; Werner, Rene; Lu, Wei; Low, Daniel; Guo, Xiaohu; Wang, Jing

2015-11-21

Finite element analysis (FEA)-based biomechanical modeling can be used to predict lung respiratory motion. In this technique, elastic models and biomechanical parameters are two important factors that determine modeling accuracy. We systematically evaluated the effects of lung and lung tumor biomechanical modeling approaches and related parameters to improve the accuracy of motion simulation of lung tumor center of mass (TCM) displacements. Experiments were conducted with four-dimensional computed tomography (4D-CT). A Quasi-Newton FEA was performed to simulate lung and related tumor displacements between end-expiration (phase 50%) and other respiration phases (0%, 10%, 20%, 30%, and 40%). Both linear isotropic and non-linear hyperelastic materials, including the neo-Hookean compressible and uncoupled Mooney-Rivlin models, were used to create a finite element model (FEM) of lung and tumors. Lung surface displacement vector fields (SDVFs) were obtained by registering the 50% phase CT to other respiration phases, using the non-rigid demons registration algorithm. The obtained SDVFs were used as lung surface displacement boundary conditions in FEM. The sensitivity of TCM displacement to lung and tumor biomechanical parameters was assessed in eight patients for all three models. Patient-specific optimal parameters were estimated by minimizing the TCM motion simulation errors between phase 50% and phase 0%. The uncoupled Mooney-Rivlin material model showed the highest TCM motion simulation accuracy. The average TCM motion simulation absolute errors for the Mooney-Rivlin material model along left-right, anterior-posterior, and superior-inferior directions were 0.80 mm, 0.86 mm, and 1.51 mm, respectively. The proposed strategy provides a reliable method to estimate patient-specific biomechanical parameters in FEM for lung tumor motion simulation.

Sensitivity of Tumor Motion Simulation Accuracy to Lung Biomechanical Modeling Approaches and Parameters

PubMed Central

Tehrani, Joubin Nasehi; Yang, Yin; Werner, Rene; Lu, Wei; Low, Daniel; Guo, Xiaohu

2015-01-01

Finite element analysis (FEA)-based biomechanical modeling can be used to predict lung respiratory motion. In this technique, elastic models and biomechanical parameters are two important factors that determine modeling accuracy. We systematically evaluated the effects of lung and lung tumor biomechanical modeling approaches and related parameters to improve the accuracy of motion simulation of lung tumor center of mass (TCM) displacements. Experiments were conducted with four-dimensional computed tomography (4D-CT). A Quasi-Newton FEA was performed to simulate lung and related tumor displacements between end-expiration (phase 50%) and other respiration phases (0%, 10%, 20%, 30%, and 40%). Both linear isotropic and non-linear hyperelastic materials, including the Neo-Hookean compressible and uncoupled Mooney-Rivlin models, were used to create a finite element model (FEM) of lung and tumors. Lung surface displacement vector fields (SDVFs) were obtained by registering the 50% phase CT to other respiration phases, using the non-rigid demons registration algorithm. The obtained SDVFs were used as lung surface displacement boundary conditions in FEM. The sensitivity of TCM displacement to lung and tumor biomechanical parameters was assessed in eight patients for all three models. Patient-specific optimal parameters were estimated by minimizing the TCM motion simulation errors between phase 50% and phase 0%. The uncoupled Mooney-Rivlin material model showed the highest TCM motion simulation accuracy. The average TCM motion simulation absolute errors for the Mooney-Rivlin material model along left-right (LR), anterior-posterior (AP), and superior-inferior (SI) directions were 0.80 mm, 0.86 mm, and 1.51 mm, respectively. The proposed strategy provides a reliable method to estimate patient-specific biomechanical parameters in FEM for lung tumor motion simulation. PMID:26531324

Design and analysis of a tendon-based computed tomography-compatible robot with remote center of motion for lung biopsy.

PubMed

Yang, Yunpeng; Jiang, Shan; Yang, Zhiyong; Yuan, Wei; Dou, Huaisu; Wang, Wei; Zhang, Daguang; Bian, Yuan

2017-04-01

Nowadays, biopsy is a decisive method of lung cancer diagnosis, whereas lung biopsy is time-consuming, complex and inaccurate. So a computed tomography-compatible robot for rapid and precise lung biopsy is developed in this article. According to the actual operation process, the robot is divided into two modules: 4-degree-of-freedom position module for location of puncture point is appropriate for patient's almost all positions and 3-degree-of-freedom tendon-based orientation module with remote center of motion is compact and computed tomography-compatible to orientate and insert needle automatically inside computed tomography bore. The workspace of the robot surrounds patient's thorax, and the needle tip forms a cone under patient's skin. A new error model of the robot based on screw theory is proposed in view of structure error and actuation error, which are regarded as screw motions. Simulation is carried out to verify the precision of the error model contrasted with compensation via inverse kinematics. The results of insertion experiment on specific phantom prove the feasibility of the robot with mean error of 1.373 mm in laboratory environment, which is accurate enough to replace manual operation.

Air Force Medical Modeling and Simulation: Bringing Virtual Reality to Reality

DTIC Science & Technology

2011-01-26

OMB control number. 1. REPORT DATE 26 JAN 2011 2. REPORT TYPE 3. DATES COVERED 00-00-2011 to 00-00-2011 4 . TITLE AND SUBTITLE Air Force...7 Over $ 4 billion added to Medicare health care cost! 2011 MHS Conference One Decade Later… 8 •10% increase inpatient deaths from medication errors in...Conference “Hub & Spoke” Simulation Network Facilities grouped into 4 -tiered system based on training requirements and simulation capability Category

A framework for human-hydrologic system model development integrating hydrology and water management: application to the Cutzamala water system in Mexico

NASA Astrophysics Data System (ADS)

Wi, S.; Freeman, S.; Brown, C.

2017-12-01

This study presents a general approach to developing computational models of human-hydrologic systems where human modification of hydrologic surface processes are significant or dominant. A river basin system is represented by a network of human-hydrologic response units (HHRUs) identified based on locations where river regulations happen (e.g., reservoir operation and diversions). Natural and human processes in HHRUs are simulated in a holistic framework that integrates component models representing rainfall-runoff, river routing, reservoir operation, flow diversion and water use processes. We illustrate the approach in a case study of the Cutzamala water system (CWS) in Mexico, a complex inter-basin water transfer system supplying the Mexico City Metropolitan Area (MCMA). The human-hydrologic system model for CWS (CUTZSIM) is evaluated in terms of streamflow and reservoir storages measured across the CWS and to water supplied for MCMA. The CUTZSIM improves the representation of hydrology and river-operation interaction and, in so doing, advances evaluation of system-wide water management consequences under altered climatic and demand regimes. The integrated modeling framework enables evaluation and simulation of model errors throughout the river basin, including errors in representation of the human component processes. Heretofore, model error evaluation, predictive error intervals and the resultant improved understanding have been limited to hydrologic processes. The general framework represents an initial step towards fuller understanding and prediction of the many and varied processes that determine the hydrologic fluxes and state variables in real river basins.

The contribution of natural variability to GCM bias: Can we effectively bias-correct climate projections?

NASA Astrophysics Data System (ADS)

McAfee, S. A.; DeLaFrance, A.

2017-12-01

Investigating the impacts of climate change often entails using projections from inherently imperfect general circulation models (GCMs) to drive models that simulate biophysical or societal systems in great detail. Error or bias in the GCM output is often assessed in relation to observations, and the projections are adjusted so that the output from impacts models can be compared to historical or observed conditions. Uncertainty in the projections is typically accommodated by running more than one future climate trajectory to account for differing emissions scenarios, model simulations, and natural variability. The current methods for dealing with error and uncertainty treat them as separate problems. In places where observed and/or simulated natural variability is large, however, it may not be possible to identify a consistent degree of bias in mean climate, blurring the lines between model error and projection uncertainty. Here we demonstrate substantial instability in mean monthly temperature bias across a suite of GCMs used in CMIP5. This instability is greatest in the highest latitudes during the cool season, where shifts from average temperatures below to above freezing could have profound impacts. In models with the greatest degree of bias instability, the timing of regional shifts from below to above average normal temperatures in a single climate projection can vary by about three decades, depending solely on the degree of bias assessed. This suggests that current bias correction methods based on comparison to 20- or 30-year normals may be inappropriate, particularly in the polar regions.

Simulation of groundwater flow, effects of artificial recharge, and storage volume changes in the Equus Beds aquifer near the city of Wichita, Kansas well field, 1935–2008

USGS Publications Warehouse

Kelly, Brian P.; Pickett, Linda L.; Hansen, Cristi V.; Ziegler, Andrew C.

2013-01-01

The Equus Beds aquifer is a primary water-supply source for Wichita, Kansas and the surrounding area because of shallow depth to water, large saturated thickness, and generally good water quality. Substantial water-level declines in the Equus Beds aquifer have resulted from pumping groundwater for agricultural and municipal needs, as well as periodic drought conditions. In March 2006, the city of Wichita began construction of the Equus Beds Aquifer Storage and Recovery project to store and later recover groundwater, and to form a hydraulic barrier to the known chloride-brine plume near Burrton, Kansas. In October 2009, the U.S. Geological Survey, in cooperation with the city of Wichita, began a study to determine groundwater flow in the area of the Wichita well field, and chloride transport from the Arkansas River and Burrton oilfield to the Wichita well field. Groundwater flow was simulated for the Equus Beds aquifer using the three-dimensional finite-difference groundwater-flow model MODFLOW-2000. The model simulates steady-state and transient conditions. The groundwater-flow model was calibrated by adjusting model input data and model geometry until model results matched field observations within an acceptable level of accuracy. The root mean square (RMS) error for water-level observations for the steady-state calibration simulation is 9.82 feet. The ratio of the RMS error to the total head loss in the model area is 0.049 and the mean error for water-level observations is 3.86 feet. The difference between flow into the model and flow out of the model across all model boundaries is -0.08 percent of total flow for the steady-state calibration. The RMS error for water-level observations for the transient calibration simulation is 2.48 feet, the ratio of the RMS error to the total head loss in the model area is 0.0124, and the mean error for water-level observations is 0.03 feet. The RMS error calculated for observed and simulated base flow gains or losses for the Arkansas River for the transient simulation is 7,916,564 cubic feet per day (91.6 cubic feet per second) and the RMS error divided by (/) the total range in streamflow (7,916,564/37,461,669 cubic feet per day) is 22 percent. The RMS error calculated for observed and simulated streamflow gains or losses for the Little Arkansas River for the transient simulation is 5,610,089 cubic feet per day(64.9 cubic feet per second) and the RMS error divided by the total range in streamflow (5,612,918/41,791,091 cubic feet per day) is 13 percent. The mean error between observed and simulated base flow gains or losses was 29,999 cubic feet per day (0.34 cubic feet per second) for the Arkansas River and -1,369,250 cubic feet per day (-15.8 cubic feet per second) for the Little Arkansas River. Cumulative streamflow gain and loss observations are similar to the cumulative simulated equivalents. Average percent mass balance difference for individual stress periods ranged from -0.46 to 0.51 percent. The cumulative mass balance for the transient calibration was 0.01 percent. Composite scaled sensitivities indicate the simulations are most sensitive to parameters with a large areal distribution. For the steady-state calibration, these parameters include recharge, hydraulic conductivity, and vertical conductance. For the transient simulation, these parameters include evapotranspiration, recharge, and hydraulic conductivity. The ability of the calibrated model to account for the additional groundwater recharged to the Equus Beds aquifer as part of the Aquifer Storage and Recovery project was assessed by using the U.S. Geological Survey subregional water budget program ZONEBUDGET and comparing those results to metered recharge for 2007 and 2008 and previous estimates of artificial recharge. The change in storage between simulations is the volume of water that estimates the recharge credit for the aquifer storage and recovery system. The estimated increase in storage of 1,607 acre-ft in the basin storage area compared to metered recharge of 1,796 acre-ft indicates some loss of metered recharge. Increased storage outside of the basin storage area of 183 acre-ft accounts for all but 6 acre-ft or 0.33 percent of the total. Previously estimated recharge credits for 2007 and 2008 are 1,018 and 600 acre-ft, respectively, and a total estimated recharge credit of 1,618 acre-ft. Storage changes calculated for this study are 4.42 percent less for 2007 and 5.67 percent more for 2008 than previous estimates. Total storage change for 2007 and 2008 is 0.68 percent less than previous estimates. The small difference between the increase in storage from artificial recharge estimated with the groundwater-flow model and metered recharge indicates the groundwater model correctly accounts for the additional water recharged to the Equus Beds aquifer as part of the Aquifer Storage and Recovery project. Small percent differences between inflows and outflows for all stress periods and all index cells in the basin storage area, improved calibration compared to the previous model, and a reasonable match between simulated and measured long-term base flow indicates the groundwater model accurately simulates groundwater flow in the study area. The change in groundwater level through recent years compared to the August 1940 groundwater level map has been documented and used to assess the change of storage volume of the Equus Beds aquifer in and near the Wichita well field for three different areas. Two methods were used to estimate changes in storage from simulation results using simulated change in groundwater levels in layer 1 between stress periods, and using ZONEBUDGET to calculate the change in storage in the same way the effects of artificial recharge were estimated within the basin storage area. The three methods indicate similar trends although the magnitude of storage changes differ. Information about the change in storage in response to hydrologic stresses is important for managing groundwater resources in the study area. The comparison between the three methods indicates similar storage change trends are estimated and each could be used to determine relative increases or decreases in storage. Use of groundwater level changes that do not include storage changes that occur in confined or semi-confined parts of the aquifer will slightly underestimate storage changes; however, use of specific yield and groundwater level changes to estimate storage change in confined or semi-confined parts of the aquifer will overestimate storage changes. Using only changes in shallow groundwater levels would provide more accurate storage change estimates for the measured groundwater levels method. The value used for specific yield is also an important consideration when estimating storage. For the Equus Beds aquifer the reported specific yield ranges between 0.08 and 0.35 and the storage coefficient (for confined conditions) ranges between 0.0004 and 0.16. Considering the importance of the value of specific yield and storage coefficient to estimates of storage change over time, and the wide range and substantial overlap for the reported values for specific yield and storage coefficient in the study area, further information on the distribution of specific yield and storage coefficient within the Equus Beds aquifer in the study area would greatly enhance the accuracy of estimated storage changes using both simulated groundwater level, simulated groundwater budget, or measured groundwater level methods.

Accounting for Relatedness in Family Based Genetic Association Studies

PubMed Central

McArdle, P.F.; O’Connell, J.R.; Pollin, T.I.; Baumgarten, M.; Shuldiner, A.R.; Peyser, P.A.; Mitchell, B.D.

2007-01-01

Objective Assess the differences in point estimates, power and type 1 error rates when accounting for and ignoring family structure in genetic tests of association. Methods We compare by simulation the performance of analytic models using variance components to account for family structure and regression models that ignore relatedness for a range of possible family based study designs (i.e., sib pairs vs. large sibships vs. nuclear families vs. extended families). Results Our analyses indicate that effect size estimates and power are not significantly affected by ignoring family structure. Type 1 error rates increase when family structure is ignored, as density of family structures increases, and as trait heritability increases. For discrete traits with moderate levels of heritability and across many common sampling designs, type 1 error rates rise from a nominal 0.05 to 0.11. Conclusion Ignoring family structure may be useful in screening although it comes at a cost of a increased type 1 error rate, the magnitude of which depends on trait heritability and pedigree configuration. PMID:17570925

Modal Correction Method For Dynamically Induced Errors In Wind-Tunnel Model Attitude Measurements

NASA Technical Reports Server (NTRS)

Buehrle, R. D.; Young, C. P., Jr.

1995-01-01

This paper describes a method for correcting the dynamically induced bias errors in wind tunnel model attitude measurements using measured modal properties of the model system. At NASA Langley Research Center, the predominant instrumentation used to measure model attitude is a servo-accelerometer device that senses the model attitude with respect to the local vertical. Under smooth wind tunnel operating conditions, this inertial device can measure the model attitude with an accuracy of 0.01 degree. During wind tunnel tests when the model is responding at high dynamic amplitudes, the inertial device also senses the centrifugal acceleration associated with model vibration. This centrifugal acceleration results in a bias error in the model attitude measurement. A study of the response of a cantilevered model system to a simulated dynamic environment shows significant bias error in the model attitude measurement can occur and is vibration mode and amplitude dependent. For each vibration mode contributing to the bias error, the error is estimated from the measured modal properties and tangential accelerations at the model attitude device. Linear superposition is used to combine the bias estimates for individual modes to determine the overall bias error as a function of time. The modal correction model predicts the bias error to a high degree of accuracy for the vibration modes characterized in the simulated dynamic environment.

A new computational account of cognitive control over reinforcement-based decision-making: Modeling of a probabilistic learning task.

PubMed

Zendehrouh, Sareh

2015-11-01

Recent work on decision-making field offers an account of dual-system theory for decision-making process. This theory holds that this process is conducted by two main controllers: a goal-directed system and a habitual system. In the reinforcement learning (RL) domain, the habitual behaviors are connected with model-free methods, in which appropriate actions are learned through trial-and-error experiences. However, goal-directed behaviors are associated with model-based methods of RL, in which actions are selected using a model of the environment. Studies on cognitive control also suggest that during processes like decision-making, some cortical and subcortical structures work in concert to monitor the consequences of decisions and to adjust control according to current task demands. Here a computational model is presented based on dual system theory and cognitive control perspective of decision-making. The proposed model is used to simulate human performance on a variant of probabilistic learning task. The basic proposal is that the brain implements a dual controller, while an accompanying monitoring system detects some kinds of conflict including a hypothetical cost-conflict one. The simulation results address existing theories about two event-related potentials, namely error related negativity (ERN) and feedback related negativity (FRN), and explore the best account of them. Based on the results, some testable predictions are also presented. Copyright © 2015 Elsevier Ltd. All rights reserved.

Command generator tracker based direct model reference adaptive tracking guidance for Mars atmospheric entry

NASA Astrophysics Data System (ADS)

Li, Shuang; Peng, Yuming

2012-01-01

In order to accurately deliver an entry vehicle through the Martian atmosphere to the prescribed parachute deployment point, active Mars entry guidance is essential. This paper addresses the issue of Mars atmospheric entry guidance using the command generator tracker (CGT) based direct model reference adaptive control to reduce the adverse effect of the bounded uncertainties on atmospheric density and aerodynamic coefficients. Firstly, the nominal drag acceleration profile meeting a variety of constraints is planned off-line in the longitudinal plane as the reference model to track. Then, the CGT based direct model reference adaptive controller and the feed-forward compensator are designed to robustly track the aforementioned reference drag acceleration profile and to effectively reduce the downrange error. Afterwards, the heading alignment logic is adopted in the lateral plane to reduce the crossrange error. Finally, the validity of the guidance algorithm proposed in this paper is confirmed by Monte Carlo simulation analysis.

An analysis of the Kalman filter in the Gamma Ray Observatory (GRO) onboard attitude determination subsystem

NASA Technical Reports Server (NTRS)

Snow, Frank; Harman, Richard; Garrick, Joseph

1988-01-01

The Gamma Ray Observatory (GRO) spacecraft needs a highly accurate attitude knowledge to achieve its mission objectives. Utilizing the fixed-head star trackers (FHSTs) for observations and gyroscopes for attitude propagation, the discrete Kalman Filter processes the attitude data to obtain an onboard accuracy of 86 arc seconds (3 sigma). A combination of linear analysis and simulations using the GRO Software Simulator (GROSS) are employed to investigate the Kalman filter for stability and the effects of corrupted observations (misalignment, noise), incomplete dynamic modeling, and nonlinear errors on Kalman filter. In the simulations, on-board attitude is compared with true attitude, the sensitivity of attitude error to model errors is graphed, and a statistical analysis is performed on the residuals of the Kalman Filter. In this paper, the modeling and sensor errors that degrade the Kalman filter solution beyond mission requirements are studied, and methods are offered to identify the source of these errors.

Optical linear algebra processors: noise and error-source modeling.

PubMed

Casasent, D; Ghosh, A

1985-06-01

The modeling of system and component noise and error sources in optical linear algebra processors (OLAP's) are considered, with attention to the frequency-multiplexed OLAP. General expressions are obtained for the output produced as a function of various component errors and noise. A digital simulator for this model is discussed.

Optical linear algebra processors - Noise and error-source modeling

NASA Technical Reports Server (NTRS)

Casasent, D.; Ghosh, A.

1985-01-01

The modeling of system and component noise and error sources in optical linear algebra processors (OLAPs) are considered, with attention to the frequency-multiplexed OLAP. General expressions are obtained for the output produced as a function of various component errors and noise. A digital simulator for this model is discussed.

Optical Enhancement of Exoskeleton-Based Estimation of Glenohumeral Angles

PubMed Central

Cortés, Camilo; Unzueta, Luis; de los Reyes-Guzmán, Ana; Ruiz, Oscar E.; Flórez, Julián

2016-01-01

In Robot-Assisted Rehabilitation (RAR) the accurate estimation of the patient limb joint angles is critical for assessing therapy efficacy. In RAR, the use of classic motion capture systems (MOCAPs) (e.g., optical and electromagnetic) to estimate the Glenohumeral (GH) joint angles is hindered by the exoskeleton body, which causes occlusions and magnetic disturbances. Moreover, the exoskeleton posture does not accurately reflect limb posture, as their kinematic models differ. To address the said limitations in posture estimation, we propose installing the cameras of an optical marker-based MOCAP in the rehabilitation exoskeleton. Then, the GH joint angles are estimated by combining the estimated marker poses and exoskeleton Forward Kinematics. Such hybrid system prevents problems related to marker occlusions, reduced camera detection volume, and imprecise joint angle estimation due to the kinematic mismatch of the patient and exoskeleton models. This paper presents the formulation, simulation, and accuracy quantification of the proposed method with simulated human movements. In addition, a sensitivity analysis of the method accuracy to marker position estimation errors, due to system calibration errors and marker drifts, has been carried out. The results show that, even with significant errors in the marker position estimation, method accuracy is adequate for RAR. PMID:27403044

Development of a Rolling Process Design Tool for Use in Improving Hot Roll Slab Recovery

DOE Office of Scientific and Technical Information (OSTI.GOV)

Couch, R; Wang, P

2003-05-06

In this quarter, our primary effort has been focused on model verification, emphasizing on consistency in result for parallel and serial simulation runs, Progress has been made in refining the parallel thermal algorithms and in diminishing discretization effects in the contact region between the rollers and slab. We have received the metrology data of the ingot profile at the end of the fifth pass from Alcoa. Detailed comparisons between the data and the initial simulation result are being performed. Forthcoming from Alcoa are modifications to the fracture model based on additional experiments at lower strain rates. The original fracture model,more » was implemented in the finite element code, but damage in the rolling simulation was not correct due to the modeling errors at lower strain rates and high stress triaxiality. Validation simulations for the fracture model will continue when the experimentally-based adjustments to the parameter values become available.« less

Radiometric calibration of an airborne multispectral scanner. [of Thematic Mapper Simulator

NASA Technical Reports Server (NTRS)

Markham, Brian L.; Ahmad, Suraiya P.; Jackson, Ray D.; Moran, M. S.; Biggar, Stuart F.; Gellman, David I.; Slater, Philip N.

1991-01-01

The absolute radiometric calibration of the NS001 Thematic Mapper Simulator reflective channels was examined based on laboratory tests and in-flight comparisons to ground measurements. The NS001 data are calibrated in-flight by reference to the NS001 internal integrating sphere source. This source's power supply or monitoring circuitry exhibited greater instability in-flight during 1988-1989 than in the laboratory. Extrapolating laboratory behavior to in-flight data resulted in 7-20 percent radiance errors relative to ground measurements and atmospheric modeling. Assuming constancy in the source's output between laboraotry and in-flight resulted in generally smaller errors. Upgrades to the source's power supply and monitoring circuitry in 1990 improved its in-flight stability, though in-flight ground reflectance based calibration tests have not yet been performed.

Growth Simulation and Discrimination of Botrytis cinerea, Rhizopus stolonifer and Colletotrichum acutatum Using Hyperspectral Reflectance Imaging

PubMed Central

Gu, Xinzhe; Wang, Zhenjie; Huang, Yangmin; Wei, Yingying; Zhang, Miaomiao; Tu, Kang

2015-01-01

This research aimed to develop a rapid and nondestructive method to model the growth and discrimination of spoilage fungi, like Botrytis cinerea, Rhizopus stolonifer and Colletotrichum acutatum, based on hyperspectral imaging system (HIS). A hyperspectral imaging system was used to measure the spectral response of fungi inoculated on potato dextrose agar plates and stored at 28°C and 85% RH. The fungi were analyzed every 12 h over two days during growth, and optimal simulation models were built based on HIS parameters. The results showed that the coefficients of determination (R2) of simulation models for testing datasets were 0.7223 to 0.9914, and the sum square error (SSE) and root mean square error (RMSE) were in a range of 2.03–53.40×10−4 and 0.011–0.756, respectively. The correlation coefficients between the HIS parameters and colony forming units of fungi were high from 0.887 to 0.957. In addition, fungi species was discriminated by partial least squares discrimination analysis (PLSDA), with the classification accuracy of 97.5% for the test dataset at 36 h. The application of this method in real food has been addressed through the analysis of Botrytis cinerea, Rhizopus stolonifer and Colletotrichum acutatum inoculated in peaches, demonstrating that the HIS technique was effective for simulation of fungal infection in real food. This paper supplied a new technique and useful information for further study into modeling the growth of fungi and detecting fruit spoilage caused by fungi based on HIS. PMID:26642054

Mixtures of Berkson and classical covariate measurement error in the linear mixed model: Bias analysis and application to a study on ultrafine particles.

PubMed

Deffner, Veronika; Küchenhoff, Helmut; Breitner, Susanne; Schneider, Alexandra; Cyrys, Josef; Peters, Annette

2018-05-01

The ultrafine particle measurements in the Augsburger Umweltstudie, a panel study conducted in Augsburg, Germany, exhibit measurement error from various sources. Measurements of mobile devices show classical possibly individual-specific measurement error; Berkson-type error, which may also vary individually, occurs, if measurements of fixed monitoring stations are used. The combination of fixed site and individual exposure measurements results in a mixture of the two error types. We extended existing bias analysis approaches to linear mixed models with a complex error structure including individual-specific error components, autocorrelated errors, and a mixture of classical and Berkson error. Theoretical considerations and simulation results show, that autocorrelation may severely change the attenuation of the effect estimations. Furthermore, unbalanced designs and the inclusion of confounding variables influence the degree of attenuation. Bias correction with the method of moments using data with mixture measurement error partially yielded better results compared to the usage of incomplete data with classical error. Confidence intervals (CIs) based on the delta method achieved better coverage probabilities than those based on Bootstrap samples. Moreover, we present the application of these new methods to heart rate measurements within the Augsburger Umweltstudie: the corrected effect estimates were slightly higher than their naive equivalents. The substantial measurement error of ultrafine particle measurements has little impact on the results. The developed methodology is generally applicable to longitudinal data with measurement error. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Evaluation of a numerical model's ability to predict bed load transport observed in braided river experiments

NASA Astrophysics Data System (ADS)

Javernick, Luke; Redolfi, Marco; Bertoldi, Walter

2018-05-01

New data collection techniques offer numerical modelers the ability to gather and utilize high quality data sets with high spatial and temporal resolution. Such data sets are currently needed for calibration, verification, and to fuel future model development, particularly morphological simulations. This study explores the use of high quality spatial and temporal data sets of observed bed load transport in braided river flume experiments to evaluate the ability of a two-dimensional model, Delft3D, to predict bed load transport. This study uses a fixed bed model configuration and examines the model's shear stress calculations, which are the foundation to predict the sediment fluxes necessary for morphological simulations. The evaluation is conducted for three flow rates, and model setup used highly accurate Structure-from-Motion (SfM) topography and discharge boundary conditions. The model was hydraulically calibrated using bed roughness, and performance was evaluated based on depth and inundation agreement. Model bed load performance was evaluated in terms of critical shear stress exceedance area compared to maps of observed bed mobility in a flume. Following the standard hydraulic calibration, bed load performance was tested for sensitivity to horizontal eddy viscosity parameterization and bed morphology updating. Simulations produced depth errors equal to the SfM inherent errors, inundation agreement of 77-85%, and critical shear stress exceedance in agreement with 49-68% of the observed active area. This study provides insight into the ability of physically based, two-dimensional simulations to accurately predict bed load as well as the effects of horizontal eddy viscosity and bed updating. Further, this study highlights how using high spatial and temporal data to capture the physical processes at work during flume experiments can help to improve morphological modeling.

Modeling validation and control analysis for controlled temperature and humidity of air conditioning system.

PubMed

Lee, Jing-Nang; Lin, Tsung-Min; Chen, Chien-Chih

2014-01-01

This study constructs an energy based model of thermal system for controlled temperature and humidity air conditioning system, and introduces the influence of the mass flow rate, heater and humidifier for proposed control criteria to achieve the controlled temperature and humidity of air conditioning system. Then, the reliability of proposed thermal system model is established by both MATLAB dynamic simulation and the literature validation. Finally, the PID control strategy is applied for controlling the air mass flow rate, humidifying capacity, and heating, capacity. The simulation results show that the temperature and humidity are stable at 541 sec, the disturbance of temperature is only 0.14 °C, 0006 kg(w)/kg(da) in steady-state error of humidity ratio, and the error rate is only 7.5%. The results prove that the proposed system is an effective controlled temperature and humidity of an air conditioning system.

A Backscatter-Lidar Forward-Operator

NASA Astrophysics Data System (ADS)

Geisinger, Armin; Behrendt, Andreas; Wulfmeyer, Volker; Vogel, Bernhard; Mattis, Ina; Flentje, Harald; Förstner, Jochen; Potthast, Roland

2015-04-01

We have developed a forward-operator which is capable of calculating virtual lidar profiles from atmospheric state simulations. The operator allows us to compare lidar measurements and model simulations based on the same measurement parameter: the lidar backscatter profile. This method simplifies qualitative comparisons and also makes quantitative comparisons possible, including statistical error quantification. Implemented into an aerosol-capable model system, the operator will act as a component to assimilate backscatter-lidar measurements. As many weather services maintain already networks of backscatter-lidars, such data are acquired already in an operational manner. To estimate and quantify errors due to missing or uncertain aerosol information, we started sensitivity studies about several scattering parameters such as the aerosol size and both the real and imaginary part of the complex index of refraction. Furthermore, quantitative and statistical comparisons between measurements and virtual measurements are shown in this study, i.e. applying the backscatter-lidar forward-operator on model output.

Modeling Validation and Control Analysis for Controlled Temperature and Humidity of Air Conditioning System

PubMed Central

Lee, Jing-Nang; Lin, Tsung-Min

2014-01-01

This study constructs an energy based model of thermal system for controlled temperature and humidity air conditioning system, and introduces the influence of the mass flow rate, heater and humidifier for proposed control criteria to achieve the controlled temperature and humidity of air conditioning system. Then, the reliability of proposed thermal system model is established by both MATLAB dynamic simulation and the literature validation. Finally, the PID control strategy is applied for controlling the air mass flow rate, humidifying capacity, and heating, capacity. The simulation results show that the temperature and humidity are stable at 541 sec, the disturbance of temperature is only 0.14°C, 0006 kgw/kgda in steady-state error of humidity ratio, and the error rate is only 7.5%. The results prove that the proposed system is an effective controlled temperature and humidity of an air conditioning system. PMID:25250390

A simulation study to quantify the impacts of exposure ...

EPA Pesticide Factsheets

BackgroundExposure measurement error in copollutant epidemiologic models has the potential to introduce bias in relative risk (RR) estimates. A simulation study was conducted using empirical data to quantify the impact of correlated measurement errors in time-series analyses of air pollution and health.MethodsZIP-code level estimates of exposure for six pollutants (CO, NOx, EC, PM2.5, SO4, O3) from 1999 to 2002 in the Atlanta metropolitan area were used to calculate spatial, population (i.e. ambient versus personal), and total exposure measurement error.Empirically determined covariance of pollutant concentration pairs and the associated measurement errors were used to simulate true exposure (exposure without error) from observed exposure. Daily emergency department visits for respiratory diseases were simulated using a Poisson time-series model with a main pollutant RR = 1.05 per interquartile range, and a null association for the copollutant (RR = 1). Monte Carlo experiments were used to evaluate the impacts of correlated exposure errors of different copollutant pairs.ResultsSubstantial attenuation of RRs due to exposure error was evident in nearly all copollutant pairs studied, ranging from 10 to 40% attenuation for spatial error, 3–85% for population error, and 31–85% for total error. When CO, NOx or EC is the main pollutant, we demonstrated the possibility of false positives, specifically identifying significant, positive associations for copoll

Modeling marine oily wastewater treatment by a probabilistic agent-based approach.

PubMed

Jing, Liang; Chen, Bing; Zhang, Baiyu; Ye, Xudong

2018-02-01

This study developed a novel probabilistic agent-based approach for modeling of marine oily wastewater treatment processes. It begins first by constructing a probability-based agent simulation model, followed by a global sensitivity analysis and a genetic algorithm-based calibration. The proposed modeling approach was tested through a case study of the removal of naphthalene from marine oily wastewater using UV irradiation. The removal of naphthalene was described by an agent-based simulation model using 8 types of agents and 11 reactions. Each reaction was governed by a probability parameter to determine its occurrence. The modeling results showed that the root mean square errors between modeled and observed removal rates were 8.73 and 11.03% for calibration and validation runs, respectively. Reaction competition was analyzed by comparing agent-based reaction probabilities, while agents' heterogeneity was visualized by plotting their real-time spatial distribution, showing a strong potential for reactor design and process optimization. Copyright © 2017 Elsevier Ltd. All rights reserved.

An investigation of motion base cueing and G-seat cueing on pilot performance in a simulator

NASA Technical Reports Server (NTRS)

Mckissick, B. T.; Ashworth, B. R.; Parrish, R. V.

1983-01-01

The effect of G-seat cueing (GSC) and motion-base cueing (MBC) on performance of a pursuit-tracking task is studied using the visual motion simulator (VMS) at Langley Research Center. The G-seat, the six-degree-of-freedom synergistic platform motion system, the visual display, the cockpit hardware, and the F-16 aircraft mathematical model are characterized. Each of 8 active F-15 pilots performed the 2-min-43-sec task 10 times for each experimental mode: no cue, GSC, MBC, and GSC + MBC; the results were analyzed statistically in terms of the RMS values of vertical and lateral tracking error. It is shown that lateral error is significantly reduced by either GSC or MBC, and that the combination of cues produces a further, significant decrease. Vertical error is significantly decreased by GSC with or without MBC, whereas MBC effects vary for different pilots. The pattern of these findings is roughly duplicated in measurements of stick force applied for roll and pitch correction.

The Gibbs free energy of homogeneous nucleation: From atomistic nuclei to the planar limit.

PubMed

Cheng, Bingqing; Tribello, Gareth A; Ceriotti, Michele

2017-09-14

In this paper we discuss how the information contained in atomistic simulations of homogeneous nucleation should be used when fitting the parameters in macroscopic nucleation models. We show how the number of solid and liquid atoms in such simulations can be determined unambiguously by using a Gibbs dividing surface and how the free energy as a function of the number of solid atoms in the nucleus can thus be extracted. We then show that the parameters (the chemical potential, the interfacial free energy, and a Tolman correction) of a model based on classical nucleation theory can be fitted using the information contained in these free-energy profiles but that the parameters in such models are highly correlated. This correlation is unfortunate as it ensures that small errors in the computed free energy surface can give rise to large errors in the extrapolated properties of the fitted model. To resolve this problem we thus propose a method for fitting macroscopic nucleation models that uses simulations of planar interfaces and simulations of three-dimensional nuclei in tandem. We show that when the chemical potentials and the interface energy are pinned to their planar-interface values, more precise estimates for the Tolman length are obtained. Extrapolating the free energy profile obtained from small simulation boxes to larger nuclei is thus more reliable.

Statistical design and analysis for plant cover studies with multiple sources of observation errors

USGS Publications Warehouse

Wright, Wilson; Irvine, Kathryn M.; Warren, Jeffrey M .; Barnett, Jenny K.

2017-01-01

Effective wildlife habitat management and conservation requires understanding the factors influencing distribution and abundance of plant species. Field studies, however, have documented observation errors in visually estimated plant cover including measurements which differ from the true value (measurement error) and not observing a species that is present within a plot (detection error). Unlike the rapid expansion of occupancy and N-mixture models for analysing wildlife surveys, development of statistical models accounting for observation error in plants has not progressed quickly. Our work informs development of a monitoring protocol for managed wetlands within the National Wildlife Refuge System.Zero-augmented beta (ZAB) regression is the most suitable method for analysing areal plant cover recorded as a continuous proportion but assumes no observation errors. We present a model extension that explicitly includes the observation process thereby accounting for both measurement and detection errors. Using simulations, we compare our approach to a ZAB regression that ignores observation errors (naïve model) and an “ad hoc” approach using a composite of multiple observations per plot within the naïve model. We explore how sample size and within-season revisit design affect the ability to detect a change in mean plant cover between 2 years using our model.Explicitly modelling the observation process within our framework produced unbiased estimates and nominal coverage of model parameters. The naïve and “ad hoc” approaches resulted in underestimation of occurrence and overestimation of mean cover. The degree of bias was primarily driven by imperfect detection and its relationship with cover within a plot. Conversely, measurement error had minimal impacts on inferences. We found >30 plots with at least three within-season revisits achieved reasonable posterior probabilities for assessing change in mean plant cover.For rapid adoption and application, code for Bayesian estimation of our single-species ZAB with errors model is included. Practitioners utilizing our R-based simulation code can explore trade-offs among different survey efforts and parameter values, as we did, but tuned to their own investigation. Less abundant plant species of high ecological interest may warrant the additional cost of gathering multiple independent observations in order to guard against erroneous conclusions.

On a more rigorous gravity field processing for future LL-SST type gravity satellite missions

NASA Astrophysics Data System (ADS)

Daras, I.; Pail, R.; Murböck, M.

2013-12-01

In order to meet the augmenting demands of the user community concerning accuracies of temporal gravity field models, future gravity missions of low-low satellite-to-satellite tracking (LL-SST) type are planned to carry more precise sensors than their precedents. A breakthrough is planned with the improved LL-SST measurement link, where the traditional K-band microwave instrument of 1μm accuracy will be complemented by an inter-satellite ranging instrument of several nm accuracy. This study focuses on investigations concerning the potential performance of the new sensors and their impact in gravity field solutions. The processing methods for gravity field recovery have to meet the new sensor standards and be able to take full advantage of the new accuracies that they provide. We use full-scale simulations in a realistic environment to investigate whether the standard processing techniques suffice to fully exploit the new sensors standards. We achieve that by performing full numerical closed-loop simulations based on the Integral Equation approach. In our simulation scheme, we simulate dynamic orbits in a conventional tracking analysis to compute pseudo inter-satellite ranges or range-rates that serve as observables. Each part of the processing is validated separately with special emphasis on numerical errors and their impact in gravity field solutions. We demonstrate that processing with standard precision may be a limiting factor for taking full advantage of new generation sensors that future satellite missions will carry. Therefore we have created versions of our simulator with enhanced processing precision with primarily aim to minimize round-off system errors. Results using the enhanced precision show a big reduction of system errors that were present at the standard precision processing even for the error-free scenario, and reveal the improvements the new sensors will bring into the gravity field solutions. As a next step, we analyze the contribution of individual error sources to the system's error budget. More specifically we analyze sensor noise from the laser interferometer and the accelerometers, errors in the kinematic orbits and the background fields as well as temporal and spatial aliasing errors. We give special care on the assessment of error sources with stochastic behavior, such as the laser interferometer and the accelerometers, and their consistent stochastic modeling in frame of the adjustment process.

Decentralized control of sound radiation using iterative loop recovery.

PubMed

Schiller, Noah H; Cabell, Randolph H; Fuller, Chris R

2010-10-01

A decentralized model-based control strategy is designed to reduce low-frequency sound radiation from periodically stiffened panels. While decentralized control systems tend to be scalable, performance can be limited due to modeling error introduced by the unmodeled interaction between neighboring control units. Since bounds on modeling error are not known in advance, it is difficult to ensure the decentralized control system will be robust without making the controller overly conservative. Therefore an iterative approach is suggested, which utilizes frequency-shaped loop recovery. The approach accounts for modeling error introduced by neighboring control loops, requires no communication between subsystems, and is relatively simple. The control strategy is evaluated numerically using a model of a stiffened aluminum panel that is representative of the sidewall of an aircraft. Simulations demonstrate that the iterative approach can achieve significant reductions in radiated sound power from the stiffened panel without destabilizing neighboring control units.

Decentralized Control of Sound Radiation Using Iterative Loop Recovery

NASA Technical Reports Server (NTRS)

Schiller, Noah H.; Cabell, Randolph H.; Fuller, Chris R.

2009-01-01

A decentralized model-based control strategy is designed to reduce low-frequency sound radiation from periodically stiffened panels. While decentralized control systems tend to be scalable, performance can be limited due to modeling error introduced by the unmodeled interaction between neighboring control units. Since bounds on modeling error are not known in advance, it is difficult to ensure the decentralized control system will be robust without making the controller overly conservative. Therefore an iterative approach is suggested, which utilizes frequency-shaped loop recovery. The approach accounts for modeling error introduced by neighboring control loops, requires no communication between subsystems, and is relatively simple. The control strategy is evaluated numerically using a model of a stiffened aluminum panel that is representative of the sidewall of an aircraft. Simulations demonstrate that the iterative approach can achieve significant reductions in radiated sound power from the stiffened panel without destabilizing neighboring control units.

Simulation of streamflow, evapotranspiration, and groundwater recharge in the Lower Frio River watershed, south Texas, 1961-2008

USGS Publications Warehouse

Lizarraga, Joy S.; Ockerman, Darwin J.

2011-01-01

The U.S. Geological Survey, in cooperation with the U.S. Army Corps of Engineers, Fort Worth District; the City of Corpus Christi; the Guadalupe-Blanco River Authority; the San Antonio River Authority; and the San Antonio Water System, configured, calibrated, and tested a watershed model for a study area consisting of about 5,490 mi2 of the Frio River watershed in south Texas. The purpose of the model is to contribute to the understanding of watershed processes and hydrologic conditions in the lower Frio River watershed. The model simulates streamflow, evapotranspiration (ET), and groundwater recharge by using a numerical representation of physical characteristics of the landscape, and meteorological and streamflow data. Additional time-series inputs to the model include wastewater-treatment-plant discharges, surface-water withdrawals, and estimated groundwater inflow from Leona Springs. Model simulations of streamflow, ET, and groundwater recharge were done for various periods of record depending upon available measured data for input and comparison, starting as early as 1961. Because of the large size of the study area, the lower Frio River watershed was divided into 12 subwatersheds; separate Hydrological Simulation Program-FORTRAN models were developed for each subwatershed. Simulation of the overall study area involved running simulations in downstream order. Output from the model was summarized by subwatershed, point locations, reservoir reaches, and the Carrizo-Wilcox aquifer outcrop. Four long-term U.S. Geological Survey streamflow-gaging stations and two short-term streamflow-gaging stations were used for streamflow model calibration and testing with data from 1991-2008. Calibration was based on data from 2000-08, and testing was based on data from 1991-99. Choke Canyon Reservoir stage data from 1992-2008 and monthly evaporation estimates from 1999-2008 also were used for model calibration. Additionally, 2006-08 ET data from a U.S. Geological Survey meteorological station in Medina County were used for calibration. Streamflow and ET calibration were considered good or very good. For the 2000-08 calibration period, total simulated flow volume and the flow volume of the highest 10 percent of simulated daily flows were calibrated to within about 10 percent of measured volumes at six U.S. Geological Survey streamflow-gaging stations. The flow volume of the lowest 50 percent of daily flows was not simulated as accurately but represented a small percent of the total flow volume. The model-fit efficiency for the weekly mean streamflow during the calibration periods ranged from 0.60 to 0.91, and the root mean square error ranged from 16 to 271 percent of the mean flow rate. The simulated total flow volumes during the testing periods at the long-term gaging stations exceeded the measured total flow volumes by approximately 22 to 50 percent at three stations and were within 7 percent of the measured total flow volumes at one station. For the longer 1961-2008 simulation period at the long-term stations, simulated total flow volumes were within about 3 to 18 percent of measured total flow volumes. The calibrations made by using Choke Canyon reservoir volume for 1992-2008, reservoir evaporation for 1999-2008, and ET in Medina County for 2006-08, are considered very good. Model limitations include possible errors related to model conceptualization and parameter variability, lack of data to better quantify certain model inputs, and measurement errors. Uncertainty regarding the degree to which available rainfall data represent actual rainfall is potentially the most serious source of measurement error. A sensitivity analysis was performed for the Upper San Miguel subwatershed model to show the effect of changes to model parameters on the estimated mean recharge, ET, and surface runoff from that part of the Carrizo-Wilcox aquifer outcrop. Simulated recharge was most sensitive to the changes in the lower-zone ET (LZ

Bayesian inversions of a dynamic vegetation model at four European grassland sites

NASA Astrophysics Data System (ADS)

Minet, J.; Laloy, E.; Tychon, B.; Francois, L.

2015-05-01

Eddy covariance data from four European grassland sites are used to probabilistically invert the CARAIB (CARbon Assimilation In the Biosphere) dynamic vegetation model (DVM) with 10 unknown parameters, using the DREAM(ZS) (DiffeRential Evolution Adaptive Metropolis) Markov chain Monte Carlo (MCMC) sampler. We focus on comparing model inversions, considering both homoscedastic and heteroscedastic eddy covariance residual errors, with variances either fixed a priori or jointly inferred together with the model parameters. Agreements between measured and simulated data during calibration are comparable with previous studies, with root mean square errors (RMSEs) of simulated daily gross primary productivity (GPP), ecosystem respiration (RECO) and evapotranspiration (ET) ranging from 1.73 to 2.19, 1.04 to 1.56 g C m-2 day-1 and 0.50 to 1.28 mm day-1, respectively. For the calibration period, using a homoscedastic eddy covariance residual error model resulted in a better agreement between measured and modelled data than using a heteroscedastic residual error model. However, a model validation experiment showed that CARAIB models calibrated considering heteroscedastic residual errors perform better. Posterior parameter distributions derived from using a heteroscedastic model of the residuals thus appear to be more robust. This is the case even though the classical linear heteroscedastic error model assumed herein did not fully remove heteroscedasticity of the GPP residuals. Despite the fact that the calibrated model is generally capable of fitting the data within measurement errors, systematic bias in the model simulations are observed. These are likely due to model inadequacies such as shortcomings in the photosynthesis modelling. Besides the residual error treatment, differences between model parameter posterior distributions among the four grassland sites are also investigated. It is shown that the marginal distributions of the specific leaf area and characteristic mortality time parameters can be explained by site-specific ecophysiological characteristics.

Improvement of Parameter Estimations in Tumor Growth Inhibition Models on Xenografted Animals: Handling Sacrifice Censoring and Error Caused by Experimental Measurement on Larger Tumor Sizes.

PubMed

Pierrillas, Philippe B; Tod, Michel; Amiel, Magali; Chenel, Marylore; Henin, Emilie

2016-09-01

The purpose of this study was to explore the impact of censoring due to animal sacrifice on parameter estimates and tumor volume calculated from two diameters in larger tumors during tumor growth experiments in preclinical studies. The type of measurement error that can be expected was also investigated. Different scenarios were challenged using the stochastic simulation and estimation process. One thousand datasets were simulated under the design of a typical tumor growth study in xenografted mice, and then, eight approaches were used for parameter estimation with the simulated datasets. The distribution of estimates and simulation-based diagnostics were computed for comparison. The different approaches were robust regarding the choice of residual error and gave equivalent results. However, by not considering missing data induced by sacrificing the animal, parameter estimates were biased and led to false inferences in terms of compound potency; the threshold concentration for tumor eradication when ignoring censoring was 581 ng.ml(-1), but the true value was 240 ng.ml(-1).

Simulation of streamflow and water quality in the Christina River subbasin and overview of simulations in other subbasins of the Christina River Basin, Pennsylvania, Maryland, and Delaware, 1994-98

USGS Publications Warehouse

Senior, Lisa A.; Koerkle, Edward H.

2003-01-01

The Christina River Basin drains 565 square miles (mi2) in Pennsylvania and Delaware and includes the major subbasins of Brandywine Creek, Red Clay Creek, White Clay Creek, and Christina River. The Christina River subbasin (exclusive of the Brandywine, Red Clay, and White Clay Creek subbasins) drains an area of 76 mi2. Streams in the Christina River Basin are used for recreation, drinking water supply, and support of aquatic life. Water quality in some parts of the Christina River Basin is impaired and does not support designated uses of the stream. A multi-agency water-quality management strategy included a modeling component to evaluate the effects of point- and nonpoint-source contributions of nutrients and suspended sediment on stream water quality. To assist in nonpoint-source evaluation, four independent models, one for each of the four main subbasins of the Christina River Basin, were developed and calibrated using the model code Hydrological Simulation Program–Fortran (HSPF). Water-quality data for model calibration were collected in each of the four main subbasins and in small subbasins predominantly covered by one land use following a nonpoint- source monitoring plan. Under this plan, stormflow and base-flow samples were collected during 1998 at two sites in the Christina River subbasin and nine sites elsewhere in the Christina River Basin.The HSPF model for the Christina River subbasin simulates streamflow, suspended sediment, and the nutrients, nitrogen and phosphorus. In addition, the model simulates water temperature, dissolved oxygen, biochemical oxygen demand, and plankton as secondary objectives needed to support the sediment and nutrient simulations. For the model, the basin was subdivided into nine reaches draining areas that ranged from 3.8 to 21.9 mi2. Ten different pervious land uses and two impervious land uses were selected for simulation. Land-use areas were determined from 1995 land-use data. The predominant land uses in the Christina River subbasin are residential, urban, forested, agricultural, and open.The hydrologic component of the model was run at an hourly time step and calibrated using streamflow data from two U.S. Geological Survey (USGS) streamflow-measurement stations for the period of October 1, 1994, through October 29, 1998. Daily precipitation data from one National Oceanic and Atmospheric Administration (NOAA) meteorologic station and hourly data from one NOAA meteorologic station were used for model input. The difference between observed and simulated streamflow volume ranged from -2.3 to 5.3 percent for a 10-month portion of the calibration period at the two calibration sites. Annual differences between observed and simulated streamflow generally were greater than the overall error for the 4-year period. For example, at Christina River at Coochs Bridge, near the bottom of the free-flowing part of the subbasin (drainage area of 21 mi2), annual differences between observed and simulated streamflow ranged from -6.9 to 6.5 percent and the overall error for the 4-year period was -1.1 percent. Calibration errors for 36 storm periods at the three calibration sites for total volume, low-flow recession rate, 50-percent lowest flows, 10-percent highest flows, and storm peaks were within the recommended criteria of 20 percent or less. Much of the error in simulating storm events on an hourly time step can be attributed to uncertainty in the rainfall data.The water-quality component of the model was calibrated using nonpoint-source monitoring data collected at two USGS streamflow-measurement stations and other water-quality monitoring data. The period of record for water-quality monitoring was variable at the stations, with a start date ranging from October 1994 to January 1998 and an end date of October 1998. Because of availability, monitoring data for suspended-solids concentrations were used as surrogates for suspended-sediment concentrations, although suspended-solids data may underestimate suspended sediment and affect apparent accuracy of the suspended-sediment simulaion. Comparison of observed to simulated loads for up to six storms in 1998 at the two nonpoint-source monitoring sites (Little Mill Creek near Newport and Christina River at Coochs Bridge, Del.) indicate that simulation error is commonly as large as an order of magnitude for suspended sediment and nutrients. The simulation error tends to be smaller for dissolved nutrients than for particulate nutrients. Errors of 40 percent or less for monthly or annual values indicate a fair to good water-quality calibration according to recommended criteria; much larger errors are possible for individual events. Assessment of the water-quality calibration under stormflow conditions is limited by the relatively small amount of available water-quality data in the subbasin.Users of the Christina River subbasin HSPF model and HSPF models for other subbasins in the Christina River Basin should be aware of model limitations and consider the following if the model is used for predictive purposes: streamflow-duration curves suggest the model simulates streamflow reasonably well when measured over a broad range of conditions and time although streamflow and the corresponding water quality for individual storm events may not be well simulated; streamflow-duration curves for the simulation period compare well with duration curves for the 8-year period ending in 2001 at Christina River at Coochs Bridge, Del., and include all but the extreme high-flow and low-flow events; and calibration for water quality was based on limited data, with the result of increasing uncertainty in the water-quality simulation.

On the appropriateness of applying chi-square distribution based confidence intervals to spectral estimates of helicopter flyover data

NASA Technical Reports Server (NTRS)

Rutledge, Charles K.

1988-01-01

The validity of applying chi-square based confidence intervals to far-field acoustic flyover spectral estimates was investigated. Simulated data, using a Kendall series and experimental acoustic data from the NASA/McDonnell Douglas 500E acoustics test, were analyzed. Statistical significance tests to determine the equality of distributions of the simulated and experimental data relative to theoretical chi-square distributions were performed. Bias and uncertainty errors associated with the spectral estimates were easily identified from the data sets. A model relating the uncertainty and bias errors to the estimates resulted, which aided in determining the appropriateness of the chi-square distribution based confidence intervals. Such confidence intervals were appropriate for nontonally associated frequencies of the experimental data but were inappropriate for tonally associated estimate distributions. The appropriateness at the tonally associated frequencies was indicated by the presence of bias error and noncomformity of the distributions to the theoretical chi-square distribution. A technique for determining appropriate confidence intervals at the tonally associated frequencies was suggested.

Precipitation and Latent Heating Distributions from Satellite Passive Microwave Radiometry. Part 2; Evaluation of Estimates Using Independent Data

NASA Technical Reports Server (NTRS)

Yang, Song; Olson, William S.; Wang, Jian-Jian; Bell, Thomas L.; Smith, Eric A.; Kummerow, Christian D.

2004-01-01

Rainfall rate estimates from space-borne k&ents are generally accepted as reliable by a majority of the atmospheric science commu&y. One-of the Tropical Rainfall Measuring Mission (TRh4M) facility rain rate algorithms is based upon passive microwave observations fiom the TRMM Microwave Imager (TMI). Part I of this study describes improvements in the TMI algorithm that are required to introduce cloud latent heating and drying as additional algorithm products. Here, estimates of surface rain rate, convective proportion, and latent heating are evaluated using independent ground-based estimates and satellite products. Instantaneous, OP5resolution estimates of surface rain rate over ocean fiom the improved TMI algorithm are well correlated with independent radar estimates (r approx. 0.88 over the Tropics), but bias reduction is the most significant improvement over forerunning algorithms. The bias reduction is attributed to the greater breadth of cloud-resolving model simulations that support the improved algorithm, and the more consistent and specific convective/stratiform rain separation method utilized. The bias of monthly, 2.5 deg. -resolution estimates is similarly reduced, with comparable correlations to radar estimates. Although the amount of independent latent heating data are limited, TMI estimated latent heating profiles compare favorably with instantaneous estimates based upon dual-Doppler radar observations, and time series of surface rain rate and heating profiles are generally consistent with those derived from rawinsonde analyses. Still, some biases in profile shape are evident, and these may be resolved with: (a) additional contextual information brought to the estimation problem, and/or; (b) physically-consistent and representative databases supporting the algorithm. A model of the random error in instantaneous, 0.5 deg-resolution rain rate estimates appears to be consistent with the levels of error determined from TMI comparisons to collocated radar. Error model modifications for non-raining situations will be required, however. Sampling error appears to represent only a fraction of the total error in monthly, 2S0-resolution TMI estimates; the remaining error is attributed to physical inconsistency or non-representativeness of cloud-resolving model simulated profiles supporting the algorithm.

Variance reduction for Fokker–Planck based particle Monte Carlo schemes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gorji, M. Hossein, E-mail: gorjih@ifd.mavt.ethz.ch; Andric, Nemanja; Jenny, Patrick

Recently, Fokker–Planck based particle Monte Carlo schemes have been proposed and evaluated for simulations of rarefied gas flows [1–3]. In this paper, the variance reduction for particle Monte Carlo simulations based on the Fokker–Planck model is considered. First, deviational based schemes were derived and reviewed, and it is shown that these deviational methods are not appropriate for practical Fokker–Planck based rarefied gas flow simulations. This is due to the fact that the deviational schemes considered in this study lead either to instabilities in the case of two-weight methods or to large statistical errors if the direct sampling method is applied.more » Motivated by this conclusion, we developed a novel scheme based on correlated stochastic processes. The main idea here is to synthesize an additional stochastic process with a known solution, which is simultaneously solved together with the main one. By correlating the two processes, the statistical errors can dramatically be reduced; especially for low Mach numbers. To assess the methods, homogeneous relaxation, planar Couette and lid-driven cavity flows were considered. For these test cases, it could be demonstrated that variance reduction based on parallel processes is very robust and effective.« less

Attitude algorithm and initial alignment method for SINS applied in short-range aircraft

NASA Astrophysics Data System (ADS)

Zhang, Rong-Hui; He, Zhao-Cheng; You, Feng; Chen, Bo

2017-07-01

This paper presents an attitude solution algorithm based on the Micro-Electro-Mechanical System and quaternion method. We completed the numerical calculation and engineering practice by adopting fourth-order Runge-Kutta algorithm in the digital signal processor. The state space mathematical model of initial alignment in static base was established, and the initial alignment method based on Kalman filter was proposed. Based on the hardware in the loop simulation platform, the short-range flight simulation test and the actual flight test were carried out. The results show that the error of pitch, yaw and roll angle is fast convergent, and the fitting rate between flight simulation and flight test is more than 85%.

Modelling space-based integral-field spectrographs and their application to Type Ia supernova cosmology

NASA Astrophysics Data System (ADS)

Shukla, Hemant; Bonissent, Alain

2017-04-01

We present the parameterized simulation of an integral-field unit (IFU) slicer spectrograph and its applications in spectroscopic studies, namely, for probing dark energy with type Ia supernovae. The simulation suite is called the fast-slicer IFU simulator (FISim). The data flow of FISim realistically models the optics of the IFU along with the propagation effects, including cosmological, zodiacal, instrumentation and detector effects. FISim simulates the spectrum extraction by computing the error matrix on the extracted spectrum. The applications for Type Ia supernova spectroscopy are used to establish the efficacy of the simulator in exploring the wider parametric space, in order to optimize the science and mission requirements. The input spectral models utilize the observables such as the optical depth and velocity of the Si II absorption feature in the supernova spectrum as the measured parameters for various studies. Using FISim, we introduce a mechanism for preserving the complete state of a system, called the partial p/partial f matrix, which allows for compression, reconstruction and spectrum extraction, we introduce a novel and efficient method for spectrum extraction, called super-optimal spectrum extraction, and we conduct various studies such as the optimal point spread function, optimal resolution, parameter estimation, etc. We demonstrate that for space-based telescopes, the optimal resolution lies in the region near R ˜ 117 for read noise of 1 e- and 7 e- using a 400 km s-1 error threshold on the Si II velocity.

Mixed effects versus fixed effects modelling of binary data with inter-subject variability.

PubMed

Murphy, Valda; Dunne, Adrian

2005-04-01

The question of whether or not a mixed effects model is required when modelling binary data with inter-subject variability and within subject correlation was reported in this journal by Yano et al. (J. Pharmacokin. Pharmacodyn. 28:389-412 [2001]). That report used simulation experiments to demonstrate that, under certain circumstances, the use of a fixed effects model produced more accurate estimates of the fixed effect parameters than those produced by a mixed effects model. The Laplace approximation to the likelihood was used when fitting the mixed effects model. This paper repeats one of those simulation experiments, with two binary observations recorded for every subject, and uses both the Laplace and the adaptive Gaussian quadrature approximations to the likelihood when fitting the mixed effects model. The results show that the estimates produced using the Laplace approximation include a small number of extreme outliers. This was not the case when using the adaptive Gaussian quadrature approximation. Further examination of these outliers shows that they arise in situations in which the Laplace approximation seriously overestimates the likelihood in an extreme region of the parameter space. It is also demonstrated that when the number of observations per subject is increased from two to three, the estimates based on the Laplace approximation no longer include any extreme outliers. The root mean squared error is a combination of the bias and the variability of the estimates. Increasing the sample size is known to reduce the variability of an estimator with a consequent reduction in its root mean squared error. The estimates based on the fixed effects model are inherently biased and this bias acts as a lower bound for the root mean squared error of these estimates. Consequently, it might be expected that for data sets with a greater number of subjects the estimates based on the mixed effects model would be more accurate than those based on the fixed effects model. This is borne out by the results of a further simulation experiment with an increased number of subjects in each set of data. The difference in the interpretation of the parameters of the fixed and mixed effects models is discussed. It is demonstrated that the mixed effects model and parameter estimates can be used to estimate the parameters of the fixed effects model but not vice versa.

Advanced Shock Position Control for Mode Transition in a Turbine Based Combined Cycle Engine Inlet Model

NASA Technical Reports Server (NTRS)

Csank, Jeffrey T.; Stueber, Thomas J.

2013-01-01

A dual flow-path inlet system is being tested to evaluate methodologies for a Turbine Based Combined Cycle (TBCC) propulsion system to perform a controlled inlet mode transition. Prior to experimental testing, simulation models are used to test, debug, and validate potential control algorithms. One simulation package being used for testing is the High Mach Transient Engine Cycle Code simulation, known as HiTECC. This paper discusses the closed loop control system, which utilizes a shock location sensor to improve inlet performance and operability. Even though the shock location feedback has a coarse resolution, the feedback allows for a reduction in steady state error and, in some cases, better performance than with previous proposed pressure ratio based methods. This paper demonstrates the design and benefit with the implementation of a proportional-integral controller, an H-Infinity based controller, and a disturbance observer based controller.

Incorporating a prediction of postgrazing herbage mass into a whole-farm model for pasture-based dairy systems.

PubMed

Gregorini, P; Galli, J; Romera, A J; Levy, G; Macdonald, K A; Fernandez, H H; Beukes, P C

2014-07-01

The DairyNZ whole-farm model (WFM; DairyNZ, Hamilton, New Zealand) consists of a framework that links component models for animal, pastures, crops, and soils. The model was developed to assist with analysis and design of pasture-based farm systems. New (this work) and revised (e.g., cow, pasture, crops) component models can be added to the WFM, keeping the model flexible and up to date. Nevertheless, the WFM does not account for plant-animal relationships determining herbage-depletion dynamics. The user has to preset the maximum allowable level of herbage depletion [i.e., postgrazing herbage mass (residuals)] throughout the year. Because residuals have a direct effect on herbage regrowth, the WFM in its current form does not dynamically simulate the effect of grazing pressure on herbage depletion and consequent effect on herbage regrowth. The management of grazing pressure is a key component of pasture-based dairy systems. Thus, the main objective of the present work was to develop a new version of the WFM able to predict residuals, and thereby simulate related effects of grazing pressure dynamically at the farm scale. This objective was accomplished by incorporating a new component model into the WFM. This model represents plant-animal relationships, for example sward structure and herbage intake rate, and resulting level of herbage depletion. The sensitivity of the new version of the WFM was evaluated and then the new WFM was tested against an experimental data set previously used to evaluate the WFM and to illustrate the adequacy and improvement of the model development. Key outputs variables of the new version pertinent to this work (milk production, herbage dry matter intake, intake rate, harvesting efficiency, and residuals) responded acceptably to a range of input variables. The relative prediction errors for monthly and mean annual residual predictions were 20 and 5%, respectively. Monthly predictions of residuals had a line bias (1.5%), with a proportion of square root of mean square prediction error (RMSPE) due to random error of 97.5%. Predicted monthly herbage growth rates had a line bias of 2%, a proportion of RMSPE due to random error of 96%, and a concordance correlation coefficient of 0.87. Annual herbage production was predicted with an RMSPE of 531 (kg of herbage dry matter/ha per year), a line bias of 11%, a proportion of RMSPE due to random error of 80%, and relative prediction errors of 2%. Annual herbage dry matter intake per cow and hectare, both per year, were predicted with RMSPE, relative prediction error, and concordance correlation coefficient of 169 and 692kg of dry matter, 3 and 4%, and 0.91 and 0.87, respectively. These results indicate that predictions of the new WFM are relatively accurate and precise, with a conclusion that incorporating a plant-animal relationship model into the WFM allows for dynamic predictions of residuals and more realistic simulations of the effect of grazing pressure on herbage production and intake at the farm level without the intervention from the user. Copyright © 2014 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Subresolution Displacements in Finite Difference Simulations of Ultrasound Propagation and Imaging.

PubMed

Pinton, Gianmarco F

2017-03-01

Time domain finite difference simulations are used extensively to simulate wave propagation. They approximate the wave field on a discrete domain with a grid spacing that is typically on the order of a tenth of a wavelength. The smallest displacements that can be modeled by this type of simulation are thus limited to discrete values that are integer multiples of the grid spacing. This paper presents a method to represent continuous and subresolution displacements by varying the impedance of individual elements in a multielement scatterer. It is demonstrated that this method removes the limitations imposed by the discrete grid spacing by generating a continuum of displacements as measured by the backscattered signal. The method is first validated on an ideal perfect correlation case with a single scatterer. It is subsequently applied to a more complex case with a field of scatterers that model an acoustic radiation force-induced displacement used in ultrasound elasticity imaging. A custom finite difference simulation tool is used to simulate propagation from ultrasound imaging pulses in the scatterer field. These simulated transmit-receive events are then beamformed into images, which are tracked with a correlation-based algorithm to determine the displacement. A linear predictive model is developed to analytically describe the relationship between element impedance and backscattered phase shift. The error between model and simulation is λ/ 1364 , where λ is the acoustical wavelength. An iterative method is also presented that reduces the simulation error to λ/ 5556 over one iteration. The proposed technique therefore offers a computationally efficient method to model continuous subresolution displacements of a scattering medium in ultrasound imaging. This method has applications that include ultrasound elastography, blood flow, and motion tracking. This method also extends generally to finite difference simulations of wave propagation, such as electromagnetic or seismic waves.

A Damping Grid Strapdown Inertial Navigation System Based on a Kalman Filter for Ships in Polar Regions.

PubMed

Huang, Weiquan; Fang, Tao; Luo, Li; Zhao, Lin; Che, Fengzhu

2017-07-03

The grid strapdown inertial navigation system (SINS) used in polar navigation also includes three kinds of periodic oscillation errors as common SINS are based on a geographic coordinate system. Aiming ships which have the external information to conduct a system reset regularly, suppressing the Schuler periodic oscillation is an effective way to enhance navigation accuracy. The Kalman filter based on the grid SINS error model which applies to the ship is established in this paper. The errors of grid-level attitude angles can be accurately estimated when the external velocity contains constant error, and then correcting the errors of the grid-level attitude angles through feedback correction can effectively dampen the Schuler periodic oscillation. The simulation results show that with the aid of external reference velocity, the proposed external level damping algorithm based on the Kalman filter can suppress the Schuler periodic oscillation effectively. Compared with the traditional external level damping algorithm based on the damping network, the algorithm proposed in this paper can reduce the overshoot errors when the state of grid SINS is switched from the non-damping state to the damping state, and this effectively improves the navigation accuracy of the system.

Near field communications technology and the potential to reduce medication errors through multidisciplinary application

PubMed Central

Pegler, Joe; Lehane, Elaine; Livingstone, Vicki; McCarthy, Nora; Sahm, Laura J.; Tabirca, Sabin; O’Driscoll, Aoife; Corrigan, Mark

2016-01-01

Background Patient safety requires optimal management of medications. Electronic systems are encouraged to reduce medication errors. Near field communications (NFC) is an emerging technology that may be used to develop novel medication management systems. Methods An NFC-based system was designed to facilitate prescribing, administration and review of medications commonly used on surgical wards. Final year medical, nursing, and pharmacy students were recruited to test the electronic system in a cross-over observational setting on a simulated ward. Medication errors were compared against errors recorded using a paper-based system. Results A significant difference in the commission of medication errors was seen when NFC and paper-based medication systems were compared. Paper use resulted in a mean of 4.09 errors per prescribing round while NFC prescribing resulted in a mean of 0.22 errors per simulated prescribing round (P=0.000). Likewise, medication administration errors were reduced from a mean of 2.30 per drug round with a Paper system to a mean of 0.80 errors per round using NFC (P<0.015). A mean satisfaction score of 2.30 was reported by users, (rated on seven-point scale with 1 denoting total satisfaction with system use and 7 denoting total dissatisfaction). Conclusions An NFC based medication system may be used to effectively reduce medication errors in a simulated ward environment. PMID:28293602

Near field communications technology and the potential to reduce medication errors through multidisciplinary application.

PubMed

O'Connell, Emer; Pegler, Joe; Lehane, Elaine; Livingstone, Vicki; McCarthy, Nora; Sahm, Laura J; Tabirca, Sabin; O'Driscoll, Aoife; Corrigan, Mark

2016-01-01

Patient safety requires optimal management of medications. Electronic systems are encouraged to reduce medication errors. Near field communications (NFC) is an emerging technology that may be used to develop novel medication management systems. An NFC-based system was designed to facilitate prescribing, administration and review of medications commonly used on surgical wards. Final year medical, nursing, and pharmacy students were recruited to test the electronic system in a cross-over observational setting on a simulated ward. Medication errors were compared against errors recorded using a paper-based system. A significant difference in the commission of medication errors was seen when NFC and paper-based medication systems were compared. Paper use resulted in a mean of 4.09 errors per prescribing round while NFC prescribing resulted in a mean of 0.22 errors per simulated prescribing round (P=0.000). Likewise, medication administration errors were reduced from a mean of 2.30 per drug round with a Paper system to a mean of 0.80 errors per round using NFC (P<0.015). A mean satisfaction score of 2.30 was reported by users, (rated on seven-point scale with 1 denoting total satisfaction with system use and 7 denoting total dissatisfaction). An NFC based medication system may be used to effectively reduce medication errors in a simulated ward environment.

Formation of an internal model of environment dynamics during upper limb reaching movements: a fuzzy approach.

PubMed

MacDonald, Chad; Moussavi, Zahra; Sarkodie-Gyan, Thompson

2007-01-01

This paper presents the development and simulation of a fuzzy logic based learning mechanism to emulate human motor learning. In particular, fuzzy inference was used to develop an internal model of a novel dynamic environment experienced during planar reaching movements with the upper limb. A dynamic model of the human arm was developed and a fuzzy if-then rule base was created to relate trajectory movement and velocity errors to internal model update parameters. An experimental simulation was performed to compare the fuzzy system's performance with that of human subjects. It was found that the dynamic model behaved as expected, and the fuzzy learning mechanism created an internal model that was capable of opposing the environmental force field to regain a trajectory closely resembling the desired ideal.

The Impact of Trajectory Prediction Uncertainty on Air Traffic Controller Performance and Acceptability

NASA Technical Reports Server (NTRS)

Mercer, Joey S.; Bienert, Nancy; Gomez, Ashley; Hunt, Sarah; Kraut, Joshua; Martin, Lynne; Morey, Susan; Green, Steven M.; Prevot, Thomas; Wu, Minghong G.

2013-01-01

A Human-In-The-Loop air traffic control simulation investigated the impact of uncertainties in trajectory predictions on NextGen Trajectory-Based Operations concepts, seeking to understand when the automation would become unacceptable to controllers or when performance targets could no longer be met. Retired air traffic controllers staffed two en route transition sectors, delivering arrival traffic to the northwest corner-post of Atlanta approach control under time-based metering operations. Using trajectory-based decision-support tools, the participants worked the traffic under varying levels of wind forecast error and aircraft performance model error, impacting the ground automations ability to make accurate predictions. Results suggest that the controllers were able to maintain high levels of performance, despite even the highest levels of trajectory prediction errors.

The impact of response measurement error on the analysis of designed experiments

DOE PAGES

Anderson-Cook, Christine Michaela; Hamada, Michael Scott; Burr, Thomas Lee

2016-11-01

This study considers the analysis of designed experiments when there is measurement error in the true response or so-called response measurement error. We consider both additive and multiplicative response measurement errors. Through a simulation study, we investigate the impact of ignoring the response measurement error in the analysis, that is, by using a standard analysis based on t-tests. In addition, we examine the role of repeat measurements in improving the quality of estimation and prediction in the presence of response measurement error. We also study a Bayesian approach that accounts for the response measurement error directly through the specification ofmore » the model, and allows including additional information about variability in the analysis. We consider the impact on power, prediction, and optimization. Copyright © 2015 John Wiley & Sons, Ltd.« less

The impact of response measurement error on the analysis of designed experiments

DOE Office of Scientific and Technical Information (OSTI.GOV)

Anderson-Cook, Christine Michaela; Hamada, Michael Scott; Burr, Thomas Lee

This study considers the analysis of designed experiments when there is measurement error in the true response or so-called response measurement error. We consider both additive and multiplicative response measurement errors. Through a simulation study, we investigate the impact of ignoring the response measurement error in the analysis, that is, by using a standard analysis based on t-tests. In addition, we examine the role of repeat measurements in improving the quality of estimation and prediction in the presence of response measurement error. We also study a Bayesian approach that accounts for the response measurement error directly through the specification ofmore » the model, and allows including additional information about variability in the analysis. We consider the impact on power, prediction, and optimization. Copyright © 2015 John Wiley & Sons, Ltd.« less

Dynamic imaging model and parameter optimization for a star tracker.

PubMed

Yan, Jinyun; Jiang, Jie; Zhang, Guangjun

2016-03-21

Under dynamic conditions, star spots move across the image plane of a star tracker and form a smeared star image. This smearing effect increases errors in star position estimation and degrades attitude accuracy. First, an analytical energy distribution model of a smeared star spot is established based on a line segment spread function because the dynamic imaging process of a star tracker is equivalent to the static imaging process of linear light sources. The proposed model, which has a clear physical meaning, explicitly reflects the key parameters of the imaging process, including incident flux, exposure time, velocity of a star spot in an image plane, and Gaussian radius. Furthermore, an analytical expression of the centroiding error of the smeared star spot is derived using the proposed model. An accurate and comprehensive evaluation of centroiding accuracy is obtained based on the expression. Moreover, analytical solutions of the optimal parameters are derived to achieve the best performance in centroid estimation. Finally, we perform numerical simulations and a night sky experiment to validate the correctness of the dynamic imaging model, the centroiding error expression, and the optimal parameters.

Adaptive control of servo system based on LuGre model

NASA Astrophysics Data System (ADS)

Jin, Wang; Niancong, Liu; Jianlong, Chen; Weitao, Geng

2018-03-01

This paper established a mechanical model of feed system based on LuGre model. In order to solve the influence of nonlinear factors on the system running stability, a nonlinear single observer is designed to estimate the parameter z in the LuGre model and an adaptive friction compensation controller is designed. Simulink simulation results show that the control method can effectively suppress the adverse effects of friction and external disturbances. The simulation show that the adaptive parameter kz is between 0.11-0.13, and the value of gamma1 is between 1.9-2.1. Position tracking error reaches level 10-3 and is stabilized near 0 values within 0.3 seconds, the compensation method has better tracking accuracy and robustness.

Breaking Computational Barriers: Real-time Analysis and Optimization with Large-scale Nonlinear Models via Model Reduction

DOE Office of Scientific and Technical Information (OSTI.GOV)

Carlberg, Kevin Thomas; Drohmann, Martin; Tuminaro, Raymond S.

2014-10-01

Model reduction for dynamical systems is a promising approach for reducing the computational cost of large-scale physics-based simulations to enable high-fidelity models to be used in many- query (e.g., Bayesian inference) and near-real-time (e.g., fast-turnaround simulation) contexts. While model reduction works well for specialized problems such as linear time-invariant systems, it is much more difficult to obtain accurate, stable, and efficient reduced-order models (ROMs) for systems with general nonlinearities. This report describes several advances that enable nonlinear reduced-order models (ROMs) to be deployed in a variety of time-critical settings. First, we present an error bound for the Gauss-Newton with Approximatedmore » Tensors (GNAT) nonlinear model reduction technique. This bound allows the state-space error for the GNAT method to be quantified when applied with the backward Euler time-integration scheme. Second, we present a methodology for preserving classical Lagrangian structure in nonlinear model reduction. This technique guarantees that important properties--such as energy conservation and symplectic time-evolution maps--are preserved when performing model reduction for models described by a Lagrangian formalism (e.g., molecular dynamics, structural dynamics). Third, we present a novel technique for decreasing the temporal complexity --defined as the number of Newton-like iterations performed over the course of the simulation--by exploiting time-domain data. Fourth, we describe a novel method for refining projection-based reduced-order models a posteriori using a goal-oriented framework similar to mesh-adaptive h -refinement in finite elements. The technique allows the ROM to generate arbitrarily accurate solutions, thereby providing the ROM with a 'failsafe' mechanism in the event of insufficient training data. Finally, we present the reduced-order model error surrogate (ROMES) method for statistically quantifying reduced- order-model errors. This enables ROMs to be rigorously incorporated in uncertainty-quantification settings, as the error model can be treated as a source of epistemic uncertainty. This work was completed as part of a Truman Fellowship appointment. We note that much additional work was performed as part of the Fellowship. One salient project is the development of the Trilinos-based model-reduction software module Razor , which is currently bundled with the Albany PDE code and currently allows nonlinear reduced-order models to be constructed for any application supported in Albany. Other important projects include the following: 1. ROMES-equipped ROMs for Bayesian inference: K. Carlberg, M. Drohmann, F. Lu (Lawrence Berkeley National Laboratory), M. Morzfeld (Lawrence Berkeley National Laboratory). 2. ROM-enabled Krylov-subspace recycling: K. Carlberg, V. Forstall (University of Maryland), P. Tsuji, R. Tuminaro. 3. A pseudo balanced POD method using only dual snapshots: K. Carlberg, M. Sarovar. 4. An analysis of discrete v. continuous optimality in nonlinear model reduction: K. Carlberg, M. Barone, H. Antil (George Mason University). Journal articles for these projects are in progress at the time of this writing.« less

Nuclear Reaction Models Responsible for Simulation of Neutron-induced Soft Errors in Microelectronics

NASA Astrophysics Data System (ADS)

Watanabe, Y.; Abe, S.

2014-06-01

Terrestrial neutron-induced soft errors in MOSFETs from a 65 nm down to a 25 nm design rule are analyzed by means of multi-scale Monte Carlo simulation using the PHITS-HyENEXSS code system. Nuclear reaction models implemented in PHITS code are validated by comparisons with experimental data. From the analysis of calculated soft error rates, it is clarified that secondary He and H ions provide a major impact on soft errors with decreasing critical charge. It is also found that the high energy component from 10 MeV up to several hundreds of MeV in secondary cosmic-ray neutrons has the most significant source of soft errors regardless of design rule.

Patient-individualized boundary conditions for CFD simulations using time-resolved 3D angiography.

PubMed

Boegel, Marco; Gehrisch, Sonja; Redel, Thomas; Rohkohl, Christopher; Hoelter, Philip; Doerfler, Arnd; Maier, Andreas; Kowarschik, Markus

2016-06-01

Hemodynamic simulations are of increasing interest for the assessment of aneurysmal rupture risk and treatment planning. Achievement of accurate simulation results requires the usage of several patient-individual boundary conditions, such as a geometric model of the vasculature but also individualized inflow conditions. We propose the automatic estimation of various parameters for boundary conditions for computational fluid dynamics (CFD) based on a single 3D rotational angiography scan, also showing contrast agent inflow. First the data are reconstructed, and a patient-specific vessel model can be generated in the usual way. For this work, we optimize the inflow waveform based on two parameters, the mean velocity and pulsatility. We use statistical analysis of the measurable velocity distribution in the vessel segment to estimate the mean velocity. An iterative optimization scheme based on CFD and virtual angiography is utilized to estimate the inflow pulsatility. Furthermore, we present methods to automatically determine the heart rate and synchronize the inflow waveform to the patient's heart beat, based on time-intensity curves extracted from the rotational angiogram. This will result in a patient-individualized inflow velocity curve. The proposed methods were evaluated on two clinical datasets. Based on the vascular geometries, synthetic rotational angiography data was generated to allow a quantitative validation of our approach against ground truth data. We observed an average error of approximately [Formula: see text] for the mean velocity, [Formula: see text] for the pulsatility. The heart rate was estimated very precisely with an average error of about [Formula: see text], which corresponds to about 6 ms error for the duration of one cardiac cycle. Furthermore, a qualitative comparison of measured time-intensity curves from the real data and patient-specific simulated ones shows an excellent match. The presented methods have the potential to accurately estimate patient-specific boundary conditions from a single dedicated rotational scan.

A two-dimensional, finite-difference model of the high plains aquifer in southern South Dakota

USGS Publications Warehouse

Kolm, K.E.; Case, H. L.

1983-01-01

The High Plains aquifer is the principal source of water for irrigation, industry, municipalities, and domestic use in south-central South Dakota. The aquifer, composed of upper sandstone units of the Arikaree Formation, and the overlying Ogallala and Sand Hills Formations, was simulated using a two-dimensional, finite-difference computer model. The maximum difference between simulated and measured potentiometric heads was less than 60 feet (1- to 4-percent error). Two-thirds of the simulated potentiometric heads were within 26 feet of the measured values (3-percent error). The estimated saturated thickness, computed from simulated potentiometric heads, was within 25-percent error of the known saturated thickness for 95 percent of the study area. (USGS)

Sensitivity analysis for future space missions with segmented telescopes for high-contrast imaging

NASA Astrophysics Data System (ADS)

Leboulleux, Lucie; Pueyo, Laurent; Sauvage, Jean-François; Mazoyer, Johan; Soummer, Remi; Fusco, Thierry; Sivaramakrishnan, Anand

2018-01-01

The detection and analysis of biomarkers on earth-like planets using direct-imaging will require both high-contrast imaging and spectroscopy at very close angular separation (10^10 star to planet flux ratio at a few 0.1”). This goal can only be achieved with large telescopes in space to overcome atmospheric turbulence, often combined with a coronagraphic instrument with wavefront control. Large segmented space telescopes such as studied for the LUVOIR mission will generate segment-level instabilities and cophasing errors in addition to local mirror surface errors and other aberrations of the overall optical system. These effects contribute directly to the degradation of the final image quality and contrast. We present an analytical model that produces coronagraphic images of a segmented pupil telescope in the presence of segment phasing aberrations expressed as Zernike polynomials. This model relies on a pair-based projection of the segmented pupil and provides results that match an end-to-end simulation with an rms error on the final contrast of ~3%. This analytical model can be applied both to static and dynamic modes, and either in monochromatic or broadband light. It retires the need for end-to-end Monte-Carlo simulations that are otherwise needed to build a rigorous error budget, by enabling quasi-instantaneous analytical evaluations. The ability to invert directly the analytical model provides direct constraints and tolerances on all segments-level phasing and aberrations.

Impact of variational assimilation using multivariate background error covariances on the simulation of monsoon depressions over India

NASA Astrophysics Data System (ADS)

Dhanya, M.; Chandrasekar, A.

2016-02-01

The background error covariance structure influences a variational data assimilation system immensely. The simulation of a weather phenomenon like monsoon depression can hence be influenced by the background correlation information used in the analysis formulation. The Weather Research and Forecasting Model Data assimilation (WRFDA) system includes an option for formulating multivariate background correlations for its three-dimensional variational (3DVar) system (cv6 option). The impact of using such a formulation in the simulation of three monsoon depressions over India is investigated in this study. Analysis and forecast fields generated using this option are compared with those obtained using the default formulation for regional background error correlations (cv5) in WRFDA and with a base run without any assimilation. The model rainfall forecasts are compared with rainfall observations from the Tropical Rainfall Measurement Mission (TRMM) and the other model forecast fields are compared with a high-resolution analysis as well as with European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim reanalysis. The results of the study indicate that inclusion of additional correlation information in background error statistics has a moderate impact on the vertical profiles of relative humidity, moisture convergence, horizontal divergence and the temperature structure at the depression centre at the analysis time of the cv5/cv6 sensitivity experiments. Moderate improvements are seen in two of the three depressions investigated in this study. An improved thermodynamic and moisture structure at the initial time is expected to provide for improved rainfall simulation. The results of the study indicate that the skill scores of accumulated rainfall are somewhat better for the cv6 option as compared to the cv5 option for at least two of the three depression cases studied, especially at the higher threshold levels. Considering the importance of utilising improved flow-dependent correlation structures for efficient data assimilation, the need for more studies on the impact of background error covariances is obvious.

Dynamical Interpolation of Mesoscale Flows in the TOPEX/Poseidon Diamond Surrounding the U.S. Joint Global Ocean Flux Study Bermuda Atlantic Time-Series Study Site

NASA Technical Reports Server (NTRS)

McGillicuddy, Dennis J., Jr.; Kosnyrev, V. K.

2001-01-01

An open boundary ocean model is configured in a domain bounded by the four TOPEX/Poseidon (T/P) ground tracks surrounding the US Joint Global Ocean Flux Study Bermuda Atlantic Time-Series Study (BATS) site. This implementation facilitates prescription of model boundary conditions directly from altimetric measurements (both TIP and ERS-2). The expected error characteristics for a domain of this size with periodically updated boundary conditions are established with idealized numerical experiments using simulated data. A hindcast simulation is then constructed using actual altimetric observations during the period October 1992 through September 1998. Quantitative evaluation of the simulation suggests significant skill. The correlation coefficient between predicted sea level anomaly and ERS observations in the model interior is 0.89; that for predicted versus observed dynamic height anomaly based on hydrography at the BATS site is 0.73. Comparison with the idealized experiments suggests that the main source of error in the hindcast is temporal undersampling of the boundary conditions. The hindcast simulation described herein provides a basis for retrospective analysis of BATS observations in the context of the mesoscale eddy field.

Dynamical Interpolation of Mesoscale Flows in the TOPEX/ Poseidon Diamond Surrounding the U.S. Joint Global Ocean Flux Study Bermuda Atlantic Time-Series Study Site

NASA Technical Reports Server (NTRS)

McGillicuddy, D. J.; Kosnyrev, V. K.

2001-01-01

An open boundary ocean model is configured in a domain bounded by the four TOPEX/Poseidon (TIP) ground tracks surrounding the U.S. Joint Global Ocean Flux Study Bermuda Atlantic Time-series Study (BATS) site. This implementation facilitates prescription of model boundary conditions directly from altimetric measurements (both TIP and ERS-2). The expected error characteristics for a domain of this size with periodically updated boundary conditions are established with idealized numerical experiments using simulated data. A hindcast simulation is then constructed using actual altimetric observations during the period October 1992 through September 1998. Quantitative evaluation of the simulation suggests significant skill. The correlation coefficient between predicted sea level anomaly and ERS observations in the model interior is 0.89; that for predicted versus observed dynamic height anomaly based on hydrography at the BATS site is 0.73. Comparison with the idealized experiments suggests that the main source of error in the hindcast is temporal undersampling of the boundary conditions. The hindcast simulation described herein provides a basis for retrospective analysis of BATS observations in the context of the mesoscale eddy field.

Tracing the source of numerical climate model uncertainties in precipitation simulations using a feature-oriented statistical model

NASA Astrophysics Data System (ADS)

Xu, Y.; Jones, A. D.; Rhoades, A.

2017-12-01

Precipitation is a key component in hydrologic cycles, and changing precipitation regimes contribute to more intense and frequent drought and flood events around the world. Numerical climate modeling is a powerful tool to study climatology and to predict future changes. Despite the continuous improvement in numerical models, long-term precipitation prediction remains a challenge especially at regional scales. To improve numerical simulations of precipitation, it is important to find out where the uncertainty in precipitation simulations comes from. There are two types of uncertainty in numerical model predictions. One is related to uncertainty in the input data, such as model's boundary and initial conditions. These uncertainties would propagate to the final model outcomes even if the numerical model has exactly replicated the true world. But a numerical model cannot exactly replicate the true world. Therefore, the other type of model uncertainty is related the errors in the model physics, such as the parameterization of sub-grid scale processes, i.e., given precise input conditions, how much error could be generated by the in-precise model. Here, we build two statistical models based on a neural network algorithm to predict long-term variation of precipitation over California: one uses "true world" information derived from observations, and the other uses "modeled world" information using model inputs and outputs from the North America Coordinated Regional Downscaling Project (NA CORDEX). We derive multiple climate feature metrics as the predictors for the statistical model to represent the impact of global climate on local hydrology, and include topography as a predictor to represent the local control. We first compare the predictors between the true world and the modeled world to determine the errors contained in the input data. By perturbing the predictors in the statistical model, we estimate how much uncertainty in the model's final outcomes is accounted for by each predictor. By comparing the statistical model derived from true world information and modeled world information, we assess the errors lying in the physics of the numerical models. This work provides a unique insight to assess the performance of numerical climate models, and can be used to guide improvement of precipitation prediction.

Attitude determination for high-accuracy submicroradian jitter pointing on space-based platforms

NASA Astrophysics Data System (ADS)

Gupta, Avanindra A.; van Houten, Charles N.; Germann, Lawrence M.

1990-10-01

A description of the requirement definition process is given for a new wideband attitude determination subsystem (ADS) for image motion compensation (IMC) systems. The subsystem consists of either lateral accelerometers functioning in differential pairs or gas-bearing gyros for high-frequency sensors using CCD-based star trackers for low-frequency sensors. To minimize error the sensor signals are combined so that the mixing filter does not allow phase distortion. The two ADS models are introduced in an IMC simulation to predict measurement error, correction capability, and residual image jitter for a variety of system parameters. The IMC three-axis testbed is utilized to simulate an incoming beam in inertial space. Results demonstrate that both mechanical and electronic IMC meet the requirements of image stabilization for space-based observation at submicroradian-jitter levels. Currently available technology may be employed to implement IMC systems.

Predicting 3D pose in partially overlapped X-ray images of knee prostheses using model-based Roentgen stereophotogrammetric analysis (RSA).

PubMed

Hsu, Chi-Pin; Lin, Shang-Chih; Shih, Kao-Shang; Huang, Chang-Hung; Lee, Chian-Her

2014-12-01

After total knee replacement, the model-based Roentgen stereophotogrammetric analysis (RSA) technique has been used to monitor the status of prosthetic wear, misalignment, and even failure. However, the overlap of the prosthetic outlines inevitably increases errors in the estimation of prosthetic poses due to the limited amount of available outlines. In the literature, quite a few studies have investigated the problems induced by the overlapped outlines, and manual adjustment is still the mainstream. This study proposes two methods to automate the image processing of overlapped outlines prior to the pose registration of prosthetic models. The outline-separated method defines the intersected points and segments the overlapped outlines. The feature-recognized method uses the point and line features of the remaining outlines to initiate registration. Overlap percentage is defined as the ratio of overlapped to non-overlapped outlines. The simulated images with five overlapping percentages are used to evaluate the robustness and accuracy of the proposed methods. Compared with non-overlapped images, overlapped images reduce the number of outlines available for model-based RSA calculation. The maximum and root mean square errors for a prosthetic outline are 0.35 and 0.04 mm, respectively. The mean translation and rotation errors are 0.11 mm and 0.18°, respectively. The errors of the model-based RSA results are increased when the overlap percentage is beyond about 9%. In conclusion, both outline-separated and feature-recognized methods can be seamlessly integrated to automate the calculation of rough registration. This can significantly increase the clinical practicability of the model-based RSA technique.

A system for EPID-based real-time treatment delivery verification during dynamic IMRT treatment.

PubMed

Fuangrod, Todsaporn; Woodruff, Henry C; van Uytven, Eric; McCurdy, Boyd M C; Kuncic, Zdenka; O'Connor, Daryl J; Greer, Peter B

2013-09-01

To design and develop a real-time electronic portal imaging device (EPID)-based delivery verification system for dynamic intensity modulated radiation therapy (IMRT) which enables detection of gross treatment delivery errors before delivery of substantial radiation to the patient. The system utilizes a comprehensive physics-based model to generate a series of predicted transit EPID image frames as a reference dataset and compares these to measured EPID frames acquired during treatment. The two datasets are using MLC aperture comparison and cumulative signal checking techniques. The system operation in real-time was simulated offline using previously acquired images for 19 IMRT patient deliveries with both frame-by-frame comparison and cumulative frame comparison. Simulated error case studies were used to demonstrate the system sensitivity and performance. The accuracy of the synchronization method was shown to agree within two control points which corresponds to approximately ∼1% of the total MU to be delivered for dynamic IMRT. The system achieved mean real-time gamma results for frame-by-frame analysis of 86.6% and 89.0% for 3%, 3 mm and 4%, 4 mm criteria, respectively, and 97.9% and 98.6% for cumulative gamma analysis. The system can detect a 10% MU error using 3%, 3 mm criteria within approximately 10 s. The EPID-based real-time delivery verification system successfully detected simulated gross errors introduced into patient plan deliveries in near real-time (within 0.1 s). A real-time radiation delivery verification system for dynamic IMRT has been demonstrated that is designed to prevent major mistreatments in modern radiation therapy.

Simulation of a navigator algorithm for a low-cost GPS receiver

NASA Technical Reports Server (NTRS)

Hodge, W. F.

1980-01-01

The analytical structure of an existing navigator algorithm for a low cost global positioning system receiver is described in detail to facilitate its implementation on in-house digital computers and real-time simulators. The material presented includes a simulation of GPS pseudorange measurements, based on a two-body representation of the NAVSTAR spacecraft orbits, and a four component model of the receiver bias errors. A simpler test for loss of pseudorange measurements due to spacecraft shielding is also noted.

Panel positioning error and support mechanism for a 30-m THz radio telescope

NASA Astrophysics Data System (ADS)

Yang, De-Hua; Okoh, Daniel; Zhou, Guo-Hua; Li, Ai-Hua; Li, Guo-Ping; Cheng, Jing-Quan

2011-06-01

A 30-m TeraHertz (THz) radio telescope is proposed to operate at 200 μm with an active primary surface. This paper presents sensitivity analysis of active surface panel positioning errors with optical performance in terms of the Strehl ratio. Based on Ruze's surface error theory and using a Monte Carlo simulation, the effects of six rigid panel positioning errors, such as piston, tip, tilt, radial, azimuthal and twist displacements, were directly derived. The optical performance of the telescope was then evaluated using the standard Strehl ratio. We graphically illustrated the various panel error effects by presenting simulations of complete ensembles of full reflector surface errors for the six different rigid panel positioning errors. Study of the panel error sensitivity analysis revealed that the piston error and tilt/tip errors are dominant while the other rigid errors are much less important. Furthermore, as indicated by the results, we conceived of an alternative Master-Slave Concept-based (MSC-based) active surface by implementating a special Series-Parallel Concept-based (SPC-based) hexapod as the active panel support mechanism. A new 30-m active reflector based on the two concepts was demonstrated to achieve correction for all the six rigid panel positioning errors in an economically feasible way.

Integrated navigation fusion strategy of INS/UWB for indoor carrier attitude angle and position synchronous tracking.

PubMed

Fan, Qigao; Wu, Yaheng; Hui, Jing; Wu, Lei; Yu, Zhenzhong; Zhou, Lijuan

2014-01-01

In some GPS failure conditions, positioning for mobile target is difficult. This paper proposed a new method based on INS/UWB for attitude angle and position synchronous tracking of indoor carrier. Firstly, error model of INS/UWB integrated system is built, including error equation of INS and UWB. And combined filtering model of INS/UWB is researched. Simulation results show that the two subsystems are complementary. Secondly, integrated navigation data fusion strategy of INS/UWB based on Kalman filtering theory is proposed. Simulation results show that FAKF method is better than the conventional Kalman filtering. Finally, an indoor experiment platform is established to verify the integrated navigation theory of INS/UWB, which is geared to the needs of coal mine working environment. Static and dynamic positioning results show that the INS/UWB integrated navigation system is stable and real-time, positioning precision meets the requirements of working condition and is better than any independent subsystem.

Multi-Dielectric Brownian Dynamics and Design-Space-Exploration Studies of Permeation in Ion Channels.

PubMed

Siksik, May; Krishnamurthy, Vikram

2017-09-01

This paper proposes a multi-dielectric Brownian dynamics simulation framework for design-space-exploration (DSE) studies of ion-channel permeation. The goal of such DSE studies is to estimate the channel modeling-parameters that minimize the mean-squared error between the simulated and expected "permeation characteristics." To address this computational challenge, we use a methodology based on statistical inference that utilizes the knowledge of channel structure to prune the design space. We demonstrate the proposed framework and DSE methodology using a case study based on the KcsA ion channel, in which the design space is successfully reduced from a 6-D space to a 2-D space. Our results show that the channel dielectric map computed using the framework matches with that computed directly using molecular dynamics with an error of 7%. Finally, the scalability and resolution of the model used are explored, and it is shown that the memory requirements needed for DSE remain constant as the number of parameters (degree of heterogeneity) increases.

Model reference tracking control of an aircraft: a robust adaptive approach

NASA Astrophysics Data System (ADS)

Tanyer, Ilker; Tatlicioglu, Enver; Zergeroglu, Erkan

2017-05-01

This work presents the design and the corresponding analysis of a nonlinear robust adaptive controller for model reference tracking of an aircraft that has parametric uncertainties in its system matrices and additive state- and/or time-dependent nonlinear disturbance-like terms in its dynamics. Specifically, robust integral of the sign of the error feedback term and an adaptive term is fused with a proportional integral controller. Lyapunov-based stability analysis techniques are utilised to prove global asymptotic convergence of the output tracking error. Extensive numerical simulations are presented to illustrate the performance of the proposed robust adaptive controller.

Improving lidar turbulence estimates for wind energy

NASA Astrophysics Data System (ADS)

Newman, J. F.; Clifton, A.; Churchfield, M. J.; Klein, P.

2016-09-01

Remote sensing devices (e.g., lidars) are quickly becoming a cost-effective and reliable alternative to meteorological towers for wind energy applications. Although lidars can measure mean wind speeds accurately, these devices measure different values of turbulence intensity (TI) than an instrument on a tower. In response to these issues, a lidar TI error reduction model was recently developed for commercially available lidars. The TI error model first applies physics-based corrections to the lidar measurements, then uses machine-learning techniques to further reduce errors in lidar TI estimates. The model was tested at two sites in the Southern Plains where vertically profiling lidars were collocated with meteorological towers. Results indicate that the model works well under stable conditions but cannot fully mitigate the effects of variance contamination under unstable conditions. To understand how variance contamination affects lidar TI estimates, a new set of equations was derived in previous work to characterize the actual variance measured by a lidar. Terms in these equations were quantified using a lidar simulator and modeled wind field, and the new equations were then implemented into the TI error model.

Improving Lidar Turbulence Estimates for Wind Energy: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Newman, Jennifer; Clifton, Andrew; Churchfield, Matthew

2016-10-01

Remote sensing devices (e.g., lidars) are quickly becoming a cost-effective and reliable alternative to meteorological towers for wind energy applications. Although lidars can measure mean wind speeds accurately, these devices measure different values of turbulence intensity (TI) than an instrument on a tower. In response to these issues, a lidar TI error reduction model was recently developed for commercially available lidars. The TI error model first applies physics-based corrections to the lidar measurements, then uses machine-learning techniques to further reduce errors in lidar TI estimates. The model was tested at two sites in the Southern Plains where vertically profiling lidarsmore » were collocated with meteorological towers. Results indicate that the model works well under stable conditions but cannot fully mitigate the effects of variance contamination under unstable conditions. To understand how variance contamination affects lidar TI estimates, a new set of equations was derived in previous work to characterize the actual variance measured by a lidar. Terms in these equations were quantified using a lidar simulator and modeled wind field, and the new equations were then implemented into the TI error model.« less

Improving Lidar Turbulence Estimates for Wind Energy

DOE PAGES

Newman, Jennifer F.; Clifton, Andrew; Churchfield, Matthew J.; ...

2016-10-03

Remote sensing devices (e.g., lidars) are quickly becoming a cost-effective and reliable alternative to meteorological towers for wind energy applications. Although lidars can measure mean wind speeds accurately, these devices measure different values of turbulence intensity (TI) than an instrument on a tower. In response to these issues, a lidar TI error reduction model was recently developed for commercially available lidars. The TI error model first applies physics-based corrections to the lidar measurements, then uses machine-learning techniques to further reduce errors in lidar TI estimates. The model was tested at two sites in the Southern Plains where vertically profiling lidarsmore » were collocated with meteorological towers. Results indicate that the model works well under stable conditions but cannot fully mitigate the effects of variance contamination under unstable conditions. To understand how variance contamination affects lidar TI estimates, a new set of equations was derived in previous work to characterize the actual variance measured by a lidar. Terms in these equations were quantified using a lidar simulator and modeled wind field, and the new equations were then implemented into the TI error model.« less

Validation and upgrading of physically based mathematical models

NASA Technical Reports Server (NTRS)

Duval, Ronald

1992-01-01

The validation of the results of physically-based mathematical models against experimental results was discussed. Systematic techniques are used for: (1) isolating subsets of the simulator mathematical model and comparing the response of each subset to its experimental response for the same input conditions; (2) evaluating the response error to determine whether it is the result of incorrect parameter values, incorrect structure of the model subset, or unmodeled external effects of cross coupling; and (3) modifying and upgrading the model and its parameter values to determine the most physically appropriate combination of changes.

Quantifying errors in surface ozone predictions associated with clouds over the CONUS: a WRF-Chem modeling study using satellite cloud retrievals

NASA Astrophysics Data System (ADS)

Ryu, Young-Hee; Hodzic, Alma; Barre, Jerome; Descombes, Gael; Minnis, Patrick

2018-05-01

Clouds play a key role in radiation and hence O3 photochemistry by modulating photolysis rates and light-dependent emissions of biogenic volatile organic compounds (BVOCs). It is not well known, however, how much error in O3 predictions can be directly attributed to error in cloud predictions. This study applies the Weather Research and Forecasting with Chemistry (WRF-Chem) model at 12 km horizontal resolution with the Morrison microphysics and Grell 3-D cumulus parameterization to quantify uncertainties in summertime surface O3 predictions associated with cloudiness over the contiguous United States (CONUS). All model simulations are driven by reanalysis of atmospheric data and reinitialized every 2 days. In sensitivity simulations, cloud fields used for photochemistry are corrected based on satellite cloud retrievals. The results show that WRF-Chem predicts about 55 % of clouds in the right locations and generally underpredicts cloud optical depths. These errors in cloud predictions can lead to up to 60 ppb of overestimation in hourly surface O3 concentrations on some days. The average difference in summertime surface O3 concentrations derived from the modeled clouds and satellite clouds ranges from 1 to 5 ppb for maximum daily 8 h average O3 (MDA8 O3) over the CONUS. This represents up to ˜ 40 % of the total MDA8 O3 bias under cloudy conditions in the tested model version. Surface O3 concentrations are sensitive to cloud errors mainly through the calculation of photolysis rates (for ˜ 80 %), and to a lesser extent to light-dependent BVOC emissions. The sensitivity of surface O3 concentrations to satellite-based cloud corrections is about 2 times larger in VOC-limited than NOx-limited regimes. Our results suggest that the benefits of accurate predictions of cloudiness would be significant in VOC-limited regions, which are typical of urban areas.

Improved assessment of gross and net primary productivity of Canada's landmass

NASA Astrophysics Data System (ADS)

Gonsamo, Alemu; Chen, Jing M.; Price, David T.; Kurz, Werner A.; Liu, Jane; Boisvenue, Céline; Hember, Robbie A.; Wu, Chaoyang; Chang, Kuo-Hsien

2013-12-01

assess Canada's gross primary productivity (GPP) and net primary productivity (NPP) using boreal ecosystem productivity simulator (BEPS) at 250 m spatial resolution with improved input parameter and driver fields and phenology and nutrient release parameterization schemes. BEPS is a process-based two-leaf enzyme kinetic terrestrial ecosystem model designed to simulate energy, water, and carbon (C) fluxes using spatial data sets of meteorology, remotely sensed land surface variables, soil properties, and photosynthesis and respiration rate parameters. Two improved key land surface variables, leaf area index (LAI) and land cover type, are derived at 250 m from Moderate Resolution Imaging Spectroradiometer sensor. For diagnostic error assessment, we use nine forest flux tower sites where all measured C flux, meteorology, and ancillary data sets are available. The errors due to input drivers and parameters are then independently corrected for Canada-wide GPP and NPP simulations. The optimized LAI use, for example, reduced the absolute bias in GPP from 20.7% to 1.1% for hourly BEPS simulations. Following the error diagnostics and corrections, daily GPP and NPP are simulated over Canada at 250 m spatial resolution, the highest resolution simulation yet for the country or any other comparable region. Total NPP (GPP) for Canada's land area was 1.27 (2.68) Pg C for 2008, with forests contributing 1.02 (2.2) Pg C. The annual comparisons between measured and simulated GPP show that the mean differences are not statistically significant (p > 0.05, paired t test). The main BEPS simulation error sources are from the driver fields.

A physiologically based pharmacokinetic model to predict the pharmacokinetics of highly protein-bound drugs and the impact of errors in plasma protein binding.

PubMed

Ye, Min; Nagar, Swati; Korzekwa, Ken

2016-04-01

Predicting the pharmacokinetics of highly protein-bound drugs is difficult. Also, since historical plasma protein binding data were often collected using unbuffered plasma, the resulting inaccurate binding data could contribute to incorrect predictions. This study uses a generic physiologically based pharmacokinetic (PBPK) model to predict human plasma concentration-time profiles for 22 highly protein-bound drugs. Tissue distribution was estimated from in vitro drug lipophilicity data, plasma protein binding and the blood: plasma ratio. Clearance was predicted with a well-stirred liver model. Underestimated hepatic clearance for acidic and neutral compounds was corrected by an empirical scaling factor. Predicted values (pharmacokinetic parameters, plasma concentration-time profile) were compared with observed data to evaluate the model accuracy. Of the 22 drugs, less than a 2-fold error was obtained for the terminal elimination half-life (t1/2 , 100% of drugs), peak plasma concentration (Cmax , 100%), area under the plasma concentration-time curve (AUC0-t , 95.4%), clearance (CLh , 95.4%), mean residence time (MRT, 95.4%) and steady state volume (Vss , 90.9%). The impact of fup errors on CLh and Vss prediction was evaluated. Errors in fup resulted in proportional errors in clearance prediction for low-clearance compounds, and in Vss prediction for high-volume neutral drugs. For high-volume basic drugs, errors in fup did not propagate to errors in Vss prediction. This is due to the cancellation of errors in the calculations for tissue partitioning of basic drugs. Overall, plasma profiles were well simulated with the present PBPK model. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Exploring Discretization Error in Simulation-Based Aerodynamic Databases

NASA Technical Reports Server (NTRS)

Aftosmis, Michael J.; Nemec, Marian

2010-01-01

This work examines the level of discretization error in simulation-based aerodynamic databases and introduces strategies for error control. Simulations are performed using a parallel, multi-level Euler solver on embedded-boundary Cartesian meshes. Discretization errors in user-selected outputs are estimated using the method of adjoint-weighted residuals and we use adaptive mesh refinement to reduce these errors to specified tolerances. Using this framework, we examine the behavior of discretization error throughout a token database computed for a NACA 0012 airfoil consisting of 120 cases. We compare the cost and accuracy of two approaches for aerodynamic database generation. In the first approach, mesh adaptation is used to compute all cases in the database to a prescribed level of accuracy. The second approach conducts all simulations using the same computational mesh without adaptation. We quantitatively assess the error landscape and computational costs in both databases. This investigation highlights sensitivities of the database under a variety of conditions. The presence of transonic shocks or the stiffness in the governing equations near the incompressible limit are shown to dramatically increase discretization error requiring additional mesh resolution to control. Results show that such pathologies lead to error levels that vary by over factor of 40 when using a fixed mesh throughout the database. Alternatively, controlling this sensitivity through mesh adaptation leads to mesh sizes which span two orders of magnitude. We propose strategies to minimize simulation cost in sensitive regions and discuss the role of error-estimation in database quality.

Interval sampling methods and measurement error: a computer simulation.

PubMed

Wirth, Oliver; Slaven, James; Taylor, Matthew A

2014-01-01

A simulation study was conducted to provide a more thorough account of measurement error associated with interval sampling methods. A computer program simulated the application of momentary time sampling, partial-interval recording, and whole-interval recording methods on target events randomly distributed across an observation period. The simulation yielded measures of error for multiple combinations of observation period, interval duration, event duration, and cumulative event duration. The simulations were conducted up to 100 times to yield measures of error variability. Although the present simulation confirmed some previously reported characteristics of interval sampling methods, it also revealed many new findings that pertain to each method's inherent strengths and weaknesses. The analysis and resulting error tables can help guide the selection of the most appropriate sampling method for observation-based behavioral assessments. © Society for the Experimental Analysis of Behavior.

On the use of log-transformation vs. nonlinear regression for analyzing biological power laws.

PubMed

Xiao, Xiao; White, Ethan P; Hooten, Mevin B; Durham, Susan L

2011-10-01

Power-law relationships are among the most well-studied functional relationships in biology. Recently the common practice of fitting power laws using linear regression (LR) on log-transformed data has been criticized, calling into question the conclusions of hundreds of studies. It has been suggested that nonlinear regression (NLR) is preferable, but no rigorous comparison of these two methods has been conducted. Using Monte Carlo simulations, we demonstrate that the error distribution determines which method performs better, with NLR better characterizing data with additive, homoscedastic, normal error and LR better characterizing data with multiplicative, heteroscedastic, lognormal error. Analysis of 471 biological power laws shows that both forms of error occur in nature. While previous analyses based on log-transformation appear to be generally valid, future analyses should choose methods based on a combination of biological plausibility and analysis of the error distribution. We provide detailed guidelines and associated computer code for doing so, including a model averaging approach for cases where the error structure is uncertain.

Sliding mode output feedback control based on tracking error observer with disturbance estimator.

PubMed

Xiao, Lingfei; Zhu, Yue

2014-07-01

For a class of systems who suffers from disturbances, an original output feedback sliding mode control method is presented based on a novel tracking error observer with disturbance estimator. The mathematical models of the systems are not required to be with high accuracy, and the disturbances can be vanishing or nonvanishing, while the bounds of disturbances are unknown. By constructing a differential sliding surface and employing reaching law approach, a sliding mode controller is obtained. On the basis of an extended disturbance estimator, a creative tracking error observer is produced. By using the observation of tracking error and the estimation of disturbance, the sliding mode controller is implementable. It is proved that the disturbance estimation error and tracking observation error are bounded, the sliding surface is reachable and the closed-loop system is robustly stable. The simulations on a servomotor positioning system and a five-degree-of-freedom active magnetic bearings system verify the effect of the proposed method. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

How to obtain accurate resist simulations in very low-k1 era?

NASA Astrophysics Data System (ADS)

Chiou, Tsann-Bim; Park, Chan-Ha; Choi, Jae-Seung; Min, Young-Hong; Hansen, Steve; Tseng, Shih-En; Chen, Alek C.; Yim, Donggyu

2006-03-01

A procedure for calibrating a resist model iteratively adjusts appropriate parameters until the simulations of the model match the experimental data. The tunable parameters may include the shape of the illuminator, the geometry and transmittance/phase of the mask, light source and scanner-related parameters that affect imaging quality, resist process control and most importantly the physical/chemical factors in the resist model. The resist model can be accurately calibrated by measuring critical dimensions (CD) of a focus-exposure matrix (FEM) and the technique has been demonstrated to be very successful in predicting lithographic performance. However, resist model calibration is more challenging in the low k1 (<0.3) regime because numerous uncertainties, such as mask and resist CD metrology errors, are becoming too large to be ignored. This study demonstrates a resist model calibration procedure for a 0.29 k1 process using a 6% halftone mask containing 2D brickwall patterns. The influence of different scanning electron microscopes (SEM) and their wafer metrology signal analysis algorithms on the accuracy of the resist model is evaluated. As an example of the metrology issue of the resist pattern, the treatment of a sidewall angle is demonstrated for the resist line ends where the contrast is relatively low. Additionally, the mask optical proximity correction (OPC) and corner rounding are considered in the calibration procedure that is based on captured SEM images. Accordingly, the average root-mean-square (RMS) error, which is the difference between simulated and experimental CDs, can be improved by considering the metrological issues. Moreover, a weighting method and a measured CD tolerance are proposed to handle the different CD variations of the various edge points of the wafer resist pattern. After the weighting method is implemented and the CD selection criteria applied, the RMS error can be further suppressed. Therefore, the resist CD and process window can be confidently evaluated using the accurately calibrated resist model. One of the examples simulates the sensitivity of the mask pattern error, which is helpful to specify the mask CD control.

Results of the GABLS3 diurnal-cycle benchmark for wind energy applications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rodrigo, J. Sanz; Allaerts, D.; Avila, M.

We present results of the GABLS3 model intercomparison benchmark revisited for wind energy applications. The case consists of a diurnal cycle, measured at the 200-m tall Cabauw tower in the Netherlands, including a nocturnal low-level jet. The benchmark includes a sensitivity analysis of WRF simulations using two input meteorological databases and five planetary boundary-layer schemes. A reference set of mesoscale tendencies is used to drive microscale simulations using RANS k-ϵ and LES turbulence models. The validation is based on rotor-based quantities of interest. Cycle-integrated mean absolute errors are used to quantify model performance. The results of the benchmark are usedmore » to discuss input uncertainties from mesoscale modelling, different meso-micro coupling strategies (online vs offline) and consistency between RANS and LES codes when dealing with boundary-layer mean flow quantities. Altogether, all the microscale simulations produce a consistent coupling with mesoscale forcings.« less

Results of the GABLS3 diurnal-cycle benchmark for wind energy applications

DOE PAGES

Rodrigo, J. Sanz; Allaerts, D.; Avila, M.; ...

2017-06-13

We present results of the GABLS3 model intercomparison benchmark revisited for wind energy applications. The case consists of a diurnal cycle, measured at the 200-m tall Cabauw tower in the Netherlands, including a nocturnal low-level jet. The benchmark includes a sensitivity analysis of WRF simulations using two input meteorological databases and five planetary boundary-layer schemes. A reference set of mesoscale tendencies is used to drive microscale simulations using RANS k-ϵ and LES turbulence models. The validation is based on rotor-based quantities of interest. Cycle-integrated mean absolute errors are used to quantify model performance. The results of the benchmark are usedmore » to discuss input uncertainties from mesoscale modelling, different meso-micro coupling strategies (online vs offline) and consistency between RANS and LES codes when dealing with boundary-layer mean flow quantities. Altogether, all the microscale simulations produce a consistent coupling with mesoscale forcings.« less

Prospective Evaluation of a Model-Based Dosing Regimen for Amikacin in Preterm and Term Neonates in Clinical Practice

PubMed Central

De Cock, R. F. W.; Allegaert, K.; Vanhaesebrouck, S.; Danhof, M.; Knibbe, C. A. J.

2015-01-01

Based on a previously derived population pharmacokinetic model, a novel neonatal amikacin dosing regimen was developed. The aim of the current study was to prospectively evaluate this dosing regimen. First, early (before and after second dose) therapeutic drug monitoring (TDM) observations were evaluated for achieving target trough (<3 mg/liter) and peak (>24 mg/liter) levels. Second, all observed TDM concentrations were compared with model-predicted concentrations, whereby the results of a normalized prediction distribution error (NPDE) were considered. Subsequently, Monte Carlo simulations were performed. Finally, remaining causes limiting amikacin predictability (i.e., prescription errors and disease characteristics of outliers) were explored. In 579 neonates (median birth body weight, 2,285 [range, 420 to 4,850] g; postnatal age 2 days [range, 1 to 30 days]; gestational age, 34 weeks [range, 24 to 41 weeks]), 90.5% of the observed early peak levels reached 24 mg/liter, and 60.2% of the trough levels were <3 mg/liter (93.4% ≤5 mg/liter). Observations were accurately predicted by the model without bias, which was confirmed by the NPDE. Monte Carlo simulations showed that peak concentrations of >24 mg/liter were reached at steady state in almost all patients. Trough values of <3 mg/liter at steady state were documented in 78% to 100% and 45% to 96% of simulated cases with and without ibuprofen coadministration, respectively; suboptimal trough levels were found in patients with postnatal age <14 days and current weight of >2,000 g. Prospective evaluation of a model-based neonatal amikacin dosing regimen resulted in optimized peak and trough concentrations in almost all patients. Slightly adapted dosing for patient subgroups with suboptimal trough levels was proposed. This model-based approach improves neonatal dosing individualization. PMID:26248375

Human-simulation-based learning to prevent medication error: A systematic review.

PubMed

Sarfati, Laura; Ranchon, Florence; Vantard, Nicolas; Schwiertz, Vérane; Larbre, Virginie; Parat, Stéphanie; Faudel, Amélie; Rioufol, Catherine

2018-01-31

In the past 2 decades, there has been an increasing interest in simulation-based learning programs to prevent medication error (ME). To improve knowledge, skills, and attitudes in prescribers, nurses, and pharmaceutical staff, these methods enable training without directly involving patients. However, best practices for simulation for healthcare providers are as yet undefined. By analysing the current state of experience in the field, the present review aims to assess whether human simulation in healthcare helps to reduce ME. A systematic review was conducted on Medline from 2000 to June 2015, associating the terms "Patient Simulation," "Medication Errors," and "Simulation Healthcare." Reports of technology-based simulation were excluded, to focus exclusively on human simulation in nontechnical skills learning. Twenty-one studies assessing simulation-based learning programs were selected, focusing on pharmacy, medicine or nursing students, or concerning programs aimed at reducing administration or preparation errors, managing crises, or learning communication skills for healthcare professionals. The studies varied in design, methodology, and assessment criteria. Few demonstrated that simulation was more effective than didactic learning in reducing ME. This review highlights a lack of long-term assessment and real-life extrapolation, with limited scenarios and participant samples. These various experiences, however, help in identifying the key elements required for an effective human simulation-based learning program for ME prevention: ie, scenario design, debriefing, and perception assessment. The performance of these programs depends on their ability to reflect reality and on professional guidance. Properly regulated simulation is a good way to train staff in events that happen only exceptionally, as well as in standard daily activities. By integrating human factors, simulation seems to be effective in preventing iatrogenic risk related to ME, if the program is well designed. © 2018 John Wiley & Sons, Ltd.

Effect of tumor amplitude and frequency on 4D modeling of Vero4DRT system.

PubMed

Miura, Hideharu; Ozawa, Shuichi; Hayata, Masahiro; Tsuda, Shintaro; Yamada, Kiyoshi; Nagata, Yasushi

2017-01-01

An important issue in indirect dynamic tumor tracking with the Vero4DRT system is the accuracy of the model predictions of the internal target position based on surrogate infrared (IR) marker measurement. We investigated the predictive uncertainty of 4D modeling using an external IR marker, focusing on the effect of the target and surrogate amplitudes and periods. A programmable respiratory motion table was used to simulate breathing induced organ motion. Sinusoidal motion sequences were produced by a dynamic phantom with different amplitudes and periods. To investigate the 4D modeling error, the following amplitudes (peak-to-peak: 10-40 mm) and periods (2-8 s) were considered. The 95th percentile 4D modeling error (4D- E 95% ) between the detected and predicted target position ( μ  + 2SD) was calculated to investigate the 4D modeling error. 4D- E 95% was linearly related to the target motion amplitude with a coefficient of determination R 2  = 0.99 and ranged from 0.21 to 0.88 mm. The 4D modeling error ranged from 1.49 to 0.14 mm and gradually decreased with increasing target motion period. We analyzed the predictive error in 4D modeling and the error due to the amplitude and period of target. 4D modeling error substantially increased with increasing amplitude and decreasing period of the target motion.

An efficient surrogate-based simulation-optimization method for calibrating a regional MODFLOW model

NASA Astrophysics Data System (ADS)

Chen, Mingjie; Izady, Azizallah; Abdalla, Osman A.

2017-01-01

Simulation-optimization method entails a large number of model simulations, which is computationally intensive or even prohibitive if the model simulation is extremely time-consuming. Statistical models have been examined as a surrogate of the high-fidelity physical model during simulation-optimization process to tackle this problem. Among them, Multivariate Adaptive Regression Splines (MARS), a non-parametric adaptive regression method, is superior in overcoming problems of high-dimensions and discontinuities of the data. Furthermore, the stability and accuracy of MARS model can be improved by bootstrap aggregating methods, namely, bagging. In this paper, Bagging MARS (BMARS) method is integrated to a surrogate-based simulation-optimization framework to calibrate a three-dimensional MODFLOW model, which is developed to simulate the groundwater flow in an arid hardrock-alluvium region in northwestern Oman. The physical MODFLOW model is surrogated by the statistical model developed using BMARS algorithm. The surrogate model, which is fitted and validated using training dataset generated by the physical model, can approximate solutions rapidly. An efficient Sobol' method is employed to calculate global sensitivities of head outputs to input parameters, which are used to analyze their importance for the model outputs spatiotemporally. Only sensitive parameters are included in the calibration process to further improve the computational efficiency. Normalized root mean square error (NRMSE) between measured and simulated heads at observation wells is used as the objective function to be minimized during optimization. The reasonable history match between the simulated and observed heads demonstrated feasibility of this high-efficient calibration framework.

Parameters Identification for Motorcycle Simulator's Platform Characterization

NASA Astrophysics Data System (ADS)

Nehaoua, L.; Arioui, H.

2008-06-01

This paper presents the dynamics modeling and parameters identification of a motorcycle simulator's platform. This model begins with some suppositions which consider that the leg dynamics can be neglected with respect to the mobile platform one. The objectif is to synthesis a simplified control scheme, adapted to driving simulation application, minimising dealys and without loss of tracking performance. Electronic system of platform actuation is described. It's based on a CAN BUS communication which offers a large transmission robustness and error handling. Despite some disadvanteges, we adapted a control solution which overcome these inconvenients and preserve the quality of tracking trajectory. A bref description of the simulator's platform is given and results are shown and justified according to our specifications.

Participatory System Dynamics Modeling: Increasing Stakeholder Engagement and Precision to Improve Implementation Planning in Systems.

PubMed

Zimmerman, Lindsey; Lounsbury, David W; Rosen, Craig S; Kimerling, Rachel; Trafton, Jodie A; Lindley, Steven E

2016-11-01

Implementation planning typically incorporates stakeholder input. Quality improvement efforts provide data-based feedback regarding progress. Participatory system dynamics modeling (PSD) triangulates stakeholder expertise, data and simulation of implementation plans prior to attempting change. Frontline staff in one VA outpatient mental health system used PSD to examine policy and procedural "mechanisms" they believe underlie local capacity to implement evidence-based psychotherapies (EBPs) for PTSD and depression. We piloted the PSD process, simulating implementation plans to improve EBP reach. Findings indicate PSD is a feasible, useful strategy for building stakeholder consensus, and may save time and effort as compared to trial-and-error EBP implementation planning.

Robust Methods for Moderation Analysis with a Two-Level Regression Model.

PubMed

Yang, Miao; Yuan, Ke-Hai

2016-01-01

Moderation analysis has many applications in social sciences. Most widely used estimation methods for moderation analysis assume that errors are normally distributed and homoscedastic. When these assumptions are not met, the results from a classical moderation analysis can be misleading. For more reliable moderation analysis, this article proposes two robust methods with a two-level regression model when the predictors do not contain measurement error. One method is based on maximum likelihood with Student's t distribution and the other is based on M-estimators with Huber-type weights. An algorithm for obtaining the robust estimators is developed. Consistent estimates of standard errors of the robust estimators are provided. The robust approaches are compared against normal-distribution-based maximum likelihood (NML) with respect to power and accuracy of parameter estimates through a simulation study. Results show that the robust approaches outperform NML under various distributional conditions. Application of the robust methods is illustrated through a real data example. An R program is developed and documented to facilitate the application of the robust methods.

The Challenge of Grounding Planning in Simulation with an Interactive Model Development Environment

NASA Technical Reports Server (NTRS)

Clement, Bradley J.; Frank, Jeremy D.; Chachere, John M.; Smith, Tristan B.; Swanson, Keith J.

2011-01-01

A principal obstacle to fielding automated planning systems is the difficulty of modeling. Physical systems are modeled conventionally based on specification documents and the modeler's understanding of the system. Thus, the model is developed in a way that is disconnected from the system's actual behavior and is vulnerable to manual error. Another obstacle to fielding planners is testing and validation. For a space mission, generated plans must be validated often by translating them into command sequences that are run in a simulation testbed. Testing in this way is complex and onerous because of the large number of possible plans and states of the spacecraft. Though, if used as a source of domain knowledge, the simulator can ease validation. This paper poses a challenge: to ground planning models in the system physics represented by simulation. A proposed, interactive model development environment illustrates the integration of planning and simulation to meet the challenge. This integration reveals research paths for automated model construction and validation.

Evaluation of Subgrid-Scale Models for Large Eddy Simulation of Compressible Flows

NASA Technical Reports Server (NTRS)

Blaisdell, Gregory A.

1996-01-01

The objective of this project was to evaluate and develop subgrid-scale (SGS) turbulence models for large eddy simulations (LES) of compressible flows. During the first phase of the project results from LES using the dynamic SGS model were compared to those of direct numerical simulations (DNS) of compressible homogeneous turbulence. The second phase of the project involved implementing the dynamic SGS model in a NASA code for simulating supersonic flow over a flat-plate. The model has been successfully coded and a series of simulations has been completed. One of the major findings of the work is that numerical errors associated with the finite differencing scheme used in the code can overwhelm the SGS model and adversely affect the LES results. Attached to this overview are three submitted papers: 'Evaluation of the Dynamic Model for Simulations of Compressible Decaying Isotropic Turbulence'; 'The effect of the formulation of nonlinear terms on aliasing errors in spectral methods'; and 'Large-Eddy Simulation of a Spatially Evolving Compressible Boundary Layer Flow'.

Modeling the response of a monopulse radar to impulsive jamming signals using the Block Oriented System Simulator (BOSS)

NASA Astrophysics Data System (ADS)

Long, Jeffrey K.

1989-09-01

This theses developed computer models of two types of amplitude comparison monopulse processors using the Block Oriented System Simulation (BOSS) software package and to determine the response to these models to impulsive input signals. This study was an effort to determine the susceptibility of monopulse tracking radars to impulsing jamming signals. Two types of amplitude comparison monopulse receivers were modeled, one using logarithmic amplifiers and the other using automatic gain control for signal normalization. Simulations of both types of systems were run under various conditions of gain or frequency imbalance between the two receiver channels. The resulting errors from the imbalanced simulations were compared to the outputs of similar, baseline simulations which had no electrical imbalances. The accuracy of both types of processors was directly affected by gain or frequency imbalances in their receiver channels. In most cases, it was possible to generate both positive and negative angular errors, dependent upon the type and degree of mismatch between the channels. The system most susceptible to induced errors was a frequency imbalanced processor which used AGC circuitry. Any errors introduced will be a function of the degree of mismatch between the channels and therefore would be difficult to exploit reliably.

Simulating the fate of water in field soil crop environment

NASA Astrophysics Data System (ADS)

Cameira, M. R.; Fernando, R. M.; Ahuja, L.; Pereira, L.

2005-12-01

This paper presents an evaluation of the Root Zone Water Quality Model(RZWQM) for assessing the fate of water in the soil-crop environment at the field scale under the particular conditions of a Mediterranean region. The RZWQM model is a one-dimensional dual porosity model that allows flow in macropores. It integrates the physical, biological and chemical processes occurring in the root zone, allowing the simulation of a wide spectrum of agricultural management practices. This study involved the evaluation of the soil, hydrologic and crop development sub-models within the RZWQM for two distinct agricultural systems, one consisting of a grain corn planted in a silty loam soil, irrigated by level basins and the other a forage corn planted in a sandy soil, irrigated by sprinklers. Evaluation was performed at two distinct levels. At the first level the model capability to fit the measured data was analyzed (calibration). At the second level the model's capability to extrapolate and predict the system behavior for conditions different than those used when fitting the model was assessed (validation). In a subsequent paper the same type of evaluation is presented for the nitrogen transformation and transport model. At the first level a change in the crop evapotranspiration (ETc) formulation was introduced, based upon the definition of the effective leaf area, resulting in a 51% decrease in the root mean square error of the ETc simulations. As a result the simulation of the root water uptake was greatly improved. A new bottom boundary condition was implemented to account for the presence of a shallow water table. This improved the simulation of the water table depths and consequently the soil water evolution within the root zone. The soil hydraulic parameters and the crop variety specific parameters were calibrated in order to minimize the simulation errors of soil water and crop development. At the second level crop yield was predicted with an error of 1.1 and 2.8% for grain and forage corn, respectively. Soil water was predicted with an efficiency ranging from 50 to 95% for the silty loam soil and between 56 and 72% for the sandy soil. The purposed calibration procedure allowed the model to predict crop development, yield and the water balance terms, with accuracy that is acceptable in practical applications for complex and spatially variable field conditions. An iterative method was required to account for the strong interaction between the different model components, based upon detailed experimental data on soils and crops.

SU-E-J-30: Benchmark Image-Based TCP Calculation for Evaluation of PTV Margins for Lung SBRT Patients

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, M; Chetty, I; Zhong, H

2014-06-01

Purpose: Tumor control probability (TCP) calculated with accumulated radiation doses may help design appropriate treatment margins. Image registration errors, however, may compromise the calculated TCP. The purpose of this study is to develop benchmark CT images to quantify registration-induced errors in the accumulated doses and their corresponding TCP. Methods: 4DCT images were registered from end-inhale (EI) to end-exhale (EE) using a “demons” algorithm. The demons DVFs were corrected by an FEM model to get realistic deformation fields. The FEM DVFs were used to warp the EI images to create the FEM-simulated images. The two images combined with the FEM DVFmore » formed a benchmark model. Maximum intensity projection (MIP) images, created from the EI and simulated images, were used to develop IMRT plans. Two plans with 3 and 5 mm margins were developed for each patient. With these plans, radiation doses were recalculated on the simulated images and warped back to the EI images using the FEM DVFs to get the accumulated doses. The Elastix software was used to register the FEM-simulated images to the EI images. TCPs calculated with the Elastix-accumulated doses were compared with those generated by the FEM to get the TCP error of the Elastix registrations. Results: For six lung patients, the mean Elastix registration error ranged from 0.93 to 1.98 mm. Their relative dose errors in PTV were between 0.28% and 6.8% for 3mm margin plans, and between 0.29% and 6.3% for 5mm-margin plans. As the PTV margin reduced from 5 to 3 mm, the mean TCP error of the Elastix-reconstructed doses increased from 2.0% to 2.9%, and the mean NTCP errors decreased from 1.2% to 1.1%. Conclusion: Patient-specific benchmark images can be used to evaluate the impact of registration errors on the computed TCPs, and may help select appropriate PTV margins for lung SBRT patients.« less

Probabilistic Wind Power Ramp Forecasting Based on a Scenario Generation Method

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Qin; Florita, Anthony R; Krishnan, Venkat K

Wind power ramps (WPRs) are particularly important in the management and dispatch of wind power and currently drawing the attention of balancing authorities. With the aim to reduce the impact of WPRs for power system operations, this paper develops a probabilistic ramp forecasting method based on a large number of simulated scenarios. An ensemble machine learning technique is first adopted to forecast the basic wind power forecasting scenario and calculate the historical forecasting errors. A continuous Gaussian mixture model (GMM) is used to fit the probability distribution function (PDF) of forecasting errors. The cumulative distribution function (CDF) is analytically deduced.more » The inverse transform method based on Monte Carlo sampling and the CDF is used to generate a massive number of forecasting error scenarios. An optimized swinging door algorithm is adopted to extract all the WPRs from the complete set of wind power forecasting scenarios. The probabilistic forecasting results of ramp duration and start-time are generated based on all scenarios. Numerical simulations on publicly available wind power data show that within a predefined tolerance level, the developed probabilistic wind power ramp forecasting method is able to predict WPRs with a high level of sharpness and accuracy.« less

Probabilistic Wind Power Ramp Forecasting Based on a Scenario Generation Method: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Qin; Florita, Anthony R; Krishnan, Venkat K

2017-08-31

Wind power ramps (WPRs) are particularly important in the management and dispatch of wind power, and they are currently drawing the attention of balancing authorities. With the aim to reduce the impact of WPRs for power system operations, this paper develops a probabilistic ramp forecasting method based on a large number of simulated scenarios. An ensemble machine learning technique is first adopted to forecast the basic wind power forecasting scenario and calculate the historical forecasting errors. A continuous Gaussian mixture model (GMM) is used to fit the probability distribution function (PDF) of forecasting errors. The cumulative distribution function (CDF) ismore » analytically deduced. The inverse transform method based on Monte Carlo sampling and the CDF is used to generate a massive number of forecasting error scenarios. An optimized swinging door algorithm is adopted to extract all the WPRs from the complete set of wind power forecasting scenarios. The probabilistic forecasting results of ramp duration and start time are generated based on all scenarios. Numerical simulations on publicly available wind power data show that within a predefined tolerance level, the developed probabilistic wind power ramp forecasting method is able to predict WPRs with a high level of sharpness and accuracy.« less

Modelling high data rate communication network access protocol

NASA Technical Reports Server (NTRS)

Khanna, S.; Foudriat, E. C.; Paterra, Frank; Maly, Kurt J.; Overstreet, C. Michael

1990-01-01

Modeling of high data rate communication systems is different from the low data rate systems. Three simulations were built during the development phase of Carrier Sensed Multiple Access/Ring Network (CSMA/RN) modeling. The first was a model using SIMCRIPT based upon the determination and processing of each event at each node. The second simulation was developed in C based upon isolating the distinct object that can be identified as the ring, the message, the node, and the set of critical events. The third model further identified the basic network functionality by creating a single object, the node which includes the set of critical events which occur at the node. The ring structure is implicit in the node structure. This model was also built in C. Each model is discussed and their features compared. It should be stated that the language used was mainly selected by the model developer because of his past familiarity. Further the models were not built with the intent to compare either structure or language but because the complexity of the problem and initial results contained obvious errors, so alternative models were built to isolate, determine, and correct programming and modeling errors. The CSMA/RN protocol is discussed in sufficient detail to understand modeling complexities. Each model is described along with its features and problems. The models are compared and concluding observations and remarks are presented.

A critical issue in model-based inference for studying trait-based community assembly and a solution.

PubMed

Ter Braak, Cajo J F; Peres-Neto, Pedro; Dray, Stéphane

2017-01-01

Statistical testing of trait-environment association from data is a challenge as there is no common unit of observation: the trait is observed on species, the environment on sites and the mediating abundance on species-site combinations. A number of correlation-based methods, such as the community weighted trait means method (CWM), the fourth-corner correlation method and the multivariate method RLQ, have been proposed to estimate such trait-environment associations. In these methods, valid statistical testing proceeds by performing two separate resampling tests, one site-based and the other species-based and by assessing significance by the largest of the two p -values (the p max test). Recently, regression-based methods using generalized linear models (GLM) have been proposed as a promising alternative with statistical inference via site-based resampling. We investigated the performance of this new approach along with approaches that mimicked the p max test using GLM instead of fourth-corner. By simulation using models with additional random variation in the species response to the environment, the site-based resampling tests using GLM are shown to have severely inflated type I error, of up to 90%, when the nominal level is set as 5%. In addition, predictive modelling of such data using site-based cross-validation very often identified trait-environment interactions that had no predictive value. The problem that we identify is not an "omitted variable bias" problem as it occurs even when the additional random variation is independent of the observed trait and environment data. Instead, it is a problem of ignoring a random effect. In the same simulations, the GLM-based p max test controlled the type I error in all models proposed so far in this context, but still gave slightly inflated error in more complex models that included both missing (but important) traits and missing (but important) environmental variables. For screening the importance of single trait-environment combinations, the fourth-corner test is shown to give almost the same results as the GLM-based tests in far less computing time.

Introducing Multisensor Satellite Radiance-Based Evaluation for Regional Earth System Modeling

NASA Technical Reports Server (NTRS)

Matsui, T.; Santanello, J.; Shi, J. J.; Tao, W.-K.; Wu, D.; Peters-Lidard, C.; Kemp, E.; Chin, M.; Starr, D.; Sekiguchi, M.;

Checking distributional assumptions for pharmacokinetic summary statistics based on simulations with compartmental models.

PubMed

Shen, Meiyu; Russek-Cohen, Estelle; Slud, Eric V

2016-08-12

Bioequivalence (BE) studies are an essential part of the evaluation of generic drugs. The most common in vivo BE study design is the two-period two-treatment crossover design. AUC (area under the concentration-time curve) and Cmax (maximum concentration) are obtained from the observed concentration-time profiles for each subject from each treatment under each sequence. In the BE evaluation of pharmacokinetic crossover studies, the normality of the univariate response variable, e.g. log(AUC) 1 or log(Cmax), is often assumed in the literature without much evidence. Therefore, we investigate the distributional assumption of the normality of response variables, log(AUC) and log(Cmax), by simulating concentration-time profiles from two-stage pharmacokinetic models (commonly used in pharmacokinetic research) for a wide range of pharmacokinetic parameters and measurement error structures. Our simulations show that, under reasonable distributional assumptions on the pharmacokinetic parameters, log(AUC) has heavy tails and log(Cmax) is skewed. Sensitivity analyses are conducted to investigate how the distribution of the standardized log(AUC) (or the standardized log(Cmax)) for a large number of simulated subjects deviates from normality if distributions of errors in the pharmacokinetic model for plasma concentrations deviate from normality and if the plasma concentration can be described by different compartmental models.

Near-Surface Meteorology During the Arctic Summer Cloud Ocean Study (ASCOS): Evaluation of Reanalyses and Global Climate Models.

NASA Technical Reports Server (NTRS)

De Boer, G.; Shupe, M.D.; Caldwell, P.M.; Bauer, Susanne E.; Persson, O.; Boyle, J.S.; Kelley, M.; Klein, S.A.; Tjernstrom, M.

2014-01-01

Atmospheric measurements from the Arctic Summer Cloud Ocean Study (ASCOS) are used to evaluate the performance of three atmospheric reanalyses (European Centre for Medium Range Weather Forecasting (ECMWF)- Interim reanalysis, National Center for Environmental Prediction (NCEP)-National Center for Atmospheric Research (NCAR) reanalysis, and NCEP-DOE (Department of Energy) reanalysis) and two global climate models (CAM5 (Community Atmosphere Model 5) and NASA GISS (Goddard Institute for Space Studies) ModelE2) in simulation of the high Arctic environment. Quantities analyzed include near surface meteorological variables such as temperature, pressure, humidity and winds, surface-based estimates of cloud and precipitation properties, the surface energy budget, and lower atmospheric temperature structure. In general, the models perform well in simulating large-scale dynamical quantities such as pressure and winds. Near-surface temperature and lower atmospheric stability, along with surface energy budget terms, are not as well represented due largely to errors in simulation of cloud occurrence, phase and altitude. Additionally, a development version of CAM5, which features improved handling of cloud macro physics, has demonstrated to improve simulation of cloud properties and liquid water amount. The ASCOS period additionally provides an excellent example of the benefits gained by evaluating individual budget terms, rather than simply evaluating the net end product, with large compensating errors between individual surface energy budget terms that result in the best net energy budget.

A study and simulation of the impact of high-order aberrations to overlay error distribution

NASA Astrophysics Data System (ADS)

Sun, G.; Wang, F.; Zhou, C.

2011-03-01

With reduction of design rules, a number of corresponding new technologies, such as i-HOPC, HOWA and DBO have been proposed and applied to eliminate overlay error. When these technologies are in use, any high-order error distribution needs to be clearly distinguished in order to remove the underlying causes. Lens aberrations are normally thought to mainly impact the Matching Machine Overlay (MMO). However, when using Image-Based overlay (IBO) measurement tools, aberrations become the dominant influence on single machine overlay (SMO) and even on stage repeatability performance. In this paper, several measurements of the error distributions of the lens of SMEE SSB600/10 prototype exposure tool are presented. Models that characterize the primary influence from lens magnification, high order distortion, coma aberration and telecentricity are shown. The contribution to stage repeatability (as measured with IBO tools) from the above errors was predicted with simulator and compared to experiments. Finally, the drift of every lens distortion that impact to SMO over several days was monitored and matched with the result of measurements.

Modeling to predict pilot performance during CDTI-based in-trail following experiments

NASA Technical Reports Server (NTRS)

Sorensen, J. A.; Goka, T.

1984-01-01

A mathematical model was developed of the flight system with the pilot using a cockpit display of traffic information (CDTI) to establish and maintain in-trail spacing behind a lead aircraft during approach. Both in-trail and vertical dynamics were included. The nominal spacing was based on one of three criteria (Constant Time Predictor; Constant Time Delay; or Acceleration Cue). This model was used to simulate digitally the dynamics of a string of multiple following aircraft, including response to initial position errors. The simulation was used to predict the outcome of a series of in-trail following experiments, including pilot performance in maintaining correct longitudinal spacing and vertical position. The experiments were run in the NASA Ames Research Center multi-cab cockpit simulator facility. The experimental results were then used to evaluate the model and its prediction accuracy. Model parameters were adjusted, so that modeled performance matched experimental results. Lessons learned in this modeling and prediction study are summarized.

A prediction model based on artificial neural network for surface temperature simulation of nickel-metal hydride battery during charging

NASA Astrophysics Data System (ADS)

Fang, Kaizheng; Mu, Daobin; Chen, Shi; Wu, Borong; Wu, Feng

2012-06-01

In this study, a prediction model based on artificial neural network is constructed for surface temperature simulation of nickel-metal hydride battery. The model is developed from a back-propagation network which is trained by Levenberg-Marquardt algorithm. Under each ambient temperature of 10 °C, 20 °C, 30 °C and 40 °C, an 8 Ah cylindrical Ni-MH battery is charged in the rate of 1 C, 3 C and 5 C to its SOC of 110% in order to provide data for the model training. Linear regression method is adopted to check the quality of the model training, as well as mean square error and absolute error. It is shown that the constructed model is of excellent training quality for the guarantee of prediction accuracy. The surface temperature of battery during charging is predicted under various ambient temperatures of 50 °C, 60 °C, 70 °C by the model. The results are validated in good agreement with experimental data. The value of battery surface temperature is calculated to exceed 90 °C under the ambient temperature of 60 °C if it is overcharged in 5 C, which might cause battery safety issues.

LiDAR error estimation with WAsP engineering

NASA Astrophysics Data System (ADS)

Bingöl, F.; Mann, J.; Foussekis, D.

2008-05-01

The LiDAR measurements, vertical wind profile in any height between 10 to 150m, are based on assumption that the measured wind is a product of a homogenous wind. In reality there are many factors affecting the wind on each measurement point which the terrain plays the main role. To model LiDAR measurements and predict possible error in different wind directions for a certain terrain we have analyzed two experiment data sets from Greece. In both sites LiDAR and met, mast data have been collected and the same conditions are simulated with RisØ/DTU software, WAsP Engineering 2.0. Finally measurement data is compared with the model results. The model results are acceptable and very close for one site while the more complex one is returning higher errors at higher positions and in some wind directions.

Hydrological changes in the Amur river basin: two approaches for assignment of climate projections into hydrological model

NASA Astrophysics Data System (ADS)

Gelfan, Alexander; Kalugin, Andrei; Motovilov, Yury

2017-04-01

A regional hydrological model was setup to assess possible impact of climate change on the hydrological regime of the Amur drainage basin (the catchment area is 1 855 000 km2). The model is based on the ECOMAG hydrological modeling platform and describes spatially distributed processes of water cycle in this great basin with account for flow regulation by the Russian and Chinese reservoirs. Earlier, the regional hydrological model was intensively evaluated against 20-year streamflow data over the whole Amur basin and, being driven by 252-station meteorological observations as input data, demonstrated good performance. In this study, we firstly assessed the reliability of the model to reproduce the historical streamflow series when Global Climate Model (GCM) simulation data are used as input into the hydrological model. Data of nine GCMs involved in CMIP5 project was utilized and we found that ensemble mean of annual flow is close to the observed flow (error is about 14%) while data of separate GCMs may result in much larger errors. Reproduction of seasonal flow for the historical period turned out weaker; first of all because of large errors in simulated seasonal precipitation, so hydrological consequences of climate change were estimated just in terms of annual flow. We analyzed the hydrological projections from the climate change scenarios. The impacts were assessed in four 20-year periods: early- (2020-2039), mid- (2040-2059) and two end-century (2060-2079; 2080-2099) periods using an ensemble of nine GCMs and four Representative Concentration Pathways (RCP) scenarios. Mean annual runoff anomalies calculated as percentages of the future runoff (simulated under 36 GCM-RCP combinations of climate scenarios) to the historical runoff (simulated under the corresponding GCM outputs for the reference 1986-2005 period) were estimated. Hydrological model gave small negative runoff anomalies for almost all GCM-RCP combinations of climate scenarios and for all 20-year periods. The largest ensemble mean anomaly was about minus 8% by the end of XXI century under the most severe RCP8.5 scenario. We compared the mean annual runoff anomalies projected under the GCM-based data for the XXI century with the corresponding anomalies projected under a modified observed climatology using the delta-change (DC) method. Use of the modified observed records as driving forces for hydrological model-based projections can be considered as an alternative to the GCM-based scenarios if the latter are uncertain. The main advantage of the DC approach is its simplicity: in its simplest version only differences between present and future climates (i.e. between the long-term means of the climatic variables) are considered as DC-factors. In this study, the DC-factors for the reference meteorological series (1986-2005) of climate parameters were calculated from the GCM-based scenarios. The modified historical data were used as input into the hydrological models. For each of four 20-year period, runoff anomalies simulated under the delta-changed historical time series were compared with runoff anomalies simulated under the corresponding GCM-data with the same mean. We found that the compared projections are closely correlated. Thus, for the Amur basin, the modified observed climatology can be used as driving force for hydrological model-based projections and considered as an alternative to the GCM-based scenarios if only annual flow projections are of the interest.

Stability of finite difference numerical simulations of acoustic logging-while-drilling with different perfectly matched layer schemes

NASA Astrophysics Data System (ADS)

Wang, Hua; Tao, Guo; Shang, Xue-Feng; Fang, Xin-Ding; Burns, Daniel R.

2013-12-01

In acoustic logging-while-drilling (ALWD) finite difference in time domain (FDTD) simulations, large drill collar occupies, most of the fluid-filled borehole and divides the borehole fluid into two thin fluid columns (radius ˜27 mm). Fine grids and large computational models are required to model the thin fluid region between the tool and the formation. As a result, small time step and more iterations are needed, which increases the cumulative numerical error. Furthermore, due to high impedance contrast between the drill collar and fluid in the borehole (the difference is >30 times), the stability and efficiency of the perfectly matched layer (PML) scheme is critical to simulate complicated wave modes accurately. In this paper, we compared four different PML implementations in a staggered grid finite difference in time domain (FDTD) in the ALWD simulation, including field-splitting PML (SPML), multiaxial PML(MPML), non-splitting PML (NPML), and complex frequency-shifted PML (CFS-PML). The comparison indicated that NPML and CFS-PML can absorb the guided wave reflection from the computational boundaries more efficiently than SPML and M-PML. For large simulation time, SPML, M-PML, and NPML are numerically unstable. However, the stability of M-PML can be improved further to some extent. Based on the analysis, we proposed that the CFS-PML method is used in FDTD to eliminate the numerical instability and to improve the efficiency of absorption in the PML layers for LWD modeling. The optimal values of CFS-PML parameters in the LWD simulation were investigated based on thousands of 3D simulations. For typical LWD cases, the best maximum value of the quadratic damping profile was obtained using one d 0. The optimal parameter space for the maximum value of the linear frequency-shifted factor ( α 0) and the scaling factor ( β 0) depended on the thickness of the PML layer. For typical formations, if the PML thickness is 10 grid points, the global error can be reduced to <1% using the optimal PML parameters, and the error will decrease as the PML thickness increases.

Simulation-based MDP verification for leading-edge masks

NASA Astrophysics Data System (ADS)

Su, Bo; Syrel, Oleg; Pomerantsev, Michael; Hagiwara, Kazuyuki; Pearman, Ryan; Pang, Leo; Fujimara, Aki

2017-07-01

For IC design starts below the 20nm technology node, the assist features on photomasks shrink well below 60nm and the printed patterns of those features on masks written by VSB eBeam writers start to show a large deviation from the mask designs. Traditional geometry-based fracturing starts to show large errors for those small features. As a result, other mask data preparation (MDP) methods have become available and adopted, such as rule-based Mask Process Correction (MPC), model-based MPC and eventually model-based MDP. The new MDP methods may place shot edges slightly differently from target to compensate for mask process effects, so that the final patterns on a mask are much closer to the design (which can be viewed as the ideal mask), especially for those assist features. Such an alteration generally produces better masks that are closer to the intended mask design. Traditional XOR-based MDP verification cannot detect problems caused by eBeam effects. Much like model-based OPC verification which became a necessity for OPC a decade ago, we see the same trend in MDP today. Simulation-based MDP verification solution requires a GPU-accelerated computational geometry engine with simulation capabilities. To have a meaningful simulation-based mask check, a good mask process model is needed. The TrueModel® system is a field tested physical mask model developed by D2S. The GPU-accelerated D2S Computational Design Platform (CDP) is used to run simulation-based mask check, as well as model-based MDP. In addition to simulation-based checks such as mask EPE or dose margin, geometry-based rules are also available to detect quality issues such as slivers or CD splits. Dose margin related hotspots can also be detected by setting a correct detection threshold. In this paper, we will demonstrate GPU-acceleration for geometry processing, and give examples of mask check results and performance data. GPU-acceleration is necessary to make simulation-based mask MDP verification acceptable.

Non-null annular subaperture stitching interferometry for aspheric test

NASA Astrophysics Data System (ADS)

Zhang, Lei; Liu, Dong; Shi, Tu; Yang, Yongying; Chong, Shiyao; Miao, Liang; Huang, Wei; Shen, Yibing; Bai, Jian

2015-10-01

A non-null annular subaperture stitching interferometry (NASSI), combining the subaperture stitching idea and non-null test method, is proposed for steep aspheric testing. Compared with standard annular subaperture stitching interferometry (ASSI), a partial null lens (PNL) is employed as an alternative to the transmission sphere, to generate different aspherical wavefronts as the references. The coverage subaperture number would thus be reduced greatly for the better performance of aspherical wavefronts in matching the local slope of aspheric surfaces. Instead of various mathematical stitching algorithms, a simultaneous reverse optimizing reconstruction (SROR) method based on system modeling and ray tracing is proposed for full aperture figure error reconstruction. All the subaperture measurements are simulated simultaneously with a multi-configuration model in a ray-tracing program, including the interferometric system modeling and subaperture misalignments modeling. With the multi-configuration model, full aperture figure error would be extracted in form of Zernike polynomials from subapertures wavefront data by the SROR method. This method concurrently accomplishes subaperture retrace error and misalignment correction, requiring neither complex mathematical algorithms nor subaperture overlaps. A numerical simulation exhibits the comparison of the performance of the NASSI and standard ASSI, which demonstrates the high accuracy of the NASSI in testing steep aspheric. Experimental results of NASSI are shown to be in good agreement with that of Zygo® VerifireTM Asphere interferometer.

Robust THP Transceiver Designs for Multiuser MIMO Downlink with Imperfect CSIT

NASA Astrophysics Data System (ADS)

Ubaidulla, P.; Chockalingam, A.

2009-12-01

We present robust joint nonlinear transceiver designs for multiuser multiple-input multiple-output (MIMO) downlink in the presence of imperfections in the channel state information at the transmitter (CSIT). The base station (BS) is equipped with multiple transmit antennas, and each user terminal is equipped with one or more receive antennas. The BS employs Tomlinson-Harashima precoding (THP) for interuser interference precancellation at the transmitter. We consider robust transceiver designs that jointly optimize the transmit THP filters and receive filter for two models of CSIT errors. The first model is a stochastic error (SE) model, where the CSIT error is Gaussian-distributed. This model is applicable when the CSIT error is dominated by channel estimation error. In this case, the proposed robust transceiver design seeks to minimize a stochastic function of the sum mean square error (SMSE) under a constraint on the total BS transmit power. We propose an iterative algorithm to solve this problem. The other model we consider is a norm-bounded error (NBE) model, where the CSIT error can be specified by an uncertainty set. This model is applicable when the CSIT error is dominated by quantization errors. In this case, we consider a worst-case design. For this model, we consider robust (i) minimum SMSE, (ii) MSE-constrained, and (iii) MSE-balancing transceiver designs. We propose iterative algorithms to solve these problems, wherein each iteration involves a pair of semidefinite programs (SDPs). Further, we consider an extension of the proposed algorithm to the case with per-antenna power constraints. We evaluate the robustness of the proposed algorithms to imperfections in CSIT through simulation, and show that the proposed robust designs outperform nonrobust designs as well as robust linear transceiver designs reported in the recent literature.

Atmospheric modeling to assess wind dependence in tracer dilution method measurements of landfill methane emissions.

PubMed

Taylor, Diane M; Chow, Fotini K; Delkash, Madjid; Imhoff, Paul T

2018-03-01

The short-term temporal variability of landfill methane emissions is not well understood due to uncertainty in measurement methods. Significant variability is seen over short-term measurement campaigns with the tracer dilution method (TDM), but this variability may be due in part to measurement error rather than fluctuations in the actual landfill emissions. In this study, landfill methane emissions and TDM-measured emissions are simulated over a real landfill in Delaware, USA using the Weather Research and Forecasting model (WRF) for two emissions scenarios. In the steady emissions scenario, a constant landfill emissions rate is prescribed at each model grid point on the surface of the landfill. In the unsteady emissions scenario, emissions are calculated at each time step as a function of the local surface wind speed, resulting in variable emissions over each 1.5-h measurement period. The simulation output is used to assess the standard deviation and percent error of the TDM-measured emissions. Eight measurement periods are simulated over two different days to look at different conditions. Results show that standard deviation of the TDM- measured emissions does not increase significantly from the steady emissions simulations to the unsteady emissions scenarios, indicating that the TDM may have inherent errors in its prediction of emissions fluctuations. Results also show that TDM error does not increase significantly from the steady to the unsteady emissions simulations. This indicates that introducing variability to the landfill emissions does not increase errors in the TDM at this site. Across all simulations, TDM errors range from -15% to 43%, consistent with the range of errors seen in previous TDM studies. Simulations indicate diurnal variations of methane emissions when wind effects are significant, which may be important when developing daily and annual emissions estimates from limited field data. Copyright © 2017 Elsevier Ltd. All rights reserved.

Research on effects of phase error in phase-shifting interferometer

NASA Astrophysics Data System (ADS)

Wang, Hongjun; Wang, Zhao; Zhao, Hong; Tian, Ailing; Liu, Bingcai

2007-12-01

Referring to phase-shifting interferometry technology, the phase shifting error from the phase shifter is the main factor that directly affects the measurement accuracy of the phase shifting interferometer. In this paper, the resources and sorts of phase shifting error were introduction, and some methods to eliminate errors were mentioned. Based on the theory of phase shifting interferometry, the effects of phase shifting error were analyzed in detail. The Liquid Crystal Display (LCD) as a new shifter has advantage as that the phase shifting can be controlled digitally without any mechanical moving and rotating element. By changing coded image displayed on LCD, the phase shifting in measuring system was induced. LCD's phase modulation characteristic was analyzed in theory and tested. Based on Fourier transform, the effect model of phase error coming from LCD was established in four-step phase shifting interferometry. And the error range was obtained. In order to reduce error, a new error compensation algorithm was put forward. With this method, the error can be obtained by process interferogram. The interferogram can be compensated, and the measurement results can be obtained by four-step phase shifting interferogram. Theoretical analysis and simulation results demonstrate the feasibility of this approach to improve measurement accuracy.

Validation of a RANS transition model using a high-order weighted compact nonlinear scheme

NASA Astrophysics Data System (ADS)

Tu, GuoHua; Deng, XiaoGang; Mao, MeiLiang

2013-04-01

A modified transition model is given based on the shear stress transport (SST) turbulence model and an intermittency transport equation. The energy gradient term in the original model is replaced by flow strain rate to saving computational costs. The model employs local variables only, and then it can be conveniently implemented in modern computational fluid dynamics codes. The fifth-order weighted compact nonlinear scheme and the fourth-order staggered scheme are applied to discrete the governing equations for the purpose of minimizing discretization errors, so as to mitigate the confusion between numerical errors and transition model errors. The high-order package is compared with a second-order TVD method on simulating the transitional flow of a flat plate. Numerical results indicate that the high-order package give better grid convergence property than that of the second-order method. Validation of the transition model is performed for transitional flows ranging from low speed to hypersonic speed.

AAA gunnermodel based on observer theory. [predicting a gunner's tracking response

NASA Technical Reports Server (NTRS)

Kou, R. S.; Glass, B. C.; Day, C. N.; Vikmanis, M. M.

1978-01-01

The Luenberger observer theory is used to develop a predictive model of a gunner's tracking response in antiaircraft artillery systems. This model is composed of an observer, a feedback controller and a remnant element. An important feature of the model is that the structure is simple, hence a computer simulation requires only a short execution time. A parameter identification program based on the least squares curve fitting method and the Gauss Newton gradient algorithm is developed to determine the parameter values of the gunner model. Thus, a systematic procedure exists for identifying model parameters for a given antiaircraft tracking task. Model predictions of tracking errors are compared with human tracking data obtained from manned simulation experiments. Model predictions are in excellent agreement with the empirical data for several flyby and maneuvering target trajectories.

Application of artificial neural networks in hydrological modeling: A case study of runoff simulation of a Himalayan glacier basin

NASA Technical Reports Server (NTRS)

Buch, A. M.; Narain, A.; Pandey, P. C.

1994-01-01

The simulation of runoff from a Himalayan Glacier basin using an Artificial Neural Network (ANN) is presented. The performance of the ANN model is found to be superior to the Energy Balance Model and the Multiple Regression model. The RMS Error is used as the figure of merit for judging the performance of the three models, and the RMS Error for the ANN model is the latest of the three models. The ANN is faster in learning and exhibits excellent system generalization characteristics.

A design of experiments approach to validation sampling for logistic regression modeling with error-prone medical records.

PubMed

Ouyang, Liwen; Apley, Daniel W; Mehrotra, Sanjay

2016-04-01

Electronic medical record (EMR) databases offer significant potential for developing clinical hypotheses and identifying disease risk associations by fitting statistical models that capture the relationship between a binary response variable and a set of predictor variables that represent clinical, phenotypical, and demographic data for the patient. However, EMR response data may be error prone for a variety of reasons. Performing a manual chart review to validate data accuracy is time consuming, which limits the number of chart reviews in a large database. The authors' objective is to develop a new design-of-experiments-based systematic chart validation and review (DSCVR) approach that is more powerful than the random validation sampling used in existing approaches. The DSCVR approach judiciously and efficiently selects the cases to validate (i.e., validate whether the response values are correct for those cases) for maximum information content, based only on their predictor variable values. The final predictive model will be fit using only the validation sample, ignoring the remainder of the unvalidated and unreliable error-prone data. A Fisher information based D-optimality criterion is used, and an algorithm for optimizing it is developed. The authors' method is tested in a simulation comparison that is based on a sudden cardiac arrest case study with 23 041 patients' records. This DSCVR approach, using the Fisher information based D-optimality criterion, results in a fitted model with much better predictive performance, as measured by the receiver operating characteristic curve and the accuracy in predicting whether a patient will experience the event, than a model fitted using a random validation sample. The simulation comparisons demonstrate that this DSCVR approach can produce predictive models that are significantly better than those produced from random validation sampling, especially when the event rate is low. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Characterizing the physical-basis of orographic rainfall retrieval errors due to terrain artifacts on GPM-DPR reflectivity profiles

NASA Astrophysics Data System (ADS)

Arulraj, M.; Barros, A. P.

2017-12-01

GPM-DPR reflectivity profiles in mountainous regions are severely handicapped by low level ground-clutter artifacts which have different error characteristics depending on landform (upwind slopes of high mountains versus complex topography in middle-mountains) and precipitation regime. These artifacts result in high detection and estimation errors especially in mid-latitude and tropical mountain regions where low-level light precipitation and complex multi-layer clouds interact with incoming storms. Here, we present results assessment studies in the Southern Appalachian Mountains (SAM) and preliminary results over the eastern slopes of the Andes using ground-based observations from the long-term hydrometeorological networks and model studies toward developing a physically-based framework to systematically identify and attribute measurement errors. Specifically, the focus is on events when GPM-DPR Ka- and Ku- Band precipitation radar misses low-level precipitation with vertical altitude less than 2 km AGL (above ground level). For this purpose, ground-based MRR and Parsivel disdrometer observations near the surface are compared with the reflectivity profiles observed by the GPM-DPR overpasses, the raindrop-size spectra are used to classify the precipitation regime associated with different classes of detection and estimation errors. This information will be used along with a coupled rainfall dynamics and radar simulator model to 1) merge the low-level GPM-DPR measured reflectivity with the MRR reflectivities optimally under strict physically-based constraints and 2) build a library of reflectivity profile corrections. Finally, preliminary 4D analysis of the organization of reflectivity correction modes, microphysical regimes, topography and storm environment will be presented toward developing a general physically-based error model.

Deep data fusion method for missile-borne inertial/celestial system

NASA Astrophysics Data System (ADS)

Zhang, Chunxi; Chen, Xiaofei; Lu, Jiazhen; Zhang, Hao

2018-05-01

Strap-down inertial-celestial integrated navigation system has the advantages of autonomy and high precision and is very useful for ballistic missiles. The star sensor installation error and inertial measurement error have a great influence for the system performance. Based on deep data fusion, this paper establishes measurement equations including star sensor installation error and proposes the deep fusion filter method. Simulations including misalignment error, star sensor installation error, IMU error are analyzed. Simulation results indicate that the deep fusion method can estimate the star sensor installation error and IMU error. Meanwhile, the method can restrain the misalignment errors caused by instrument errors.

Group navigation and the "many-wrongs principle" in models of animal movement.

PubMed

Codling, E A; Pitchford, J W; Simpson, S D

2007-07-01

Traditional studies of animal navigation over both long and short distances have usually considered the orientation ability of the individual only, without reference to the implications of group membership. However, recent work has suggested that being in a group can significantly improve the ability of an individual to align toward and reach a target direction or point, even when all group members have limited navigational ability and there are no leaders. This effect is known as the "many-wrongs principle" since the large number of individual navigational errors across the group are suppressed by interactions and group cohesion. In this paper, we simulate the many-wrongs principle using a simple individual-based model of movement based on a biased random walk that includes group interactions. We study the ability of the group as a whole to reach a target given different levels of individual navigation error, group size, interaction radius, and environmental turbulence. In scenarios with low levels of environmental turbulence, simulation results demonstrate a navigational benefit from group membership, particularly for small group sizes. In contrast, when movement takes place in a highly turbulent environment, simulation results suggest that the best strategy is to navigate as individuals rather than as a group.

Genomic Prediction Accounting for Residual Heteroskedasticity

PubMed Central

Ou, Zhining; Tempelman, Robert J.; Steibel, Juan P.; Ernst, Catherine W.; Bates, Ronald O.; Bello, Nora M.

2015-01-01

Whole-genome prediction (WGP) models that use single-nucleotide polymorphism marker information to predict genetic merit of animals and plants typically assume homogeneous residual variance. However, variability is often heterogeneous across agricultural production systems and may subsequently bias WGP-based inferences. This study extends classical WGP models based on normality, heavy-tailed specifications and variable selection to explicitly account for environmentally-driven residual heteroskedasticity under a hierarchical Bayesian mixed-models framework. WGP models assuming homogeneous or heterogeneous residual variances were fitted to training data generated under simulation scenarios reflecting a gradient of increasing heteroskedasticity. Model fit was based on pseudo-Bayes factors and also on prediction accuracy of genomic breeding values computed on a validation data subset one generation removed from the simulated training dataset. Homogeneous vs. heterogeneous residual variance WGP models were also fitted to two quantitative traits, namely 45-min postmortem carcass temperature and loin muscle pH, recorded in a swine resource population dataset prescreened for high and mild residual heteroskedasticity, respectively. Fit of competing WGP models was compared using pseudo-Bayes factors. Predictive ability, defined as the correlation between predicted and observed phenotypes in validation sets of a five-fold cross-validation was also computed. Heteroskedastic error WGP models showed improved model fit and enhanced prediction accuracy compared to homoskedastic error WGP models although the magnitude of the improvement was small (less than two percentage points net gain in prediction accuracy). Nevertheless, accounting for residual heteroskedasticity did improve accuracy of selection, especially on individuals of extreme genetic merit. PMID:26564950

Novel parametric reduced order model for aeroengine blade dynamics

NASA Astrophysics Data System (ADS)

Yuan, Jie; Allegri, Giuliano; Scarpa, Fabrizio; Rajasekaran, Ramesh; Patsias, Sophoclis

2015-10-01

The work introduces a novel reduced order model (ROM) technique to describe the dynamic behavior of turbofan aeroengine blades. We introduce an equivalent 3D frame model to describe the coupled flexural/torsional mode shapes, with their relevant natural frequencies and associated modal masses. The frame configurations are identified through a structural identification approach based on a simulated annealing algorithm with stochastic tunneling. The cost functions are constituted by linear combinations of relative errors associated to the resonance frequencies, the individual modal assurance criteria (MAC), and on either overall static or modal masses. When static masses are considered the optimized 3D frame can represent the blade dynamic behavior with an 8% error on the MAC, a 1% error on the associated modal frequencies and a 1% error on the overall static mass. When using modal masses in the cost function the performance of the ROM is similar, but the overall error increases to 7%. The approach proposed in this paper is considerably more accurate than state-of-the-art blade ROMs based on traditional Timoshenko beams, and provides excellent accuracy at reduced computational time when compared against high fidelity FE models. A sensitivity analysis shows that the proposed model can adequately predict the global trends of the variations of the natural frequencies when lumped masses are used for mistuning analysis. The proposed ROM also follows extremely closely the sensitivity of the high fidelity finite element models when the material parameters are used in the sensitivity.

Model-Based Engine Control Architecture with an Extended Kalman Filter

NASA Technical Reports Server (NTRS)

Csank, Jeffrey T.; Connolly, Joseph W.

2016-01-01

This paper discusses the design and implementation of an extended Kalman filter (EKF) for model-based engine control (MBEC). Previously proposed MBEC architectures feature an optimal tuner Kalman Filter (OTKF) to produce estimates of both unmeasured engine parameters and estimates for the health of the engine. The success of this approach relies on the accuracy of the linear model and the ability of the optimal tuner to update its tuner estimates based on only a few sensors. Advances in computer processing are making it possible to replace the piece-wise linear model, developed off-line, with an on-board nonlinear model running in real-time. This will reduce the estimation errors associated with the linearization process, and is typically referred to as an extended Kalman filter. The non-linear extended Kalman filter approach is applied to the Commercial Modular Aero-Propulsion System Simulation 40,000 (C-MAPSS40k) and compared to the previously proposed MBEC architecture. The results show that the EKF reduces the estimation error, especially during transient operation.

Model-Based Engine Control Architecture with an Extended Kalman Filter

NASA Technical Reports Server (NTRS)

Csank, Jeffrey T.; Connolly, Joseph W.

2016-01-01

This paper discusses the design and implementation of an extended Kalman filter (EKF) for model-based engine control (MBEC). Previously proposed MBEC architectures feature an optimal tuner Kalman Filter (OTKF) to produce estimates of both unmeasured engine parameters and estimates for the health of the engine. The success of this approach relies on the accuracy of the linear model and the ability of the optimal tuner to update its tuner estimates based on only a few sensors. Advances in computer processing are making it possible to replace the piece-wise linear model, developed off-line, with an on-board nonlinear model running in real-time. This will reduce the estimation errors associated with the linearization process, and is typically referred to as an extended Kalman filter. The nonlinear extended Kalman filter approach is applied to the Commercial Modular Aero-Propulsion System Simulation 40,000 (C-MAPSS40k) and compared to the previously proposed MBEC architecture. The results show that the EKF reduces the estimation error, especially during transient operation.